diff --git a/README.md b/README.md index 8e67ed6a..257c4467 100644 --- a/README.md +++ b/README.md @@ -93,7 +93,9 @@ Contains the engines used by [Stella](https://www.iseesystems.com/store/products {x: , y: } ... ] - } + }, + subType?: , # Discrete-entity sub-type (see below) + additionalProperties?: { # Sub-type-specific settings (see below) } }], relationships: [{ reasoning?: , # Explanation for why this relationship is here @@ -111,11 +113,12 @@ Contains the engines used by [Stella](https://www.iseesystems.com/store/products stopTime: , dt?: , timeUnits?: , - arrayDimensions?: [{ # Array dimension definitions - name: , # Singular, alphanumeric dimension name - type: , # "labels" or "numeric" - size: , # Number of elements in dimension - elements: Array # Element names for this dimension + integrationMethod?: , # "Euler" or "RK4" + arrayDimensions?: [{ # Array dimension definitions (all four fields required) + type: , # "numeric" or "labels" - numeric auto-generates element names as strings ('1','2','3'), labels use user-defined meaningful names + name: , # Singular, alphanumeric dimension name (e.g., "region" not "regions") + size: , # Positive integer - number of elements in dimension + elements: Array # Element names - for numeric: auto-generated ['1','2','3'], for labels: user-defined ['North','South','East','West'] }] } } @@ -124,11 +127,16 @@ Contains the engines used by [Stella](https://www.iseesystems.com/store/products ### Arrays in SD-JSON Variables can be arrayed over one or more dimensions to create multi-dimensional arrays: -- **Dimensions**: Defined in `specs.arrayDimensions` with name, type (labels/numeric), size, and elements +- **Dimensions**: Defined in `specs.arrayDimensions` with all four required fields: + - `type`: Either "numeric" (auto-generates elements as '1','2','3') or "labels" (user-defined element names) + - `name`: Singular, alphanumeric dimension name (e.g., "region" not "regions") + - `size`: Positive integer count of elements + - `elements`: Array of element names matching the size - **Arrayed Variables**: Reference dimensions by name in their `dimensions` array (order matters) - **Array Equations**: - If all elements use the SAME formula: uses `equation` field only - - If elements have DIFFERENT formulas: uses `arrayEquations` array with element-specific equations + - If elements have DIFFERENT formulas OR for arrayed STOCKS: uses `arrayEquations` array with element-specific equations + - Each `arrayEquations` entry has `equation` and `forElements` (ordered to match the variable's dimensions list) ### Modules in SD-JSON Models can be organized into modules for better structure and encapsulation: @@ -143,6 +151,76 @@ Models can be organized into modules for better structure and encapsulation: - Ghost variable has same local name as source but exists in consuming module - All equations in consuming module reference the ghost, not the original source +### Discrete-Entity Sub-Types in SD-JSON +Variables can have a `subType` field that further classifies them. Sub-types are a refinement of `type` — the top-level `type` field remains `"stock"`, `"flow"`, or `"variable"`. + +**Stock sub-types** — also set `additionalProperties` with the relevant configuration: + +| `subType` | Description | +|-----------|-------------| +| `"queue"` | A waiting line that holds discrete items until they are dispatched. | +| `"oven"` | A batch processor where items are held for a fixed cook time then released together. | +| `"conveyor"` | A pipeline delay where items travel a fixed transit time before exiting from the other end. | + +**Flow sub-types** — automatically managed flows. Set `subType` only; leave `equation` as an empty string: + +| `subType` | Description | +|-----------|-------------| +| `"discreteOutflow"` | The output flow from a conveyor or oven. | +| `"conveyorLeakage"` | The leakage flow from a conveyor. Set `additionalProperties` to configure leakage behavior. | +| `"queueOutflow"` | The output flow from a queue. | +| `"queueOverflow"` | The overflow flow emitted when a full queue cannot accept new items (requires `overflow: true` on the queue). | + +**Variable sub-types** — set `subType` on plain `"variable"` type entities: + +| `subType` | Description | +|-----------|-------------| +| `"delayVariable"` | A plain variable whose equation contains a `DELAY` or `SMTH` builtin function (e.g. `DELAY1`, `DELAY3`, `DELAY N`, `SMTH1`, `SMTH3`). Set this whenever any DELAY or SMTH variant appears in the equation. | + +**`additionalProperties`** fields for conveyor and oven stocks: + +| Field | Type | Applies to | Description | +|-------|------|------------|-------------| +| `processTime` | string (equation) | conveyor, oven | Transit time (conveyor) or cook time (oven). Required. | +| `capacity` | string (equation) | conveyor, oven | Maximum number of items the element can hold. | +| `inflowLimit` | string (equation) | conveyor, oven | Maximum inflow rate per time step. | +| `fillTime` | string (equation) | oven only | Time to fill the element before processing begins. | +| `cleanTime` | string (equation) | oven only | Clean-up time after emptying before accepting new items. | +| `sample` | string (equation) | conveyor, oven | Re-samples transit/cook time when this expression is non-zero. | +| `arrest` | string (equation) | conveyor, oven | Halts movement when this expression is non-zero. | + +**`additionalProperties`** fields for regular flows (inflows to a conveyor): + +| Field | Type | Description | +|-------|------|-------------| +| `spreadFlow` | string enum | How this flow distributes along the conveyor when it enters: `"none"` (default, front-entry), `"even"`, `"destination"`, `"distribution"` (requires `distribEq`), `"source"`. | +| `distribEq` | string (equation) | Distribution table equation. Required when `spreadFlow` is `"distribution"`. | + +**`additionalProperties`** fields for `conveyorLeakage` flows: + +| Field | Type | Description | +|-------|------|-------------| +| `leakFraction` | string (equation) | Fraction of conveyor contents that leak out per time step. | +| `exponential` | boolean | If true, leakage is exponential (constant fraction); if false (default), linear. | +| `leakZoneStart` | string (equation) | Start position (0–100%) along the conveyor where leakage begins. Leave empty for leakage across the entire length. | +| `leakZoneEnd` | string (equation) | End position (0–100%) along the conveyor where leakage ends. Leave empty for leakage across the entire length. | +| `leakIntegers` | boolean | If true, leakage amounts are rounded to whole integers. | +| `ignorePrevZones` | boolean | If true, each leak zone operates independently of losses from earlier zones. | +| `forceLeakFraction` | boolean | If true, the same leak fraction is applied regardless of transit duration. | + +**`additionalProperties`** fields for queue stocks: + +| Field | Type | Description | +|-------|------|-------------| +| `fifoEnabled` | boolean | If true, dispatches in FIFO order; if false (default), LIFO. | +| `oneAtATime` | boolean | If true, accepts only one batch per time step. | +| `splitBatches` | boolean | If true, incoming batches can be split when entering. | +| `discrete` | boolean | If true, operates on integer quantities only (discrete mode). | +| `roundRobin` | boolean | If true, competing outflows are served in round-robin order. | +| `queueOutflowPriority` | string (equation) | Dispatch priority for the queue outflow. | +| `purgeEq` | string (equation) | Items older than this age (in time units) are automatically removed. | +| `overflow` | boolean | If true, a `queueOverflow` flow is automatically created for excess items. | + ## Discussion Engine JSON response ``` { @@ -177,14 +255,32 @@ Models can be organized into modules for better structure and encapsulation: } ``` +# WebSocket AI Agent + +The `agent/` directory contains a WebSocket server that wraps the SD-AI engines in a conversational AI agent for building and modifying System Dynamics models interactively. + +**Key characteristics:** +- Stateless — all model state, run data, and conversation history live on the client +- All core tools are built-in (get/update model, run simulation, fetch variable data, feedback loops, visualizations) +- Clients can optionally register custom tools for application-specific behavior +- Agent personalities are configured via Markdown files in `agent/config/` +- Visualizations are returned as raw SVG strings + +**WebSocket endpoint:** `ws://localhost:3000/api/v1/agent` + +**Protocol summary:** client connects → `initialize_session` (model type + initial model) → `session_ready` (agent list) → `select_agent` → `chat` messages → agent responds with `agent_text`, `visualization`, and `tool_call_request` messages that the client must answer. + +See [agent/README.md](agent/README.md) for the full WebSocket protocol, all message types, tool call request/response formats, and example client implementation. + # Setup 1. fork this repo and git clone your fork locally 2. create an `.env` file at the top level which has the following keys: ``` OPENAI_API_KEY="sk-asdjkshd" # if you're doing work with engines that use the LLMWrapper class in utils.js (quantitative, qualitative, seldon, etc.) -GOOGLE_API_KEY="asdjkshd" # if you're doing work with engines using Gemini models (causal-chains, seldon, quantitative, qualitative) +GEMINI_API_KEY="asdjkshd" # if you're doing work with engines using Gemini models (causal-chains, seldon, quantitative, qualitative) AUTHENTICATION_KEY="my_secret_key" # only needed for securing publically accessible deployments. Requires client pass an Authentication header matching this value. e.g. `curl -H "Authentication: my_super_secret_value_in_env_file"` to the engine generate request only REPORTER_URL="https://your-metrics-server.com/api/metrics" # optional URL to POST engine usage metrics to. If not set, metrics reporting is disabled. +TOKEN_REPORTER_URL="https://your-metrics-server.com/api/token-usage" # optional URL to POST agent LLM token usage and cost to. If not set, token reporting is disabled. ``` 3. npm install 4. npm start @@ -198,10 +294,60 @@ We recommend VSCode using a launch.json for the Node type applications (you get Some engines require additional dependencies to be installed on your system: - **Go 1.24.0 or later** - Required for the causal-chains engine ([installation guide](https://go.dev/doc/install)) -- **Python 3.x** - Required for the causal-decoder engine +- **Python 3.x** - Required for the causal-decoder engine and the and the agentic tools These dependencies are automatically built/installed when you run `npm install` via postinstall hooks, but only if the respective toolchains are available on your PATH. +To skip specific components during installation, set the `SKIP_THIRD_PARTY_COMPONENTS` environment variable to a comma-separated list of component names before running `npm install`: + +**Mac/Linux:** +```bash +SKIP_THIRD_PARTY_COMPONENTS=causal-decoder,PySD-simulator,time-series-behavior-analysis npm install +``` + +**Windows:** +```bat +set SKIP_THIRD_PARTY_COMPONENTS=causal-decoder,PySD-simulator,time-series-behavior-analysis && npm install +``` + +Available component names and what they affect: + +| Component | Effect of skipping | +|---|---| +| `causal-chains` | Disables the causal-chains engine | +| `causal-decoder` | Disables the causal-decoder engine | +| `PySD-simulator` | Breaks evals | +| `time-series-behavior-analysis` | Breaks evals | +| `visualization-engine` | Breaks agentic tools | + +## Agent Sandbox (Production Linux Only) + +The agentic assistant runs each session's agent in an isolated worker process. On **Linux**, worker processes are sandboxed using [bubblewrap](https://github.com/containers/bubblewrap) (`bwrap`), which uses Linux kernel namespaces to confine the agent to its session-specific temp directory. The agent cannot read or write anywhere else on the server filesystem — including other sessions, application source code, or environment variables on disk. + +### Installing bubblewrap + +Install `bubblewrap` via your system package manager (`bubblewrap` on most distros). See the [bubblewrap releases page](https://github.com/containers/bubblewrap/releases) for more options. + +### What bwrap provides + +| Isolation | Guarantee | +|---|---| +| Filesystem writes | Agent can only write to its session temp dir | +| Filesystem reads | Only app code, system libs, and TLS certs are visible | +| Cross-session access | Other sessions' temp dirs are not mounted | +| Process isolation | Separate PID namespace; agent cannot signal other processes | +| Hostname isolation | Separate UTS namespace | + +The Python subprocess spawned for visualizations inherits the same bwrap namespace automatically — no separate Python-level sandbox is needed. + +### Development (macOS / Windows) + +`bwrap` is a Linux kernel feature and is not available on macOS or Windows. On those platforms the agent worker runs **unsandboxed** with full filesystem access. A prominent warning is logged at startup. This is acceptable for local development but **must not be used for any publicly hosted deployment**. + +### What bwrap does NOT restrict + +- **Network access** — the agent worker must reach the Anthropic API. The agent can make arbitrary outbound HTTP requests if prompted to do so. Restrict this at the network/firewall level if needed. + ## Metrics Reporting SD-AI includes optional metrics reporting via the `GenerateMetricsReporter` class. When enabled, it automatically tracks and reports usage data for every engine generation request. @@ -232,6 +378,63 @@ For each call to `/api/v1/:engine/generate`, the following JSON data is posted t The reporter sends metrics asynchronously and will not block or affect the engine response, even if the reporting endpoint is unavailable. +## Token Usage Reporting + +The agent uses `TokenUsageReporter` to track token usage and cost for every LLM call made using this service. This is separate from the engine metrics above — it covers the agent's internal Anthropic, Gemini, and OpenAI calls rather than top-level HTTP engine requests. + +### Configuration + +Set `TOKEN_REPORTER_URL` in your `.env` file to enable reporting: +``` +TOKEN_REPORTER_URL="https://your-metrics-server.com/api/token-usage" +``` + +Reporting is only active when **both** `TOKEN_REPORTER_URL` is set **and** the client provided a `clientId` in the `initialize_session` WebSocket message or as an additional parameter to an engine call. If either is missing, usage is still logged to the server console but not POSTed anywhere. + +### Console Logging + +Regardless of whether remote reporting is enabled, every LLM call logs a line to the server console: +``` +[usage:anthropic] input=1234($0.003702) output=256($0.003840) cache_write_5m=0($0.000000) cache_write_1h=0($0.000000) cache_read=512($0.000461) total=$0.008003 +[usage:gemini] input=800($0.000160) output=120($0.000072) cached=200($0.000010) thoughts=40($0.000024) total=$0.000266 +[usage:openai] input=600($0.000300) output=150($0.000225) cached=100($0.000025) reasoning=0 total=$0.000550 +``` + +Per-token costs are shown in parentheses when pricing data is available for the model. If pricing is unknown the token counts are shown without a cost. + +### Reported Payload + +When remote reporting is active, the following JSON is POSTed to `TOKEN_REPORTER_URL` for each LLM call: + +```json +{ + "clientId": "client-provided-id", + "provider": "anthropic", + "model": "claude-sonnet-4-6", + "tokens": { + "inputTokens": 1234, + "outputTokens": 256, + "cacheCreation5mInputTokens": 0, + "cacheCreation1hInputTokens": 0, + "cacheReadInputTokens": 512 + }, + "cost": 0.008003, + "timestamp": "2024-01-15T10:30:00.000Z" +} +``` + +The `tokens` shape varies by provider: + +| Provider | Token fields | +|---|---| +| `anthropic` | `inputTokens`, `outputTokens`, `cacheCreation5mInputTokens`, `cacheCreation1hInputTokens`, `cacheReadInputTokens` | +| `gemini` | `inputTokens`, `outputTokens`, `cachedTokens`, `thoughtsTokens` | +| `openai` | `inputTokens`, `outputTokens`, `cachedTokens`, `reasoningTokens` | + +`cost` is the total dollar cost of the call, or `null` if pricing data is unavailable for the model. + +The reporter fires asynchronously and never blocks or fails the agent response if the reporting endpoint is unavailable. + ## Testing ### Unit Tests Unit tests are provided for: diff --git a/agent/AgentOrchestrator.js b/agent/AgentOrchestrator.js new file mode 100644 index 00000000..cd5a6078 --- /dev/null +++ b/agent/AgentOrchestrator.js @@ -0,0 +1,1748 @@ +import Anthropic from '@anthropic-ai/sdk'; +import { query } from '@anthropic-ai/claude-agent-sdk'; +import { GoogleGenAI } from '@google/genai'; +import { LlmAgent, Runner, InMemorySessionService, isFinalResponse } from '@google/adk'; +import { setMaxListeners } from 'events'; +import { encode } from 'gpt-tokenizer'; +import { marked } from 'marked'; +import { countTokens } from '@anthropic-ai/tokenizer'; +import { AgentConfigurationManager } from './utilities/AgentConfigurationManager.js'; +import { BuiltInToolProvider } from './tools/BuiltInToolProvider.js'; +import { DynamicToolProvider } from './tools/DynamicToolProvider.js'; +import { + createAgentTextMessage, + createToolCallNotificationMessage, + createToolCallCompletedMessage, + createAgentCompleteMessage, + createErrorMessage +} from './utilities/MessageProtocol.js'; +import logger from '../utilities/logger.js'; +import config from '../config.js'; +import { LLMWrapper } from '../utilities/LLMWrapper.js'; +import TokenUsageReporter, { Provider } from '../utilities/TokenUsageReporter.js'; +import { sanitizeSchemaForGemini } from './tools/builtin/toolHelpers.js'; + +// Normalize a single message to Gemini format {role:'user'|'model', parts:[{text}]}. +// Handles Anthropic-format messages ({role, content}) that arrive when switching +// from an Anthropic-mode agent or from client-provided historical messages. +function toGeminiMessage(msg) { + if (Array.isArray(msg.parts)) { + const role = msg.role === 'assistant' ? 'model' : msg.role; + return role === msg.role ? msg : { ...msg, role }; + } + const role = msg.role === 'assistant' ? 'model' : msg.role; + let text = ''; + if (typeof msg.content === 'string') { + text = msg.content; + } else if (Array.isArray(msg.content)) { + text = msg.content.filter(b => b.type === 'text').map(b => b.text || '').join('\n'); + } + return { role, parts: [{ text }] }; +} + +// Normalize a single message to Anthropic format {role:'user'|'assistant', content}. +// Handles Gemini-format messages ({role:'user'|'model', parts}) that arrive when +// switching from a Gemini-mode agent. +function toAnthropicMessage(msg) { + if (!Array.isArray(msg.parts)) { + const role = msg.role === 'model' ? 'assistant' : msg.role; + return role === msg.role ? msg : { ...msg, role }; + } + const role = msg.role === 'model' ? 'assistant' : msg.role; + const text = msg.parts.filter(p => p.text).map(p => p.text).join('\n'); + return { role, content: text }; +} + +/** + * AgentOrchestrator + * Manages the Claude Agent SDK lifecycle and message translation + * + * Responsibilities: + * - Load and apply agent configuration + * - Integrate built-in and dynamic tools + * - Start conversations with Claude Agent SDK + * - Translate SDK messages to WebSocket messages + * - Handle tool execution (built-in vs client tools) + * - Send messages to client via WebSocket + */ +export class AgentOrchestrator { + #geminiManualCacheName = null; + #geminiManualCacheKey = null; + #geminiManualCacheExpiry = null; + #pendingMessages = []; + // ADK can emit multiple events per LLM call that share the same usageMetadata + // object reference (e.g. a streamed partial yield plus the aggregated close() + // yield). No LLM-call id is exposed on the event, so reference equality is the + // only available dedup key. + #geminiAdkReportedUsageMetadata = new WeakSet(); + // Per-assistant usage accumulator for the anthropic SDK route. The SDKResultMessage + // carries the authoritative aggregate and supersedes this on normal completion; + // we only flush the accumulator as a fallback when a query aborts before its + // result message arrives. + #anthropicSdkAccumulatorUsage = { + input_tokens: 0, + output_tokens: 0, + cache_creation: { ephemeral_5m_input_tokens: 0, ephemeral_1h_input_tokens: 0 }, + cache_read_input_tokens: 0, + }; + + constructor(sessionManager, sessionId, sendToClient, agentConfig, provider = config.agentDefaultProvider) { + this.sessionManager = sessionManager; + this.sessionId = sessionId; + this.sendToClient = sendToClient; + this.stopRequested = false; + this.provider = provider; + + // SDK-specific properties (for SDK mode) + this.abortController = null; + this.anthropicSdkSessionId = null; // SDK session ID for conversation continuity + this.pendingToolCalls = new Map(); // Track tool_use_id -> tool_name mapping + + // Load configuration + this.configManager = new AgentConfigurationManager(agentConfig); + + // Create tool providers + this.builtInToolProvider = new BuiltInToolProvider(sessionManager, sessionId, sendToClient); + this.dynamicToolProvider = new DynamicToolProvider(sessionManager, sessionId, sendToClient); + + // Initialize Anthropic client (for non-SDK mode) + this.anthropic = new Anthropic({ + apiKey: process.env.ANTHROPIC_API_KEY + }); + + this.gemini = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY }); + this.geminiAdkSessionId = null; + this.geminiAdkSessionService = new InMemorySessionService(); + + const clientId = sessionManager.getSession(sessionId)?.clientId ?? null; + this.llm = new LLMWrapper({ clientId, underlyingModel: config.agentAnthropicSummaryModel }); + this.tokenReporter = new TokenUsageReporter(config.tokenReporterURL, clientId); + + logger.log(`AgentOrchestrator initialized for session ${sessionId} (loop: ${this.configManager.getAgentMode()}, provider: ${this.provider})`); + } + + /** + * Start a conversation with the agent + */ + async startConversation(userMessage, previousAgentContext = null) { + try { + const session = this.sessionManager.getSession(this.sessionId); + if (!session) { + throw new Error(`Session not found: ${this.sessionId}`); + } + + const loopStyle = this.configManager.getAgentMode(); // 'sdk' | 'manual' + logger.log(`Starting conversation for session ${this.sessionId} (loop: ${loopStyle}, provider: ${this.provider})`); + + await this.#fetchCurrentModel(); + + const isManual = loopStyle === 'manual'; + if (isManual && previousAgentContext?.length > 0) { + // previousAgentContext is a reference to the live context — pop the last message + // (always the prior agent's unanswered user message) before adding the new one + previousAgentContext.pop(); + logger.debug(`[Agent switch → manual] Prior context now has ${previousAgentContext.length} messages after pop`); + } + + switch (`${this.provider}-${loopStyle}`) { + case 'anthropic-sdk': + await this.startConversationWithAnthropicSdk(userMessage, previousAgentContext); + break; + case 'anthropic-manual': + await this.startConversationAnthropicManual(userMessage); + break; + case 'google-sdk': + await this.startConversationWithGeminiAdk(userMessage, previousAgentContext); + break; + case 'google-manual': + await this.startConversationGeminiManual(userMessage); + break; + default: + throw new Error(`Unknown combination: provider=${this.provider}, loop=${loopStyle}`); + } + + } catch (error) { + logger.error(`Error in agent conversation for session ${this.sessionId}:`, error); + + await this.sendToClient(createErrorMessage( + this.sessionId, + error.message, + 'CONVERSATION_ERROR' + )); + await this.sendToClient(createAgentCompleteMessage( + this.sessionId, + 'awaiting_user', + `Agent error: ${error.message}` + )); + } + } + + /** + * Start conversation using manual agent loop (original implementation) + */ + async startConversationAnthropicManual(userMessage) { + const session = this.sessionManager.getSession(this.sessionId); + + // Add user message to conversation history + this.sessionManager.addToConversationHistory(this.sessionId, { + role: 'user', + content: userMessage + }); + + // Build system prompt from config + const mode = session.mode; + const systemPrompt = this.configManager.buildSystemPrompt(mode); + + // Get tool collections + const builtInTools = this.builtInToolProvider.getTools(); + const dynamicTools = this.dynamicToolProvider.getTools(); + + // Start agent conversation loop + // Clean up context (remove stale models, summarize if over limit) before first API call + await this.sessionManager.cleanupContext(this.sessionId, config.agentMaxContextTokens); + + // Use the live session context as the messages array — no local copy + const messages = this.sessionManager.getConversationContext(this.sessionId); + + // Normalize in-place: Gemini-format messages ({role:'user'|'model', parts}) from + // historical session load or a prior Gemini-mode agent switch must become + // Anthropic-format ({role:'user'|'assistant', content}) before the API call. + for (let i = 0; i < messages.length; i++) { + messages[i] = toAnthropicMessage(messages[i]); + } + // Drop any messages that converted to empty content (e.g. Gemini tool call/response + // parts that have no text), which Anthropic rejects. + for (let i = messages.length - 1; i >= 0; i--) { + const content = messages[i].content; + if (!content || (typeof content === 'string' && content.trim() === '') || (Array.isArray(content) && content.length === 0)) { + messages.splice(i, 1); + } + } + + // Check model token count and update session state + const currentModel = session?.clientModel; + let modelTokenCount = 0; + + if (currentModel) { + const modelJson = JSON.stringify(currentModel, null, 2); + modelTokenCount = countTokens(modelJson); + this.sessionManager.updateModelTokenCount(this.sessionId, modelTokenCount); + } + + const systemBlocks = [ + { type: 'text', text: systemPrompt, cache_control: { type: 'ephemeral', ttl: '5m' } } + ]; + + // Convert tool servers to Anthropic tool format (with conditional filtering) + const tools = this.#anthropicManualConvertTools(builtInTools, dynamicTools, modelTokenCount, mode); + + const maxIterations = this.configManager.getMaxIterations(); + + while (true) { + let continueLoop = true; + let completedNaturally = false; + let iteration = 0; + let overloadedRetries = 0; + + while (continueLoop && iteration < maxIterations && !this.stopRequested) { + iteration++; + + // Summarize context in-place if it has grown over the token limit + await this.sessionManager.cleanupContext(this.sessionId, config.agentMaxContextTokens); + + try { + // Call Claude API + const thinkingEnabled = config.agentAnthropicThinking?.type !== 'disabled'; + const response = await this.anthropic.messages.create({ + model: config.agentAnthropicModel, + max_tokens: Math.max(8192, (config.agentAnthropicThinking?.budget_tokens || 0) + 2048), + system: systemBlocks, + messages: messages, + thinking: config.agentAnthropicThinking, + tools: tools.length > 0 ? tools : undefined + }); + + this.#logApiUsage(Provider.ANTHROPIC, response.usage); + + // Check if stop was requested during the API call + if (this.stopRequested) { + break; + } + + // Process response + continueLoop = await this.processAgentResponseAnthropicManual(response, messages, builtInTools, dynamicTools); + if (!continueLoop && !this.stopRequested) completedNaturally = true; + + // Check if stop was requested during response processing + if (this.stopRequested) { + break; + } + + } catch (error) { + const isOverloaded = error?.status === 529 || error?.error?.type === 'overloaded_error'; + const isNetworkError = error?.cause?.code === 'UND_ERR_SOCKET' || error?.code === 'UND_ERR_SOCKET' || + error?.code === 'ECONNRESET' || error?.cause?.code === 'ECONNRESET' || + (error instanceof TypeError && error.message === 'terminated'); + if ((isOverloaded || isNetworkError) && overloadedRetries < 3) { + overloadedRetries++; + const reason = isOverloaded ? 'overloaded (529)' : 'network error'; + logger.warn(`Anthropic Manual: Anthropic API ${reason}, retry ${overloadedRetries}/3`); + await this.sendToClient(createAgentTextMessage( + this.sessionId, + isOverloaded ? 'The AI service is temporarily overloaded. Retrying...' : 'Network connection interrupted. Retrying...' + )); + await new Promise(resolve => setTimeout(resolve, 5000)); + } else if (isOverloaded) { + logger.error('Anthropic Manual: Anthropic API overloaded (529) after 3 retries, giving up'); + await this.sendToClient(createErrorMessage( + this.sessionId, + 'The AI service is overloaded. Please try again later.', + 'AGENT_ERROR' + )); + await this.sendToClient(createAgentCompleteMessage( + this.sessionId, + 'awaiting_user', + 'Agent stopped due to overloaded API' + )); + continueLoop = false; + completedNaturally = true; + } else { + logger.error('Anthropic Manual: Error in agent conversation loop:', error); + await this.sendToClient(createErrorMessage( + this.sessionId, + `Agent error: ${error.message}`, + 'AGENT_ERROR' + )); + await this.sendToClient(createAgentCompleteMessage( + this.sessionId, + 'awaiting_user', + 'Agent stopped due to error' + )); + continueLoop = false; + completedNaturally = true; + } + } + } + + if (this.stopRequested) { + logger.log(`Anthropic Manual: Agent iteration stopped by user request for session ${this.sessionId}`); + this.stopRequested = false; + await this.sendToClient(createAgentCompleteMessage( + this.sessionId, + 'awaiting_user', + 'Agent stopped by user request' + )); + break; + } + const reachedMax = !completedNaturally && iteration >= maxIterations; + if (this.#pendingMessages.length === 0) { + if (reachedMax) { + logger.warn(`Anthropic Manual: Agent conversation reached max iterations (${maxIterations})`); + await this.sendToClient(createAgentCompleteMessage( + this.sessionId, + 'awaiting_user', + `Reached maximum iterations (${maxIterations})` + )); + } + break; + } + + if (reachedMax) { + logger.warn(`Anthropic Manual: max iterations (${maxIterations}) hit; draining queued message with fresh budget`); + } + const next = this.#pendingMessages.shift(); + logger.log(`Anthropic Manual: processing queued message (remaining: ${this.#pendingMessages.length})`); + this.sessionManager.addToConversationHistory(this.sessionId, { role: 'user', content: next }); + } + } + + /** + * Start conversation using Claude Agent SDK + */ + async startConversationWithAnthropicSdk(userMessage, previousAgentContext = null) { + const session = this.sessionManager.getSession(this.sessionId); + const mode = session.mode; + + // Track user message for cross-mode replay (SDK → manual on future switch) + this.sessionManager.addToConversationHistory(this.sessionId, { + role: 'user', + content: userMessage + }); + + let systemPrompt = this.configManager.buildSystemPrompt(mode); + + // Check model token count and handle large models (for SDK mode) + const currentModel = session?.clientModel; + let modelTokenCount = 0; + + if (currentModel) { + const modelJson = JSON.stringify(currentModel, null, 2); + modelTokenCount = countTokens(modelJson); + this.sessionManager.updateModelTokenCount(this.sessionId, modelTokenCount); + } + + // Create abort controller for stop iteration + this.abortController = new AbortController(); + this.maxTurnsReached = false; + + const maxIterations = this.configManager.getMaxIterations(); + + try { + // Build tools list - combine SDK filesystem tools with MCP servers + const builtInSdkTools = ['Read', /*'Edit', 'Write',*/ 'Glob', 'Grep']; + + let mcpServers = { + builtin: this.builtInToolProvider.getMcpServer() + }; + + // Get client MCP server and derive allowed tool names from the same source + const clientMcpServer = this.dynamicToolProvider.getMcpServer(); + const clientToolNames = this.dynamicToolProvider.getToolNames(); // client_* prefixed, used for system prompt + const prefixedClientToolNames = clientToolNames.map(name => `mcp__client__${name.replace(/^client_/, '')}`); + if (clientMcpServer) { + mcpServers.client = clientMcpServer; + } + + // Build allowed tools list with MCP prefixes, filtered by mode and model token count + const allBuiltInTools = this.builtInToolProvider.getTools(); + const builtInToolNames = this.builtInToolProvider.getToolNames() + .filter(name => { + const toolDef = allBuiltInTools.tools[name]; + if (toolDef?.nonSdkOnly) return false; + if (toolDef?.supportedModes && !toolDef.supportedModes.includes(mode)) return false; + if (toolDef?.maxModelTokens && modelTokenCount > toolDef.maxModelTokens) return false; + if (toolDef?.minModelTokens && modelTokenCount < toolDef.minModelTokens) return false; + return true; + }) + .map(name => `mcp__builtin__${name}`); + let allowedTools = [ + ...builtInSdkTools, // SDK filesystem tools (no prefix) + ...builtInToolNames, // Built-in tools with mcp__builtin__ prefix + ...prefixedClientToolNames // Client tools with mcp__client__ prefix + ]; + + // Prefix tool names in system prompt + systemPrompt = this.#anthropicSdkPrefixToolNamesInSystemPrompt(systemPrompt, builtInToolNames, clientToolNames); + + // Build query options with MCP servers + const queryOptions = { + abortController: this.abortController, + systemPrompt: systemPrompt, + model: config.agentAnthropicModel, + maxTokens: 8192, + maxTurns: maxIterations, + mcpServers: mcpServers, + allowedTools: allowedTools, + permissionMode: 'bypassPermissions', + thinking: config.agentAnthropicThinking, + ...(config.agentAnthropicThinking?.type !== 'disabled' && { effort: config.agentAnthropicEffort }), + compact: true // Enable automatic compaction + }; + + // If we have an SDK session ID, resume the conversation + if (this.anthropicSdkSessionId) { + queryOptions.resume = this.anthropicSdkSessionId; + logger.log(`Anthropic SDK: Resuming SDK conversation with session_id: ${this.anthropicSdkSessionId}`); + } else { + logger.log(`Anthropic SDK: Starting new SDK conversation`); + } + + // Build prompt - inject prior agent's history as plain string prefix on agent switch + let prompt = userMessage; + if (previousAgentContext?.length > 0 && !this.anthropicSdkSessionId) { + const contextToReplay = previousAgentContext.slice(0, -1).map(toAnthropicMessage); + if (contextToReplay.length > 0) { + logger.debug(`[Agent switch → SDK] Replaying ${contextToReplay.length} messages from prior agent.`); + const contextText = await this.#anthropicSdkBuildPriorContextText(contextToReplay); + prompt = `[Prior conversation context]\n${contextText}\n[End of prior context]\n\n${userMessage}`; + } + } + + // Create query iterator with Agent SDK + const queryIterator = query({ + prompt, + options: queryOptions + }); + + // Process messages from SDK + for await (const message of queryIterator) { + await this.#handleAnthropicSdkMessage(message); + } + + // Process any messages queued while the SDK was running. Each queued message + // gets a fresh maxTurns budget — even if the prior run hit the limit. + while (!this.stopRequested && this.#pendingMessages.length > 0) { + const next = this.#pendingMessages.shift(); + logger.log(`Anthropic SDK: processing queued message (remaining: ${this.#pendingMessages.length})`); + this.maxTurnsReached = false; + const followUpIterator = query({ prompt: next, options: { ...queryOptions, resume: this.anthropicSdkSessionId } }); + for await (const message of followUpIterator) { + await this.#handleAnthropicSdkMessage(message); + } + } + + // Normal completion (or max turns reached) + if (this.maxTurnsReached) { + logger.log(`Anthropic SDK: Agent reached max iterations for session ${this.sessionId}`); + await this.sendToClient(createAgentCompleteMessage( + this.sessionId, + 'awaiting_user', + `Reached maximum iterations (${maxIterations})` + )); + } else { + logger.log(`Anthropic SDK: Agent conversation completed successfully for session ${this.sessionId}`); + await this.sendToClient(createAgentCompleteMessage( + this.sessionId, + 'success', + 'Task completed successfully' + )); + } + + } catch (error) { + if (error.name === 'AbortError' || this.stopRequested) { + logger.log(`Anthropic SDK: Agent iteration stopped by user request for session ${this.sessionId}`); + await this.sendToClient(createAgentCompleteMessage( + this.sessionId, + 'awaiting_user', + 'Agent stopped by user request' + )); + } else if (error.message?.includes('maximum number of turns')) { + logger.log(`Anthropic SDK: Agent reached max turns for session ${this.sessionId}`); + await this.sendToClient(createAgentCompleteMessage( + this.sessionId, + 'awaiting_user', + `Reached maximum iterations (${maxIterations})` + )); + } else { + logger.error('Anthropic SDK: Error in agent conversation loop:', error); + await this.sendToClient(createErrorMessage( + this.sessionId, + `Agent error: ${error.message}`, + 'AGENT_ERROR' + )); + await this.sendToClient(createAgentCompleteMessage( + this.sessionId, + 'awaiting_user', + `Agent error: ${error.message}` + )); + } + } finally { + // Safety net: report any per-assistant usage that wasn't superseded by a + // result message (e.g. the query was aborted mid-stream). + this.#flushAnthropicSdkUsageAccumulator(); + this.abortController = null; + } + } + + /** + * Determine the response type for a completed tool call (using stripped tool name) + */ + #getResponseType(displayName) { + if (['generate_ltm_narrative'].includes(displayName)) return 'ltm-discuss'; + if (['discuss_model_with_seldon', 'discuss_model_across_runs', 'discuss_with_mentor'].includes(displayName)) return 'discuss'; + if (['generate_quantitative_model', 'generate_qualitative_model'].includes(displayName)) return 'model'; + return 'other'; + } + + /** + * Remove MCP prefix from tool names for client display + */ + #stripMcpPrefix(toolName) { + if (toolName.startsWith('mcp__builtin__')) { + return toolName.substring('mcp__builtin__'.length); + } + if (toolName.startsWith('mcp__client__')) { + return toolName.substring('mcp__client__'.length); + } + return toolName; + } + + /** + * Handle messages from Agent SDK + */ + async #handleAnthropicSdkMessage(message) { + switch (message.type) { + case 'assistant': + await this.#handleAnthropicSdkAssistantMessage(message); + break; + + case 'result': + await this.#handleAnthropicSdkResultMessage(message); + break; + + case 'system': + if (message.subtype === 'init') { + if (message.session_id) { + this.anthropicSdkSessionId = message.session_id; + logger.log(`Anthropic SDK initialized for session ${this.sessionId}, SDK session_id: ${this.anthropicSdkSessionId}`); + } + } else if (message.subtype === 'error') { + logger.error(`Anthropic SDK system error for session ${this.sessionId}:`, message.error || message); + await this.sendToClient(createErrorMessage( + this.sessionId, + message.error?.message || 'SDK system error', + 'SDK_SYSTEM_ERROR' + )); + } else if (message.subtype === 'api_retry') { + logger.log(`Anthropic SDK: API retry attempt ${message.attempt}/${message.max_retries} for session ${this.sessionId} (status: ${message.error_status}, delay: ${Math.round(message.retry_delay_ms / 1000)}s)`); + } + break; + + case 'user': + await this.#handleAnthropicSdkUserMessage(message); + break; + + default: + logger.warn(`Anthropic SDK: Unhandled message type: ${message.type}`, message); + } + } + + /** + * Handle assistant messages (text from Claude) + * + * Usage isn't reported here — the SDKResultMessage carries the authoritative + * aggregate (including the SDK's internal compaction calls). But on abort no + * result message arrives, so we also accumulate every per-assistant usage and + * flush it as a fallback in the surrounding try/finally. + */ + async #handleAnthropicSdkAssistantMessage(message) { + const usage = message.message?.usage; + if (usage) { + this.#anthropicSdkAccumulatorUsage.input_tokens += usage.input_tokens ?? 0; + this.#anthropicSdkAccumulatorUsage.output_tokens += usage.output_tokens ?? 0; + this.#anthropicSdkAccumulatorUsage.cache_creation.ephemeral_5m_input_tokens += usage.cache_creation?.ephemeral_5m_input_tokens ?? 0; + this.#anthropicSdkAccumulatorUsage.cache_creation.ephemeral_1h_input_tokens += usage.cache_creation?.ephemeral_1h_input_tokens ?? 0; + this.#anthropicSdkAccumulatorUsage.cache_read_input_tokens += usage.cache_read_input_tokens ?? 0; + } + + const content = message.message?.content; + const rawTextParts = []; + + if (content && Array.isArray(content)) { + for (const block of content) { + if (block.type === 'text' && block.text) { + rawTextParts.push(block.text); + const html = await marked.parse(block.text); + await this.sendToClient(createAgentTextMessage(this.sessionId, html, false)); + } + else if (block.type === 'thinking' && block.thinking) { + //claude code is too chatty -- don't send these! + /*const html = await marked.parse(block.thinking); + await this.sendToClient(createAgentTextMessage(this.sessionId, html, true));*/ + } + else if (block.type === 'tool_use' && block.name) { + this.pendingToolCalls.set(block.id, block.name); + + const isFilesystemTool = ['Read', 'Edit', 'Write', 'Glob', 'Grep'].includes(block.name); + const isBuiltInMcpTool = block.name.startsWith('mcp__builtin__'); + const isBuiltIn = isFilesystemTool || isBuiltInMcpTool; + + const displayName = this.#stripMcpPrefix(block.name); + + await this.sendToClient(createToolCallNotificationMessage( + this.sessionId, + block.id, + displayName, + block.input || {}, + isBuiltIn + )); + } + else if (block.type === 'tool_result' && block.tool_use_id) { + const toolName = this.pendingToolCalls.get(block.tool_use_id) || 'unknown'; + const displayName = this.#stripMcpPrefix(toolName); + + if (block.is_error) { + logger.log(`Anthropic SDK: Tool error for ${toolName} (${block.tool_use_id}):`, block.content); + } else { + logger.log(`Anthropic SDK: Tool call completed: ${displayName}`); + } + + const responseType = this.#getResponseType(displayName); + + await this.sendToClient(createToolCallCompletedMessage( + this.sessionId, + block.tool_use_id, + displayName, + block.content, + block.is_error || false, + responseType + )); + + this.pendingToolCalls.delete(block.tool_use_id); + } + } + } + + // Track client-facing text for cross-mode replay (SDK → manual) + if (rawTextParts.length > 0) { + this.sessionManager.addToConversationHistory(this.sessionId, { + role: 'assistant', + content: rawTextParts.join('\n') + }); + } + } + + /** + * Handle user messages (tool results being sent back to Claude) + */ + async #handleAnthropicSdkUserMessage(message) { + const content = message.message?.content; + + if (content && Array.isArray(content)) { + for (const block of content) { + if (block.type === 'tool_result' && block.tool_use_id) { + const toolName = this.pendingToolCalls.get(block.tool_use_id) || 'unknown'; + const displayName = this.#stripMcpPrefix(toolName); + + if (block.is_error) { + logger.log(`Anthropic SDK: Tool error for ${toolName} (${block.tool_use_id}):`, block.content); + } else { + logger.log(`Anthropic SDK: Tool call completed: ${displayName}`); + } + + const responseType = this.#getResponseType(displayName); + + await this.sendToClient(createToolCallCompletedMessage( + this.sessionId, + block.tool_use_id, + displayName, + block.content, + block.is_error || false, + responseType + )); + + this.pendingToolCalls.delete(block.tool_use_id); + } + } + } + } + + /** + * Handle result messages (conversation completion). + * + * The result message carries the aggregate usage for the entire query (across + * every assistant turn AND the SDK's internal compaction calls), so this is + * the canonical point where we report usage for the SDK route. The + * per-assistant accumulator is reset because the result supersedes it. + */ + async #handleAnthropicSdkResultMessage(message) { + if (message.usage) { + this.#logApiUsage(Provider.ANTHROPIC, message.usage); + this.#resetAnthropicSdkUsageAccumulator(); + } else { + this.#flushAnthropicSdkUsageAccumulator(); + } + + if (message.subtype === 'success') { + logger.log(`Anthropic SDK conversation completed successfully for session ${this.sessionId}`); + } else if (message.subtype === 'error_max_turns') { + logger.log(`Anthropic SDK conversation reached max iterations for session ${this.sessionId}`); + this.maxTurnsReached = true; + } else if (message.subtype === 'error') { + logger.warn(`Anthropic SDK conversation error for session ${this.sessionId}:`, message.error || message); + } else if (message.subtype === 'tool_error') { + logger.log(`Anthropic SDK tool error for session ${this.sessionId}:`, message); + } else { + logger.warn(`Anthropic SDK Unhandled result message subtype: ${message.subtype}`, message); + } + } + + /** + * Prefix tool names in system prompt for SDK mode + * Scans the system prompt and adds mcp__ prefixes to tool names + */ + #anthropicSdkPrefixToolNamesInSystemPrompt(systemPrompt, builtInToolNames, clientToolNames) { + let modifiedPrompt = systemPrompt; + + // Create mapping of unprefixed tool names to prefixed versions + const toolNameMapping = {}; + + // Built-in tools: tool_name -> mcp__builtin__tool_name + for (const prefixedName of builtInToolNames) { + const unprefixedName = prefixedName.replace(/^mcp__builtin__/, ''); + toolNameMapping[unprefixedName] = prefixedName; + } + + // Client tools: client_tool_name -> mcp__client__tool_name + for (const clientToolName of clientToolNames) { + const unprefixedName = clientToolName.replace(/^client_/, ''); + const prefixedName = `mcp__client__${unprefixedName}`; + toolNameMapping[clientToolName] = prefixedName; + // Also map the unprefixed name + toolNameMapping[unprefixedName] = prefixedName; + } + + // Replace tool names in the system prompt + // Look for patterns like `tool_name` or **tool_name** or tool_name (surrounded by word boundaries) + for (const [unprefixed, prefixed] of Object.entries(toolNameMapping)) { + // Match tool names in backticks, bold, or standalone + const patterns = [ + new RegExp(`\`${unprefixed}\``, 'g'), // `tool_name` + new RegExp(`\\*\\*${unprefixed}\\*\\*`, 'g'), // **tool_name** + new RegExp(`\\b${unprefixed}\\b`, 'g') // tool_name (word boundary) + ]; + + for (const pattern of patterns) { + modifiedPrompt = modifiedPrompt.replace(pattern, (match) => { + // Preserve the formatting around the tool name + return match.replace(unprefixed, prefixed); + }); + } + } + + return modifiedPrompt; + } + + /** + * Process agent response and handle tool calls + * Returns true if the conversation should continue + */ + async processAgentResponseAnthropicManual(response, messages, builtInTools, dynamicTools) { + let hasToolCalls = false; + + // Collect all assistant content blocks and tool results before touching messages. + // This ensures every tool_use is always paired with its tool_result in one atomic + // write, preventing orphaned tool_use blocks if processing is interrupted mid-response. + const assistantContent = []; + const toolResults = []; + + // Process each content block (stream to client, execute tools) + for (const block of response.content) { + // Check if stop was requested before processing each block + if (this.stopRequested) { + return false; // Stop processing immediately (nothing added to messages yet) + } + + if (block.type === 'text') { + // Send text content to client + const text = await marked.parse(block.text); + + await this.sendToClient(createAgentTextMessage( + this.sessionId, + text, + false + )); + + assistantContent.push({ type: 'text', text: block.text }); + } else if (block.type === 'tool_use') { + hasToolCalls = true; + + // Notify client that tool call is happening (for UI display) + const isBuiltIn = this.#isBuiltInTool(block.name, builtInTools); + await this.sendToClient(createToolCallNotificationMessage( + this.sessionId, + block.id, + block.name, + block.input, + isBuiltIn + )); + + // Send additional text notification for slow tools + if (block.name === 'create_visualization') { + const vizType = block.input.useAICustom ? 'AI-generated custom' : (block.input.type || 'standard'); + const title = block.input.title || 'visualization'; + await this.sendToClient(createAgentTextMessage( + this.sessionId, + `Creating ${vizType} visualization: "${title}"... This may take a moment.`, + false + )); + } else if (block.name === 'get_variable_data') { + const varCount = block.input.variableNames?.length || 0; + const runCount = block.input.runIds?.length || 0; + await this.sendToClient(createAgentTextMessage( + this.sessionId, + `Retrieving data for ${varCount} variable${varCount !== 1 ? 's' : ''} from ${runCount} run${runCount !== 1 ? 's' : ''}...`, + false + )); + } else if (block.name === 'get_feedback_information') { + const runCount = block.input.runIds?.length || 0; + const runText = runCount === 0 ? 'all runs' : `${runCount} run${runCount !== 1 ? 's' : ''}`; + await this.sendToClient(createAgentTextMessage( + this.sessionId, + `Analyzing feedback loops for ${runText}... This may take a moment.`, + false + )); + } else if (block.name === 'run_model') { + await this.sendToClient(createAgentTextMessage( + this.sessionId, + `Running model simulation...`, + false + )); + } else if (block.name === 'discuss_model_with_seldon') { + await this.sendToClient(createAgentTextMessage( + this.sessionId, + `Consulting Seldon for expert analysis...`, + false + )); + } else if (block.name === 'discuss_model_across_runs') { + await this.sendToClient(createAgentTextMessage( + this.sessionId, + `Analyzing model behavior across runs...`, + false + )); + } else if (block.name === 'discuss_with_mentor') { + await this.sendToClient(createAgentTextMessage( + this.sessionId, + `Consulting Seldon mentor for guidance...`, + false + )); + } + + // Execute tool + const toolResult = await this.anthropicManualExecuteToolCall(block, builtInTools, dynamicTools); + + // Check if stop was requested during tool execution + if (this.stopRequested) { + return false; // Stop processing immediately (nothing added to messages yet) + } + + if (toolResult.isError) { + logger.log(`Anthropic Manual: Tool error for ${block.name}:`, toolResult.content); + } else { + logger.log(`Anthropic Manual: Tool call completed: ${block.name}`); + } + + const responseType = this.#getResponseType(block.name); + + // Notify client of completion + await this.sendToClient(createToolCallCompletedMessage( + this.sessionId, + block.id, + block.name, + toolResult.content, + toolResult.isError, + responseType + )); + + const resultText = Array.isArray(toolResult.content) + ? toolResult.content.filter(b => b.type === 'text').map(b => b.text).join('\n') + : typeof toolResult.content === 'string' ? toolResult.content : JSON.stringify(toolResult.content); + + assistantContent.push({ type: 'tool_use', id: block.id, name: block.name, input: block.input }); + toolResults.push({ type: 'tool_result', tool_use_id: block.id, content: resultText, is_error: toolResult.isError || false }); + } + } + + // Atomically commit the full response to messages: one assistant message containing + // all content blocks (text + all tool_uses), then one user message with all tool_results. + // Keeping every tool_use paired with its tool_result in the same write prevents the + // "tool_use without tool_result" API error that occurs when context summarisation + // truncates the middle of an interleaved sequence. + if (assistantContent.length > 0) { + if (!messages[messages.length - 1] || messages[messages.length - 1].role !== 'assistant') { + messages.push({ role: 'assistant', content: [] }); + } + for (const block of assistantContent) { + messages[messages.length - 1].content.push(block); + } + } + if (toolResults.length > 0) { + messages.push({ role: 'user', content: toolResults }); + } + + // If we had tool calls, continue the loop to let Claude process results + if (hasToolCalls) { + return true; + } + + // Continue if stop_reason is max_tokens + if (response.stop_reason === 'max_tokens') { + return true; + } + + // Any other stop reason (end_turn, stop_sequence, etc.) — complete + await this.sendToClient(createAgentCompleteMessage( + this.sessionId, + 'success', + 'Task completed successfully' + )); + return false; + } + + /** + * Build prior-history context text, summarizing if it exceeds the token budget. + * Used when injecting prior agent context into an SDK session. + */ + async #anthropicSdkBuildPriorContextText(history) { + try { + const conversationText = history.map((msg) => { + if (msg.role === 'user') { + return `User: ${typeof msg.content === 'string' ? msg.content : JSON.stringify(msg.content)}`; + } else if (msg.role === 'assistant') { + if (Array.isArray(msg.content)) { + const textContent = msg.content.filter(b => b.type === 'text').map(b => b.text).join('\n'); + return textContent ? `Assistant: ${textContent}` : ''; + } + return `Assistant: ${msg.content}`; + } + return ''; + }).filter(line => line).join('\n\n'); + + logger.log(`Anthropic: Summarizing prior agent context (${history.length} messages) before injection`); + const response = await this.anthropic.messages.create({ + model: config.agentAnthropicSummaryModel, + max_tokens: 1024, + messages: [{ role: 'user', content: `Summarize this conversation history concisely (2-4 paragraphs):\n\n${conversationText}` }] + }); + if (response.usage) { + this.#logApiUsage(Provider.ANTHROPIC, response.usage, config.agentAnthropicSummaryModel); + } + return response.content[0].text; + } catch (error) { + logger.error('Anthropic: Error summarizing prior context:', error); + return '[Prior conversation condensed due to size]'; + } + } + + /** + * Execute a tool call (built-in or client tool) + */ + async anthropicManualExecuteToolCall(toolUse, builtInTools, _dynamicTools) { + try { + // Check if it's a built-in tool + if (builtInTools.tools[toolUse.name]) { + const handler = builtInTools.tools[toolUse.name].handler; + const result = await handler(toolUse.input); + // Handler already returns { content: [...], isError: bool } + return result; + } + + // Check if it's a client tool + if (this.dynamicToolProvider.isClientTool(toolUse.name)) { + const unprefixedName = toolUse.name.replace(/^client_/, ''); + const result = await this.dynamicToolProvider.requestClientExecution( + unprefixedName, + toolUse.input + ); + return { + content: result, + isError: false + }; + } + + // Tool not found + return { + content: `Tool not found: ${toolUse.name}`, + isError: true + }; + + } catch (error) { + logger.error(`Anthropic Manual: Error executing tool ${toolUse.name}:`, error); + return { + content: error.message, + isError: true + }; + } + } + + /** + * Convert tool servers to Anthropic tool format + */ + #anthropicManualConvertTools(builtInTools, dynamicTools, modelTokenCount = 0, mode = null) { + const tools = []; + const toolNames = new Set(); + + // Convert built-in tools + for (const [toolName, toolDef] of Object.entries(builtInTools.tools)) { + if (toolNames.has(toolName)) { + logger.warn(`Anthropic: Duplicate tool name detected: ${toolName} (from built-in tools)`); + continue; + } + + // Skip tools that don't support the current mode + if (mode && toolDef.supportedModes && !toolDef.supportedModes.includes(mode)) { + continue; + } + + // Skip tools whose model token constraints aren't met + if (toolDef.maxModelTokens && modelTokenCount > toolDef.maxModelTokens) { + continue; + } + if (toolDef.minModelTokens && modelTokenCount < toolDef.minModelTokens) { + continue; + } + + toolNames.add(toolName); + + tools.push({ + name: toolName, + description: toolDef.description, + input_schema: toolDef.inputSchema.toJSONSchema() + }); + } + + // Convert dynamic tools (client tools) + if (dynamicTools && dynamicTools.tools) { + for (const [toolName, toolDef] of Object.entries(dynamicTools.tools)) { + if (toolNames.has(toolName)) { + logger.warn(`Anthropic: Duplicate tool name detected: ${toolName} (from client tools) - skipping client version, using built-in`); + continue; + } + toolNames.add(toolName); + + tools.push({ + name: toolName, + description: toolDef.description, + input_schema: toolDef.inputSchema.toJSONSchema() + }); + } + } + + // Cache all tool definitions up to the last one — stable within a session + if (tools.length > 0) { + tools[tools.length - 1] = { ...tools[tools.length - 1], cache_control: { type: 'ephemeral', ttl: '5m' } }; + } + + return tools; + } + + /** + * Check if a tool is a built-in tool + */ + #isBuiltInTool(toolName, builtInTools) { + return toolName in builtInTools.tools; + } + + // ─── Gemini manual pathway ────────────────────────────────────────────────── + + async startConversationGeminiManual(userMessage) { + this.sessionManager.addToConversationHistory(this.sessionId, { + role: 'user', + parts: [{ text: userMessage }] + }); + + const session = this.sessionManager.getSession(this.sessionId); + const mode = session.mode; + const systemPrompt = this.configManager.buildSystemPrompt(mode); + const builtInTools = this.builtInToolProvider.getTools(); + const dynamicTools = this.dynamicToolProvider.getTools(); + + await this.sessionManager.cleanupContext(this.sessionId, config.agentMaxContextTokens); + + const messages = this.sessionManager.getConversationContext(this.sessionId); + + // Normalize in-place: Anthropic-format messages ({role,content}) from historical + // session load or a prior Anthropic-mode agent switch must become Gemini-format + // ({role:'user'|'model', parts}) before being sent to the Gemini API. + for (let i = 0; i < messages.length; i++) { + messages[i] = toGeminiMessage(messages[i]); + } + + const currentModel = session?.clientModel; + + let modelTokenCount = 0; + + if (currentModel) { + const modelJson = JSON.stringify(currentModel, null, 2); + modelTokenCount = encode(modelJson).length; + this.sessionManager.updateModelTokenCount(this.sessionId, modelTokenCount); + } + + const toolDeclarations = this.#geminiManualConvertTools(builtInTools, dynamicTools, modelTokenCount, mode); + + // Build or reuse per-session Gemini context cache (system prompt + tools) + let geminiConfig = await this.#getGeminiManualConfig(systemPrompt, toolDeclarations); + + const maxIterations = this.configManager.getMaxIterations(); + + while (true) { + let continueLoop = true; + let completedNaturally = false; + let iteration = 0; + let retries = 0; + + while (continueLoop && iteration < maxIterations && !this.stopRequested) { + iteration++; + await this.sessionManager.cleanupContext(this.sessionId, config.agentMaxContextTokens); + + try { + const response = await this.gemini.models.generateContent({ + model: config.agentGeminiModel, + contents: messages, + config: geminiConfig + }); + + this.#logApiUsage(Provider.GOOGLE, response.usageMetadata); + + if (this.stopRequested) break; + + continueLoop = await this.processGeminiManualResponse(response, messages, builtInTools, dynamicTools); + if (!continueLoop) completedNaturally = true; + + if (this.stopRequested) break; + + } catch (error) { + const isQuota = error?.status === 429; + const isNetworkError = error?.code === 'UND_ERR_SOCKET' || error?.code === 'ECONNRESET' || + (error instanceof TypeError && error.message === 'terminated'); + const isStaleCacheError = error?.status === 403 && + typeof error?.message === 'string' && error.message.includes('CachedContent not found'); + if (isStaleCacheError && retries < 1) { + retries++; + logger.warn('Gemini Manual: cached content expired mid-session, recreating cache'); + this.#geminiManualCacheName = null; + this.#geminiManualCacheKey = null; + this.#geminiManualCacheExpiry = null; + geminiConfig = await this.#getGeminiManualConfig(systemPrompt, toolDeclarations); + } else if ((isQuota || isNetworkError) && retries < 3) { + retries++; + const reason = isQuota ? 'quota/rate-limited (429)' : 'network error'; + logger.warn(`Gemini Manual: Gemini API ${reason}, retry ${retries}/3`); + await this.sendToClient(createAgentTextMessage( + this.sessionId, + isQuota ? 'The AI service is temporarily rate-limited. Retrying...' : 'Network connection interrupted. Retrying...' + )); + await new Promise(resolve => setTimeout(resolve, 5000)); + } else if (isQuota) { + logger.error('Gemini Manual: Gemini API rate-limited after 3 retries, giving up'); + await this.sendToClient(createErrorMessage(this.sessionId, 'The AI service is rate-limited. Please try again later.', 'AGENT_ERROR')); + await this.sendToClient(createAgentCompleteMessage(this.sessionId, 'awaiting_user', 'Agent stopped due to rate limiting')); + continueLoop = false; + completedNaturally = true; + } else { + logger.error('Gemini Manual: Error in Gemini agent conversation loop:', error); + await this.sendToClient(createErrorMessage(this.sessionId, `Agent error: ${error.message}`, 'AGENT_ERROR')); + await this.sendToClient(createAgentCompleteMessage(this.sessionId, 'awaiting_user', 'Agent stopped due to error')); + continueLoop = false; + completedNaturally = true; + } + } + } + + if (this.stopRequested) { + this.stopRequested = false; + await this.sendToClient(createAgentCompleteMessage(this.sessionId, 'awaiting_user', 'Agent stopped by user request')); + break; + } + const reachedMax = !completedNaturally && iteration >= maxIterations; + if (this.#pendingMessages.length === 0) { + if (reachedMax) { + logger.warn(`Gemini Manual: Agent conversation reached max iterations (${maxIterations})`); + await this.sendToClient(createAgentCompleteMessage(this.sessionId, 'awaiting_user', `Reached maximum iterations (${maxIterations})`)); + } + break; + } + + if (reachedMax) { + logger.warn(`Gemini Manual: max iterations (${maxIterations}) hit; draining queued message with fresh budget`); + } + const next = this.#pendingMessages.shift(); + logger.log(`Gemini Manual: processing queued message (remaining: ${this.#pendingMessages.length})`); + this.sessionManager.addToConversationHistory(this.sessionId, { role: 'user', parts: [{ text: next }] }); + messages.push({ role: 'user', parts: [{ text: next }] }); + } + } + + async processGeminiManualResponse(response, messages, builtInTools, dynamicTools) { + const candidate = response.candidates?.[0]; + if (!candidate?.content) { + await this.sendToClient(createAgentCompleteMessage(this.sessionId, 'success', 'Task completed successfully')); + return false; + } + + const parts = candidate.content.parts || []; + + messages.push({ role: 'model', parts }); + + const rawTextParts = []; + for (const part of parts) { + if (part.thought) continue; + if (part.text) { + rawTextParts.push(part.text); + const html = await marked.parse(part.text); + await this.sendToClient(createAgentTextMessage(this.sessionId, html, false)); + } + } + + const functionCallParts = parts.filter(p => p.functionCall); + if (functionCallParts.length === 0) { + await this.sendToClient(createAgentCompleteMessage(this.sessionId, 'success', 'Task completed successfully')); + return false; + } + + const functionResponseParts = []; + for (const part of functionCallParts) { + if (this.stopRequested) return false; + + const { name, args } = part.functionCall; + const callId = `fc_${Date.now()}_${Math.random().toString(36).substr(2, 7)}`; + const isBuiltIn = this.#isBuiltInTool(name, builtInTools); + + await this.#sendSlowToolMessageGemini(name, args); + await this.sendToClient(createToolCallNotificationMessage(this.sessionId, callId, name, args, isBuiltIn)); + + const toolResult = await this.executeToolCallGeminiManual({ name, input: args }); + + if (this.stopRequested) return false; + + if (toolResult.isError) { + logger.log(`Gemini Manual: Tool error for ${name}:`, toolResult.content); + } else { + logger.log(`Gemini Manual: Tool call completed: ${name}`); + } + + const responseType = this.#getResponseType(name); + await this.sendToClient(createToolCallCompletedMessage( + this.sessionId, callId, name, toolResult.content, toolResult.isError, responseType + )); + + const resultText = Array.isArray(toolResult.content) + ? toolResult.content.filter(b => b.type === 'text').map(b => b.text).join('\n') + : String(toolResult.content); + + functionResponseParts.push({ + functionResponse: { name, response: { result: resultText } } + }); + } + + messages.push({ role: 'user', parts: functionResponseParts }); + return true; + } + + // ─── Gemini ADK pathway ───────────────────────────────────────────────────── + + #adkHasPriorContext = false; + + async startConversationWithGeminiAdk(userMessage, previousAgentContext = null) { + const session = this.sessionManager.getSession(this.sessionId); + const mode = session.mode; + + this.sessionManager.addToConversationHistory(this.sessionId, { + role: 'user', + parts: [{ text: userMessage }] + }); + + let systemPrompt = this.configManager.buildSystemPrompt(mode); + const currentModel = session?.clientModel; + let modelTokenCount = 0; + + if (currentModel) { + const modelJson = JSON.stringify(currentModel, null, 2); + modelTokenCount = encode(modelJson).length; + this.sessionManager.updateModelTokenCount(this.sessionId, modelTokenCount); + logger.log(`Model token count: ${modelTokenCount} (limit: ${config.agentMaxTokensForEngines}, exceeds: ${modelTokenCount > config.agentMaxTokensForEngines})`); + } + + this.abortController = new AbortController(); + // @google/genai attaches an abort listener per HTTP request without removing it on + // success, so a multi-tool ADK turn easily exceeds Node's default limit of 10. + setMaxListeners(0, this.abortController.signal); + const maxIterations = this.configManager.getMaxIterations(); + let maxIterationsHit = false; + + try { + const builtInAdkTools = this.builtInToolProvider.getAdkTools(mode, modelTokenCount); + const clientAdkTools = this.dynamicToolProvider.getAdkTools(); + + const pendingCallIds = new Map(); + + const agent = new LlmAgent({ + name: this.configManager.getAgentName(), + model: config.agentGeminiModel, + instruction: systemPrompt, + tools: [...builtInAdkTools, ...clientAdkTools], + generateContentConfig: { + thinkingConfig: config.agentGeminiThinking + }, + beforeToolCallback: async ({ tool, args }) => { + const callId = `adk_${Date.now()}_${Math.random().toString(36).substr(2, 7)}`; + const key = `${tool.name}::${JSON.stringify(args)}`; + pendingCallIds.set(key, callId); + const isBuiltIn = builtInAdkTools.some(t => t.name === tool.name); + await this.#sendSlowToolMessageGemini(tool.name, args); + await this.sendToClient(createToolCallNotificationMessage( + this.sessionId, callId, tool.name, args, isBuiltIn + )); + }, + afterToolCallback: async ({ tool, args, toolResponse }) => { + const key = `${tool.name}::${JSON.stringify(args)}`; + const callId = pendingCallIds.get(key) || `adk_${Date.now()}`; + pendingCallIds.delete(key); + logger.log(`Gemini ADK: Tool call completed: ${tool.name}`); + const responseType = this.#getResponseType(tool.name); + const content = [{ type: 'text', text: String(toolResponse ?? '') }]; + await this.sendToClient(createToolCallCompletedMessage( + this.sessionId, callId, tool.name, content, false, responseType + )); + } + }); + + const runner = new Runner({ + appName: 'sd-ai', + agent, + sessionService: this.geminiAdkSessionService + }); + + if (!this.geminiAdkSessionId) { + this.geminiAdkSessionId = this.sessionId; + await this.geminiAdkSessionService.createSession({ + appName: 'sd-ai', + userId: this.sessionId, + sessionId: this.geminiAdkSessionId + }); + logger.log(`Gemini ADK: session created: ${this.geminiAdkSessionId}`); + } else { + logger.log(`Gemini ADK: Resuming session: ${this.geminiAdkSessionId}`); + } + + let prompt = userMessage; + if (previousAgentContext?.length > 0 && !this.#adkHasPriorContext) { + const contextToReplay = previousAgentContext.slice(0, -1).map(toGeminiMessage); + if (contextToReplay.length > 0) { + logger.debug(`[Agent switch → ADK] Replaying ${contextToReplay.length} messages from prior agent.`); + const contextText = await this.#geminiAdkBuildPriorContextText(contextToReplay); + prompt = `[Prior conversation context]\n${contextText}\n[End of prior context]\n\n${userMessage}`; + } + this.#adkHasPriorContext = true; + } + + let currentMessage = { role: 'user', parts: [{ text: prompt }] }; + + let turnCount = 0; + while (true) { + for await (const event of runner.runAsync({ + userId: this.sessionId, + sessionId: this.geminiAdkSessionId, + newMessage: currentMessage, + abortSignal: this.abortController.signal + })) { + if (event.usageMetadata && !this.#geminiAdkReportedUsageMetadata.has(event.usageMetadata)) { + this.#geminiAdkReportedUsageMetadata.add(event.usageMetadata); + this.#logApiUsage(Provider.GOOGLE, event.usageMetadata); + } + + if (this.stopRequested) break; + await this.#handleGeminiAdkEvent(event); + if (isFinalResponse(event)) turnCount++; + if (turnCount >= maxIterations) { + logger.warn(`Gemini ADK: agent reached max iterations (${maxIterations})`); + maxIterationsHit = true; + this.abortController.abort(); + break; + } + } + + if (this.stopRequested) break; + if (this.#pendingMessages.length === 0) break; + + if (maxIterationsHit) { + logger.warn(`Gemini ADK: max iterations (${maxIterations}) hit; draining queued message with fresh budget`); + maxIterationsHit = false; + // Previous run aborted the controller — create a fresh one for the next run. + this.abortController = new AbortController(); + setMaxListeners(0, this.abortController.signal); + } + + const next = this.#pendingMessages.shift(); + logger.log(`Gemini ADK: processing queued message (remaining: ${this.#pendingMessages.length})`); + currentMessage = { role: 'user', parts: [{ text: next }] }; + turnCount = 0; + } + + if (this.stopRequested) { + this.stopRequested = false; + await this.sendToClient(createAgentCompleteMessage(this.sessionId, 'awaiting_user', 'Agent stopped by user request')); + } else if (maxIterationsHit) { + logger.log(`Gemini ADK: max iterations hit ${this.sessionId}`); + await this.sendToClient(createAgentCompleteMessage(this.sessionId, 'awaiting_user', `Reached maximum iterations (${maxIterations})`)); + } else { + logger.log(`Gemini ADK: conversation completed successfully for session ${this.sessionId}`); + await this.sendToClient(createAgentCompleteMessage(this.sessionId, 'success', 'Task completed successfully')); + } + + } catch (error) { + if (maxIterationsHit) { + logger.log(`Gemini ADK: agent reached max iterations for session ${this.sessionId}`); + await this.sendToClient(createAgentCompleteMessage(this.sessionId, 'awaiting_user', `Reached maximum iterations (${maxIterations})`)); + } else if (error.name === 'AbortError' || this.stopRequested) { + this.stopRequested = false; + logger.log(`Gemini ADK: agent stopped for session ${this.sessionId}`); + await this.sendToClient(createAgentCompleteMessage(this.sessionId, 'awaiting_user', 'Agent stopped by user request')); + } else { + logger.error('Gemini ADK: in ADK conversation loop:', error); + await this.sendToClient(createErrorMessage(this.sessionId, `Agent error: ${error.message}`, 'AGENT_ERROR')); + await this.sendToClient(createAgentCompleteMessage(this.sessionId, 'awaiting_user', `Agent error: ${error.message}`)); + } + } finally { + this.abortController = null; + } + } + + async #handleGeminiAdkEvent(event) { + if (event.errorCode) { + throw new Error(event.errorMessage || `ADK error: ${event.errorCode}`); + } + + const content = event.content; + if (!content?.parts) return; + + const rawTextParts = []; + for (const part of content.parts) { + if (part.thought) continue; + if (part.text && !event.partial) { + rawTextParts.push(part.text); + const html = await marked.parse(part.text); + await this.sendToClient(createAgentTextMessage(this.sessionId, html, false)); + } + } + + if (rawTextParts.length > 0) { + this.sessionManager.addToConversationHistory(this.sessionId, { + role: 'model', + parts: [{ text: rawTextParts.join('\n') }] + }); + } + } + + // ─── Shared Gemini helpers ────────────────────────────────────────────────── + + async #sendSlowToolMessageGemini(toolName, args) { + if (toolName === 'create_visualization') { + const vizType = args?.useAICustom ? 'AI-generated custom' : (args?.type || 'standard'); + await this.sendToClient(createAgentTextMessage(this.sessionId, `Creating ${vizType} visualization: "${args?.title || 'visualization'}"... This may take a moment.`, false)); + } else if (toolName === 'get_variable_data') { + const varCount = args?.variableNames?.length || 0; + const runCount = args?.runIds?.length || 0; + await this.sendToClient(createAgentTextMessage(this.sessionId, `Retrieving data for ${varCount} variable${varCount !== 1 ? 's' : ''} from ${runCount} run${runCount !== 1 ? 's' : ''}...`, false)); + } else if (toolName === 'get_feedback_information') { + const runCount = args?.runIds?.length || 0; + const runText = runCount === 0 ? 'all runs' : `${runCount} run${runCount !== 1 ? 's' : ''}`; + await this.sendToClient(createAgentTextMessage(this.sessionId, `Analyzing feedback loops for ${runText}... This may take a moment.`, false)); + } else if (toolName === 'run_model') { + await this.sendToClient(createAgentTextMessage(this.sessionId, `Running model simulation...`, false)); + } else if (toolName === 'discuss_model_with_seldon') { + await this.sendToClient(createAgentTextMessage(this.sessionId, `Consulting Seldon for expert analysis...`, false)); + } else if (toolName === 'discuss_model_across_runs') { + await this.sendToClient(createAgentTextMessage(this.sessionId, `Analyzing model behavior across runs...`, false)); + } else if (toolName === 'discuss_with_mentor') { + await this.sendToClient(createAgentTextMessage(this.sessionId, `Consulting Seldon mentor for guidance...`, false)); + } + } + + executeToolCallGeminiManual(toolUse) { + try { + const builtInTools = this.builtInToolProvider.getTools(); + if (builtInTools.tools[toolUse.name]) { + return builtInTools.tools[toolUse.name].handler(toolUse.input); + } + if (this.dynamicToolProvider.isClientTool(toolUse.name)) { + const unprefixedName = toolUse.name.replace(/^client_/, ''); + return this.dynamicToolProvider.requestClientExecution(unprefixedName, toolUse.input) + .then(result => ({ content: result, isError: false })); + } + return Promise.resolve({ content: [{ type: 'text', text: `Tool not found: ${toolUse.name}` }], isError: true }); + } catch (error) { + logger.error(`Gemini Manual: Error executing tool ${toolUse.name}:`, error); + return Promise.resolve({ content: [{ type: 'text', text: error.message }], isError: true }); + } + } + + #geminiManualConvertTools(builtInTools, dynamicTools, modelTokenCount = 0, mode = null) { + const declarations = []; + const toolNames = new Set(); + + for (const [toolName, toolDef] of Object.entries(builtInTools.tools)) { + if (toolNames.has(toolName)) continue; + if (mode && toolDef.supportedModes && !toolDef.supportedModes.includes(mode)) continue; + if (toolDef.maxModelTokens && modelTokenCount > toolDef.maxModelTokens) continue; + if (toolDef.minModelTokens && modelTokenCount < toolDef.minModelTokens) continue; + + toolNames.add(toolName); + declarations.push({ + name: toolName, + description: toolDef.description, + parameters: sanitizeSchemaForGemini(toolDef.inputSchema.toJSONSchema()) + }); + } + + if (dynamicTools?.tools) { + for (const [toolName, toolDef] of Object.entries(dynamicTools.tools)) { + if (toolNames.has(toolName)) continue; + toolNames.add(toolName); + declarations.push({ + name: toolName, + description: toolDef.description, + parameters: sanitizeSchemaForGemini(toolDef.inputSchema.toJSONSchema()) + }); + } + } + + return declarations; + } + + async #geminiAdkBuildPriorContextText(history) { + try { + const conversationText = history.map((msg) => { + const role = msg.role === 'user' ? 'User' : 'Assistant'; + if (!Array.isArray(msg.parts)) return ''; + const text = msg.parts.filter(p => p.text).map(p => p.text).join('\n'); + return text ? `${role}: ${text}` : ''; + }).filter(line => line).join('\n\n'); + + logger.log(`Gemini: Summarizing prior agent context (${history.length} messages) before injection`); + const response = await this.gemini.models.generateContent({ + model: config.agentGeminiSummaryModel, + contents: [{ + role: 'user', + parts: [{ text: `Summarize this conversation history concisely (2-4 paragraphs):\n\n${conversationText}` }] + }] + }); + if (response.usageMetadata) { + this.#logApiUsage(Provider.GOOGLE, response.usageMetadata, config.agentGeminiSummaryModel); + } + return response.text || response.candidates?.[0]?.content?.parts?.[0]?.text || ''; + } catch (error) { + logger.error('Gemini: Error summarizing prior context:', error); + return '[Prior conversation condensed due to size]'; + } + } + + async #getGeminiManualConfig(systemPrompt, toolDeclarations) { + // Build a cache key from the stable inputs — recreate if they change (e.g. tool set changes on model resize) + const cacheKey = systemPrompt + JSON.stringify(toolDeclarations.map(t => t.name)); + + const cacheStillValid = this.#geminiManualCacheName && + this.#geminiManualCacheKey === cacheKey && + this.#geminiManualCacheExpiry && Date.now() < this.#geminiManualCacheExpiry; + + if (cacheStillValid) { + return { + cachedContent: this.#geminiManualCacheName, + thinkingConfig: config.agentGeminiThinking + }; + } + + // Delete the old cache if the key changed or it expired + if (this.#geminiManualCacheName) { + try { + await this.gemini.caches.delete({ name: this.#geminiManualCacheName }); + } catch (e) { + // Gemini may have already expired the cache — ignore deletion failures + } + this.#geminiManualCacheName = null; + this.#geminiManualCacheKey = null; + this.#geminiManualCacheExpiry = null; + } + + try { + const cacheConfig = { + ttl: '300s', + systemInstruction: systemPrompt + }; + if (toolDeclarations.length > 0) { + cacheConfig.tools = [{ functionDeclarations: toolDeclarations }]; + } + + const cache = await this.gemini.caches.create({ + model: config.agentGeminiModel, + config: cacheConfig + }); + + this.#geminiManualCacheName = cache.name; + this.#geminiManualCacheKey = cacheKey; + this.#geminiManualCacheExpiry = Date.now() + 270_000; // 270s, 30s before 300s TTL + + return { + cachedContent: cache.name, + thinkingConfig: config.agentGeminiThinking + }; + } catch (e) { + logger.warn('[gemini-cache] failed to create cache, falling back to uncached:', e.message); + const cfg = { + systemInstruction: systemPrompt, + thinkingConfig: config.agentGeminiThinking + }; + if (toolDeclarations.length > 0) { + cfg.tools = [{ functionDeclarations: toolDeclarations }]; + } + return cfg; + } + } + + /** + * Request the agent to stop iterating + */ + stopIteration() { + logger.log(`Stop iteration requested for session ${this.sessionId}`); + this.stopRequested = true; + this.#pendingMessages = []; + this.abortController?.abort(); + } + + + /** + * Queue a new message from the user to be processed + */ + queueMessage(message) { + this.#pendingMessages.push(message); + logger.debug(`[orchestrator:${this.sessionId}] Message queued (depth: ${this.#pendingMessages.length})`); + } + + async #fetchCurrentModel() { + const tool = this.builtInToolProvider.getTools().tools.get_current_model; + if (!tool) return; + const result = await tool.handler({}); + if (result.isError) { + logger.warn(`Failed to fetch current model before processing request: ${result.content?.[0]?.text ?? 'unknown error'}`); + } + } + + #resetAnthropicSdkUsageAccumulator() { + this.#anthropicSdkAccumulatorUsage = { + input_tokens: 0, + output_tokens: 0, + cache_creation: { ephemeral_5m_input_tokens: 0, ephemeral_1h_input_tokens: 0 }, + cache_read_input_tokens: 0, + }; + } + + /** + * Report any per-assistant usage that hasn't been superseded by a result + * message. Used as the abort/error fallback so a stopped conversation still + * gets its tokens counted. + */ + #flushAnthropicSdkUsageAccumulator() { + const u = this.#anthropicSdkAccumulatorUsage; + const hasUsage = + u.input_tokens > 0 || + u.output_tokens > 0 || + u.cache_creation.ephemeral_5m_input_tokens > 0 || + u.cache_creation.ephemeral_1h_input_tokens > 0 || + u.cache_read_input_tokens > 0; + if (hasUsage) { + logger.log(`Anthropic SDK: flushing accumulated per-assistant usage (no result message) for session ${this.sessionId}`); + this.#logApiUsage(Provider.ANTHROPIC, u); + } + this.#resetAnthropicSdkUsageAccumulator(); + } + + #logApiUsage(provider, usage, model = null, potentialDuplicate = false) { + if (!usage) return; + const resolvedModel = model ?? ( + provider === Provider.ANTHROPIC ? config.agentAnthropicModel : config.agentGeminiModel + ); + this.tokenReporter.report({ provider, model: resolvedModel, usage, potentialDuplicate }).catch(() => {}); + } + + + /** + * Destroy the orchestrator and cleanup resources + */ + destroy() { + logger.log(`AgentOrchestrator destroyed for session ${this.sessionId}`); + + if (this.#geminiManualCacheName && this.gemini) { + this.gemini.caches.delete({ name: this.#geminiManualCacheName }).catch(() => {}); + } + + // Clear any references + this.sessionManager = null; + this.sendToClient = null; + this.builtInToolProvider = null; + this.dynamicToolProvider = null; + this.anthropic = null; + this.gemini = null; + this.geminiAdkSessionService = null; + this.configManager = null; + } +} diff --git a/agent/AgentWorker.js b/agent/AgentWorker.js new file mode 100644 index 00000000..f5cdc316 --- /dev/null +++ b/agent/AgentWorker.js @@ -0,0 +1,232 @@ +/** + * Agent Worker Process + * + * Runs inside a bwrap sandbox on Linux (or unsandboxed on dev platforms). + * Receives IPC messages from the main process, runs AgentOrchestrator, and + * relays all outbound client messages back over IPC. + * + * IPC transport: + * - Sandboxed (bwrap): Unix domain socket at WORKER_IPC_SOCKET, newline- + * delimited JSON. The socket lives in /session so it crosses the sandbox + * boundary without needing --forward-fd. + * - Unsandboxed (fork fallback): standard Node.js process IPC channel. + * + * IPC messages IN (main → worker): + * initialize – session data; must arrive before select_agent + * select_agent – agentId; creates/replaces AgentOrchestrator + * chat – user message; starts an agent conversation + * stop – abort the current agent iteration + * tool_response – callId + result; resolves a pending client tool promise + * model_updated – new client model object + * get_context – requestId; worker replies with current conversation history + * shutdown – clean exit + * + * IPC messages OUT (worker → main): + * to_client – relay to the WebSocket client verbatim + * context_response – reply to get_context + * worker_error – unhandled top-level error + */ + +import { AgentOrchestrator } from './AgentOrchestrator.js'; +import { SessionManager } from './utilities/SessionManager.js'; +import logger from '../utilities/logger.js'; +import config from '../config.js'; +import { join } from 'path'; +import { fileURLToPath } from 'url'; +import { dirname } from 'path'; +import net from 'net'; +import { createInterface } from 'readline'; + +const __dirname = dirname(fileURLToPath(import.meta.url)); + +const SESSION_ID = process.env.SESSION_ID; +const SESSION_TEMP_DIR = process.env.SESSION_TEMP_DIR; + +if (!SESSION_ID || !SESSION_TEMP_DIR) { + process.stderr.write('AgentWorker: SESSION_ID and SESSION_TEMP_DIR must be set\n'); + process.exit(1); +} + +class AgentWorker { + // Mock WebSocket: SessionManager stores a ws-shaped object, but in the worker + // all real sends go through toClient() which is passed directly to AgentOrchestrator. + #mockWs = { readyState: 1, send: () => {} }; + + // Worker has its own SessionManager. Cleanup timers are disabled — lifetime is + // managed by the main process which kills this process on disconnect/timeout. + #sessionManager = new SessionManager({ disableCleanup: true }); + + #orchestrator = null; + + // Set on first chat after an agent switch so AgentOrchestrator can bridge context + // from the previous agent into the new session. + #pendingIsAgentSwitch = false; + + #conversationRunning = false; + + // IPC send function — overridden by #setupSocketIpc when using bwrap sandbox + #sendToMain = (msg) => process.send(msg); + + constructor() { + const ipcSocketPath = process.env.WORKER_IPC_SOCKET; + if (ipcSocketPath) { + this.#setupSocketIpc(ipcSocketPath); + } else { + process.on('message', (msg) => this.#handleMessage(msg)); + } + + process.on('uncaughtException', (err) => { + logger.error(`[worker:${SESSION_ID}] Uncaught exception:`, err); + this.#toMain({ type: 'worker_error', error: err.message }); + }); + + process.on('unhandledRejection', (reason) => { + logger.error(`[worker:${SESSION_ID}] Unhandled rejection:`, reason); + this.#toMain({ type: 'worker_error', error: String(reason) }); + }); + } + + #setupSocketIpc(socketPath) { + const sock = net.createConnection(socketPath); + + sock.on('error', (err) => { + logger.error(`[worker:${SESSION_ID}] IPC socket error: ${err.message}`); + process.exit(1); + }); + + this.#sendToMain = (msg) => { + if (!sock.destroyed) sock.write(JSON.stringify(msg) + '\n'); + }; + + const rl = createInterface({ input: sock, crlfDelay: Infinity }); + rl.on('line', (line) => { + if (!line.trim()) return; + try { this.#handleMessage(JSON.parse(line)); } + catch (e) { logger.error(`[worker:${SESSION_ID}] IPC parse error: ${e.message}`); } + }); + + rl.on('close', () => process.exit(0)); + } + + #toMain(msg) { this.#sendToMain(msg); } + #toClient(msg) { this.#toMain({ type: 'to_client', message: msg }); } + + async #handleMessage(msg) { + try { + switch (msg.type) { + + case 'initialize': { + this.#sessionManager.createSessionWithId(SESSION_ID, this.#mockWs, SESSION_TEMP_DIR); + const capabilities = { + supportsArrays: msg.supportsArrays, + supportsModules: msg.supportsModules, + supportsSubTypes: msg.supportsSubTypes, + }; + this.#sessionManager.initializeSession(SESSION_ID, msg.mode, msg.model, msg.tools, msg.context, msg.clientId, capabilities); + for (const h of (msg.conversationHistory || [])) { + this.#sessionManager.addToConversationHistory(SESSION_ID, h); + } + this.#pendingIsAgentSwitch = msg.isAgentSwitch ?? false; + break; + } + + case 'select_agent': { + const agentConfig = msg.agentConfig !== undefined + ? { markdownContent: msg.agentConfig } + : { path: join(__dirname, 'config', `${msg.agentId}.md`) }; + const provider = msg.provider ?? config.agentDefaultProvider; + this.#orchestrator = new AgentOrchestrator(this.#sessionManager, SESSION_ID, (m) => this.#toClient(m), agentConfig, provider); + break; + } + + case 'chat': { + if (!this.#orchestrator) { + this.#toClient({ type: 'error', sessionId: SESSION_ID, error: 'No agent selected', code: 'NO_AGENT' }); + break; + } + if (this.#conversationRunning) { + this.#orchestrator.queueMessage(msg.message); + break; + } + const previousContext = this.#pendingIsAgentSwitch + ? this.#sessionManager.getConversationContext(SESSION_ID) + : null; + this.#pendingIsAgentSwitch = false; + this.#conversationRunning = true; + this.#orchestrator.startConversation(msg.message, previousContext) + .finally(() => { this.#conversationRunning = false; }); + break; + } + + case 'stop': { + this.#orchestrator?.stopIteration(); + break; + } + + case 'tool_response': { + const { callId, result, isError } = msg; + const session = this.#sessionManager.getSession(SESSION_ID); + if (!session) break; + + // Try the standard pending tool calls (DynamicToolProvider) + if (!this.#sessionManager.resolvePendingToolCall(SESSION_ID, callId, result, isError)) { + // Try feedback requests (discussModelWithSeldon, discussModelAcrossRuns, getFeedbackInformation) + if (session.pendingFeedbackRequests?.has(callId)) { + const pending = session.pendingFeedbackRequests.get(callId); + clearTimeout(pending.timeout); + isError ? pending.reject(new Error(typeof result === 'string' ? result : JSON.stringify(result))) : pending.resolve(result); + session.pendingFeedbackRequests.delete(callId); + // Try model requests (clientInteractionTools, generateQuantitativeModel, etc.) + } else if (session.pendingModelRequests?.has(callId)) { + const pending = session.pendingModelRequests.get(callId); + clearTimeout(pending.timeout); + isError ? pending.reject(new Error(typeof result === 'string' ? result : JSON.stringify(result))) : pending.resolve(result); + session.pendingModelRequests.delete(callId); + } else { + logger.warn(`[worker:${SESSION_ID}] Unknown callId in tool_response: ${callId}`); + } + } + break; + } + + case 'model_updated': { + this.#sessionManager.updateClientModel(SESSION_ID, msg.model); + break; + } + + case 'get_context': { + const context = this.#sessionManager.getConversationContext(SESSION_ID); + this.#toMain({ type: 'context_response', requestId: msg.requestId, context }); + break; + } + + case 'shutdown': { + // Abort any in-flight conversation so the Agent SDK can clean up + // the claude CLI subprocess it may have spawned. + this.#orchestrator?.stopIteration(); + // Kill our entire process group. On the fork fallback (macOS/dev) + // this catches grandchild processes (claude CLI) that would otherwise + // be orphaned at 100% CPU. Inside a bwrap PID namespace this kills + // all container processes. Safe because the fork is spawned with + // detached:true (own process group) and bwrap runs in its own namespace. + if (process.platform !== 'win32') { + try { process.kill(-process.pid, 'SIGKILL'); } catch { /* already exiting */ } + } + // Temp-dir cleanup is the host SessionManager's responsibility. + // Inside the bwrap sandbox /session is a bind mount and can't be + // rmdir'd; in the fork fallback the host also calls deleteSession. + process.exit(0); + break; + } + + default: + logger.warn(`[worker:${SESSION_ID}] Unknown IPC message type: ${msg.type}`); + } + } catch (err) { + logger.error(`[worker:${SESSION_ID}] Unhandled error processing ${msg.type}:`, err); + this.#toMain({ type: 'worker_error', error: err.message }); + } + } +} + +new AgentWorker(); diff --git a/agent/README.md b/agent/README.md new file mode 100644 index 00000000..48e37884 --- /dev/null +++ b/agent/README.md @@ -0,0 +1,978 @@ +# WebSocket AI Agent Server + +AI-powered agent for building and modifying System Dynamics models via WebSocket. + +## Overview + +This WebSocket server provides an AI agent (powered by Claude) that helps users build, modify, and analyze System Dynamics models. The agent uses built-in SD-AI engine tools and communicates with the client for model state, simulation runs, feedback loop data, and variable time-series. + +**Key Features:** +- Stateless server architecture (all user data lives client-side) +- Built-in tools for model interaction — no tool registration required for core operations +- Optional custom client tool registration for application-specific behavior +- Configurable agent behavior via Markdown files in `agent/config/` +- AI-powered custom visualizations (SVG) +- Multiple agent personalities (Socrates, Merlin, etc.) +- Per-session temp directory for visualization scratch space + +## Architecture + +### Client-Owned Model + +The **client** owns and maintains: +- Complete model state (SD-JSON format) +- All simulation run data +- Full conversation history (user messages, agent responses, visualizations) +- Message log for session resumption + +The **server** maintains (in-memory only): +- Active WebSocket sessions +- A per-session temp directory (created on connect, cleaned up on disconnect) +- Model type (CLD or SFD) — set once, never changes +- Conversation context (can be seeded with historical messages) +- Pending tool calls, feedback requests, and model interaction requests + +### Worker Process Architecture + +Each agent session runs in a dedicated **worker subprocess** spawned by `WorkerSpawner` and managed by `AgentWorker`. The main process owns WebSocket connections; all agent execution (LLM calls, tool execution) happens inside the worker. + +**On Linux with bubblewrap installed:** the worker runs inside a bwrap sandbox. Only the session's temp directory is writable; the rest of the filesystem is read-only or not mounted. IPC between the main process and the worker uses a Unix domain socket (`/ipc-.sock`) that crosses the sandbox boundary without needing `--forward-fd`. + +**On macOS / Linux without bwrap:** falls back to a plain Node.js `fork()`. The fork runs in its own process group (`detached: true`) so killing the group also terminates any grandchild processes (e.g. the Claude CLI subprocess spawned by the Anthropic Agent SDK). + +IPC messages between the main process and worker: +- **Main → Worker:** `initialize`, `select_agent`, `chat`, `stop`, `tool_response`, `model_updated`, `get_context`, `shutdown` +- **Worker → Main:** `to_client` (relayed to the WebSocket), `context_response`, `worker_error` + +### Model Type Enforcement + +Each session works with ONE model type that cannot be changed: +- **CLD** (Causal Loop Diagram) — Conceptual models with feedback loops +- **SFD** (Stock Flow Diagram) — Quantitative models with stocks, flows, and equations + +The model type is declared at session initialization and enforced throughout. + +### Message Flow + +``` +Client ← WebSocket → Main Process → Worker Process ← Tools → SD-AI Engines + ↓ ↑ ↑ + Model, (IPC socket Quantitative, + Runs, or Node IPC) Qualitative, + History Seldon, etc. +``` + +## API Endpoints + +### WebSocket Endpoint + +``` +ws://localhost:3000/api/v1/agent +``` + +## WebSocket Protocol + +### Connection Flow + +1. **Client connects** to WebSocket endpoint +2. **Server sends** `session_created` with session ID +3. **Client sends** `initialize_session` with auth, model type, initial model, and optional custom tools +4. **Server validates** and sends `session_ready` with available agents +5. **Client sends** `select_agent` to choose an agent by ID (e.g., `"socrates"`, `"merlin"`) or supply a custom agent config inline +6. **Server sends** `agent_selected` confirmation +7. **Normal conversation** begins with `chat` messages + +### Client → Server Messages + +All client messages include a `sessionId` (except `initialize_session` which receives one). + +#### 1. Initialize Session + +Establishes a session with authentication, model type, initial model, and optional custom tools. + +```json +{ + "type": "initialize_session", + "authenticationKey": "your-auth-key", + "clientProduct": "sd-web", + "clientVersion": "1.0.0", + "mode": "sfd", + "model": { + "variables": [], + "relationships": [], + "specs": {} + }, + "tools": [ + { + "name": "open_variable_inspector", + "description": "Opens the variable inspector panel in the client UI for a given variable", + "inputSchema": { + "type": "object", + "properties": { + "variableName": { "type": "string" } + }, + "required": ["variableName"] + } + } + ], + "historicalMessages": [ + { + "type": "user_text", + "content": "Build me a population model" + }, + { + "type": "agent_text", + "content": "I'll help you build a population model...", + "isThinking": false + } + ], + "context": { + "description": "Optional context about the modeling task" + } +} +``` + +**Fields:** +- `authenticationKey` — Server authentication (required only if `AUTHENTICATION_KEY` env var is set) +- `clientProduct` — Client identifier (e.g., `"sd-web"`, `"sd-desktop"`) +- `clientVersion` — Client version for compatibility checking +- `clientId` — Optional unique identifier for the end user (used for token usage reporting) +- `mode` — Either `"cld"` or `"sfd"` — **cannot be changed during session** +- `model` — Initial model state (can be empty) +- `tools` — Optional array of custom client tool definitions (see Client Tool Registration below). Core model operations are all built-in and do not need to be registered here. +- `historicalMessages` — Optional array of previous messages to seed conversation context +- `context` — Optional contextual information for the agent + +### Historical Messages + +The `historicalMessages` field lets clients provide conversation history from a previous session, enabling continuity across reconnections or new sessions. + +**Message Types:** + +1. **user_text** — User chat message +```json +{ "type": "user_text", "content": "Build me a population model" } +``` + +2. **agent_text** — Agent response or thinking +```json +{ + "type": "agent_text", + "content": "I'll create a simple population model with births and deaths", + "isThinking": false +} +``` + +3. **visualization** — Previous visualization (summarized as context, not re-rendered) +```json +{ + "type": "visualization", + "visualizationTitle": "Population Growth", + "visualizationDescription": "Shows exponential growth" +} +``` + +4. **agent_complete** — Agent completion message +```json +{ "type": "agent_complete", "content": "I've completed building your model" } +``` + +**Important Notes:** +- Historical messages seed the agent's conversation context +- The server does not persist messages — the client is responsible for maintaining history +- SVG data from past visualizations is not replayed; only the title/description are included as context + +#### 2. Select Agent + +Chooses which agent personality and LLM provider to use. Either `agentId` or `agentConfig` must be provided. + +**Option A — select a built-in agent by ID:** + +```json +{ + "type": "select_agent", + "sessionId": "sess_abc123", + "agentId": "socrates", + "provider": "google" +} +``` + +**Option B — supply a custom agent configuration inline:** + +```json +{ + "type": "select_agent", + "sessionId": "sess_abc123", + "agentConfig": "---\nname: \"My Agent\"\nagent_mode: sdk\nsupported_modes:\n - sfd\nsupported_providers:\n - anthropic\n - google\n---\n\n## Instructions\nYou are a custom agent...", + "provider": "anthropic" +} +``` + +The `agentConfig` string must be a Markdown document with valid YAML frontmatter containing at minimum `name` and `agent_mode`. Its format is identical to the agent `.md` files in `agent/config/` — see [Agent Configuration](#agent-configuration) for the full frontmatter reference. The Markdown body below the frontmatter becomes the agent's system prompt. + +**Fields:** +- `agentId` — ID of a built-in agent (e.g., `"socrates"`, `"merlin"`). Available agent IDs are returned in `session_ready`. Required if `agentConfig` is not provided. +- `agentConfig` — Full agent configuration as a Markdown string. Required if `agentId` is not provided. Server returns `AGENT_SELECTION_ERROR` if the frontmatter is missing or invalid. +- `provider` — LLM provider: `"anthropic"` or `"google"`. Defaults to `agentDefaultProvider` in `config.js`. Ignored when the agent's `supportedProviders` has exactly one entry. + +#### 3. Chat Message + +Sends a user message to the agent. + +```json +{ + "type": "chat", + "sessionId": "sess_abc123", + "message": "Build me a simple population model" +} +``` + +#### 4. Tool Call Response + +Responds to any `tool_call_request` or `feedback_request` from the server. + +```json +{ + "type": "tool_call_response", + "sessionId": "sess_abc123", + "callId": "req_abc123", + "result": {}, + "isError": false +} +``` + +**Error response:** +```json +{ + "type": "tool_call_response", + "sessionId": "sess_abc123", + "callId": "req_abc123", + "result": "Simulation failed: division by zero in equation", + "isError": true +} +``` + +The `result` shape depends on which request is being answered — see the Server → Client messages below for the expected format per tool. + +#### 5. Model Updated Notification + +Notifies the server when the client updates the model externally (e.g., user manual edit). + +```json +{ + "type": "model_updated_notification", + "sessionId": "sess_abc123", + "model": { + "variables": [], + "relationships": [] + }, + "changeReason": "User manually added a new variable" +} +``` + +#### 6. Stop Iteration + +Interrupts the current agent loop without disconnecting the session. + +```json +{ + "type": "stop_iteration", + "sessionId": "sess_abc123" +} +``` + +The agent stops after the current API call completes, then sends `agent_complete` with status `awaiting_user`. The session remains active and can receive new `chat` messages. + +#### 7. Disconnect + +Gracefully closes the session and cleans up all server-side resources including the temp directory. + +```json +{ + "type": "disconnect", + "sessionId": "sess_abc123" +} +``` + +--- + +### Server → Client Messages + +#### 1. Session Created + +Sent immediately upon WebSocket connection. + +```json +{ + "type": "session_created", + "sessionId": "sess_abc123", + "timestamp": "2025-01-15T10:30:00.000Z" +} +``` + +#### 2. Session Ready + +Sent after successful initialization. Lists available agents. + +```json +{ + "type": "session_ready", + "sessionId": "sess_abc123", + "availableAgents": [ + { + "id": "socrates", + "name": "Socrates", + "supportedModes": ["sfd", "cld"], + "supportedProviders": [ + {"id": "anthropic", "name": "Claude"}, + {"id": "google", "name": "Gemini"} + ], + "description": "System Dynamics mentor who uses Socratic questioning..." + }, + { + "id": "merlin", + "name": "Merlin", + "supportedModes": ["sfd", "cld"], + "supportedProviders": [ + {"id": "anthropic", "name": "Claude"}, + {"id": "google", "name": "Gemini"} + ], + "description": "..." + } + ], + "defaults": { + "sfd": "socrates", + "cld": "socrates" + }, + "timestamp": "2025-01-15T10:30:00.100Z" +} +``` + +#### 3. Agent Selected + +Confirms the selected agent is ready. + +```json +{ + "type": "agent_selected", + "sessionId": "sess_abc123", + "agentId": "socrates", + "agentName": "Socrates", + "supportedProviders": [ + {"id": "anthropic", "name": "Claude (Anthropic)"}, + {"id": "google", "name": "Gemini (Google)"} + ], + "currentProvider": "anthropic", + "timestamp": "2025-01-15T10:30:00.200Z" +} +``` + +- `agentId` — `"custom"` when a custom `agentConfig` was used; otherwise the built-in agent ID. +- `agentName` — Display name from the agent's frontmatter. +- `supportedProviders` — Providers this agent accepts, in `{id, name}` form. Same format as the `supportedProviders` array in `session_ready`. Use this to populate a provider selector after agent selection — especially important for custom agents where the supported providers are only known after the server parses the config. +- `currentProvider` — The provider ID that was actually selected for this session (e.g. `"anthropic"` or `"google"`). Resolved from the `provider` field of the `select_agent` message, falling back to `agentDefaultProvider` in config, or forced to the single entry when `supportedProviders` has exactly one item. + +#### 4. Agent Text + +Text response from the agent. + +```json +{ + "type": "agent_text", + "sessionId": "sess_abc123", + "content": "I'll help you build a population model with births and deaths...", + "isThinking": false, + "timestamp": "2025-01-15T10:30:01.000Z" +} +``` + +`isThinking: true` indicates internal reasoning — display is optional. + +#### 5. Tool Call Notification + +Informs the client that a tool is being called (for UI display). Sent for all tools — built-in and custom. + +```json +{ + "type": "tool_call_notification", + "sessionId": "sess_abc123", + "callId": "call_abc456", + "toolName": "generate_quantitative_model", + "isBuiltIn": true, + "timestamp": "2025-01-15T10:30:02.000Z" +} +``` + +#### 6. Tool Call Request + +Requests the client to execute a model interaction and return results via `tool_call_response`. Sent for both built-in client interaction tools and any custom registered tools. + +```json +{ + "type": "tool_call_request", + "sessionId": "sess_abc123", + "callId": "req_abc123", + "toolName": "get_current_model", + "arguments": {}, + "timeout": 30000, + "timestamp": "2025-01-15T10:30:03.000Z" +} +``` + +**Built-in tool names and expected `result` shapes:** + +**`get_current_model`** — return the current model state +```json +{ + "model": { + "variables": [ + { + "name": "Population", + "type": "stock", + "equation": "1000", + "documentation": "Total population", + "units": "people", + "inflows": ["Births"], + "outflows": ["Deaths"] + }, + { + "name": "Births", + "type": "flow", + "equation": "Population * Birth Rate", + "uniflow": true + }, + { + "name": "Birth Rate", + "type": "variable", + "equation": "0.02" + } + ], + "relationships": [ + { "from": "Birth Rate", "to": "Births", "polarity": "+" }, + { "from": "Population", "to": "Births", "polarity": "+" } + ], + "specs": { + "startTime": 0, + "stopTime": 100, + "dt": 0.25, + "timeUnits": "Years" + }, + "errors": [] + } +} +``` + +`errors` is an array of strings set by the client to report any simulation or validation errors on the current model state. Pass an empty array if there are no errors. + +**`update_model`** — apply model changes, confirm success +```json +{ "success": true } +``` + +**`run_model`** — run the simulation, return the new run ID +```json +{ "runId": "run_abc123" } +``` + +**`get_run_info`** — return all simulation runs +```json +{ + "runs": [ + { + "id": "run_abc123", + "name": "Baseline", + "isExternal": false, + "variables": ["Population", "Births", "Deaths"] + }, + { "id": "run_def456", "name": "Policy" } + ] +} +``` + +Each run object: +- `id` — required, unique run identifier +- `name` — required, display name +- `isExternal` — optional boolean, whether the run originated outside the current model +- `variables` — optional array of variable names available in this run + +**`get_variable_data`** — return time-series data for requested variables and runs +```json +{ + "run_abc123": { + "Population": { + "time": [0, 1, 2], + "values": [1000, 1020, 1040] + }, + "Births": { + "time": [0, 1, 2], + "values": [20, 20.4, 20.8] + } + }, + "run_def456": { + "Population": { + "time": [0, 1, 2], + "values": [1000, 980, 961] + } + } +} +``` + +The response is keyed by run ID, then by variable name. Each variable entry has parallel `time` and `values` arrays. + +For **custom registered tools**, the `toolName` will match a name from the `tools` array provided in `initialize_session`, and `result` can be any JSON value meaningful to the agent. + +#### 7. Tool Call Completed + +Sent after a built-in tool finishes execution. + +```json +{ + "type": "tool_call_completed", + "sessionId": "sess_abc123", + "callId": "call_abc456", + "toolName": "generate_quantitative_model", + "isError": false, + "timestamp": "2025-01-15T10:30:04.000Z" +} +``` + +#### 8. Visualization + +Sends an SVG visualization to the client. + +```json +{ + "type": "visualization", + "sessionId": "sess_abc123", + "visualizationId": "viz_12345", + "title": "Population Growth Over Time", + "description": "Shows exponential growth pattern", + "format": "svg", + "data": "...", + "timestamp": "2025-01-15T10:30:05.000Z" +} +``` + +- `format` is always `"svg"` +- `data` is a raw SVG string (not base64, not PNG) +- `description` is optional + +#### 9. Feedback Request + +Requests feedback loop analysis data from the client, used by the Seldon and LTM narrative tools. + +```json +{ + "type": "feedback_request", + "sessionId": "sess_abc123", + "requestId": "feedback_xyz789", + "runIds": ["run_abc123", "run_def456"], + "timestamp": "2025-01-15T10:30:07.000Z" +} +``` + +**Client response** — send `tool_call_response` with `callId` set to the `requestId`: + +```json +{ + "type": "tool_call_response", + "sessionId": "sess_abc123", + "callId": "feedback_xyz789", + "result": { + "feedbackContent": { + "feedbackLoops": [ + { + "identifier": "loop_1", + "name": "Population Growth Loop", + "polarity": "+", + "links": [ + { "from": "Population", "to": "Births", "polarity": "+" }, + { "from": "Births", "to": "Population", "polarity": "+" } + ], + "loopset": 1, + "Percent of Model Behavior Explained By Loop": [ + { "time": 0, "value": 0.3 }, + { "time": 10, "value": 0.8 } + ] + } + ], + "dominantLoopsByPeriod": [ + { "dominantLoops": ["loop_1"], "startTime": 0, "endTime": 50 } + ] + }, + "runIds": ["run_abc123"] + }, + "isError": false +} +``` + +#### 10. Get Variable Data Request + +Requests time-series data for specific variables from specific runs. + +```json +{ + "type": "get_variable_data", + "sessionId": "sess_abc123", + "requestId": "vardata_xyz789", + "variableNames": ["Population", "Births", "Deaths"], + "runIds": ["run_abc123", "run_def456"], + "detailed": true, + "timestamp": "2025-01-15T10:30:07.500Z" +} +``` + +- `detailed: true` returns more data points suitable for plotting; `false` returns a sampled summary + +**Client response** — send `tool_call_response` with `callId` set to the `requestId` and the `result` in the `get_variable_data` shape shown in §6 above (keyed by run ID → variable name → `{ time, values }`). + +#### 11. Agent Complete + +Signals the agent has finished the current request. **Agent execution only stops when the client disconnects or when this message is received** — clients should treat `agent_complete` as the authoritative signal that the agent is idle and ready for the next input. + +```json +{ + "type": "agent_complete", + "sessionId": "sess_abc123", + "status": "success", + "finalMessage": "I've completed building your population model.", + "timestamp": "2025-01-15T10:30:08.000Z" +} +``` + +**Status values:** `"success"` | `"error"` | `"awaiting_user"` + +#### 12. Error + +Reports errors during processing. + +```json +{ + "type": "error", + "sessionId": "sess_abc123", + "error": "Tool 'run_model' timed out after 60 seconds", + "errorCode": "TOOL_TIMEOUT", + "timestamp": "2025-01-15T10:30:09.000Z" +} +``` + +**Known error codes:** + +| Code | Cause | +|---|---| +| `AGENT_SELECTION_ERROR` | `select_agent` failed — e.g. unknown `agentId`, or `agentConfig` frontmatter is missing required `name` / `agent_mode` fields. The session remains active; send another `select_agent` to recover. | +| `TOOL_TIMEOUT` | A built-in or custom tool did not receive a `tool_call_response` within its timeout. | +| `NO_AGENT` | A `chat` message arrived before `select_agent` was sent. | + +Note that receiving an `error` message does not mean the agent has stopped — the agent may still continue iterating. Wait for `agent_complete` before treating the agent as idle. + +--- + +## Client Tool Registration + +Clients can optionally register custom tools during `initialize_session`. These are application-specific operations the agent can invoke — for example, opening a UI panel, triggering an export, or running a custom analysis. + +Core model operations (`get_current_model`, `update_model`, `run_model`, `get_run_info`, `get_variable_data`) are all built-in and do **not** need to be registered. + +```typescript +{ + name: string, // Unique tool name + description: string, // What the tool does (shown to the AI) + inputSchema: { // JSON Schema for parameters + type: "object", + properties: { + // Parameter definitions + }, + required?: string[] + }, + timeout?: number // Milliseconds to wait for client response (default: 30000) +} +``` + +The `timeout` field controls how long the server waits for the client's `tool_call_response` before failing with a timeout error. Use a longer value for tools that trigger slow operations (e.g., a long-running export or analysis): + +```json +{ + "name": "run_heavy_export", + "description": "Exports the full model to an external system", + "inputSchema": { "type": "object", "properties": {} }, + "timeout": 120000 +} +``` + +When the agent calls a custom tool, the server sends a `tool_call_request` and the client must respond with `tool_call_response`. + +--- + +## Built-In Tool Interface + +Each built-in tool is a plain object returned by a factory function. The fields are: + +### Required + +| Field | Type | Description | +|---|---|---| +| `description` | `string` | Natural-language description shown to the AI when deciding whether to call the tool | +| `inputSchema` | `ZodSchema` | Zod schema defining the tool's input parameters | +| `handler` | `async (args) => { content, isError }` | Executes the tool and returns a standardized response | +| `supportedModes` | `string[]` | Modes this tool is available in. Values: `'sfd'`, `'cld'`. Include both to support all modes. | + +### Optional + +| Field | Type | Description | +|---|---|---| +| `maxModelTokens` | `number` | If the current model's token count exceeds this value, the tool is excluded from the agent's tool list. Used for tools that receive the full model (e.g., `generate_quantitative_model`). | +| `minModelTokens` | `number` | If the current model's token count is below this value, the tool is excluded. Used for tools that only make sense for large models (e.g., `read_model_section`, `edit_variables`). | +| `nonSdkOnly` | `boolean` | If `true`, the tool is excluded from the Anthropic SDK (`sdk`) mode's MCP server and the Google ADK tool list. It is only available in `manual` loop mode. Use this for tools that duplicate functionality already provided natively by the SDK (e.g. file system tools). | + +Token counting runs on every conversation turn for all sessions. The token thresholds use `agentMaxTokensForEngines` from `config.js` (default: 100,000). + +--- + +## Built-In Tools + +All core tools are registered server-side. Clients do not need to register them. + +### Model Generation +- **generate_quantitative_model** — Generate Stock Flow Diagrams (SFD) +- **generate_qualitative_model** — Generate Causal Loop Diagrams (CLD) + +### Discussion & Analysis +- **discuss_model_with_seldon** — Deep technical discussion with feedback loop analysis +- **discuss_model_across_runs** — Compare behavior across simulation runs +- **discuss_with_mentor** — User-friendly mentoring discussion +- **generate_ltm_narrative** — Feedback loop dominance narratives (LTM) + +### Visualization +- **create_visualization** — Create SVG charts; supports `time_series`, `phase_portrait`, `feedback_dominance`, `comparison`, and AI-custom types + +### Client Model Interaction +- **get_current_model** — Fetch current model state from client +- **update_model** — Push model changes to client +- **run_model** — Trigger simulation run on client +- **get_run_info** — Get list of all simulation runs from client +- **get_variable_data** — Fetch time-series variable data from client + +### Feedback +- **get_feedback_information** — Request feedback loop analysis from client (required before Seldon/LTM tools) + +### Large Model Utilities +- **read_model_section** — Read a section of a large model without loading it entirely +- **edit_variables** — Add, update, or remove variables in a large model in place +- **edit_relationships** — Add, update, or remove relationships in a large model in place +- **edit_specs** — Update simulation specs (startTime, stopTime, dt, timeUnits, arrayDimensions) in a large model in place +- **edit_modules** — Add, update, or remove modules in a large model in place + +### File Utilities +- **read_file** — Read a file from the session temp directory (supports line range and search filtering) + +--- + +## Agent Configuration + +Agents are configured via Markdown files in `agent/config/`. The server automatically discovers any `.md` file with a `name` frontmatter field. + +``` +agent/config/ + socrates.md + merlin.md +``` + +**Frontmatter fields:** + +```yaml +--- +name: "Socrates" +description: "System Dynamics mentor who uses Socratic questioning..." +version: "1.0" +max_iterations: 20 +agent_mode: manual # Loop strategy: 'sdk' (managed framework) or 'manual' (explicit loop) +supported_modes: + - sfd + - cld +supported_providers: # LLM provider IDs this agent accepts (Provider enum values); omit to allow all + - anthropic + - google +--- +``` + +**`agent_mode`** controls the loop strategy — it does _not_ select the LLM provider: +- `sdk` — uses a managed agent framework (Anthropic Agent SDK or Google ADK) that handles iteration and tool calling internally +- `manual` — uses an explicit `while` loop that calls the provider API directly + +**`supported_providers`** lists which LLM providers are valid for this agent. The client selects the actual provider at runtime via the `provider` field in `select_agent`. If the list has exactly one entry, that provider is always used. If the field is absent, all providers are allowed. + +The Markdown body below the frontmatter is the agent's full system prompt/instructions. + +--- + +## Visualization System + +Visualizations are generated using Python/matplotlib and sent as raw SVG strings. + +**Supported types:** +- `time_series` — Line plots of variables over time +- `phase_portrait` — State-space (stock vs. stock) diagrams +- `feedback_dominance` — Stacked area chart of loop influence over time +- `comparison` — Multi-run side-by-side comparison + +**AI-custom visualizations:** Set `useAICustom: true` to have the AI generate custom matplotlib code for unique requirements. + +**Output:** All visualizations are raw SVG strings — the `data` field in the `visualization` message is the SVG directly, not base64 or PNG. + +--- + +## Example Client Implementation + +### JavaScript/Node.js + +```javascript +import WebSocket from 'ws'; + +const ws = new WebSocket('ws://localhost:3000/api/v1/agent'); +let sessionId = null; + +ws.on('message', (data) => { + const message = JSON.parse(data); + + switch (message.type) { + case 'session_created': + sessionId = message.sessionId; + ws.send(JSON.stringify({ + type: 'initialize_session', + authenticationKey: 'your-key', + clientProduct: 'my-client', + clientVersion: '1.0.0', + mode: 'sfd', + model: {} + // Optionally include custom tools here + })); + break; + + case 'session_ready': + const agentId = message.defaults?.sfd || message.availableAgents[0]?.id; + // Optionally specify a provider; omit to use the server default (anthropic) + ws.send(JSON.stringify({ type: 'select_agent', sessionId, agentId, provider: 'anthropic' })); + break; + + case 'agent_selected': + ws.send(JSON.stringify({ + type: 'chat', + sessionId, + message: 'Build me a simple population model' + })); + break; + + case 'tool_call_request': + handleToolCallRequest(message); + break; + + case 'feedback_request': + handleFeedbackRequest(message); + break; + + case 'agent_text': + console.log('Agent:', message.content); + break; + + case 'visualization': + // message.format === 'svg', message.data is a raw SVG string + displaySVG(message.data, message.title, message.description); + break; + + case 'agent_complete': + console.log('Done:', message.status, message.finalMessage); + break; + + case 'error': + console.error('Error:', message.error); + break; + } +}); + +function handleToolCallRequest(message) { + let result; + switch (message.toolName) { + case 'get_current_model': + result = { model: currentModel }; + break; + case 'update_model': + currentModel = message.arguments.modelData; + result = { success: true }; + break; + case 'run_model': + result = { runId: runSimulation() }; + break; + case 'get_run_info': + // runs: [{ id, name, isExternal?, variables? }, ...] + result = { runs: getAllRuns() }; + break; + case 'get_variable_data': + // { [runId]: { [varName]: { times: number[], values: number[] } } } + result = getVariableData(message.arguments); + break; + default: + // Custom registered tool + result = executeCustomTool(message.toolName, message.arguments); + } + ws.send(JSON.stringify({ + type: 'tool_call_response', + sessionId, + callId: message.callId, + result, + isError: false + })); +} + +function handleFeedbackRequest(message) { + const feedbackContent = getFeedbackLoops(message.runIds); + ws.send(JSON.stringify({ + type: 'tool_call_response', + sessionId, + callId: message.requestId, + result: { feedbackContent, runIds: message.runIds }, + isError: false + })); +} + +function stopAgent() { + ws.send(JSON.stringify({ type: 'stop_iteration', sessionId })); +} +``` + +--- + +## Security & Scalability + +### Authentication + +Set `AUTHENTICATION_KEY` environment variable to enable authentication: + +```bash +export AUTHENTICATION_KEY="your-secret-key" +``` + +Clients must include this in `initialize_session`. If the env var is not set, authentication is disabled. + +### Stateless Design + +- No user data persisted server-side +- Sessions exist only in RAM, but do make use of a temporary directory for large model edits and visualization generation +- Per-session temp directory created on connect, deleted on disconnect +- Safe for multi-user deployment + +### Scaling + +- Horizontal scaling supported with sticky sessions at the load balancer + +--- + +## Development + +### Running the Server + +```bash +npm start +``` + +WebSocket server available at: `ws://localhost:3000/api/v1/agent` diff --git a/agent/WebSocket.js b/agent/WebSocket.js new file mode 100644 index 00000000..d7ddf901 --- /dev/null +++ b/agent/WebSocket.js @@ -0,0 +1,562 @@ +import { WorkerSpawner } from './WorkerSpawner.js'; +import { AgentConfigurationManager } from './utilities/AgentConfigurationManager.js'; +import { + validateClientMessage, + createSessionCreatedMessage, + createSessionReadyMessage, + createAgentSelectedMessage, + createAgentTextMessage, + createErrorMessage +} from './utilities/MessageProtocol.js'; +import { join } from 'path'; +import { fileURLToPath } from 'url'; +import { dirname } from 'path'; +import { readdirSync, readFileSync } from 'fs'; +import logger from '../utilities/logger.js'; +import utils from '../utilities/utils.js'; +import config from '../config.js'; +import { ProviderDisplayNames } from '../utilities/TokenUsageReporter.js'; + +const __dirname = dirname(fileURLToPath(import.meta.url)); + +function getAvailableAgents() { + const configDir = join(__dirname, 'config'); + const agents = []; + + try { + const files = readdirSync(configDir).filter(f => f.endsWith('.md')); + + for (const file of files) { + try { + const content = readFileSync(join(configDir, file), 'utf8'); + const metadata = AgentConfigurationManager.parseContent(content).metadata; + + if (metadata?.name) { + agents.push({ + id: file.replace('.md', ''), + name: metadata.name || file.replace('.md', ''), + role: metadata.role || 'Agent', + supportedModes: metadata.supported_modes || [], + supportedProviders: (metadata.supported_providers?.length ? metadata.supported_providers : ['anthropic', 'google']) + .map(id => ({ id, name: ProviderDisplayNames[id] ?? id })), + description: metadata.description || '' + }); + } + } catch (err) { + logger.warn(`Failed to load agent config from ${file}:`, err.message); + } + } + } catch (err) { + logger.error('Failed to scan agent config directory:', err); + } + + // Hardcoded defaults - socrates is the default agent for all model types + const defaults = { + sfd: 'socrates', + cld: 'socrates' + }; + + return { agents, defaults }; +} + +// Registry of all live worker processes so signal handlers can kill them all. +const liveWorkers = new Set(); + +// Kill a worker and all its descendant processes. +// +// IpcWorker (bwrap sandbox): w.pid is undefined. We kill only the bwrap process; +// the kernel kills everything in the PID namespace when its init (bwrap) exits. +// +// ChildProcess (fork fallback): w.pid is a number. The fork is spawned with +// detached:true so it leads its own process group. Killing the group +// (process.kill(-pid, signal)) also kills grandchildren like the claude CLI +// subprocess launched by the Agent SDK — without this they become orphans at +// 100% CPU after the worker is gone. +function killWorkerProcess(w, signal) { + if (typeof w.pid === 'number' && process.platform !== 'win32') { + process.kill(-w.pid, signal); + } else { + w.kill(signal); + } +} + +export class WebSocketHandler { + #ws; + #sessionManager; + #sessionId = null; + #worker = null; + // Promise for an in-flight WorkerSpawner.spawn(). #onClose/#onError/disconnect + // must await this before deleteSession runs rmSync on the session temp dir — + // otherwise the bwrap bind-mount source vanishes mid-spawn and the worker + // hits ENOENT on /session/ipc-*.sock (during connect) or /session/model.sdjson + // (during writes). Null when no spawn is in flight. + #workerSpawnPromise = null; + // True on the first chat message after a select_agent — tells worker to bridge context + #pendingAgentSwitch = false; + + // SIGKILL every live worker immediately. Called by process signal handlers so + // workers don't outlive the main process as orphans. + static killAll() { + for (const w of liveWorkers) { + try { killWorkerProcess(w, 'SIGKILL'); } catch { /* already dead */ } + } + liveWorkers.clear(); + } + + constructor(ws, sessionManager) { + this.#ws = ws; + this.#sessionManager = sessionManager; + this.#setup(); + } + + #setup() { + try { + this.#sessionId = this.#sessionManager.createSession(this.#ws); + this.#ws.send(JSON.stringify(createSessionCreatedMessage(this.#sessionId))); + logger.log(`WebSocket connected: ${this.#sessionId}`); + } catch (error) { + logger.error('Failed to create session:', error); + this.#ws.close(1011, error.message); + return; + } + + this.#ws.on('message', (data) => this.#onMessage(data)); + this.#ws.on('close', (code, reason) => this.#onClose(code, reason)); + this.#ws.on('error', (error) => this.#onError(error)); + } + + async #sendToClient(message) { + if (this.#ws.readyState === 1) { + this.#ws.send(JSON.stringify(message)); + } + } + + async #onMessage(data) { + try { + const rawMessage = JSON.parse(data.toString()); + const validation = validateClientMessage(rawMessage); + if (!validation.success) { + await this.#sendToClient(createErrorMessage(this.#sessionId, `Invalid message: ${validation.error}`, 'INVALID_MESSAGE')); + return; + } + + const message = validation.data; + + switch (message.type) { + case 'initialize_session': + await this.#handleInitializeSession(message); + break; + case 'select_agent': + await this.#handleSelectAgent(message); + break; + case 'chat': + await this.#handleChat(message); + break; + case 'tool_call_response': + await this.#handleToolCallResponse(message); + break; + case 'model_updated_notification': + await this.#handleModelUpdated(message); + break; + case 'stop_iteration': + await this.#handleStopIteration(message); + break; + case 'disconnect': { + const sessionId = this.#sessionId; + await this.#waitForSpawnAndKill(); + this.#sessionManager.deleteSession(sessionId); + this.#ws.close(1000, 'Client requested disconnect'); + break; + } + default: + await this.#sendToClient(createErrorMessage(this.#sessionId, `Unknown message type: ${message.type}`, 'UNKNOWN_MESSAGE_TYPE')); + } + } catch (error) { + logger.error(`Error handling message for session ${this.#sessionId}:`, error); + await this.#sendToClient(createErrorMessage(this.#sessionId, error.message, 'MESSAGE_PROCESSING_ERROR')); + } + } + + async #handleInitializeSession(message) { + try { + const authenticationKey = process.env.AUTHENTICATION_KEY; + if (authenticationKey) { + if (message.authenticationKey !== authenticationKey) { + this.#ws.close(1008, 'Unauthorized, please pass valid Authentication key.'); + return; + } + } + + if (!utils.supportedPlatform(message.clientProduct, message.clientVersion)) { + this.#ws.close(1008, 'Your client application is not currently supported.'); + return; + } + + if (!message.mode || !['cld', 'sfd'].includes(message.mode)) { + throw new Error('Invalid or missing mode. Must be "cld" or "sfd".'); + } + + const capabilities = { + supportsArrays: message?.supportsArrays, + supportsModules: message?.supportsModules, + supportsSubTypes: message?.supportsSubTypes + }; + + if (message.clientProduct === 'Stella Architect Beta' && message.clientVersion === '4.3') { + capabilities.supportsArrays = true; + capabilities.supportsModules = true; + capabilities.supportsSubTypes = false; + } + this.#sessionManager.initializeSession(this.#sessionId, message.mode, message.model, message.tools, message.context, message.clientId, capabilities); + + if (message.historicalMessages && message.historicalMessages.length > 0) { + for (const histMsg of message.historicalMessages) { + let role = 'assistant'; + let content = ''; + + switch (histMsg.type) { + case 'user_text': + role = 'user'; + content = histMsg.content || ''; + break; + case 'agent_text': + case 'agent_complete': + role = 'assistant'; + content = histMsg.content || ''; + break; + case 'visualization': + role = 'assistant'; + content = `[Created visualization: ${histMsg.visualizationTitle || 'Untitled'}]`; + if (histMsg.visualizationDescription) content += ` ${histMsg.visualizationDescription}`; + break; + } + + if (content) { + this.#sessionManager.addToConversationHistory(this.#sessionId, { role, content }); + } + } + + await this.#sessionManager.cleanupContext(this.#sessionId, config.agentMaxContextTokens); + logger.log(`Loaded ${message.historicalMessages.length} historical messages for session ${this.#sessionId}`); + } + + const { agents, defaults } = getAvailableAgents(); + await this.#sendToClient(createSessionReadyMessage(this.#sessionId, agents, defaults)); + logger.log(`Session initialized: ${this.#sessionId}`); + } catch (error) { + logger.error(`Failed to initialize session ${this.#sessionId}:`, error); + await this.#sendToClient(createErrorMessage(this.#sessionId, `Initialization failed: ${error.message}`, 'INITIALIZATION_ERROR')); + } + } + + async #handleSelectAgent(message) { + try { + let selectedAgent; + + if (message.agentConfig) { + const metadata = AgentConfigurationManager.parseContent(message.agentConfig).metadata; + if (!metadata.name || !metadata.agent_mode) { + throw new Error('agentConfig must have valid YAML frontmatter with name and agent_mode fields'); + } + selectedAgent = { + id: 'custom', + name: metadata.name, + supportedProviders: (metadata.supported_providers?.length ? metadata.supported_providers : ['anthropic', 'google']) + .map(id => ({ id, name: ProviderDisplayNames[id] ?? id })) + }; + } else { + const { agents } = getAvailableAgents(); + selectedAgent = agents.find(agent => agent.id === message.agentId); + if (!selectedAgent) { + throw new Error(`Agent '${message.agentId}' not found. Available agents: ${agents.map(a => a.id).join(', ')}`); + } + } + + const isSwitching = this.#worker !== null; + + // When switching agents, ask the running worker for its current conversation + // history so the new worker can bridge context across the handoff. + let conversationHistory = this.#sessionManager.getConversationContext(this.#sessionId); + if (isSwitching) { + try { + conversationHistory = await this.#getWorkerContext(this.#worker); + } catch (err) { + logger.warn(`[session:${this.#sessionId}] Could not retrieve context from old worker: ${err.message}`); + } + // Must await — both spawn (below) and any concurrent #onClose use the + // same tempDir. Spawning a new bwrap while the old worker is still + // alive shares the bind-mount source; letting #onClose race ahead would + // rmSync that source out from under either worker. + await this.#killWorker(); + } + + // Guard: the WS may have closed during the async context fetch above. + // #onClose already killed the worker and deleted the session — bail out + // before spawning a new worker that would never be cleaned up. + if (this.#ws.readyState !== 1) return; + + const tempDir = this.#sessionManager.getSessionTempDir(this.#sessionId); + // Publish the in-flight spawn so #onClose/#onError/disconnect can await + // it before deleteSession runs rmSync on tempDir. Without this, a WS + // close arriving during bwrap retry delays (up to 9s) lets the cleanup + // path rm the bind-mount source mid-spawn — the worker then hits + // ENOENT on /session/ipc-*.sock the moment it tries to connect. + const spawnPromise = WorkerSpawner.spawn(this.#sessionId, tempDir); + this.#workerSpawnPromise = spawnPromise; + try { + this.#worker = await spawnPromise; + } finally { + if (this.#workerSpawnPromise === spawnPromise) { + this.#workerSpawnPromise = null; + } + } + + // Guard: WS may have closed during bwrap retry delays (up to 9s). + if (this.#ws.readyState !== 1) { + // Await — the worker process is alive and bind-mounted to tempDir. + // cleanupSessionTempDir below rmSync's that source synchronously, so + // the worker must be reaped first or it'll write into a vanished + // bind mount (root cause of the model.sdjson ENOENT). + await this.#killWorker(); + // If the session was already deleted by #onClose, the spawn's + // mkdirSync may have re-created the temp dir after deleteSession's + // rmSync removed it. Clean it up so it doesn't become orphaned. + if (!this.#sessionManager.getSession(this.#sessionId)) { + this.#sessionManager.cleanupSessionTempDir(tempDir); + } + return; + } + + liveWorkers.add(this.#worker); + this.#setupWorkerRelay(this.#worker); + + // Let SessionManager's stale-cleanup path await worker exit before + // rmSync'ing the bwrap bind-mount source. + this.#sessionManager.setWorkerTeardown(this.#sessionId, () => this.#killWorker()); + + const session = this.#sessionManager.getSession(this.#sessionId); + if (!this.#worker.connected) { + throw new Error('Worker process failed to start (sandbox may not be available)'); + } + this.#worker.send({ + type: 'initialize', + mode: session.mode, + model: session.clientModel, + tools: session.clientTools, + context: session.context, + clientId: session.clientId, + conversationHistory, + isAgentSwitch: isSwitching, + supportsArrays: session.supportsArrays, + supportsModules: session.supportsModules, + supportsSubTypes: session.supportsSubTypes, + }); + + const supportedProviders = selectedAgent.supportedProviders; // [{id, name}] + const provider = supportedProviders.length === 1 + ? supportedProviders[0].id + : (message.provider ?? config.agentDefaultProvider); + const workerSelectMsg = message.agentConfig + ? { type: 'select_agent', agentConfig: message.agentConfig, provider } + : { type: 'select_agent', agentId: message.agentId, provider }; + this.#worker.send(workerSelectMsg); + this.#pendingAgentSwitch = isSwitching; + + await this.#sendToClient(createAgentSelectedMessage(this.#sessionId, selectedAgent.id, selectedAgent.name, selectedAgent.supportedProviders, provider)); + const providerLabel = ProviderDisplayNames[provider] ?? provider; + if (isSwitching) { + await this.#sendToClient(createAgentTextMessage(this.#sessionId, `I've switched to ${selectedAgent.name} (${providerLabel}). How can I help you?`, false)); + logger.log(`Agent switched to: ${selectedAgent.id} (${provider}) for session ${this.#sessionId}`); + } else { + await this.#sendToClient(createAgentTextMessage(this.#sessionId, `${selectedAgent.name} (${providerLabel}) — What can I do for you today?`, false)); + logger.log(`Agent selected: ${selectedAgent.id} (${provider}) for session ${this.#sessionId}`); + } + } catch (error) { + logger.error(`Failed to select agent for session ${this.#sessionId}:`, error); + await this.#sendToClient(createErrorMessage(this.#sessionId, `Agent selection failed: ${error.message}`, 'AGENT_SELECTION_ERROR')); + } + } + + async #handleChat(message) { + try { + if (!this.#worker) { + throw new Error('No agent selected. Send select_agent first.'); + } + this.#worker.send({ type: 'chat', message: message.message }); + // isAgentSwitch flag is carried in the worker's own pendingIsAgentSwitch state, + // set during initialize — no need to pass it again here. + this.#pendingAgentSwitch = false; + } catch (error) { + logger.error(`Error in chat for session ${this.#sessionId}:`, error); + await this.#sendToClient(createErrorMessage(this.#sessionId, error.message, 'CHAT_ERROR')); + } + } + + // Forward to worker which owns all pending promise maps + async #handleToolCallResponse(message) { + try { + if (!this.#worker) { + logger.warn(`Received tool_call_response for ${message.callId} but no worker is running`); + return; + } + this.#worker.send({ + type: 'tool_response', + callId: message.callId, + result: message.result, + isError: message.isError, + }); + } catch (error) { + logger.error(`Error forwarding tool response for session ${this.#sessionId}:`, error); + await this.#sendToClient(createErrorMessage(this.#sessionId, error.message, 'TOOL_RESPONSE_ERROR')); + } + } + + async #handleModelUpdated(message) { + try { + // Keep main-process SessionManager in sync (used to initialize new workers on agent switch) + this.#sessionManager.updateClientModel(this.#sessionId, message.model); + // Forward to worker so AgentOrchestrator sees the updated model token count + this.#worker?.send({ type: 'model_updated', model: message.model }); + logger.log(`Model updated for session ${this.#sessionId}: ${message.changeReason}`); + } catch (error) { + logger.error(`Error updating model for session ${this.#sessionId}:`, error); + } + } + + async #handleStopIteration() { + try { + if (!this.#worker) { + throw new Error('No active agent to stop'); + } + logger.log(`Stop iteration requested for session ${this.#sessionId}`); + this.#worker.send({ type: 'stop' }); + } catch (error) { + logger.error(`Error stopping iteration for session ${this.#sessionId}:`, error); + await this.#sendToClient(createErrorMessage(this.#sessionId, error.message, 'STOP_ITERATION_ERROR')); + } + } + + async #onClose(code, reason) { + logger.log(`WebSocket closed: ${this.#sessionId} (code: ${code}, reason: ${reason})`); + if (this.#sessionId) { + const sessionId = this.#sessionId; + const startedAt = Date.now(); + await this.#waitForSpawnAndKill(); + const elapsed = Date.now() - startedAt; + logger.log(`[session:${sessionId}] Worker shutdown completed in ${elapsed}ms; deleting session`); + this.#sessionManager.deleteSession(sessionId); + } + } + + async #onError(error) { + logger.error(`WebSocket error for session ${this.#sessionId}:`, error); + if (this.#sessionId) { + const sessionId = this.#sessionId; + await this.#waitForSpawnAndKill(); + this.#sessionManager.deleteSession(sessionId); + } + } + + // Cleanup-path helper: wait for any in-flight WorkerSpawner.spawn() to settle, + // then kill the resulting worker. Callers must use this (not bare #killWorker) + // anywhere they're about to deleteSession or rmSync the session temp dir — + // otherwise a WS close arriving mid-spawn lets the cleanup path race ahead of + // bwrap's --bind setup and the worker hits ENOENT on /session. + async #waitForSpawnAndKill() { + if (this.#workerSpawnPromise) { + try { await this.#workerSpawnPromise; } catch { /* spawn rejection is fine — nothing to kill */ } + } + await this.#killWorker(); + } + + // Returns a promise that resolves once the worker process has actually exited + // (or after the SIGKILL fallback fires). Callers that destroy the session temp + // directory MUST await this — bwrap's `--bind` source vanishing under a live + // sandbox produces ENOENT on writes from inside the container. + #killWorker() { + if (!this.#worker) return Promise.resolve(); + const w = this.#worker; + const sessionId = this.#sessionId; + this.#worker = null; + liveWorkers.delete(w); + if (w.connected) { + try { w.send({ type: 'shutdown' }); } catch { /* already dead */ } + } + return new Promise((resolve) => { + let settled = false; + const settle = () => { if (!settled) { settled = true; resolve(); } }; + + const sigkillTimer = setTimeout(() => { + logger.warn(`[worker:${sessionId}] did not exit within 2s of shutdown — sending SIGKILL`); + try { killWorkerProcess(w, 'SIGKILL'); } catch { /* already dead */ } + }, 2000); + + // Safety: if 'exit' was already emitted before we attached (or never + // fires), don't hang the session-cleanup path forever. + const fallbackTimer = setTimeout(() => { + logger.warn(`[worker:${sessionId}] exit event not received 4s after shutdown — proceeding with cleanup`); + settle(); + }, 4000); + + w.once('exit', () => { + clearTimeout(sigkillTimer); + clearTimeout(fallbackTimer); + settle(); + }); + }); + } + + /** + * Wire up the IPC relay for a freshly spawned worker. + * - Forwards all to_client messages to the WebSocket. + * - Logs worker stdout/stderr. + * - Cleans up on unexpected exit. + */ + #setupWorkerRelay(w) { + w.on('message', async (msg) => { + if (msg.type === 'to_client') { + // Only forward if this is still the active worker; drop stale messages + // from a worker that has been replaced or is in its shutdown grace period. + if (this.#worker === w && this.#ws.readyState === 1) { + this.#ws.send(JSON.stringify(msg.message)); + } + } else if (msg.type === 'worker_error') { + logger.error(`[worker:${this.#sessionId}] ${msg.error}`); + } + // context_response is handled inside #getWorkerContext via its own listener + }); + + w.on('error', (err) => logger.error(`[worker:${this.#sessionId}] process error: ${err.message}`)); + + w.on('exit', (code, signal) => { + logger.log(`[worker:${this.#sessionId}] exited (code=${code} signal=${signal})`); + liveWorkers.delete(w); + if (this.#worker === w) this.#worker = null; + }); + } + + /** + * Ask a running worker for its current conversation context. + * Returns a promise that resolves with the history array. + */ + #getWorkerContext(w) { + return new Promise((resolve, reject) => { + const requestId = `ctx_${Date.now()}_${Math.random().toString(36).slice(2)}`; + const timeout = setTimeout(() => { + w.off('message', handler); + reject(new Error('Context request timed out')); + }, 5000); + + function handler(msg) { + if (msg.type === 'context_response' && msg.requestId === requestId) { + clearTimeout(timeout); + w.off('message', handler); + resolve(msg.context); + } + } + + w.on('message', handler); + w.send({ type: 'get_context', requestId }); + }); + } +} diff --git a/agent/WorkerSpawner.js b/agent/WorkerSpawner.js new file mode 100644 index 00000000..bf33d317 --- /dev/null +++ b/agent/WorkerSpawner.js @@ -0,0 +1,439 @@ +import { spawn, fork } from 'child_process'; +import { existsSync, readFileSync, statSync, unlink, unlinkSync, mkdirSync } from 'fs'; +import { randomBytes } from 'crypto'; +import { fileURLToPath } from 'url'; +import { dirname, join } from 'path'; +import { execSync } from 'child_process'; +import net from 'net'; +import { EventEmitter } from 'events'; +import logger from '../utilities/logger.js'; + +const __dirname = dirname(fileURLToPath(import.meta.url)); +const APP_ROOT = dirname(__dirname); // sd-ai root (parent of agent/) + +/** + * Wraps a bwrap ChildProcess with a Unix-socket-based IPC channel. + * + * bwrap cannot pass the Node.js IPC fd (fd 3) into the sandbox. + * Instead we create a Unix domain socket inside the session temp dir + * (which maps to /session in the container) and use newline-delimited + * JSON over that socket as a drop-in replacement. + * + * The public API intentionally mirrors the subset of ChildProcess that + * WebSocket.js uses (.send, on('message'), .connected, .stdout, .stderr, + * .kill, on('exit'), on('error')). + */ +class IpcWorker extends EventEmitter { + #proc = null; + #server; + #socketPath; + #socket = null; + #sendQueue = []; + #connected = true; + #socketConnected = false; + + /** + * Create the server socket and wait until it is bound and listening. + * The socket file exists on disk before this promise resolves, so bwrap + * can be spawned immediately after with no race condition. + * Call worker.attach(proc) right after spawning the sandboxed process. + */ + static async listen(socketPath) { + const server = net.createServer(); + try { unlinkSync(socketPath); } catch { /* no stale socket to remove */ } + await new Promise((resolve, reject) => { + server.once('listening', resolve); + server.once('error', reject); + server.listen(socketPath); + }); + return new IpcWorker(server, socketPath); + } + + constructor(server, socketPath) { + super(); + this.#server = server; + this.#socketPath = socketPath; + + server.on('connection', (socket) => { + // Defensive: only one connection is expected per worker, but if a second + // arrives (e.g. retry inside the sandbox), tear the old one down rather + // than orphan its FD and listeners. + if (this.#socket && !this.#socket.destroyed) this.#socket.destroy(); + this.#socket = socket; + this.#socketConnected = true; + this.emit('socket-connected'); + + for (const chunk of this.#sendQueue) socket.write(chunk); + this.#sendQueue = []; + + let buf = ''; + socket.on('data', (d) => { + buf += d.toString(); + let nl; + while ((nl = buf.indexOf('\n')) !== -1) { + const line = buf.slice(0, nl).trim(); + buf = buf.slice(nl + 1); + if (line) { + try { this.emit('message', JSON.parse(line)); } + catch { /* ignore malformed line */ } + } + } + }); + + socket.once('close', () => { this.#connected = false; }); + socket.on('error', (err) => this.emit('error', err)); + }); + + server.on('error', (err) => this.emit('error', err)); + } + + /** + * Tear down the server + socket file without going through attach(). + * Use only when spawn() failed before attach() was called — once attached, + * proc.on('exit') owns the cleanup. + */ + dispose() { + this.#socket?.destroy(); + try { this.#server.close(); } catch { /* already closing */ } + try { unlinkSync(this.#socketPath); } catch { /* already gone */ } + } + + /** Wire up the sandboxed process after the socket is already listening. */ + attach(proc) { + this.#proc = proc; + proc.on('error', (err) => this.emit('error', err)); + proc.on('exit', (code, signal) => { + this.#connected = false; + this.#socket?.destroy(); + this.#server.close(); + unlink(this.#socketPath, () => {}); + this.emit('exit', code, signal); + }); + } + + get stdout() { return this.#proc.stdout; } + get stderr() { return this.#proc.stderr; } + get stdin() { return this.#proc.stdin; } + get connected() { return this.#connected; } + get socketConnected() { return this.#socketConnected; } + + kill(signal) { this.#proc.kill(signal); } + + send(msg) { + const chunk = JSON.stringify(msg) + '\n'; + if (this.#socket && !this.#socket.destroyed) { + this.#socket.write(chunk); + } else if (this.#connected) { + this.#sendQueue.push(chunk); // worker hasn't connected yet; drain on connect + } + // silently drop if the process has already exited + } +} + +export class WorkerSpawner { + static CONTAINER_SESSION_PATH = '/session'; + static #WORKER_PATH = join(__dirname, 'AgentWorker.js'); + static #bwrapBroken = false; // set true on first bwrap sandbox failure + // Set ALLOW_UNSANDBOXED_FALLBACK=true to allow unsandboxed fork workers when + // bwrap fails at runtime. Defaults to false so a sandbox failure is a hard + // error rather than a silent security regression. + static #allowUnsandboxedFallback = process.env.ALLOW_UNSANDBOXED_FALLBACK === 'true'; + + static #findBinary(name) { + try { return execSync(`which ${name}`, { encoding: 'utf8' }).trim(); } + catch { return null; } + } + + static #logBwrapDiagnostics(bwrapBin) { + const lines = ['bwrap sandbox diagnostics:']; + + let isSetuid = false; + try { + const st = statSync(bwrapBin); + isSetuid = (st.mode & 0o4000) !== 0; + lines.push(` bwrap binary : ${bwrapBin} (mode=${st.mode.toString(8)}, setuid=${isSetuid})`); + } catch (e) { + lines.push(` bwrap binary : stat failed — ${e.message}`); + } + + for (const sysctl of [ + '/proc/sys/kernel/unprivileged_userns_clone', + '/proc/sys/user/max_user_namespaces', + '/proc/sys/kernel/apparmor_restrict_unprivileged_userns', + ]) { + try { + lines.push(` ${sysctl} = ${readFileSync(sysctl, 'utf8').trim()}`); + } catch { + lines.push(` ${sysctl} = (not present)`); + } + } + + try { + const caps = readFileSync('/proc/self/status', 'utf8'); + const capEff = caps.match(/^CapEff:\s+(\S+)/m)?.[1]; + lines.push(` process CapEff: ${capEff ?? 'unknown'}`); + } catch { /* ignore */ } + + for (const f of ['/.dockerenv', '/run/.containerenv']) { + if (existsSync(f)) { lines.push(` container marker: ${f}`); break; } + } + + lines.push(''); + if (!isSetuid) { + lines.push(' Most reliable fix: sudo chmod u+s ' + bwrapBin); + lines.push(' Ubuntu 24.04 alternative: sudo sysctl -w kernel.apparmor_restrict_unprivileged_userns=0'); + lines.push(' LXC/Proxmox: enable nested user namespaces in the container config'); + } + + logger.error(lines.join('\n')); + } + + /** + * Build bwrap argument list for a sandboxed worker process. + * + * Mount strategy: + * - /usr (+ /lib, /lib64 if present): Node.js runtime + system libraries (read-only) + * - /etc/ssl, /etc/resolv.conf, /etc/hosts: TLS certs + DNS for Anthropic API (read-only) + * - APP_ROOT → /app: application code including node_modules (read-only) + * - Node binary dir (if outside /usr, e.g. nvm): additional read-only bind + * - Claude binary dir (if outside /usr): additional read-only bind + * - Any non-/usr directories in PATH (e.g. python venv): read-only bind of + * the venv root (detected via pyvenv.cfg) or the directory itself + * - sessionTempDir → /session: the ONLY writable location; also hosts ipc.sock + * - /dev, /proc: required pseudo-filesystems for Node.js + * - /tmp: tmpfs (ephemeral scratch) + * + * IPC is handled via a Unix domain socket at /session/ipc.sock rather than + * Node.js IPC fd forwarding, so no --forward-fd flag is needed. + */ + static #buildBwrapArgs(sessionTempDir) { + const nodeBin = process.execPath; + const nodeBinDir = dirname(nodeBin); + const claudeBin = WorkerSpawner.#findBinary('claude'); + + const args = [ + '--ro-bind', '/usr', '/usr', + ]; + + for (const lib of ['/lib', '/lib64', '/lib/x86_64-linux-gnu', '/lib/aarch64-linux-gnu']) { + if (existsSync(lib)) args.push('--ro-bind', lib, lib); + } + + for (const path of ['/etc/ssl', '/etc/resolv.conf', '/etc/hosts', '/etc/nsswitch.conf', '/etc/gai.conf']) { + if (existsSync(path)) args.push('--ro-bind', path, path); + } + + args.push('--ro-bind', APP_ROOT, '/app'); + + if (!nodeBin.startsWith('/usr/')) { + const parts = nodeBinDir.split('/').filter(Boolean); + for (let i = 1; i <= parts.length; i++) { + args.push('--dir', '/' + parts.slice(0, i).join('/')); + } + args.push('--ro-bind', nodeBinDir, nodeBinDir); + } + + if (claudeBin && !claudeBin.startsWith('/usr/')) { + const claudeDir = dirname(claudeBin); + const parts = claudeDir.split('/').filter(Boolean); + for (let i = 1; i <= parts.length; i++) { + args.push('--dir', '/' + parts.slice(0, i).join('/')); + } + args.push('--ro-bind', claudeDir, claudeDir); + } + + // Mount any non-/usr directories from PATH (e.g. python venv). + // If a directory's parent contains pyvenv.cfg we mount the whole venv root + // so that the Python interpreter can find its site-packages and stdlib. + const alreadyMounted = new Set(); + for (const dir of (process.env.PATH || '').split(':')) { + if (!dir || dir.startsWith('/usr') || !existsSync(dir)) continue; + const parent = dirname(dir); + const mountTarget = existsSync(join(parent, 'pyvenv.cfg')) ? parent : dir; + if (alreadyMounted.has(mountTarget)) continue; + alreadyMounted.add(mountTarget); + const parts = mountTarget.split('/').filter(Boolean); + for (let i = 1; i < parts.length; i++) { + args.push('--dir', '/' + parts.slice(0, i).join('/')); + } + args.push('--ro-bind', mountTarget, mountTarget); + } + + args.push( + '--bind', sessionTempDir, WorkerSpawner.CONTAINER_SESSION_PATH, + '--dev', '/dev', + '--proc', '/proc', + '--tmpfs', '/tmp', + '--unshare-pid', + '--unshare-uts', '--hostname', 'agent', + '--', + nodeBin, + '/app/agent/AgentWorker.js' + ); + + return args; + } + + /** + * Spawn a sandboxed agent worker process for the given session. + * + * On Linux with bwrap installed: runs inside a bubblewrap container where + * only the session temp dir is writable and most of the filesystem is + * either read-only or not mounted at all. IPC uses a Unix domain socket + * at /ipc.sock (mapped to /session/ipc.sock in the sandbox) + * rather than Node.js IPC fd forwarding, so no --forward-fd support is needed. + * + * On Linux without bwrap, macOS, or Windows: falls back to a plain fork + * with a prominent warning. Use Linux + bwrap for any publicly hosted + * deployment. + * + * Returns an IpcWorker (bwrap) or ChildProcess (fork) — both expose the + * same .send() / on('message') / .connected interface used by WebSocket.js. + */ + static async spawn(sessionId, sessionTempDir) { + if (process.platform === 'linux') { + const bwrapBin = WorkerSpawner.#findBinary('bwrap'); + if (bwrapBin && !WorkerSpawner.#bwrapBroken) { + const MAX_ATTEMPTS = 3; + for (let attempt = 1; attempt <= MAX_ATTEMPTS; attempt++) { + const attemptLabel = attempt > 1 ? ` (attempt ${attempt}/${MAX_ATTEMPTS})` : ''; + logger.log(`[worker:${sessionId}] Spawning sandboxed worker via bwrap${attemptLabel}`); + + mkdirSync(sessionTempDir, { recursive: true }); + // Unique name per spawn so the old IpcWorker's async unlink-on-exit + // never races with the new IpcWorker's socket (agent-switch scenario). + const socketName = `ipc-${randomBytes(4).toString('hex')}.sock`; + const socketPath = join(sessionTempDir, socketName); + const workerEnv = { + OPENAI_API_KEY: process.env.OPENAI_API_KEY, + GEMINI_API_KEY: process.env.GEMINI_API_KEY, + ANTHROPIC_API_KEY: process.env.ANTHROPIC_API_KEY, + SESSION_ID: sessionId, + SESSION_TEMP_DIR: WorkerSpawner.CONTAINER_SESSION_PATH, + WORKER_IPC_SOCKET: `${WorkerSpawner.CONTAINER_SESSION_PATH}/${socketName}`, + // claude CLI requires HOME to locate ~/.claude/ for config and session state. + // Point it at /session so each sandbox gets a fresh, writable home dir. + HOME: WorkerSpawner.CONTAINER_SESSION_PATH, + PATH: process.env.PATH, + }; + const bwrapArgs = WorkerSpawner.#buildBwrapArgs(sessionTempDir); + logger.log(`[worker:${sessionId}] bwrap args: ${bwrapArgs.join(' ')}`); + + // Socket file is on disk before bwrap starts — no race condition. + const worker = await IpcWorker.listen(socketPath); + + let proc; + try { + proc = spawn(bwrapBin, bwrapArgs, { + env: workerEnv, + // Pipe stderr (instead of inheriting) so we can prefix lines with + // the session id — concurrent workers' stderr would otherwise + // interleave under a single anonymous fd, making post-mortems + // (like the "IPC socket error: connect ENOENT /session/ipc-*.sock" + // failure seen under concurrent spawns) impossible to attribute. + stdio: ['inherit', 'inherit', 'pipe'], + }); + } catch (err) { + // spawn rarely throws synchronously (most failures emit 'error'), + // but bad options can. Tear down the listening server + socket file + // so we don't leak FDs across retries. + worker.dispose(); + throw err; + } + if (proc.stderr) { + let buf = ''; + proc.stderr.on('data', (chunk) => { + buf += chunk.toString(); + let nl; + while ((nl = buf.indexOf('\n')) !== -1) { + const line = buf.slice(0, nl); + buf = buf.slice(nl + 1); + if (line.length > 0) logger.error(`[bwrap:${sessionId}] ${line}`); + } + }); + proc.stderr.on('end', () => { + if (buf.length > 0) logger.error(`[bwrap:${sessionId}] ${buf}`); + }); + } + worker.attach(proc); + + // Wait for either a successful IPC connection or an early bwrap exit. + // Each handler removes its sibling so the loser doesn't stay attached + // for the worker's lifetime firing into an already-resolved promise. + const earlyExit = await new Promise((resolve) => { + const onConnected = () => { + worker.off('exit', onExit); + resolve(null); + }; + const onExit = (code, signal) => { + worker.off('socket-connected', onConnected); + if (!worker.socketConnected) resolve({ code, signal }); + }; + worker.once('socket-connected', onConnected); + worker.once('exit', onExit); + }); + + if (earlyExit === null) return worker; // socket connected — worker is up + + const { code, signal } = earlyExit; + if (attempt < MAX_ATTEMPTS) { + logger.warn( + `[worker:${sessionId}] bwrap exited early (code=${code} signal=${signal}) — attempt ${attempt}/${MAX_ATTEMPTS}, retrying in 3s...` + ); + await new Promise(r => setTimeout(r, 3000)); + } else { + WorkerSpawner.#bwrapBroken = true; + const fallbackNote = WorkerSpawner.#allowUnsandboxedFallback + ? 'Future workers will fall back to unsandboxed fork (ALLOW_UNSANDBOXED_FALLBACK=true).' + : 'Worker spawning will now FAIL until bwrap is fixed (set ALLOW_UNSANDBOXED_FALLBACK=true to override).'; + logger.error( + `[worker:${sessionId}] bwrap exited early (code=${code} signal=${signal}) — sandbox unavailable after ${MAX_ATTEMPTS} attempts. See stderr above.\n` + + fallbackNote + '\n' + + 'Fix: update bubblewrap (apt-get upgrade bubblewrap) or ensure user namespaces are enabled.' + ); + WorkerSpawner.#logBwrapDiagnostics(bwrapBin); + } + } + // All attempts failed — fall through to bwrapBroken handling below. + } + if (WorkerSpawner.#bwrapBroken) { + if (!WorkerSpawner.#allowUnsandboxedFallback) { + throw new Error('bwrap sandbox is unavailable and ALLOW_UNSANDBOXED_FALLBACK is not set — refusing to spawn unsandboxed worker'); + } + logger.warn(`[worker:${sessionId}] bwrap sandbox unavailable — spawning unsandboxed worker`); + } else { + logger.error( + '================================================================================\n' + + 'SECURITY WARNING: bwrap (bubblewrap) not found on Linux!\n' + + 'Agent workers will run WITHOUT filesystem sandbox isolation.\n' + + 'Install bubblewrap to enable sandboxing: apt install bubblewrap\n' + + 'DO NOT run this configuration for any publicly hosted service.\n' + + '================================================================================' + ); + } + } else { + logger.warn( + '================================================================================\n' + + `SECURITY WARNING: Running on ${process.platform} — bwrap sandboxing is unavailable.\n` + + 'Agent workers can read and write the ENTIRE server filesystem.\n' + + 'This configuration is for LOCAL DEVELOPMENT ONLY.\n' + + 'Deploy on Linux with bubblewrap installed for any hosted environment.\n' + + '================================================================================' + ); + } + + // Unsandboxed fallback: plain fork. + // detached: true puts the worker in its own process group so that killing + // the group (process.kill(-pid, signal)) also kills grandchildren like the + // claude CLI subprocess spawned by the Agent SDK. + return fork(WorkerSpawner.#WORKER_PATH, [], { + env: { ...process.env, SESSION_ID: sessionId, SESSION_TEMP_DIR: sessionTempDir }, + stdio: ['inherit', 'inherit', 'inherit', 'ipc'], + // detached only on Unix: puts the worker in its own process group so + // process.kill(-pid) can kill grandchildren (e.g. the claude CLI). + // On Windows, detached + inherited stdio breaks the IPC channel (EBADF), + // and negative-PID group killing isn't supported anyway. + detached: process.platform !== 'win32', + }); + } +} diff --git a/agent/config/merlin.md b/agent/config/merlin.md new file mode 100644 index 00000000..c23f81a1 --- /dev/null +++ b/agent/config/merlin.md @@ -0,0 +1,361 @@ +--- +name: "Merlin" +role: "Craftsman" +description: "Expert Modeler who builds sophisticated System Dynamics models efficiently. Asks only necessary questions, uses arrays and modules when appropriate, and is comfortable with technical complexity." +version: "1.0" +max_iterations: 50 +agent_mode: sdk +supported_modes: + - sfd + - cld +supported_providers: + - anthropic + - google +--- + +You are Merlin, an efficient and expert System Dynamics modeler with deep knowledge of SD theory and practice. +Your responses should be direct, technically precise, and action-oriented. +Use proper SD terminology freely - your users are comfortable with jargon. +Ask only the essential questions needed to build accurate models. + +CRITICAL RULE — FEEDBACK STRUCTURE: +NEVER describe, summarize, or discuss feedback loop structure, loop polarities, loop dominance, or causal mechanisms in any response unless you have called get_feedback_information in the current conversation turn. This applies to model build summaries, modification summaries, simulation summaries, and all other responses. If you have not called get_feedback_information, describe what the model is composed of (stocks, flows, variables) but say nothing about feedback loops or causal behavior. Violating this rule is a critical error. + +IMPORTANT RULES: +1. To see the current model, call get_current_model +2. To modify the model, call update_model with proposed changes +3. To run simulations, call run_model - it automatically uses the client's current model +4. NEVER assume you know the model structure - always call get_current_model first +5. Always validate models rigorously before recommending simulations +6. Explain the theoretical basis for your modeling decisions +7. CRITICAL: Use LTM to understand model structure by asking for feedback information! +8. Assume NO limits on complexity - build comprehensive models as needed +9. Always refer to runs by their name, not their runId — when communicating with the user, use the human-readable run name rather than the numeric ID. +10. After building or significantly modifying a model, explicitly critique it for structural issues (loop polarities, missing feedbacks, unrealistic formulations) and behavioral credibility (reference mode fit, extreme conditions, conservation laws). Do not proceed to sensitivity analysis or optimization until the model has earned its credibility. + +## Loops That Matter (LTM) +LTM (Loops That Matter) is a feedback-loop dominance analysis technique that ranks loops by instantaneous impact, showing how dominance shifts over time. Use it extensively via get_feedback_information → discuss_model_with_seldon to understand WHY behavior occurs, validate causal mechanisms, and design effective policies. + + +## Modeling Workflow +When building or modifying models, work efficiently: +1. PROBLEM ARTICULATION: Ask only essential questions to understand the problem +2. DYNAMIC HYPOTHESIS: Quickly develop causal theories about feedback structure +3. FORMULATION: Create comprehensive equations with dimensional consistency + - Assume NO limits on model complexity - build as complex as needed + - Use arrays when modeling groups of similar entities + - Use modules when structure can be componentized + - Use sub-types when discrete entity specializations are appropriate + - Include all relevant variables and relationships for completeness +4. TESTING: Run structural validity tests - including LTM if possible to verify right behavior for the right reasons. +5. POLICY ANALYSIS: Identify high-leverage intervention points +6. DOCUMENTATION: Document key assumptions and limitations + + +## Modification Workflow +When modifying existing models: +1. Call get_current_model to review current structure +2. If necessary, use discuss_model_with_seldon to quickly analyze existing feedback loops and their implications +3. Make changes efficiently, explaining technical rationale +4. Use update_model with clear theoretical reasoning +5. Recommend testing after significant modifications + + +## Validation Rules +Enforce strict validation: +- All stocks must have valid initial values with units +- All equations must be dimensionally consistent +- Verify conservation laws (mass, energy, etc.) +- Ensure model boundaries are appropriate +- Validate against reference modes +- If possible, verify behavior comes from correct feedback mechanisms using LTM and Seldon +- Explicitly critique model structure: check loop polarities, missing feedbacks, and unrealistic formulations +- Explicitly critique model behavior: verify reference mode fit, test extreme conditions, and confirm conservation laws hold +- A model has not earned credibility until it passes both structural and behavioral critique +- Ask users for their assessment of model validity by describing the important processes within the model + + +## Visualization Guidelines +**NEVER create visualizations automatically.** Only create charts, plots, or feedback dominance analyses when the user explicitly requests them or confirms after a suggestion. +- After a simulation, briefly mention what would be informative to visualize, then STOP and wait for the user to ask +- Do NOT auto-run get_feedback_information or create_visualization after building or running a model + +## Tool Usage Policies + +### get_current_model *(sfd + cld)* +**When to use:** Always before any analysis or modification +**Frequency:** At start of every modeling conversation + +### update_model *(sfd + cld)* +**When to use:** After editing the model file on disk — this tool reads the session model file and pushes it to the client. Edit the file first, then call this with no arguments. +**Always explain** your reasoning when using this tool + +### run_model *(sfd only)* +**When to use:** After structural validation passes +**Auto-suggest** this tool when appropriate + +### get_run_info *(sfd only)* +**When to use:** Both before and after simulations. Call it proactively at the start of any calibration or visualization request to see what run data already exists — you may not need to run a new simulation or ask the user to load data. +**Frequency:** Before calling `get_variable_data`; also before `load_calibration_data` to check whether calibration data is already present + +### get_variable_data *(sfd only)* +**When to use:** After `get_run_info`, to fetch time-series data for specific variables +**IMPORTANT:** Always pass `detailed=true` to get enough data points for plotting +**Frequency:** Every time before `create_visualization` + +### generate_ltm_narrative *(sfd only)* +**When to use:** When deep feedback loop analysis would help explain complex behavior +**Frequency:** As needed for understanding causal mechanisms + +### discuss_model_with_seldon *(sfd + cld)* +**When to use:** Only when the user asks for feedback loop analysis or causal explanation — do not call automatically +**Frequency:** On request; after simulations, suggest it rather than running it automatically + +### discuss_model_across_runs *(sfd only)* +**When to use:** Use to understand what causes behavioral differences across runs - analyzes how different scenarios or parameter changes produce different outcomes by examining underlying feedback loop dynamics +**Frequency:** When comparing simulation results from different runs or scenarios + +### generate_quantitative_model *(sfd only)* +**When to use:** For sfd models - use arrays, modules, and sub-types when appropriate + +### generate_qualitative_model *(cld only)* +**When to use:** For cld models - can be comprehensive + +### create_visualization *(sfd only)* +**When to use:** Only when the user explicitly requests a chart or graph, or confirms after a suggestion — do not create automatically after simulations + +### get_feedback_information *(sfd + cld)* +**When to use:** ALWAYS before discuss_model_with_seldon, discuss_model_across_runs, or generate_ltm_narrative — no exceptions + +## Action Sequences + +### On New Model Request +1. Ask only critical questions needed (time horizon, key variables, problem statement) +2. Generate the model (generate_qualitative_model, generate_quantitative_model) +3. **VALIDATE** — do all of the following before continuing: + a. Call get_current_model, fix all errors and warnings + b. *(SFD only)* Inspect equations structurally: do physical-quantity stocks have first-order control on outflows to prevent going negative? Are graphical functions normalized? Do equations have embedded constants? + c. *(SFD only)* Run the model (run_model), then get_variable_data for key stocks — check whether anything goes negative that physically cannot, whether conservation laws hold, and whether behavior matches the reference mode. Fix any structural violations before proceeding (do NOT use MIN/MAX clamps — fix the structure). +4. STOP — ask the user what they want to do next. Do NOT auto-visualize or auto-analyze feedback. + +### On Modification Request +1. Inspect the current model (get_current_model) +2. Describe why changes are needed +3. Apply the changes (update_model) +4. **VALIDATE** — same as step 3 above: fix errors/warnings, check structural integrity, run and verify behavior for SFDs +5. STOP — ask the user what they want to do next. + +### On Plot / Visualization Request (user asks for a chart or graph, not explicitly a run) +1. Call `get_run_info` to check whether existing run data is available +2. If usable data exists, go straight to `get_variable_data` and `create_visualization` — do not run the model +3. If no suitable data exists, run the simulation first (run_model), then proceed with `get_variable_data` and `create_visualization` +4. After showing the visualization, suggest that the user ask for an explanation of behavior (i.e. use Seldon and get_feedback_information) + +### On Simulation Request (user explicitly asks to run, or model was just modified) +1. Check all parameters defined, equations valid, units consistent +2. Run the simulation (run_model) +3. Report the run completed. Ask what the user wants to do next — do NOT automatically create visualizations or run feedback analysis. + +## Communication Style +**Style:** direct, technical, efficient +- Always explain your reasoning +- Use examples to clarify concepts +- System Dynamics terminology is acceptable + +**Response Format:** +- thinking: Concise theoretical reasoning from SD principles +- actions: Direct descriptions of tools and their purpose +- results: Technical interpretation in terms of feedback structure and SD theory +- next steps: Recommend next modeling steps or validation tests + +**Verbosity level:** medium +**Tone:** professional, confident, efficient + +## Constraints +**Maximum model complexity:** +- variables: Unlimited - build as complex as needed for accuracy +- feedback_loops: Unlimited - include all relevant feedback structure +- All variables must have documentation +- All variables must have units +- All equations must be validated + + +## Client-Specific Tools *(sfd only)* + +These tools are available when connected to a Stella client. They expose the optimization, calibration, and sensitivity analysis subsystems directly. + +### Tool Reference + +#### Calibration & Payoff Tools + +**`load_calibration_data`** +Prompts the user to select an external data file and loads it as a calibration run. +- `requestedVariables` (array of strings, optional) — variables to suggest in the load dialog +- Returns: `{ runId, runName, variables }` where `variables` lists every variable in the loaded file +- **CRITICAL:** Always call before creating a new calibration payoff. The returned `runId` is required as `calibrationRunId`, and the `variables` array defines which model variables have data — use exactly those as payoff elements. + +**`create_payoff`** +Defines what the optimization targets. +- `name` (string, required) +- `isCalibration` (boolean) — true for calibration; weights computed automatically +- `calibrationRunId` (integer) — `runId` from `load_calibration_data`; required when `isCalibration` is true +- `elements` (array of `{ variableName, weight? }`) — for calibration payoffs use the `variables` from `load_calibration_data` +- Returns: `{ status: "created", payoffIndex }` + +**`edit_payoff`** +Modifies an existing payoff. Requires `payoffIndex` (integer); all other fields from `create_payoff` are optional. +Returns: `{ status: "updated", payoffIndex }` + +**`list_payoffs`** +Lists all defined payoffs with elements and calibration references. No parameters. + +#### Optimization Tools + +**`create_optimization`** +Creates a Powell optimization. +- `name` (string, required) +- `parameters` (array of `{ variableName, min?, max?, stepMult? }`) — `stepMult` scales the global `initialStep` for this parameter +- `payoff` (`{ payoffName, action }`) — `action`: `"maximize"` | `"minimize"` | `"lt"` | `"lte"`; calibration payoffs use `"minimize"` +- `initialStep` (number, default 1.0) — expected parameter magnitude to reach optimum +- `numSims` (integer, default 5000) — max optimizer evaluations; -1 for unlimited +- `sensitivityAnalysis` (string, optional) — name of a sensitivity analysis to optimize over (each evaluation runs the full analysis) +- `worstCase` (boolean, optional) — when using a sensitivity analysis, optimize for worst case +- Returns: `{ status: "created", optimizationIndex }` + +**`edit_optimization`** +Modifies an existing optimization. Requires `optimizationIndex` (integer); all other fields optional. +Returns: `{ status: "updated", optimizationIndex }` + +**`list_optimization_analyses`** +Lists all defined optimizations. No parameters. Returns `{ optimizations: [...], activeIndex }`. + +**`run_optimization`** +Runs an optimization. Long-running (minutes to hours). +- `optimizationIndex` (integer, optional) — use -1 or omit for the active one +- Returns: `{ status: "completed" }` + +#### Sensitivity Analysis Tools + +**`create_sensitivity_analysis`** +Creates a sensitivity analysis. +- `name` (string, required) +- `method` (enum: `"sobolSequence"` [default], `"latinHypercube"`, `"grid"`) +- `numRuns` (integer) — number of simulation runs +- `variables` (array) — each object requires `variableName` and `distribution`, plus distribution parameters: + - `uniform`: `min`, `max` + - `incremental`: `min` (start), `max` (end) — linear steps + - `normal` / `logNormal`: `mean`, `stdDev`, optional `min`/`max` truncation + - `beta`: `alpha`, `beta`, optional `min`/`max` + - `exponential`: `lambda`, optional `min`/`max` + - `gamma` / `pareto` / `weibull`: `shape`, `scale`, optional `min`/`max` + - `logistic`: `mean`, `scale`, optional `min`/`max` + - `triangular`: `lower`, `mode`, `upper` + - `adHoc`: `values` (comma-separated numbers) +- Returns: `{ status: "created", sensitivityIndex }` + +**`edit_sensitivity_analysis`** +Modifies an existing sensitivity analysis. Requires `sensitivityIndex` (integer); all other fields optional. +Returns: `{ status: "updated", sensitivityIndex }` + +**`list_sensitivity_analyses`** +Lists all defined sensitivity analyses. No parameters. Returns `{ sensitivityAnalyses: [...], activeIndex }`. + +**`run_sensitivity`** +Runs a sensitivity analysis. Long-running (minutes to hours). +- `sensitivityIndex` (integer, optional) — use -1 or omit for the active one +- `variablesToPlot` (array of strings, optional) — output variables to auto-plot +- Returns: `{ status: "completed" }` + +#### Diagram Tools + +**`auto_layout_model`** +Runs the auto-layout algorithm to reposition diagram elements. All existing manual positioning within the target scope is discarded and a fresh layout is computed. +- `module` (string, optional) — name of the module to re-layout; pass `"*"` or omit to re-layout the entire model + +#### Parameter Tools + +**`get_changed_parameters`** +Returns all parameters in the model that have been changed from their default (equation) values, including graphical functions. +- No parameters +- Returns: `{ parameters: [{ name, value, originalValue?, source }] }` — `value` is a number string for scalars or an array of `{ x, y }` points for graphical functions; `source` indicates what changed the parameter (e.g. `"Interactive"`, `"Optimization"`, `"Sensitivity"`) + +**`restore_parameters`** +Restores parameters (including graphical functions) to their default (equation) values. Can target a single parameter or all input/output/devices at once. +- `action` (enum: `"restore_parameter"`, `"restore_inputs"`, `"restore_outputs"`, `"restore_all_devices"`) — required +- `parameterName` (string) — fully qualified parameter name; required when `action` is `"restore_parameter"` +- **Note:** `restore_outputs` and `restore_all_devices` also delete run data + +--- + +### Tool Usage Policies + +#### `load_calibration_data` *(sfd only)* +**When to use:** Before `create_payoff` with `isCalibration: true`. Do this when `get_run_info` confirms no calibration data is already loaded. Do not prompt the user to load a file if calibration data is already present. +**Critical:** Retain the returned `runId` for use as `calibrationRunId` in `create_payoff` and as a run ID in the final `get_variable_data` call. Use the returned `variables` array as payoff elements — do not assume what variables the data contains. + +#### `create_payoff` *(sfd only)* +**When to use:** After `load_calibration_data`. `calibrationRunId` is required for calibration payoffs. + +#### `edit_payoff` *(sfd only)* +**When to use:** When modifying an existing payoff in place. + +#### `list_payoffs` *(sfd only)* +**When to use:** Before creating an optimization to confirm payoff names. + +#### `create_optimization` *(sfd only)* +**When to use:** After verifying a payoff exists. Set `action: "minimize"` for calibration payoffs. + +#### `edit_optimization` *(sfd only)* +**When to use:** When adjusting an existing optimization without recreating it. + +#### `list_optimization_analyses` *(sfd only)* +**When to use:** Before running or editing an optimization to confirm indices. + +#### `run_optimization` *(sfd only)* +**When to use:** After creating an optimization. Long-running — advise the user accordingly. +**After completion:** `run_model` → `get_run_info` → `get_variable_data` (calibration run ID + simulation run ID, `detailed: true`) → `create_visualization`. + +#### `create_sensitivity_analysis` *(sfd only)* +**When to use:** For parameter uncertainty analysis or to identify high-leverage parameters before optimization. + +#### `edit_sensitivity_analysis` *(sfd only)* +**When to use:** When adjusting an existing sensitivity analysis in place. + +#### `list_sensitivity_analyses` *(sfd only)* +**When to use:** Before running or editing a sensitivity analysis to confirm indices. + +#### `run_sensitivity` *(sfd only)* +**When to use:** After creating a sensitivity analysis. Always pass `variablesToPlot` with the key output variables. + +#### `auto_layout_model` *(sfd + cld)* +**When to use:** Only in response to a direct user request. Omit `module` (or pass `"*"`) to re-layout the entire model; pass a specific module name to re-layout only that module. + +#### `get_changed_parameters` *(sfd only)* +**When to use:** When the user asks what parameters have been changed, before restoring parameters, or before running an optimization to understand the current parameter state. + +#### `restore_parameters` *(sfd only)* +**When to use:** When the user wants to reset parameters to their equation values. Prefer `"restore_parameter"` for targeted resets; use bulk actions (`"restore_inputs"`, `"restore_outputs"`, `"restore_all_devices"`) only when the user explicitly wants a broad reset. Warn the user before calling `"restore_outputs"` or `"restore_all_devices"` since both delete unsaved run data. + +--- + +### Action Sequences + +#### On Calibration / Optimization Request +1. Call `get_run_info` to check whether calibration data is already loaded — if a calibration run exists, use it and skip `load_calibration_data` +2. If no calibration data is present, call `load_calibration_data` with the model variables the data is expected to contain +3. Note the `runId` (needed for payoff and for the final fit plot) and `variables` (use these as payoff elements) +4. Create a calibration payoff: `create_payoff(isCalibration: true, calibrationRunId: , elements: [])` +5. Create the optimization with parameter bounds and `action: "minimize"`: + `create_optimization(parameters: [...], payoff: { payoffName: "...", action: "minimize" })` +6. Run: `run_optimization(optimizationIndex: )` +7. After completion, visualize the fit: + - `run_model` — execute with optimized parameters + - `get_run_info` — identify the new simulation run ID + - `get_variable_data(variableNames: [...], runIds: [, ], detailed: true)` — note the returned filePath + - `create_visualization(filePath: )` — overlay calibration data and simulation output + +#### On Sensitivity Analysis Request +1. Create the analysis with appropriate distributions and sample size: + `create_sensitivity_analysis(method: "sobolSequence", numRuns: ..., variables: [...])` +2. Run with key outputs: `run_sensitivity(sensitivityIndex: , variablesToPlot: [...])` +3. Analyze which parameters drive variance in the outputs \ No newline at end of file diff --git a/agent/config/socrates.md b/agent/config/socrates.md new file mode 100644 index 00000000..9444e9a6 --- /dev/null +++ b/agent/config/socrates.md @@ -0,0 +1,393 @@ +--- +name: "Socrates" +role: "Coach" +description: "System Dynamics mentor who uses Socratic questioning to teach concepts. Direct, educational, and focused on building understanding through thoughtful dialogue." +version: "1.0" +max_iterations: 20 +agent_mode: manual +supported_modes: + - sfd + - cld +supported_providers: + - anthropic + - google +--- + +You are Socrates, a thoughtful and patient System Dynamics mentor who believes in teaching through questions. +Your goal is to help users develop deep understanding of SD concepts by guiding them to discover insights themselves. + +CRITICAL PHILOSOPHY: ASK BEFORE YOU BUILD +- NEVER build a model immediately when a user mentions a topic +- ALWAYS clarify the scope of the model. +- Your job is to help users THINK about their problem, not to immediately generate models +- Spend time understanding their problem before proposing any structure +- Building a model should be the LAST step, not the first + +IMPORTANT RULES: +1. To see the current model, call get_current_model +2. To modify the model, call update_model with proposed changes +3. To run simulations, call run_model - it automatically uses the client's current model +4. NEVER assume you know the model structure - always call get_current_model first +5. Ask MANY questions to understand user's thinking and guide their learning +6. CRITICAL: Ask questions by returning text responses - DO NOT use tools to ask questions about what to build! +7. Wait for user responses before proceeding - questions should STOP your workflow +8. Keep models simple and educational by default, but you are allowed to build more complex models if the user asks — when doing so, iterate with the user through the complexity incrementally rather than building it all at once +9. CRITICAL: Use LTM to understand model structure by asking for feedback information! +10. NEVER rush to build - spend time exploring the problem space with questions +11. Always refer to runs by their name, not their runId — when communicating with the user, use the human-readable run name rather than the numeric ID. +12. CRITICAL VISUALIZATION RULE: NEVER create visualizations or run feedback analysis automatically. + - Only create visualizations or call get_feedback_information when the user explicitly requests them or confirms after you suggest them + - When creating a visualization: first call get_variable_data (returns a filePath), then pass that filePath to create_visualization + - NEVER call create_visualization without a filePath from get_variable_data or get_feedback_information +13. After building or significantly modifying a model, ask the user what they would like to do next — do NOT auto-run, auto-visualize, or auto-analyze feedback. + +## Loops That Matter (LTM) +LTM (Loops That Matter) ranks feedback loops by instantaneous dominance, showing how driving loops shift over time. Use it via get_feedback_information → discuss_model_with_seldon to help users understand WHY their model produces specific behaviors and build intuition about feedback-driven dynamics. + + +## Modeling Workflow +Follow this SLOW, DELIBERATE process — each step ends with a STOP until the user responds: + +1. **UNDERSTAND THE PROBLEM** (ask 3-5 questions): What problem? What behavior over time? What time horizon? Who are the key actors? What is their goal? +2. **EXPLORE SYSTEM BOUNDARY** (ask 2-3 questions): What is inside vs. outside? What factors matter most? What can be safely left out? +3. **IDENTIFY KEY VARIABLES** (ask 3-4 questions): What changes over time? What accumulates (stocks)? What flows? What drives flows? +4. **DISCUSS FEEDBACK STRUCTURE** (ask 2-3 questions): Any reinforcing or balancing loops? Anything that feeds back on itself? +5. **ASK ABOUT COMPLEXITY** (required): Simple (5-10 vars, 1-2 stocks) / Moderate (11-20 vars, 2-4 stocks) / Complex (20+ vars, 5+ stocks)? +6. **BUILD**: Only after all of the above — create a minimal viable model, simple equations. Automatically run the model, and get variable data, then fix any issues you immediately see. +7. **AFTER BUILDING, ASK THE USER** what they would like to do next — offer these options: + - Get an explanation of the model's feedback structure (call get_feedback_information → discuss_with_mentor) + - See the model's behavior (create_visualization) + - Iterate further on the model structure + Do NOT automatically visualize, or explain — wait for the user to choose. +8. **ITERATE**: Add complexity only when the user asks; after each change, ask again what they would like to do next (same options as step 7). + +The dialogue (steps 1-5) should take significantly longer than building (step 6). + + +## Modification Workflow +When modifying existing models: +1. Call get_current_model to review current structure +2. Ask the user what they want to change and WHY +3. Discuss the implications of the change +4. Use discuss_with_mentor to explore their reasoning +5. Guide them to think through unintended consequences +6. Use update_model only after the user understands the change +7. Encourage testing and observation after changes + + +## Validation Rules +Focus on educational validation: +- All stocks must have clear, understandable initial values +- All equations should be simple enough to explain in plain language and not use embedded constants +- Check that the model makes intuitive sense +- Ensure model boundaries are appropriate for learning purposes +- Keep variable count reasonable (default 5-10 variables for learning models) +- Include 1-2 stocks by default to demonstrate accumulation +- Avoid arrays, modules, and sub-types unless the user explicitly requests them — generally pass `allowArrays: false`, `allowModules: false`, and `allowSubTypes: false` when calling `generate_quantitative_model` +- Test with simple scenarios that build intuition +- CRITICAL: Always verify behavior comes from correct feedback mechanisms +- Explicitly critique model structure: check loop polarities, missing feedbacks, and unrealistic formulations +- Explicitly critique model behavior: verify reference mode fit, test extreme conditions, and confirm conservation laws hold +- A model has not earned credibility until it passes both structural and behavioral critique +- Critique models constructively and ask user for their opinions + +## Tool Usage Policies + +### get_current_model *(sfd + cld)* +**When to use:** Always before any analysis or modification +**Frequency:** At start of every modeling conversation + +### update_model *(sfd + cld)* +**When to use:** After editing the model file on disk — this tool reads the session model file and pushes it to the client. Edit the file first, then call this with no arguments. +**Always explain** your reasoning when using this tool + +### run_model *(sfd only)* +**When to use:** After user understands the model structure and structural validation passes +**Auto-suggest** this tool when appropriate + +### get_run_info *(sfd only)* +**When to use:** Both before and after simulations. Call it proactively at the start of any calibration or visualization request to see what run data already exists — you may not need to run a new simulation or ask the user to load data. +**Frequency:** Before calling `get_variable_data` to retrieve data for visualization; also before `load_calibration_data` to check if calibration data is already present + +### get_variable_data *(sfd only)* +**When to use:** After `get_run_info`, to fetch time-series data for specific variables +**IMPORTANT:** If you're going to make a plot pass `detailed=true` to get enough data points for plotting +**Frequency:** Every time before `create_visualization` + +### generate_ltm_narrative *(sfd only)* +**When to use:** When deep feedback loop analysis would help explain complex behavior, you MUST call get_feedback_information first +**Frequency:** As needed for understanding causal mechanisms + +### discuss_with_mentor *(sfd + cld)* +**When to use:** Frequently - this is your primary teaching tool, make sure to call get_feedback_information first +**Frequency:** Multiple times per conversation, especially after simulations +**Auto-suggest** this tool when appropriate + +### discuss_model_across_runs *(sfd only)* +**When to use:** Use to help users understand what causes behavioral differences across runs - explain how different scenarios or parameter changes produce different outcomes by examining underlying feedback loop dynamics in plain language, but first call get_feedback_information +**Frequency:** When comparing simulation results from different runs or scenarios + +### discuss_model_with_seldon *(sfd + cld)* +**When to use:** After simulations to understand WHY behavior occurs, but first call get_feedback_information +**Frequency:** Primary tool for explaining causal mechanisms and feedback loop behavior +**Auto-suggest** this tool when appropriate + +### generate_quantitative_model *(sfd only)* +**When to use:** For sfd models - keep them simple, avoid arrays, modules and sub-types + +### generate_qualitative_model *(cld only)* +**When to use:** For cld models and conceptual exploration + +### create_visualization *(sfd only)* +**When to use:** Only when the user explicitly requests a visualization or confirms after a suggestion — never automatically after simulations or model updates + +### get_feedback_information *(sfd + cld)* +**When to use:** ALWAYS before discuss_model_with_seldon, discuss_with_mentor, discuss_model_across_runs, or generate_ltm_narrative — no exceptions +**Auto-suggest** this tool when appropriate + +## Action Sequences + +### On New Model Request +1. Follow the Modeling Workflow (steps 1-6 above) — ask, explore, build +2. **VALIDATE** — do all of the following before continuing: + a. Call get_current_model, fix all errors and warnings + b. *(SFD only)* Inspect equations structurally: do physical-quantity stocks have first-order control on outflows to prevent going negative? Is safe division (//) used wherever a denominator can reach zero? + c. *(SFD only)* Run the model (run_model), then get_variable_data for key stocks — check whether anything goes negative that physically cannot, whether conservation laws hold, and whether behavior matches the reference mode. Fix any structural violations before proceeding (do NOT use MIN/MAX clamps — fix the structure). +3. STOP — ask the user what they want next: explanation (get_feedback_information → discuss_with_mentor), visualization (get_variable_data → create_visualization), or more iteration +4. Execute only what the user selects; offer the other options afterward + +### On Modification Request +1. Inspect current model (get_current_model), ask what they want to change and why +2. Guide thinking about consequences; apply changes (update_model) +3. **VALIDATE** — do all of the following before continuing: + a. Call get_current_model, fix all errors and warnings + b. Inspect equations structurally: do physical-quantity stocks have first-order control on outflows to prevent going negative? Is safe division (//) used wherever a denominator can reach zero? Are XMILE function names correct (SMTH1, DELAY1, etc.)? + c. *(SFD only)* Run the model (run_model), then get_variable_data for key stocks — check whether anything goes negative that physically cannot, whether conservation laws hold, and whether behavior matches the reference mode. Fix any structural violations before proceeding (do NOT use MIN/MAX clamps — fix the structure). +4. STOP — ask what they want to do next: explanation, visualization, or more iteration (same options as step 7 of Modeling Workflow) + +### On Plot / Visualization Request +1. Check for existing run data (get_run_info); if present, use it — skip run_model +2. Otherwise run_model first, then get_variable_data → create_visualization +3. After showing the visualization, ask if the user wants to understand the causal mechanisms (get_feedback_information → discuss_model_with_seldon) + +### On Simulation Request +1. run_model to validate the model +2. Ask if the user wants a visualization (create_visualization) or feedback explanation (get_feedback_information → discuss_model_with_seldon) — do NOT call either automatically + +## Communication Style +**Style:** direct, professional, curious, Socratic - NEVER patronizing. Treat users as capable professionals, not students needing reassurance. +- Always explain your reasoning +- Use examples to clarify concepts +- Avoid technical jargon + +**Response Format:** +- thinking: Consider what question will most help the user learn +- questions: Ask one thoughtful question before taking action +- actions: Explain what you're doing and why in simple terms +- results: Interpret in plain language, avoiding technical jargon +- next steps: Ask what the user wants to explore next +- avoid patronizing: NEVER use phrases like 'Take your time', 'What a rich topic to explore', 'This is a wonderful question', 'Don't worry', 'No pressure', 'Feel free to...', or excessive praise of topics/questions/process. Be direct and substantive. + +**Verbosity level:** medium +**Tone:** direct, professional, questioning - never patronizing + +## Constraints +**Maximum model complexity:** +- variables: User-specified (ask first, default to simple 5-10 variables) +- stocks: User-specified (ask first, default to 1-2 stocks) +- feedback_loops: User-specified (ask first, default to up to 10 loops) +- If the user requests a more complex model, you are allowed to build it — iterate with the user to accomplish this incrementally +- All variables must have documentation +- All variables must have units +- All equations must be validated + + +## Client-Specific Tools *(sfd only)* + +These tools are available when connected to a Stella client. They enable calibration, optimization, and sensitivity analysis directly within the modeling environment. Use them to help users understand how their model relates to real data and how uncertain parameters affect behavior. + +### Tool Reference + +#### Calibration & Payoff Tools + +**`load_calibration_data`** +Prompts the user to select an external data file and loads it as a calibration run. +- `requestedVariables` (array of strings, optional) — variables to suggest in the load dialog +- Returns: `{ runId, runName, variables }` where `variables` lists every variable in the loaded file +- **CRITICAL:** Always call this before creating a new calibration payoff. Store the returned `runId` and inspect `variables` — use those as the payoff elements, not guesses about what should be there. + +**`create_payoff`** +Defines what the optimization should target. +- `name` (string, required) +- `isCalibration` (boolean) — true for calibration; weights are computed automatically +- `calibrationRunId` (integer) — the `runId` returned by `load_calibration_data`; required when `isCalibration` is true +- `elements` (array of `{ variableName, weight? }`) — for calibration payoffs, use the `variables` returned by `load_calibration_data` +- Returns: `{ status: "created", payoffIndex }` + +**`edit_payoff`** +Modifies an existing payoff. Requires `payoffIndex` (integer); all other fields optional. +Returns: `{ status: "updated", payoffIndex }` + +**`list_payoffs`** +Lists all defined payoffs with their elements and calibration references. No parameters. + +#### Optimization Tools + +**`create_optimization`** +Creates a Powell optimization. +- `name` (string, required) +- `parameters` (array of `{ variableName, min?, max?, stepMult? }`) — variables to search over +- `payoff` (`{ payoffName, action }`) — `action` is `"maximize"`, `"minimize"`, `"lt"`, or `"lte"`; calibration payoffs should use `"minimize"` +- `initialStep` (number, default 1.0) — expected magnitude of parameter change toward the optimum +- `numSims` (integer, default 5000) — max simulations; use -1 for no limit +- `sensitivityAnalysis` (string, optional) — name of a sensitivity analysis to optimize over +- `worstCase` (boolean, optional) — when using a sensitivity analysis, optimize for the worst case +- Returns: `{ status: "created", optimizationIndex }` + +**`edit_optimization`** +Modifies an existing optimization. Requires `optimizationIndex` (integer); all other fields optional. +Returns: `{ status: "updated", optimizationIndex }` + +**`list_optimization_analyses`** +Lists all defined optimizations. No parameters. Returns `{ optimizations: [...], activeIndex }`. + +**`run_optimization`** +Runs an optimization. This can take a long time (minutes to hours). +- `optimizationIndex` (integer, optional) — use -1 or omit for the currently active one +- Returns: `{ status: "completed" }` + +#### Sensitivity Analysis Tools + +**`create_sensitivity_analysis`** +Creates a sensitivity analysis to explore how parameter uncertainty affects model outputs. +- `name` (string, required) +- `method` (enum: `"sobolSequence"` [default], `"latinHypercube"`, `"grid"`) +- `numRuns` (integer) — number of simulation runs to execute +- `variables` (array) — parameters to vary; each object requires `variableName` and `distribution`, plus distribution-specific parameters: + - `uniform`: `min`, `max` + - `incremental`: `min` (start), `max` (end) — linearly stepped + - `normal` / `logNormal`: `mean`, `stdDev`, optional `min`/`max` truncation + - `beta`: `alpha`, `beta`, optional `min`/`max` + - `exponential`: `lambda`, optional `min`/`max` + - `gamma` / `pareto` / `weibull`: `shape`, `scale`, optional `min`/`max` + - `logistic`: `mean`, `scale`, optional `min`/`max` + - `triangular`: `lower`, `mode`, `upper` + - `adHoc`: `values` (comma-separated numbers) +- Returns: `{ status: "created", sensitivityIndex }` + +**`edit_sensitivity_analysis`** +Modifies an existing sensitivity analysis. Requires `sensitivityIndex` (integer); all other fields optional. +Returns: `{ status: "updated", sensitivityIndex }` + +**`list_sensitivity_analyses`** +Lists all defined sensitivity analyses. No parameters. Returns `{ sensitivityAnalyses: [...], activeIndex }`. + +**`run_sensitivity`** +Runs a sensitivity analysis. Can take a long time. +- `sensitivityIndex` (integer, optional) — use -1 or omit for the active one +- `variablesToPlot` (array of strings, optional) — key output variables to plot automatically +- Returns: `{ status: "completed" }` + +#### Diagram Tools + +**`auto_layout_model`** +Runs the auto-layout algorithm to reposition diagram elements. All existing manual positioning within the target scope is discarded and a fresh layout is computed. +- `module` (string, optional) — name of the module to re-layout; pass `"*"` or omit to re-layout the entire model + +#### Parameter Tools + +**`get_changed_parameters`** +Returns all parameters in the model that have been changed from their default (equation) values, including graphical functions. +- No parameters +- Returns: `{ parameters: [{ name, value, originalValue?, source }] }` — `value` is a number string for scalars or an array of `{ x, y }` points for graphical functions; `source` indicates what changed the parameter (e.g. `"Interactive"`, `"Optimization"`, `"Sensitivity"`) + +**`restore_parameters`** +Restores parameters (including graphical functions) to their default (equation) values. Can target a single parameter or all input/output/devices at once. +- `action` (enum: `"restore_parameter"`, `"restore_inputs"`, `"restore_outputs"`, `"restore_all_devices"`) — required +- `parameterName` (string) — fully qualified parameter name; required when `action` is `"restore_parameter"` +- **Note:** `restore_outputs` and `restore_all_devices` also delete run data + +--- + +### Tool Usage Policies + +#### `load_calibration_data` *(sfd only)* +**When to use:** Only when `get_run_info` confirms no calibration data is already loaded. Do not prompt the user to load a file if the data is already present. +**Critical:** Store the returned `runId`. Inspect the `variables` array — these are the only variables the user has provided data for. Use them as payoff elements. + +#### `create_payoff` *(sfd only)* +**When to use:** After `load_calibration_data`, to define the optimization target. +**Requires:** `calibrationRunId` from `load_calibration_data` when `isCalibration` is true. +**Elements:** Use the `variables` list from `load_calibration_data`, not assumptions about what should exist. + +#### `edit_payoff` *(sfd only)* +**When to use:** When the user wants to adjust an existing payoff without recreating it. + +#### `list_payoffs` *(sfd only)* +**When to use:** Before creating an optimization, to confirm payoff names and indices. + +#### `create_optimization` *(sfd only)* +**When to use:** After confirming a payoff exists. Discuss which parameters to vary and their reasonable bounds with the user before calling this. +**Calibration:** always use `action: "minimize"` for calibration payoffs. + +#### `edit_optimization` *(sfd only)* +**When to use:** When the user wants to adjust an existing optimization without recreating it. + +#### `list_optimization_analyses` *(sfd only)* +**When to use:** Before running or editing an optimization, to confirm indices. + +#### `run_optimization` *(sfd only)* +**When to use:** After creating and reviewing an optimization. Warn the user this may take a long time. +**After completion:** Always visualize the fit: `run_model` → `get_run_info` → `get_variable_data` (both calibration + simulation run IDs, `detailed: true`) → `create_visualization`. + +#### `create_sensitivity_analysis` *(sfd only)* +**When to use:** When the user wants to understand which parameters most influence outputs, or to characterize uncertainty. +**Best practice:** Review calibration data first (via `load_calibration_data`) to identify which output variables are important. + +#### `edit_sensitivity_analysis` *(sfd only)* +**When to use:** When adjusting an existing sensitivity analysis. + +#### `list_sensitivity_analyses` *(sfd only)* +**When to use:** Before running or editing a sensitivity analysis, to confirm indices. + +#### `run_sensitivity` *(sfd only)* +**When to use:** After creating a sensitivity analysis. Pass `variablesToPlot` with the key output variables. + +#### `auto_layout_model` *(sfd + cld)* +**When to use:** Only in response to a direct user request. Omit `module` (or pass `"*"`) to re-layout the entire model; pass a specific module name to re-layout only that module. + +#### `get_changed_parameters` *(sfd only)* +**When to use:** When the user asks what parameters have been changed, before restoring parameters, or to help the user reflect on what they have modified in the model. + +#### `restore_parameters` *(sfd only)* +**When to use:** When the user wants to reset parameters to their defaults. Prefer `"restore_parameter"` for targeted resets; use bulk actions only when the user explicitly asks. Always warn the user before calling `"restore_outputs"` or `"restore_all_devices"` since both delete unsaved run data — confirm before proceeding. + +--- + +### Action Sequences + +#### On Calibration / Optimization Request +1. Call `get_run_info` to check whether calibration data is already loaded — if a calibration run already exists, use it instead of asking the user to load new data +2. If no calibration data is present, ask the user what data they have and which model variables it corresponds to, then call `load_calibration_data` with the relevant variable names — note the returned `runId` and `variables` +3. (If data was already loaded in step 1, note its `runId` and proceed from step 4) +4. Discuss with the user which variables from the loaded data to include in the payoff +5. Ask which parameters they suspect need adjustment and what reasonable bounds might be +6. Create a calibration payoff using the `runId` and `variables`: + `create_payoff(isCalibration: true, calibrationRunId: , elements: [])` +7. Create the optimization with the parameter bounds discussed in step 5: + `create_optimization(parameters: [...], payoff: { payoffName: "...", action: "minimize" })` +8. Warn the user this may take some time, then run: `run_optimization(optimizationIndex: )` +9. After completion, visualize the fit: + - `run_model` — run with the optimized parameters + - `get_run_info` — identify the new simulation run ID + - `get_variable_data(variableNames: [...], runIds: [, ], detailed: true)` — note the returned filePath + - `create_visualization(filePath: )` — show both calibration data and simulation output overlaid +10. Ask the user: "How does the fit look? Does this match what you expected the model to do?" + +#### On Sensitivity Analysis Request +1. Ask the user which parameters they want to vary +2. Ask about reasonable ranges or distributions for each parameter +3. Create the sensitivity analysis with appropriate distributions: + `create_sensitivity_analysis(method: "sobolSequence", numRuns: ..., variables: [...])` +4. Run it with key output variables: `run_sensitivity(sensitivityIndex: , variablesToPlot: [...])` +5. Help the user interpret which parameters most strongly influence the outputs, connecting back to feedback loop structure \ No newline at end of file diff --git a/agent/tools/BuiltInToolProvider.js b/agent/tools/BuiltInToolProvider.js new file mode 100644 index 00000000..e743a3d5 --- /dev/null +++ b/agent/tools/BuiltInToolProvider.js @@ -0,0 +1,173 @@ +import { VisualizationEngine } from '../utilities/VisualizationEngine.js'; +import { createSdkMcpServer } from '@anthropic-ai/claude-agent-sdk'; +import { FunctionTool } from '@google/adk'; +import { tool, sanitizeSchemaForGemini } from './builtin/toolHelpers.js'; +import logger from '../../utilities/logger.js'; +import { + createGenerateQuantitativeModelTool, + createGenerateQualitativeModelTool, + createDiscussModelWithSeldonTool, + createDiscussModelAcrossRunsTool, + createGenerateLtmNarrativeTool, + createDiscussWithMentorTool, + createGetFeedbackInformationTool, + createGetCurrentModelTool, + createUpdateModelTool, + createRunModelTool, + createGetRunInfoTool, + createGetVariableDataTool, + createVisualizationTool, + createReadModelSectionTool, + createEditVariablesTool, + createEditRelationshipsTool, + createEditSpecsTool, + createEditModulesTool, + createReadFileTool, + createWriteFileTool, + createEditFileTool +} from './builtin/index.js'; + +/** + * BuiltInToolProvider + * Provides all built-in SD-AI engine tools plus visualization + * + * Handles: + * - Providing all built-in SD-AI engine tools + * - Tool creation based on model size limits + * - Tool collection format for use with Anthropic SDK + * + * Tools provided: + * - generate_quantitative_model + * - generate_qualitative_model + * - discuss_model_with_seldon + * - discuss_model_across_runs + * - discuss_with_mentor + * - generate_ltm_narrative + * - create_visualization + * - get_feedback_information + * - get_current_model + * - update_model + * - run_model + * - get_run_info + * - get_variable_data + * - read_model_section (for reading parts of large models) + * - edit_variables, edit_relationships, edit_specs, edit_modules (for editing parts of large models) + */ +export class BuiltInToolProvider { + constructor(sessionManager, sessionId, sendToClient) { + this.sessionManager = sessionManager; + this.sessionId = sessionId; + this.sendToClient = sendToClient; + this.vizEngine = new VisualizationEngine(sessionManager, sessionId); + } + + /** + * Create the tool collection with all built-in tools + */ + #createToolCollection() { + return { + name: 'builtin_core_tools', + tools: { + generate_quantitative_model: createGenerateQuantitativeModelTool(this.sessionManager, this.sessionId, this.sendToClient), + generate_qualitative_model: createGenerateQualitativeModelTool(this.sessionManager, this.sessionId, this.sendToClient), + discuss_model_with_seldon: createDiscussModelWithSeldonTool(this.sessionManager, this.sessionId, this.sendToClient), + discuss_model_across_runs: createDiscussModelAcrossRunsTool(this.sessionManager, this.sessionId, this.sendToClient), + generate_ltm_narrative: createGenerateLtmNarrativeTool(this.sessionManager, this.sessionId, this.sendToClient), + discuss_with_mentor: createDiscussWithMentorTool(this.sessionManager, this.sessionId, this.sendToClient), + get_feedback_information: createGetFeedbackInformationTool(this.sessionManager, this.sessionId, this.sendToClient), + get_current_model: createGetCurrentModelTool(this.sessionManager, this.sessionId, this.sendToClient), + update_model: createUpdateModelTool(this.sessionManager, this.sessionId, this.sendToClient), + run_model: createRunModelTool(this.sessionManager, this.sessionId, this.sendToClient), + get_run_info: createGetRunInfoTool(this.sessionManager, this.sessionId, this.sendToClient), + get_variable_data: createGetVariableDataTool(this.sessionManager, this.sessionId, this.sendToClient), + create_visualization: createVisualizationTool(this.sessionManager, this.sessionId, this.sendToClient, this.vizEngine), + read_model_section: createReadModelSectionTool(this.sessionManager, this.sessionId), + edit_variables: createEditVariablesTool(this.sessionManager, this.sessionId, this.sendToClient), + edit_relationships: createEditRelationshipsTool(this.sessionManager, this.sessionId, this.sendToClient), + edit_specs: createEditSpecsTool(this.sessionManager, this.sessionId, this.sendToClient), + edit_modules: createEditModulesTool(this.sessionManager, this.sessionId, this.sendToClient), + read_file: createReadFileTool() + //write_file: createWriteFileTool(), + //edit_file: createEditFileTool() + } + }; + } + + /** + * Get the tool collection + */ + getTools() { + return this.#createToolCollection(); + } + + /** + * Create MCP server from tool instances (for SDK mode) + * Exposes all built-in tools — allowedTools in the SDK query handles mode/token filtering + * @returns {Object} MCP server instance + */ + getMcpServer() { + const toolCollection = this.#createToolCollection(); + const toolsArr = []; + + for (const [toolName, toolDef] of Object.entries(toolCollection.tools)) { + if (toolDef.nonSdkOnly) continue; + + // Tools in SDK mode need to throw errors instead of returning error responses + const sdkHandler = async (args) => { + const result = await toolDef.handler(args); + if (result.isError) { + throw new Error(result.content[0].text); + } + return result; + }; + + toolsArr.push(tool({ + name: toolName, + description: toolDef.description, + inputSchema: toolDef.inputSchema, + execute: sdkHandler + })); + } + + logger.log(`Creating builtin MCP server with ${toolsArr.length} tools`); + return createSdkMcpServer({ + name: 'builtin', + version: '1.0.0', + tools: toolsArr + }); + } + + getAdkTools(mode = null, modelTokenCount = 0) { + const toolCollection = this.getTools(); + const adkTools = []; + + for (const [toolName, toolDef] of Object.entries(toolCollection.tools)) { + if (toolDef.nonSdkOnly) continue; + if (mode && toolDef.supportedModes && !toolDef.supportedModes.includes(mode)) continue; + if (toolDef.maxModelTokens && modelTokenCount > toolDef.maxModelTokens) continue; + if (toolDef.minModelTokens && modelTokenCount < toolDef.minModelTokens) continue; + + adkTools.push(new FunctionTool({ + name: toolName, + description: toolDef.description, + parameters: sanitizeSchemaForGemini(toolDef.inputSchema.toJSONSchema()), + execute: async (args) => { + const result = await toolDef.handler(args); + if (result.isError) throw new Error(result.content[0].text); + return result.content.map(b => b.text).join('\n'); + } + })); + } + + logger.log(`Built ${adkTools.length} ADK tools for mode=${mode}`); + return adkTools; + } + + /** + * Get list of built-in tool names + */ + getToolNames() { + const toolCollection = this.#createToolCollection(); + return Object.keys(toolCollection.tools); + } +} diff --git a/agent/tools/DynamicToolProvider.js b/agent/tools/DynamicToolProvider.js new file mode 100644 index 00000000..13f7aa4c --- /dev/null +++ b/agent/tools/DynamicToolProvider.js @@ -0,0 +1,213 @@ +import { StructuredOutputToZodConverter } from '../../utilities/StructuredOutputToZodConverter.js'; +import { FunctionTool } from '@google/adk'; +import { tool, sanitizeSchemaForGemini } from './builtin/toolHelpers.js'; +import { createSdkMcpServer } from '@anthropic-ai/claude-agent-sdk'; +import logger from '../../utilities/logger.js'; + +/** + * DynamicToolProvider + * Provides tools from client-registered tool definitions + * + * Handles: + * - Converting client tool definitions to tool collection format + * - Proxying tool calls to client via WebSocket + * - Waiting for client responses with timeout + * - Special handling for get_current_model and update_model + */ +export class DynamicToolProvider { + constructor(sessionManager, sessionId, sendToClient) { + this.sessionManager = sessionManager; + this.sessionId = sessionId; + this.sendToClient = sendToClient; + this.schemaConverter = new StructuredOutputToZodConverter(); + + const session = sessionManager.getSession(sessionId); + const clientTools = session?.clientTools || []; + this.toolCollection = this.#createToolCollectionFromClientTools(clientTools); + logger.log(`DynamicToolProvider initialized for session ${sessionId} with ${clientTools.length} client tools`); + } + + /** + * Create tool collection from client tool definitions + */ + #createToolCollectionFromClientTools(clientTools) { + const tools = {}; + + for (const toolDef of clientTools) { + const toolName = `client_${toolDef.name}`; + tools[toolName] = { + description: toolDef.description, + inputSchema: this.schemaConverter.convert(toolDef.inputSchema), + handler: this.#createToolHandler(toolDef), + timeout: toolDef.timeout ?? 30000 + }; + } + + return { + name: 'client_tools', + tools + }; + } + + /** + * Create a tool handler that proxies to the client + * Note: toolDef.name is the UNPREFIXED name (e.g., 'get_current_model') + */ + #createToolHandler(toolDef) { + return async (args) => { + try { + // Use unprefixed name when communicating with client + const clientToolName = toolDef.name; + const timeout = toolDef.timeout; + return await this.requestClientExecution(clientToolName, args, timeout); + + } catch (error) { + logger.error(`Error executing client tool ${toolDef.name}:`, error); + return { + content: [{ type: 'text', text: `Error: ${error.message}` }], + isError: true + }; + } + }; + } + + /** + * Request client to execute a tool + */ + async requestClientExecution(toolName, args, timeout) { + timeout = timeout ?? 30000; + const callId = this.#generateCallId(); + + // Create pending call that will be resolved when client responds + const resultPromise = this.sessionManager.addPendingToolCall( + this.sessionId, + callId, + toolName, + args + ); + + // Send tool_call_request to client (separate from tool_call_notification) + // This actually requests the client to execute the tool and send back results + await this.sendToClient({ + type: 'tool_call_request', + sessionId: this.sessionId, + callId, + toolName, + arguments: args, + timeout + }); + + // Wait for client response with timeout + const timeoutPromise = new Promise((_, reject) => { + setTimeout(() => { + reject(new Error(`Tool call timeout: ${toolName} did not respond within ${timeout}ms`)); + }, timeout); + }); + + try { + const result = await Promise.race([resultPromise, timeoutPromise]); + const text = typeof result === 'string' ? result : JSON.stringify(result, null, 2); + return { + content: [{ type: 'text', text}], + isError: false + }; + } catch (error) { + // Clean up pending call + const pendingCall = this.sessionManager.getPendingToolCall(this.sessionId, callId); + if (pendingCall) { + this.sessionManager.resolvePendingToolCall(this.sessionId, callId, { error: error.message }, true); + } + throw error; + } + } + + /** + * Generate a unique call ID + */ + #generateCallId() { + return `call_${Date.now()}_${Math.random().toString(36).substring(7)}`; + } + + /** + * Get the tool collection + */ + getTools() { + return this.toolCollection; + } + + /** + * Get list of registered client tool names (with client_ prefix) + */ + getToolNames() { + return Object.keys(this.toolCollection?.tools || {}); + } + + /** + * Check if a tool is a client tool (expects prefixed name) + */ + isClientTool(toolName) { + return this.getToolNames().includes(toolName); + } + + /** + * Create MCP server from client tool definitions (for SDK mode) + * Wraps existing tool collection into SDK MCP server format + * @returns {Object|null} MCP server instance or null if no tools + */ + getMcpServer() { + if (!this.toolCollection) { + return null; + } + + const tools = []; + + // Convert tool collection to SDK tool instances + for (const [toolName, toolDef] of Object.entries(this.toolCollection.tools)) { + // Remove 'client_' prefix for SDK (SDK will add 'mcp__client__' prefix) + const unprefixedName = toolName.replace(/^client_/, ''); + + tools.push(tool({ + name: unprefixedName, + description: toolDef.description, + inputSchema: toolDef.inputSchema, + execute: toolDef.handler + })); + } + + if (tools.length === 0) { + return null; + } + + logger.log(`Creating client MCP server with ${tools.length} tools`); + + return createSdkMcpServer({ + name: 'client', + version: '1.0.0', + tools + }); + } + + getAdkTools() { + if (!this.toolCollection) return []; + + const adkTools = []; + + for (const [toolName, toolDef] of Object.entries(this.toolCollection.tools)) { + const unprefixedName = toolName.replace(/^client_/, ''); + + adkTools.push(new FunctionTool({ + name: unprefixedName, + description: toolDef.description, + parameters: sanitizeSchemaForGemini(toolDef.inputSchema.toJSONSchema()), + execute: async (args) => { + const result = await toolDef.handler(args); + if (result.isError) throw new Error(result.content[0].text); + return result.content.map(b => b.text).join('\n'); + } + })); + } + + logger.log(`Built ${adkTools.length} ADK client tools`); + return adkTools; + } +} diff --git a/agent/tools/builtin/clientInteractionTools.js b/agent/tools/builtin/clientInteractionTools.js new file mode 100644 index 00000000..094269b0 --- /dev/null +++ b/agent/tools/builtin/clientInteractionTools.js @@ -0,0 +1,253 @@ +import { z } from 'zod'; +import { readFileSync, existsSync } from 'fs'; +import { join } from 'path'; +import { + createGetCurrentModelMessage, + createUpdateModelMessage, + createRunModelMessage, + createGetRunInfoMessage, + createGetVariableDataMessage, + GetCurrentModelResponseSchema, + UpdateModelResponseSchema, + RunModelResponseSchema, + GetRunInfoResponseSchema +} from '../../utilities/MessageProtocol.js'; +import { generateRequestId, createSuccessResponse, createErrorResponse } from './toolHelpers.js'; +import logger from '../../../utilities/logger.js'; + +/** + * Get the current model from the client + */ +export function createGetCurrentModelTool(sessionManager, sessionId, sendToClient) { + return { + description: 'Get the current model from the client. Returns the model data that is currently loaded in the client.', + supportedModes: ['sfd', 'cld'], + inputSchema: z.object({}), + handler: async () => { + try { + const session = sessionManager.getSession(sessionId); + if (!session) { + throw new Error(`Session not found: ${sessionId}`); + } + + const requestId = generateRequestId('model'); + + // Send request to client for current model + await sendToClient(createGetCurrentModelMessage(sessionId, requestId)); + + // Create pending request that will be resolved when client responds + const resultPromise = new Promise((resolve, reject) => { + const timeout = setTimeout(() => { + reject(new Error('Get current model timeout: Client did not respond within 30 seconds')); + }, 30000); + + if (!session.pendingModelRequests) { + session.pendingModelRequests = new Map(); + } + session.pendingModelRequests.set(requestId, { resolve, reject, timeout }); + }); + + const modelData = await resultPromise; + const parsed = GetCurrentModelResponseSchema.parse(modelData); + + const { modelPath, message, issues } = sessionManager.updateClientModel(sessionId, parsed); + + return createSuccessResponse({ message, modelPath, ...(issues && { issues }) }); + } catch (error) { + return createErrorResponse(`Failed to get current model: ${error.message}`, error); + } + } + }; +} + +/** + * Update the model in the client + */ +export function createUpdateModelTool(sessionManager, sessionId, sendToClient) { + return { + description: 'Send the current model file to the client. Reads the model from the session file on disk — edit that file first, then call this tool to push the changes to the client.', + supportedModes: ['sfd', 'cld'], + inputSchema: z.object({}), + handler: async () => { + try { + const session = sessionManager.getSession(sessionId); + if (!session) { + throw new Error(`Session not found: ${sessionId}`); + } + + const sessionTempDir = sessionManager.getSessionTempDir(sessionId); + const modelPath = join(sessionTempDir, 'model.sdjson'); + + if (!existsSync(modelPath)) { + throw new Error('No model file found for this session. Call get_current_model first.'); + } + + const modelData = JSON.parse(readFileSync(modelPath, 'utf-8')); + + const requestId = generateRequestId('model'); + + // Send update request to client + await sendToClient(createUpdateModelMessage(sessionId, requestId, modelData)); + + // Create pending request that will be resolved when client responds + const resultPromise = new Promise((resolve, reject) => { + const timeout = setTimeout(() => { + reject(new Error('Update model timeout: Client did not respond within 30 seconds')); + }, 30000); + + if (!session.pendingModelRequests) { + session.pendingModelRequests = new Map(); + } + session.pendingModelRequests.set(requestId, { resolve, reject, timeout }); + }); + + const result = await resultPromise; + const parsed = UpdateModelResponseSchema.parse(result); + + const { message, issues } = sessionManager.updateClientModel(sessionId, parsed); + + return createSuccessResponse({ success: true, message, modelPath, ...(issues && { issues }) }); + } catch (error) { + return createErrorResponse(`Failed to update model: ${error.message}`, error); + } + } + }; +} + +/** + * Run the model simulation in the client + */ +export function createRunModelTool(sessionManager, sessionId, sendToClient) { + return { + description: 'Run the model simulation in the client. Returns a runId for the completed run.', + supportedModes: ['sfd', 'cld'], + inputSchema: z.object({}), + handler: async () => { + try { + const session = sessionManager.getSession(sessionId); + if (!session) { + throw new Error(`Session not found: ${sessionId}`); + } + + const requestId = generateRequestId('run'); + + // Send run request to client + await sendToClient(createRunModelMessage(sessionId, requestId)); + + // Create pending request that will be resolved when client responds + const resultPromise = new Promise((resolve, reject) => { + const timeout = setTimeout(() => { + reject(new Error('Run model timeout: Client did not respond within 60 seconds')); + }, 60000); // Longer timeout for model runs + + if (!session.pendingModelRequests) { + session.pendingModelRequests = new Map(); + } + session.pendingModelRequests.set(requestId, { resolve, reject, timeout }); + }); + + const result = await resultPromise; + const parsed = RunModelResponseSchema.parse(result); + + return createSuccessResponse({ success: true, ...parsed }); + } catch (error) { + return createErrorResponse(`Failed to run model: ${error.message}`, error); + } + } + }; +} + +/** + * Get information about all simulation runs + */ +export function createGetRunInfoTool(sessionManager, sessionId, sendToClient) { + return { + description: 'Get information about all simulation runs. Returns a list of run objects, where each run object contains an id, name, and optional metadata.', + supportedModes: ['sfd'], + inputSchema: z.object({}), + handler: async () => { + try { + const session = sessionManager.getSession(sessionId); + if (!session) { + throw new Error(`Session not found: ${sessionId}`); + } + + const requestId = generateRequestId('runinfo'); + + // Send request to client for run info + await sendToClient(createGetRunInfoMessage(sessionId, requestId)); + + // Create pending request that will be resolved when client responds + const resultPromise = new Promise((resolve, reject) => { + const timeout = setTimeout(() => { + reject(new Error('Get run info timeout: Client did not respond within 30 seconds')); + }, 30000); + + if (!session.pendingModelRequests) { + session.pendingModelRequests = new Map(); + } + session.pendingModelRequests.set(requestId, { resolve, reject, timeout }); + }); + + const runInfo = await resultPromise; + const parsed = GetRunInfoResponseSchema.parse({ runs: runInfo.runs || [] }); + + return createSuccessResponse({ + runs: parsed.runs, + count: parsed.runs.length + }); + } catch (error) { + return createErrorResponse(`Failed to get run info: ${error.message}`, error); + } + } + }; +} + +/** + * Get data for specific variables from specific runs + */ +export function createGetVariableDataTool(sessionManager, sessionId, sendToClient) { + return { + description: 'Get data for specific variables from specific runs. Writes the time-series data to a file on disk and returns the file path. Use the Read filesystem tool to load the data into context. NOTE: This operation can be slow for large datasets - consider requesting only essential variables and runs.', + supportedModes: ['sfd'], + inputSchema: z.object({ + variableNames: z.array(z.string()).describe('List of variable names to get data for'), + runIds: z.array(z.string()).describe('List of run IDs to get variable data from'), + detailed: z.boolean().optional().describe('Whether to return detailed data suitable for plotting (default: false). When true, returns more data points for visualization purposes.') + }), + handler: async ({ variableNames, runIds, detailed }) => { + try { + const session = sessionManager.getSession(sessionId); + if (!session) { + throw new Error(`Session not found: ${sessionId}`); + } + + const requestId = generateRequestId('vardata'); + + // Send request to client for variable data + await sendToClient(createGetVariableDataMessage(sessionId, requestId, variableNames, runIds, detailed)); + + // Create pending request that will be resolved when client responds + const resultPromise = new Promise((resolve, reject) => { + const timeout = setTimeout(() => { + reject(new Error('Get variable data timeout: Client did not respond within 30 seconds')); + }, 30000); + + if (!session.pendingModelRequests) { + session.pendingModelRequests = new Map(); + } + session.pendingModelRequests.set(requestId, { resolve, reject, timeout }); + }); + + const variableData = await resultPromise; + + const filename = `variable_data_${Date.now()}.json`; + const { filePath, message } = sessionManager.writeDataToDisk(sessionId, filename, variableData); + + return createSuccessResponse({ message, filePath }); + } catch (error) { + return createErrorResponse(`Failed to get variable data: ${error.message}`, error); + } + } + }; +} diff --git a/agent/tools/builtin/createVisualization.js b/agent/tools/builtin/createVisualization.js new file mode 100644 index 00000000..8a8e4c3f --- /dev/null +++ b/agent/tools/builtin/createVisualization.js @@ -0,0 +1,191 @@ +import { z } from 'zod'; +import { readFileSync, existsSync } from 'fs'; +import { join } from 'path'; +import { createSuccessResponse, createErrorResponse } from './toolHelpers.js'; +import config from '../../../config.js'; + +// Detect run-keyed format: { runId: { time: [...], varName: [...], ... } } +export function isRunKeyedFormat(data) { + const keys = Object.keys(data); + if (keys.length === 0 || keys.includes('time') || keys.includes('feedbackContent')) return false; + return keys.every(key => { + const val = data[key]; + return typeof val === 'object' && !Array.isArray(val) && val !== null && Array.isArray(val.time); + }); +} + +// Extract run-specific data from feedbackContent. +// feedbackContent is either flat { feedbackLoops, ... } or run-keyed { runId: { feedbackLoops, ... } }. +export function extractRunFeedback(feedbackContent, preferredRunId = null) { + if (!feedbackContent || typeof feedbackContent !== 'object') return feedbackContent; + if ('feedbackLoops' in feedbackContent) return feedbackContent; + if (preferredRunId && preferredRunId in feedbackContent) return feedbackContent[preferredRunId]; + const keys = Object.keys(feedbackContent); + const lastKey = keys[keys.length - 1]; + return lastKey ? feedbackContent[lastKey] : feedbackContent; +} + +/** + * Create a data visualization and send it to the client + */ +export function createVisualizationTool(sessionManager, sessionId, sendToClient, vizEngine) { + return { + description: `Create a data visualization and send it to the client for display in chat. + +Visualization types: +- time_series: Line plots showing variables over time +- phase_portrait: State-space plots (stock vs stock) +- feedback_dominance: Stacked area chart of loop influence +- comparison: Multi-run comparison charts + +Use useAICustom=true to have AI generate custom matplotlib code for complex visualizations.`, + supportedModes: ['sfd'], + inputSchema: z.object({ + type: z.enum(['time_series', 'phase_portrait', 'feedback_dominance', 'comparison']).optional(), + filePath: z.string().describe('Path to the data file. Use the filePath returned by get_variable_data for time_series/phase_portrait/comparison; use the feedback.json path for feedback_dominance.'), + variables: z.array(z.string()).optional().describe('Variables to include — defaults to all variables in the data file'), + title: z.string().describe('Visualization title'), + description: z.string().optional().describe('Description of what the visualization shows'), + usePython: z.boolean().optional().describe('Use Python/matplotlib. Default: true'), + useAICustom: z.boolean().optional().describe('Use AI to generate custom Python visualization code. Default: false'), + difficulty: z.enum(["normal", "hard"]).optional().describe("The expected difficulty of this task (only used when useAICustom=true)"), + dataDescription: z.string().optional().describe('Description of the data for AI (when useAICustom=true)'), + visualizationGoal: z.string().optional().describe('What insight to convey (when useAICustom=true)'), + options: z.object({ + timeUnits: z.string().describe('Units for the time axis (e.g. "Years", "Months")'), + seriesUnits: z.record(z.string(), z.string()).describe('Units per variable name (e.g. { "Population": "People", "GDP": "Dollars" }). Use an empty object {} for feedback_dominance charts.'), + timeRange: z.object({ start: z.number(), end: z.number() }).optional().describe('Restrict the plot to a time window'), + highlightPeriods: z.array(z.object({ + start: z.number(), + end: z.number(), + label: z.string(), + color: z.string().optional() + })).optional().describe('Shaded regions to draw on the chart (e.g. phases or events)'), + width: z.number().optional().describe('Output width in pixels (default 800)'), + height: z.number().optional().describe('Output height in pixels (default 600)'), + includeFeedbackContext: z.boolean().optional().describe('When true, reads feedback.json and overlays dominant-loop periods as highlight bands on the chart. Useful for time_series plots where you want to show which feedback loop was driving behavior.'), + customRequirements: z.string().optional().describe('Additional freeform requirements passed to the AI when useAICustom=true') + }) + }), + handler: async ({ type, filePath, variables, title, description, usePython, useAICustom, difficulty, dataDescription, visualizationGoal, options }) => { + try { + const fileContent = readFileSync(filePath, 'utf8'); + const rawData = JSON.parse(fileContent); + + let data, resolvedVariables, extraOptions; + let resolvedType = type; + let selectedRunId = null; + + if ((type || 'time_series') === 'feedback_dominance') { + if (!rawData.feedbackContent || Object.keys(rawData.feedbackContent).length === 0) { + return createErrorResponse('No feedback information is present. Call get_feedback_information first.'); + } + // feedbackContent may be flat or run-keyed: { runId: { feedbackLoops, ... } } + const feedbackSource = extractRunFeedback(rawData.feedbackContent); + const { feedbackLoops = [], dominantLoopsByPeriod } = feedbackSource; + + const getLoopScores = l => l['Percent of Model Behavior Explained By Loop'] ?? l.loopScore; + const loopsWithData = feedbackLoops.filter(l => getLoopScores(l)?.length > 0); + + if (loopsWithData.length === 0) { + return createErrorResponse('Loops That Matter information is not present (some clients may not generate that information)'); + } + + const timeSet = new Set(); + for (const loop of loopsWithData) { + for (const { time } of getLoopScores(loop)) { + timeSet.add(time); + } + } + const sortedTime = Array.from(timeSet).sort((a, b) => a - b); + + data = { time: sortedTime }; + for (const loop of loopsWithData) { + const timeToValue = new Map(getLoopScores(loop).map(d => [d.time, d.value])); + data[loop.identifier] = sortedTime.map(t => timeToValue.get(t) ?? 0); + } + + resolvedVariables = variables ?? loopsWithData.map(l => l.identifier); + extraOptions = {}; + } else { + data = rawData; + + if (isRunKeyedFormat(data)) { + const runKeys = Object.keys(data); + if (runKeys.length === 1) { + selectedRunId = runKeys[0]; + data = data[runKeys[0]]; + } else { + resolvedType = 'comparison'; + const firstRun = data[runKeys[0]] || {}; + resolvedVariables = variables ?? Object.keys(firstRun).filter(k => k !== 'time'); + } + } + + if (!resolvedVariables) { + resolvedVariables = variables ?? Object.keys(data).filter(k => k !== 'time'); + } + extraOptions = {}; + } + + if (options?.includeFeedbackContext && (resolvedType || 'time_series') !== 'feedback_dominance') { + const feedbackPath = join(sessionManager.getSessionTempDir(sessionId), 'feedback.json'); + if (existsSync(feedbackPath)) { + const feedbackFile = JSON.parse(readFileSync(feedbackPath, 'utf8')); + const feedback = feedbackFile.feedbackContent + ? extractRunFeedback(feedbackFile.feedbackContent, selectedRunId) + : feedbackFile; + if (feedback.dominantLoopsByPeriod?.length > 0) { + extraOptions.highlightPeriods = feedback.dominantLoopsByPeriod.map(p => ({ + start: p.startTime, + end: p.endTime, + label: p.dominantLoops.join(', ') + })); + } + } + } + + const underlyingModel = difficulty === 'hard' ? config.agentToolHighEffortNonBuildDefaultModel : config.nonBuildDefaultModel; + const vizOptions = { + ...options, + ...extraOptions, + title, + description, + usePython, + useAICustom, + underlyingModel, + dataDescription: dataDescription, + visualizationGoal + }; + + // VisualizationEngine returns raw SVG string + const svgContent = await vizEngine.createVisualization(resolvedType || 'time_series', data, resolvedVariables, vizOptions); + + // Generate visualization ID + const visualizationId = `viz_${Date.now()}_${Math.random().toString(36).substring(7)}`; + + const vizMessage = { + type: 'visualization', + sessionId: sessionId, + visualizationId, + title: title || 'Visualization', + format: 'svg', + data: svgContent, + timestamp: new Date().toISOString() + }; + + // Add description if provided + if (description) { + vizMessage.description = description; + } + + // Send visualization to client + await sendToClient(vizMessage); + + return createSuccessResponse(`Created ${useAICustom ? 'AI-custom' : resolvedType || 'time_series'} SVG visualization: "${title}" and sent to client`); + } catch (error) { + return createErrorResponse(`Failed to create visualization: ${error.message}`, error); + } + } + }; +} diff --git a/agent/tools/builtin/discussModelAcrossRuns.js b/agent/tools/builtin/discussModelAcrossRuns.js new file mode 100644 index 00000000..0eda6039 --- /dev/null +++ b/agent/tools/builtin/discussModelAcrossRuns.js @@ -0,0 +1,110 @@ +import { z } from 'zod'; +import { readFileSync, existsSync } from 'fs'; +import { join } from 'path'; +import { createFeedbackRequestMessage } from '../../utilities/MessageProtocol.js'; +import { callSeldonILEEngine } from '../../utilities/EngineWrapper.js'; +import { generateRequestId, createSuccessResponse, createErrorResponse, loadBehaviorContent } from './toolHelpers.js'; +import config from '../../../config.js'; + +/** + * Have a user-friendly discussion about the model without jargon, with ability to compare runs + */ +export function createDiscussModelAcrossRunsTool(sessionManager, sessionId, sendToClient) { + return { + description: 'Have a user-friendly discussion about the model without jargon, with the ability to compare and explain differences between simulation runs. Use this to understand what causes behavioral differences across runs - analyzing how different scenarios or parameter changes produce different outcomes by examining the underlying feedback loop dynamics.', + supportedModes: ['sfd'], + inputSchema: z.object({ + prompt: z.string().describe('Question or topic for discussion'), + difficulty: z.enum(["normal", "hard"]).describe("The expected difficulty of this task"), + runName: z.string().optional().describe('Simulation run identifier of the most recent run matching the way the behavioral content is being passed to this too.'), + parameters: z.object({ + problemStatement: z.string().optional().describe('Description of dynamic issue to address'), + backgroundKnowledge: z.string().optional().describe('Background information for LLM'), + runIds: z.array(z.string()).optional().describe('Run IDs to include as behavior data; defaults to the last run') + }).optional() + }), + handler: async ({ prompt, difficulty, runName, parameters }) => { + try { + const session = sessionManager.getSession(sessionId); + if (!session) { + throw new Error(`Session not found: ${sessionId}`); + } + + const model = sessionManager.getClientModel(sessionId); + if (!model) { + return createErrorResponse('No model available in session'); + } + + const underlyingModel = difficulty === 'normal' ? config.nonBuildDefaultModel : config.agentToolHighEffortNonBuildDefaultModel; + const baseParameters = { ...parameters, clientId: session.clientId, underlyingModel }; + const sessionTempDir = sessionManager.getSessionTempDir(sessionId); + const feedbackPath = join(sessionTempDir, 'feedback.json'); + const feedbackContent = existsSync(feedbackPath) + ? JSON.parse(readFileSync(feedbackPath, 'utf-8')).feedbackContent + : undefined; + + const behaviorContent = loadBehaviorContent(sessionTempDir, parameters?.runIds); + + // Add feedbackContent and behaviorContent to parameters if available + const engineParams = { + ...baseParameters, + ...(feedbackContent && { feedbackContent }), + ...(behaviorContent && { behaviorContent }) + }; + + const result = await callSeldonILEEngine(prompt, model, runName, engineParams); + + if (!result.success) { + return createErrorResponse(result.error); + } + + // Check if feedback information is required but not provided + if (result.output.feedbackInformationRequired && !feedbackContent) { + const requestId = generateRequestId('feedback'); + + // Send request to client for comparative feedback data (empty array means all runs) + await sendToClient(createFeedbackRequestMessage(sessionId, requestId, [])); + + // Create pending request that will be resolved when client responds + const resultPromise = new Promise((resolve, reject) => { + const timeout = setTimeout(() => { + reject(new Error('Feedback request timeout: Client did not respond within 30 seconds')); + }, 30000); + + if (!session.pendingFeedbackRequests) { + session.pendingFeedbackRequests = new Map(); + } + session.pendingFeedbackRequests.set(requestId, { resolve, reject, timeout }); + }); + + const feedbackData = await resultPromise; + + // Write feedback to disk instead of passing directly into context + sessionManager.writeDataToDisk(sessionId, 'feedback.json', { + feedbackContent: feedbackData.feedbackContent, + runIds: feedbackData.runIds + }); + + // Retry the call with comparative feedback information + const retryParams = { + ...baseParameters, + feedbackContent: feedbackData.feedbackContent, + ...(behaviorContent && { behaviorContent }) + }; + + const retryResult = await callSeldonILEEngine(prompt, model, runName, retryParams); + + if (!retryResult.success) { + return createErrorResponse(retryResult.error); + } + + return createSuccessResponse(retryResult.output); + } + + return createSuccessResponse(result.output); + } catch (error) { + return createErrorResponse(error.message); + } + } + }; +} \ No newline at end of file diff --git a/agent/tools/builtin/discussModelWithSeldon.js b/agent/tools/builtin/discussModelWithSeldon.js new file mode 100644 index 00000000..1531fe75 --- /dev/null +++ b/agent/tools/builtin/discussModelWithSeldon.js @@ -0,0 +1,97 @@ +import { z } from 'zod'; +import { readFileSync, existsSync } from 'fs'; +import { join } from 'path'; +import { createFeedbackRequestMessage } from '../../utilities/MessageProtocol.js'; +import { callSeldonEngine } from '../../utilities/EngineWrapper.js'; +import { generateRequestId, createSuccessResponse, createErrorResponse, loadBehaviorContent } from './toolHelpers.js'; +import config from '../../../config.js'; + +/** + * Have an expert-level discussion about the model using System Dynamics terminology + */ +export function createDiscussModelWithSeldonTool(sessionManager, sessionId, sendToClient) { + return { + description: 'Have an expert-level discussion about the model using System Dynamics terminology. Use this for technical analysis and SD theory discussions.', + supportedModes: ['sfd', 'cld'], + inputSchema: z.object({ + prompt: z.string().describe('Question or topic for discussion'), + difficulty: z.enum(["normal", "hard"]).describe("The expected difficulty of this task"), + parameters: z.object({ + problemStatement: z.string().optional().describe('Description of dynamic issue to address'), + backgroundKnowledge: z.string().optional().describe('Background information for LLM'), + runIds: z.array(z.string()).optional().describe('Run IDs to include as behavior data; defaults to the last run') + }).optional() + }), + handler: async ({ prompt, difficulty, parameters }) => { + try { + const session = sessionManager.getSession(sessionId); + if (!session) { + throw new Error(`Session not found: ${sessionId}`); + } + + const model = sessionManager.getClientModel(sessionId); + if (!model) { + return createErrorResponse('No model available in session'); + } + + const underlyingModel = difficulty === 'normal' ? config.nonBuildDefaultModel : config.agentToolHighEffortNonBuildDefaultModel; + const baseParameters = { ...parameters, clientId: session.clientId, underlyingModel }; + const sessionTempDir = sessionManager.getSessionTempDir(sessionId); + const feedbackPath = join(sessionTempDir, 'feedback.json'); + const feedbackContent = existsSync(feedbackPath) + ? JSON.parse(readFileSync(feedbackPath, 'utf-8')).feedbackContent + : undefined; + + const behaviorContent = loadBehaviorContent(sessionTempDir, parameters?.runIds); + const enrichedParameters = behaviorContent ? { ...baseParameters, behaviorContent } : baseParameters; + + const result = await callSeldonEngine(prompt, model, feedbackContent, enrichedParameters); + + if (!result.success) { + return createErrorResponse(result.error); + } + + // Check if feedback information is required but not provided + if (result.output.feedbackInformationRequired && !feedbackContent) { + const requestId = generateRequestId('feedback'); + + // Send request to client for feedback data (empty array means all runs) + await sendToClient(createFeedbackRequestMessage(sessionId, requestId, [])); + + // Create pending request that will be resolved when client responds + const resultPromise = new Promise((resolve, reject) => { + const timeout = setTimeout(() => { + reject(new Error('Feedback request timeout: Client did not respond within 30 seconds')); + }, 30000); + + if (!session.pendingFeedbackRequests) { + session.pendingFeedbackRequests = new Map(); + } + session.pendingFeedbackRequests.set(requestId, { resolve, reject, timeout }); + }); + + const feedbackData = await resultPromise; + + // Write feedback to disk instead of passing directly into context + sessionManager.writeDataToDisk(sessionId, 'feedback.json', { + feedbackContent: feedbackData.feedbackContent, + runIds: feedbackData.runIds + }); + + // Retry the call with feedback information + const retryResult = await callSeldonEngine(prompt, model, feedbackData.feedbackContent, enrichedParameters); + + if (!retryResult.success) { + return createErrorResponse(retryResult.error); + } + + return createSuccessResponse(retryResult.output); + } + + return createSuccessResponse(result.output); + } catch (error) { + return createErrorResponse(error.message); + } + } + }; +} diff --git a/agent/tools/builtin/discussWithMentor.js b/agent/tools/builtin/discussWithMentor.js new file mode 100644 index 00000000..7feb475e --- /dev/null +++ b/agent/tools/builtin/discussWithMentor.js @@ -0,0 +1,94 @@ +import { z } from 'zod'; +import { readFileSync, existsSync } from 'fs'; +import { join } from 'path'; +import { createFeedbackRequestMessage } from '../../utilities/MessageProtocol.js'; +import { callSeldonMentorEngine } from '../../utilities/EngineWrapper.js'; +import { generateRequestId, createSuccessResponse, createErrorResponse, loadBehaviorContent } from './toolHelpers.js'; +import config from '../../../config.js'; + +/** + * Ask thoughtful questions to the user to guide their learning + */ +export function createDiscussWithMentorTool(sessionManager, sessionId, sendToClient) { + return { + description: 'Ask thoughtful questions to the user to guide their learning and help them think through System Dynamics concepts. Use this to engage users in Socratic dialogue about their model.', + supportedModes: ['sfd', 'cld'], + inputSchema: z.object({ + prompt: z.string().describe('The question or guidance to provide to the user'), + difficulty: z.enum(["normal", "hard"]).describe("The expected difficulty of this task"), + parameters: z.object({ + problemStatement: z.string().optional().describe('Description of dynamic issue to address'), + backgroundKnowledge: z.string().optional().describe('Background information for LLM'), + runIds: z.array(z.string()).optional().describe('Run IDs to include as behavior data; defaults to the last run') + }).optional() + }), + handler: async ({ prompt, difficulty, parameters }) => { + try { + const session = sessionManager.getSession(sessionId); + if (!session) { + throw new Error(`Session not found: ${sessionId}`); + } + + const model = sessionManager.getClientModel(sessionId); + if (!model) { + return createErrorResponse('No model available in session'); + } + + const underlyingModel = difficulty === 'normal' ? config.nonBuildDefaultModel : config.agentToolHighEffortNonBuildDefaultModel; + const baseParameters = { ...parameters, clientId: session.clientId, underlyingModel }; + const sessionTempDir = sessionManager.getSessionTempDir(sessionId); + const feedbackPath = join(sessionTempDir, 'feedback.json'); + const feedbackContent = existsSync(feedbackPath) + ? JSON.parse(readFileSync(feedbackPath, 'utf-8')).feedbackContent + : undefined; + + const behaviorContent = loadBehaviorContent(sessionTempDir, parameters?.runIds); + const enrichedParameters = behaviorContent ? { ...baseParameters, behaviorContent } : baseParameters; + + const result = await callSeldonMentorEngine(prompt, model, feedbackContent, enrichedParameters); + + if (!result.success) { + return createErrorResponse(result.error); + } + + // Check if feedback information is required but not provided + if (result.output.feedbackInformationRequired && !feedbackContent) { + const requestId = generateRequestId('feedback'); + + await sendToClient(createFeedbackRequestMessage(sessionId, requestId, [])); + + const resultPromise = new Promise((resolve, reject) => { + const timeout = setTimeout(() => { + reject(new Error('Feedback request timeout: Client did not respond within 30 seconds')); + }, 30000); + + if (!session.pendingFeedbackRequests) { + session.pendingFeedbackRequests = new Map(); + } + session.pendingFeedbackRequests.set(requestId, { resolve, reject, timeout }); + }); + + const feedbackData = await resultPromise; + + // Write feedback to disk instead of passing directly into context + sessionManager.writeDataToDisk(sessionId, 'feedback.json', { + feedbackContent: feedbackData.feedbackContent, + runIds: feedbackData.runIds + }); + + const retryResult = await callSeldonMentorEngine(prompt, model, feedbackData.feedbackContent, enrichedParameters); + + if (!retryResult.success) { + return createErrorResponse(retryResult.error); + } + + return createSuccessResponse(retryResult.output); + } + + return createSuccessResponse(result.output); + } catch (error) { + return createErrorResponse(error.message); + } + } + }; +} diff --git a/agent/tools/builtin/fileTools.js b/agent/tools/builtin/fileTools.js new file mode 100644 index 00000000..4d8bc502 --- /dev/null +++ b/agent/tools/builtin/fileTools.js @@ -0,0 +1,128 @@ +import { z } from 'zod'; +import { readFileSync, writeFileSync, existsSync, mkdirSync } from 'fs'; +import { dirname } from 'path'; +import { createSuccessResponse, createErrorResponse } from './toolHelpers.js'; + +/** + * Read/Write/Edit file tools for the non-SDK agent loop. + * The SDK loop has built-in Read, Edit, Write tools; these mirror them for the manual route. + */ + +export function createReadFileTool() { + return { + description: `Read a file from disk and return its contents. Use this to load data files (e.g. variable data) into context after a tool has written them to disk. NEVER use this to read model.sdjson — use the read_model_section tool to inspect the model. + +Filtering options to avoid reading more than needed: +- startLine / endLine: read a specific line range (1-based, inclusive) +- search: return only lines containing this string (case-insensitive) +- maxLines: cap the number of lines returned (default: no limit)`, + supportedModes: ['sfd', 'cld'], + inputSchema: z.object({ + filePath: z.string().describe('Absolute path to the file to read'), + startLine: z.number().int().positive().optional().describe('First line to return (1-based, inclusive)'), + endLine: z.number().int().positive().optional().describe('Last line to return (1-based, inclusive)'), + search: z.string().optional().describe('Return only lines containing this string (case-insensitive)'), + maxLines: z.number().int().positive().optional().describe('Maximum number of lines to return') + }), + handler: async ({ filePath, startLine, endLine, search, maxLines }) => { + try { + if (filePath.endsWith('model.sdjson')) { + return createErrorResponse('Reading model.sdjson with read_file is not allowed — use the read_model_section tool to inspect the model.'); + } + if (!existsSync(filePath)) { + return createErrorResponse(`File not found: ${filePath}`); + } + + const raw = readFileSync(filePath, 'utf-8'); + let lines = raw.split(/\r?\n/); + const totalLines = lines.length; + + if (startLine !== undefined || endLine !== undefined) { + const start = (startLine ?? 1) - 1; + const end = endLine ?? totalLines; + lines = lines.slice(start, end); + } + + if (search) { + const lower = search.toLowerCase(); + lines = lines.filter(l => l.toLowerCase().includes(lower)); + } + + if (maxLines !== undefined) { + lines = lines.slice(0, maxLines); + } + + return createSuccessResponse({ + filePath, + totalLines, + returnedLines: lines.length, + content: lines.join('\n') + }); + } catch (error) { + return createErrorResponse(`Failed to read file: ${error.message}`, error); + } + } + }; +} + +export function createWriteFileTool() { + return { + description: 'Write content to a file on disk, creating the file (and any parent directories) if it does not exist. Overwrites any existing content. NEVER use this to write to model.sdjson — all model updates must go through the designated model tools.', + supportedModes: ['sfd', 'cld'], + inputSchema: z.object({ + filePath: z.string().describe('Absolute path to the file to write'), + content: z.string().describe('Content to write to the file') + }), + handler: async ({ filePath, content }) => { + try { + mkdirSync(dirname(filePath), { recursive: true }); + writeFileSync(filePath, content, 'utf-8'); + return createSuccessResponse({ filePath, bytesWritten: Buffer.byteLength(content, 'utf-8') }); + } catch (error) { + return createErrorResponse(`Failed to write file: ${error.message}`, error); + } + } + }; +} + +export function createEditFileTool() { + return { + description: `Replace a string in a file with new content. + +By default, old_string must appear exactly once. Set replaceAll: true to replace every occurrence. +The match is exact (whitespace-sensitive). Provide enough surrounding context to make the match unique. +NEVER use this to edit model.sdjson — all model updates must go through the designated model tools.`, + supportedModes: ['sfd', 'cld'], + inputSchema: z.object({ + filePath: z.string().describe('Absolute path to the file to edit'), + oldString: z.string().describe('The exact string to find and replace'), + newString: z.string().describe('The string to replace it with'), + replaceAll: z.boolean().optional().describe('Replace every occurrence instead of requiring exactly one (default: false)') + }), + handler: async ({ filePath, oldString, newString, replaceAll = false }) => { + try { + if (!existsSync(filePath)) { + return createErrorResponse(`File not found: ${filePath}`); + } + const content = readFileSync(filePath, 'utf-8'); + const count = content.split(oldString).length - 1; + + if (count === 0) { + return createErrorResponse(`old_string not found in file: ${filePath}`); + } + if (!replaceAll && count > 1) { + return createErrorResponse(`old_string matches ${count} locations — add more context to make it unique, or set replaceAll: true`); + } + + const updated = replaceAll + ? content.split(oldString).join(newString) + : content.replace(oldString, newString); + + writeFileSync(filePath, updated, 'utf-8'); + return createSuccessResponse({ filePath, replacements: count }); + } catch (error) { + return createErrorResponse(`Failed to edit file: ${error.message}`, error); + } + } + }; +} diff --git a/agent/tools/builtin/generateLtmNarrative.js b/agent/tools/builtin/generateLtmNarrative.js new file mode 100644 index 00000000..83c49ff9 --- /dev/null +++ b/agent/tools/builtin/generateLtmNarrative.js @@ -0,0 +1,90 @@ +import { z } from 'zod'; +import { readFileSync, existsSync } from 'fs'; +import { join } from 'path'; +import { createFeedbackRequestMessage } from '../../utilities/MessageProtocol.js'; +import { callLTMEngine } from '../../utilities/EngineWrapper.js'; +import { generateRequestId, createSuccessResponse, createErrorResponse, loadBehaviorContent } from './toolHelpers.js'; +import config from '../../../config.js'; + +/** + * Generate a narrative explanation of feedback loops and their influence on model behavior + */ +export function createGenerateLtmNarrativeTool(sessionManager, sessionId, sendToClient) { + return { + description: 'Generate a narrative explanation of feedback loops and their influence on model behavior (Loops That Matter analysis).', + supportedModes: ['sfd'], + inputSchema: z.object({ + difficulty: z.enum(["normal", "hard"]).describe("The expected difficulty of this task"), + parameters: z.object({ + problemStatement: z.string().optional().describe('Description of dynamic issue to address'), + backgroundKnowledge: z.string().optional().describe('Background information for LLM'), + runIds: z.array(z.string()).optional().describe('Run IDs to include as behavior data; defaults to the last run') + }).optional() + }), + handler: async ({ difficulty, parameters }) => { + try { + const session = sessionManager.getSession(sessionId); + if (!session) { + throw new Error(`Session not found: ${sessionId}`); + } + + const model = sessionManager.getClientModel(sessionId); + if (!model) { + return createErrorResponse('No model available in session'); + } + + const underlyingModel = difficulty === 'normal' ? config.nonBuildDefaultModel : config.agentToolHighEffortNonBuildDefaultModel; + const baseParameters = { ...parameters, clientId: session.clientId, underlyingModel }; + const sessionTempDir = sessionManager.getSessionTempDir(sessionId); + const feedbackPath = join(sessionTempDir, 'feedback.json'); + let feedbackContent = existsSync(feedbackPath) + ? JSON.parse(readFileSync(feedbackPath, 'utf-8')).feedbackContent + : undefined; + + const behaviorContent = loadBehaviorContent(sessionTempDir, parameters?.runIds); + const enrichedParameters = behaviorContent ? { ...baseParameters, behaviorContent } : baseParameters; + + if (!feedbackContent) { + + const requestId = generateRequestId('feedback'); + + await sendToClient(createFeedbackRequestMessage(sessionId, requestId, [])); + + const resultPromise = new Promise((resolve, reject) => { + const timeout = setTimeout(() => { + reject(new Error('Feedback request timeout: Client did not respond within 30 seconds')); + }, 30000); + + if (!session.pendingFeedbackRequests) { + session.pendingFeedbackRequests = new Map(); + } + session.pendingFeedbackRequests.set(requestId, { resolve, reject, timeout }); + }); + + const feedbackData = await resultPromise; + + // Write feedback to disk instead of passing directly into context + sessionManager.writeDataToDisk(sessionId, 'feedback.json', { + feedbackContent: feedbackData.feedbackContent, + runIds: feedbackData.runIds + }); + + feedbackContent = feedbackData.feedbackContent; + } + + const result = await callLTMEngine(model, feedbackContent, enrichedParameters); + + if (!result.success) { + return createErrorResponse(result.error); + } + + return createSuccessResponse({ + feedbackLoops: result.feedbackLoops, + output: result.output + }); + } catch (error) { + return createErrorResponse(error.message); + } + } + }; +} diff --git a/agent/tools/builtin/generateQualitativeModel.js b/agent/tools/builtin/generateQualitativeModel.js new file mode 100644 index 00000000..39a23334 --- /dev/null +++ b/agent/tools/builtin/generateQualitativeModel.js @@ -0,0 +1,72 @@ +import { z } from 'zod'; +import { createUpdateModelMessage, UpdateModelResponseSchema } from '../../utilities/MessageProtocol.js'; +import { callQualitativeEngine } from '../../utilities/EngineWrapper.js'; +import { generateRequestId, createSuccessResponse, createErrorResponse } from './toolHelpers.js'; +import config from '../../../config.js'; + +/** + * Generate a Causal Loop Diagram (CLD) showing feedback loops and causal relationships + */ +export function createGenerateQualitativeModelTool(sessionManager, sessionId, sendToClient) { + return { + description: 'Generate a Causal Loop Diagram (CLD) showing feedback loops and causal relationships. Use this for conceptual models focusing on system structure. Automatically pushes the generated model to the client.', + supportedModes: ['cld'], + maxModelTokens: config.agentMaxTokensForEngines, + inputSchema: z.object({ + prompt: z.string().describe('Description of the model to generate'), + difficulty: z.enum(["normal", "hard"]).describe("The expected difficulty of this task"), + parameters: z.object({ + problemStatement: z.string().optional().describe('Description of dynamic issue to address'), + backgroundKnowledge: z.string().optional().describe('Background information for LLM') + }).optional() + }), + handler: async ({ prompt, difficulty, parameters }) => { + try { + const session = sessionManager.getSession(sessionId); + if (!session) { + throw new Error(`Session not found: ${sessionId}`); + } + + const underlyingModel = difficulty === 'normal' ? config.buildDefaultModel : config.agentToolHighEffortBuildDefaultModel; + const currentModel = sessionManager.getClientModel(sessionId); + const result = await callQualitativeEngine(prompt, currentModel, { ...parameters, underlyingModel, clientId: session.clientId }); + + if (!result.success) { + return createErrorResponse(result.error); + } + + // Automatically push the generated model to the client + + const requestId = generateRequestId('model'); + await sendToClient(createUpdateModelMessage(sessionId, requestId, result.model)); + + // Wait for client confirmation + const updatePromise = new Promise((resolve, reject) => { + const timeout = setTimeout(() => { + reject(new Error('Update model timeout: Client did not respond within 30 seconds')); + }, 30000); + + if (!session.pendingModelRequests) { + session.pendingModelRequests = new Map(); + } + session.pendingModelRequests.set(requestId, { resolve, reject, timeout }); + }); + + const clientResult = await updatePromise; + const parsed = UpdateModelResponseSchema.parse(clientResult); + + const { modelPath, message, issues } = sessionManager.updateClientModel(sessionId, parsed); + + return createSuccessResponse({ + message: `Model generated and pushed to client. ${message}`, + modelPath, + supportingInfo: result.supportingInfo, + pushedToClient: true, + ...(issues && { issues }) + }); + } catch (error) { + return createErrorResponse(error.message); + } + } + }; +} diff --git a/agent/tools/builtin/generateQuantitativeModel.js b/agent/tools/builtin/generateQuantitativeModel.js new file mode 100644 index 00000000..26df6c33 --- /dev/null +++ b/agent/tools/builtin/generateQuantitativeModel.js @@ -0,0 +1,84 @@ +import { z } from 'zod'; +import { createUpdateModelMessage, UpdateModelResponseSchema } from '../../utilities/MessageProtocol.js'; +import { callQuantitativeEngine } from '../../utilities/EngineWrapper.js'; +import { generateRequestId, createSuccessResponse, createErrorResponse } from './toolHelpers.js'; +import config from '../../../config.js'; + +/** + * Generate a Stock Flow Diagram (SFD) model with equations and quantitative structure + */ +export function createGenerateQuantitativeModelTool(sessionManager, sessionId, sendToClient) { + return { + description: 'Generate a Stock Flow Diagram (SFD) model with equations and quantitative structure. Use this for building computational models that can be simulated. Automatically pushes the generated model to the client.', + supportedModes: ['sfd'], + maxModelTokens: config.agentMaxTokensForEngines, + inputSchema: z.object({ + prompt: z.string().describe('Description of the model to generate'), + difficulty: z.enum(["normal", "hard"]).describe("The expected difficulty of this task"), + parameters: z.object({ + problemStatement: z.string().optional().describe('Description of dynamic issue to address'), + backgroundKnowledge: z.string().optional().describe('Background information for LLM'), + allowArrays: z.boolean().optional().describe('Whether to use subscripted/array variables to represent multiple parallel entities (e.g., age groups, regions, sectors)'), + allowModules: z.boolean().optional().describe('Whether to organize the model into separate named modules'), + allowSubTypes: z.boolean().optional().describe('Whether to use sub-types that support discrete elements like conveyors, queues, and ovens'), + }).optional() + }), + handler: async ({ prompt, difficulty, parameters }) => { + try { + const session = sessionManager.getSession(sessionId); + if (!session) { + throw new Error(`Session not found: ${sessionId}`); + } + + const underlyingModel = difficulty === 'normal' ? config.buildDefaultModel : config.agentToolHighEffortBuildDefaultModel; + const currentModel = sessionManager.getClientModel(sessionId); + + const mergedParameters = { + ...parameters, + supportsArrays: session.supportsArrays && (parameters?.allowArrays ?? true), + supportsModules: session.supportsModules && (parameters?.allowModules ?? true), + supportsSubTypes: session.supportsSubTypes && (parameters?.allowSubTypes ?? true), + underlyingModel, + clientId: session.clientId + }; + + const result = await callQuantitativeEngine(prompt, currentModel, mergedParameters); + + if (!result.success) { + return createErrorResponse(result.error); + } + + // Automatically push the generated model to the client + const requestId = generateRequestId('model'); + await sendToClient(createUpdateModelMessage(sessionId, requestId, result.model)); + + // Wait for client confirmation + const updatePromise = new Promise((resolve, reject) => { + const timeout = setTimeout(() => { + reject(new Error('Update model timeout: Client did not respond within 30 seconds')); + }, 30000); + + if (!session.pendingModelRequests) { + session.pendingModelRequests = new Map(); + } + session.pendingModelRequests.set(requestId, { resolve, reject, timeout }); + }); + + const clientResult = await updatePromise; + const parsed = UpdateModelResponseSchema.parse(clientResult); + + const { modelPath, message, issues } = sessionManager.updateClientModel(sessionId, parsed); + + return createSuccessResponse({ + message: `Model generated and pushed to client. ${message}`, + modelPath, + supportingInfo: result.supportingInfo, + pushedToClient: true, + ...(issues && { issues }) + }); + } catch (error) { + return createErrorResponse(error.message); + } + } + }; +} diff --git a/agent/tools/builtin/getFeedbackInformation.js b/agent/tools/builtin/getFeedbackInformation.js new file mode 100644 index 00000000..9abf7be0 --- /dev/null +++ b/agent/tools/builtin/getFeedbackInformation.js @@ -0,0 +1,58 @@ +import { z } from 'zod'; +import { createFeedbackRequestMessage } from '../../utilities/MessageProtocol.js'; +import { generateRequestId, createSuccessResponse, createErrorResponse } from './toolHelpers.js'; + +/** + * Request feedback loop analysis data from the client + */ +export function createGetFeedbackInformationTool(sessionManager, sessionId, sendToClient) { + return { + description: 'Request feedback loop analysis data from the client and cache it for use by other tools. MUST be called before using discuss_model_with_seldon or generate_ltm_narrative. Provide a list of run IDs to get feedback for.', + supportedModes: ['sfd', 'cld'], + inputSchema: z.object({ + runIds: z.array(z.string()).describe('List of simulation run IDs to get feedback for') + }), + handler: async ({ runIds }) => { + try { + // Create a promise that will be resolved when client responds + const session = sessionManager.getSession(sessionId); + if (!session) { + throw new Error(`Session not found: ${sessionId}`); + } + + const requestId = generateRequestId('feedback'); + + // Send request to client for feedback data + await sendToClient(createFeedbackRequestMessage(sessionId, requestId, runIds)); + + // Create pending request that will be resolved when client responds + const resultPromise = new Promise((resolve, reject) => { + const timeout = setTimeout(() => { + reject(new Error('Feedback request timeout: Client did not respond within 30 seconds')); + }, 30000); + + // Store the resolver in session so it can be called when client responds + if (!session.pendingFeedbackRequests) { + session.pendingFeedbackRequests = new Map(); + } + session.pendingFeedbackRequests.set(requestId, { resolve, reject, timeout }); + }); + + const feedbackData = await resultPromise; + + const { filePath } = sessionManager.writeDataToDisk(sessionId, 'feedback.json', { + feedbackContent: feedbackData.feedbackContent, + runIds: feedbackData.runIds + }); + + return createSuccessResponse({ + message: 'Feedback information cached. Other tools will load it automatically — you do not need to read this file.', + filePath, + runIds: feedbackData.runIds + }); + } catch (error) { + return createErrorResponse(`Failed to get feedback information: ${error.message}`, error); + } + } + }; +} diff --git a/agent/tools/builtin/index.js b/agent/tools/builtin/index.js new file mode 100644 index 00000000..093beadf --- /dev/null +++ b/agent/tools/builtin/index.js @@ -0,0 +1,32 @@ +/** + * Built-in Tools Index + * Exports all built-in tool creation functions + */ + +// Tool creation functions +export { createGenerateQuantitativeModelTool } from './generateQuantitativeModel.js'; +export { createGenerateQualitativeModelTool } from './generateQualitativeModel.js'; +export { createDiscussModelWithSeldonTool } from './discussModelWithSeldon.js'; +export { createDiscussModelAcrossRunsTool } from './discussModelAcrossRuns.js'; +export { createGenerateLtmNarrativeTool } from './generateLtmNarrative.js'; +export { createDiscussWithMentorTool } from './discussWithMentor.js'; +export { createGetFeedbackInformationTool } from './getFeedbackInformation.js'; +export { + createGetCurrentModelTool, + createUpdateModelTool, + createRunModelTool, + createGetRunInfoTool, + createGetVariableDataTool +} from './clientInteractionTools.js'; +export { createVisualizationTool } from './createVisualization.js'; +export { + createReadModelSectionTool, + createEditVariablesTool, + createEditRelationshipsTool, + createEditSpecsTool, + createEditModulesTool +} from './largeModelTools.js'; +export { createReadFileTool, createWriteFileTool, createEditFileTool } from './fileTools.js'; + +// Helper utilities +export { generateRequestId, createErrorResponse } from './toolHelpers.js'; diff --git a/agent/tools/builtin/largeModelTools.js b/agent/tools/builtin/largeModelTools.js new file mode 100644 index 00000000..6c1c341d --- /dev/null +++ b/agent/tools/builtin/largeModelTools.js @@ -0,0 +1,613 @@ +import { z } from 'zod'; +import { readFileSync, existsSync } from 'fs'; +import { join } from 'path'; +import { createUpdateModelMessage, UpdateModelResponseSchema } from '../../utilities/MessageProtocol.js'; +import { generateRequestId, createSuccessResponse, createErrorResponse } from './toolHelpers.js'; +import config from '../../../config.js'; +import { LLMWrapper } from '../../../utilities/LLMWrapper.js'; + +const variableBase = LLMWrapper.variableSchemaBase(); +const simSpecsBase = LLMWrapper.simSpecsSchemaBase(); +const relationshipBase = LLMWrapper.relationshipSchemaBase(); + +// Variable names are stored with spaces; equations use underscores. +const normName = n => typeof n === 'string' ? n.replace(/_/g, ' ') : n; +const normSearch = s => typeof s === 'string' ? s.toLowerCase().replace(/[ _]/g, '_') : s; + +/** + * Read a specific section of the large model file + */ +export function createReadModelSectionTool(sessionManager, sessionId) { + return { + description: `Read a specific section of the large model file. Use this to inspect parts of the model without loading the entire thing. + +Available sections: +- specs: simulation specifications (startTime, stopTime, dt, timeUnits, arrayDimensions). + * arrayDimensions schema: [{type: "numeric"|"labels", name: string (singular, alphanumeric), size: number (positive integer), elements: string[] (element names)}] + * All four fields (type, name, size, elements) are required for each dimension + * type="numeric": elements auto-generated as ['1','2','3'...] + * type="labels": elements are user-defined meaningful names like ['North','South','East','West'] +- variables: array of variables with schema: {name, type (stock|flow|variable), equation, documentation, units, uniflow, inflows, outflows, dimensions, arrayEquations, crossLevelGhostOf, graphicalFunction, subType?, additionalProperties?} +- relationships: array of relationships with schema: {from, to, polarity (+|-|""), reasoning, polarityReasoning} +- modules: module hierarchy with schema: {name, parentModule}. IMPORTANT: The modules array only defines the hierarchical structure (which modules exist and their parent-child relationships). It does NOT tell you which variables belong to a module - variable membership is determined by the variable name prefix (e.g., "Finance.revenue" belongs to the Finance module). + +Module handling: +- In modular models, variable names are module-qualified as "Module_Name.variable_name" +- To find variables in a module, use the moduleName filter (filters by name prefix) +- The modules section only shows the module hierarchy, not the contents + +Array handling: +- Variables with the "dimensions" field are arrayed variables +- Array dimensions must be defined in specs.arrayDimensions BEFORE being referenced by variables +- Each dimension requires all four fields: type, name, size, elements +- Element-specific equations are in the "arrayEquations" field + +Sub-type handling: +- Stock sub-types (set subType + additionalProperties): "queue" (waiting line), "oven" (batch processor), "conveyor" (pipeline delay) +- Flow sub-types (set subType only, equation = ""): "discreteOutflow" (output from conveyor/oven), "conveyorLeakage" (leakage from conveyor), "queueOutflow" (output from queue), "queueOverflow" (overflow from full queue) +- Variable sub-types: "delayVariable" (plain variable whose equation uses a DELAY or SMTH builtin function) +- additionalProperties fields by subType: + * conveyor/oven: {processTime (required), capacity?, inflowLimit?, fillTime? (oven only), cleanTime? (oven only), sample?, arrest?} + * conveyorLeakage: {leakFraction? (units 1/time_unit when exponential, dimensionless otherwise), exponential?, leakZoneStart?, leakZoneEnd?, leakIntegers?, ignorePrevZones?, forceLeakFraction?} + * queue: {fifoEnabled?, oneAtATime?, splitBatches?, discrete?, roundRobin?, queueOutflowPriority?, purgeEq?, overflow?} + * inflow to conveyor (regular flow): {spreadFlow? ("none"|"even"|"destination"|"distribution"|"source"), distribEq? (required when spreadFlow="distribution")} + +Filtering: +- variableNames filter matches base names (e.g., "cost" matches "Module_1.cost", "Module_2.cost", and "cost") +- moduleName filter gets all variables from a specific module (by name prefix) +- usedInEquation filter finds all variables whose equations reference a given variable (case-insensitive, matches XMILE format with underscores) +- subType filter gets all variables with a specific discrete-entity sub-type (e.g., filter all queues or all conveyors)`, + supportedModes: ['sfd', 'cld'], + inputSchema: z.object({ + section: z.enum(['specs', 'variables', 'relationships', 'modules']).describe('Which section to read'), + filter: z.object({ + variableNames: z.array(z.string()).optional().describe('Filter variables by base name (matches both qualified and unqualified names, e.g., "cost" matches "Module_1.cost", "Module_2.cost", and "cost")'), + variableType: z.enum(['stock', 'flow', 'variable']).optional().describe('Filter variables by type'), + subType: z.enum(['queue', 'oven', 'conveyor', 'discreteOutflow', 'conveyorLeakage', 'queueOutflow', 'queueOverflow', 'delayVariable']).optional().describe('Filter variables by sub-type (e.g., find all conveyors, all queues, or all delay variables)'), + moduleName: z.string().optional().describe('Filter variables by module (e.g., "Module_Name" - variable names are module-qualified as Module_Name.variable_name)'), + usedInEquation: z.string().optional().describe('Find variables whose equations reference this variable (case-insensitive). Searches in both equation and arrayEquations fields.'), + relationshipFrom: z.string().optional().describe('Filter relationships by source variable'), + relationshipTo: z.string().optional().describe('Filter relationships by target variable'), + limit: z.number().optional().describe('Limit number of results returned (default: 500)') + }).optional().describe('Optional filters for variables/relationships/modules') + }), + handler: async ({ section, filter }) => { + try { + const sessionTempDir = sessionManager.getSessionTempDir(sessionId); + const modelPath = join(sessionTempDir, 'model.sdjson'); + + if (!existsSync(modelPath)) { + return createErrorResponse('Error: Model file not found. The model may not have exceeded the token limit yet.'); + } + + const modelContent = readFileSync(modelPath, 'utf-8'); + const model = JSON.parse(modelContent); + + const norm = s => s.toLowerCase().replace(/[ _]/g, '_'); + const limit = filter?.limit || 500; + let result = {}; + + switch (section) { + case 'specs': + result = model.specs || {}; + break; + + case 'variables': + let variables = model.variables || []; + + // Apply filters (case-insensitive, spaces and underscores treated as equivalent) + if (filter?.variableNames && filter.variableNames.length > 0) { + const normFilterNames = filter.variableNames.map(name => norm(name)); + variables = variables.filter(v => { + if (normFilterNames.includes(norm(v.name))) return true; + const baseName = v.name.includes('.') ? v.name.split('.').pop() : v.name; + return normFilterNames.includes(norm(baseName)); + }); + } + if (filter?.variableType) { + variables = variables.filter(v => v.type === filter.variableType); + } + if (filter?.subType) { + variables = variables.filter(v => v.subType === filter.subType); + } + if (filter?.moduleName) { + const normModule = norm(filter.moduleName); + variables = variables.filter(v => norm(v.name).startsWith(normModule + '.')); + } + if (filter?.usedInEquation) { + const searchTerm = norm(filter.usedInEquation); + variables = variables.filter(v => { + if (v.equation && norm(v.equation).includes(searchTerm)) { + return true; + } + if (v.arrayEquations && Array.isArray(v.arrayEquations)) { + return v.arrayEquations.some(ae => + ae.equation && norm(ae.equation).includes(searchTerm) + ); + } + return false; + }); + } + + const total = variables.length; + variables = variables.slice(0, limit); + + variables = variables.map(v => ({ + ...v, + name: v.name.replace(/ /g, '_') + })); + + result = { + variables, + total, + returned: variables.length, + truncated: total > limit + }; + break; + + case 'relationships': + let relationships = model.relationships || []; + + if (filter?.relationshipFrom) { + const normFrom = norm(filter.relationshipFrom); + relationships = relationships.filter(r => norm(r.from) === normFrom); + } + if (filter?.relationshipTo) { + const normTo = norm(filter.relationshipTo); + relationships = relationships.filter(r => norm(r.to) === normTo); + } + + const totalRels = relationships.length; + relationships = relationships.slice(0, limit); + + result = { + relationships, + total: totalRels, + returned: relationships.length, + truncated: totalRels > limit + }; + break; + + case 'modules': + let modules = model.modules || []; + + if (filter?.moduleName) { + const normModule = norm(filter.moduleName); + modules = modules.filter(m => norm(m.name) === normModule); + } + + result = { + modules, + total: modules.length + }; + break; + } + + return createSuccessResponse(result); + } catch (error) { + return createErrorResponse(`Failed to read model section: ${error.message}`, error); + } + } + }; +} + +/** + * Load the on-disk model for the session, applying a mutation, then push to client. + * Shared by all per-section edit tools. + * + * @param {Object} args + * @param {Object} args.sessionManager + * @param {string} args.sessionId + * @param {Function} args.sendToClient + * @param {string} args.section - For the response message + * @param {string} args.operation - For the response message + * @param {Function} args.mutate - (model) => string|null; return error message to abort + */ +async function applyEdit({ sessionManager, sessionId, sendToClient, section, operation, mutate }) { + const session = sessionManager.getSession(sessionId); + if (!session) { + throw new Error(`Session not found: ${sessionId}`); + } + + const sessionTempDir = sessionManager.getSessionTempDir(sessionId); + const modelPath = join(sessionTempDir, 'model.sdjson'); + + if (!existsSync(modelPath)) { + return createErrorResponse('Error: Model file not found. Call get_current_model to get it.'); + } + + const modelContent = readFileSync(modelPath, 'utf-8'); + const model = JSON.parse(modelContent); + + const mutationError = mutate(model); + if (mutationError) { + return createErrorResponse(mutationError); + } + + if (!model.variables || !Array.isArray(model.variables)) { + return createErrorResponse('Model validation failed: model.variables must be an array.'); + } + + if (!model.relationships || !Array.isArray(model.relationships)) { + return createErrorResponse('Model validation failed: model.relationships must be an array.'); + } + + const updateRequestId = generateRequestId('model'); + await sendToClient(createUpdateModelMessage(sessionId, updateRequestId, model)); + + const updatePromise = new Promise((resolve, reject) => { + const timeout = setTimeout(() => { + reject(new Error('Update model timeout: Client did not respond within 30 seconds')); + }, 30000); + + if (!session.pendingModelRequests) { + session.pendingModelRequests = new Map(); + } + session.pendingModelRequests.set(updateRequestId, { resolve, reject, timeout }); + }); + + const clientResult = await updatePromise; + const parsed = UpdateModelResponseSchema.parse(clientResult); + + const { issues } = sessionManager.updateClientModel(sessionId, parsed); + + return createSuccessResponse({ + message: `Successfully edited ${section} section (${operation} operation). The model has been validated, processed, and sent to the client.`, + ...(issues && { issues }) + }); +} + +function specsMutator(data) { + return (model) => { + model.specs = model.specs || {}; + if (data.startTime !== undefined) model.specs.startTime = data.startTime; + if (data.stopTime !== undefined) model.specs.stopTime = data.stopTime; + if (data.dt !== undefined) model.specs.dt = data.dt; + if (data.timeUnits !== undefined) model.specs.timeUnits = data.timeUnits; + + if (data.arrayDimensions !== undefined) { + if (Array.isArray(data.arrayDimensions)) { + for (const dim of data.arrayDimensions) { + if (!dim.type || !dim.name || dim.size === undefined || !Array.isArray(dim.elements)) { + return `Error: Array dimension "${dim.name || 'unknown'}" is missing required fields. All dimensions must have: type ("numeric" or "labels"), name (singular, alphanumeric), size (positive integer), and elements (array of element names).`; + } + if (dim.type !== 'numeric' && dim.type !== 'labels') { + return `Error: Array dimension "${dim.name}" has invalid type "${dim.type}". Must be "numeric" or "labels".`; + } + if (typeof dim.size !== 'number' || dim.size <= 0) { + return `Error: Array dimension "${dim.name}" size must be a positive integer, got: ${dim.size}`; + } + if (dim.elements.length !== dim.size) { + return `Error: Array dimension "${dim.name}" has size=${dim.size} but elements array has ${dim.elements.length} items. They must match.`; + } + } + } + model.specs.arrayDimensions = data.arrayDimensions; + } + return null; + }; +} + +function variablesMutator(operation, data) { + return (model) => { + model.variables = model.variables || []; + if (operation === 'add') { + if (!Array.isArray(data)) { + return 'Error: For add operation, data must be an array of variable objects. Example: [{name: "var1", type: "stock", equation: "100"}]'; + } + for (const v of data) { if (v.name) v.name = normName(v.name); } + const errors = []; + for (let i = 0; i < data.length; i++) { + const v = data[i]; + const varLabel = data.length > 1 ? `Variable ${i + 1} (${v.name || 'unnamed'})` : `Variable "${v.name || 'unnamed'}"`; + + if (!v.name || !v.type) { + errors.push(`${varLabel}: Missing required fields. Must have "name" and "type".`); + } else if (!['stock', 'flow', 'variable'].includes(v.type)) { + errors.push(`${varLabel}: Invalid type "${v.type}". Must be "stock", "flow", or "variable".`); + } + } + + if (errors.length > 0) { + return `Error adding ${data.length} variable(s):\n\n${errors.join('\n')}\n\nProvide an array of variable objects: [{name: "var1", type: "stock", equation: "100"}, {name: "var2", type: "variable", equation: "20"}]`; + } + + model.variables.push(...data); + } else if (operation === 'update') { + if (!Array.isArray(data)) { + return 'Error: For update operation, data must be an array of variable objects. Example: [{name: "Population", equation: "2000"}]'; + } + for (const update of data) { + const varName = normName(update.name); + update.name = varName; + if (update.newName) update.newName = normName(update.newName); + if (!varName) { + return 'Error: Must specify "name" field to update a variable'; + } + const index = model.variables.findIndex(v => normSearch(v.name) === normSearch(varName)); + if (index >= 0) { + const oldVariable = model.variables[index]; + const oldName = oldVariable.name; + + const isRenamed = update.newName && update.newName !== oldName; + + if (isRenamed) { + const newName = update.newName; + const oldNameXMILE = oldName.replace(/ /g, '_'); + const newNameXMILE = newName.replace(/ /g, '_'); + + const varRegex = new RegExp(`\\b${oldNameXMILE.replace(/[.*+?^${}()|[\]\\]/g, '\\$&')}\\b`, 'gi'); + + for (const variable of model.variables) { + if (variable.equation && varRegex.test(variable.equation)) { + variable.equation = variable.equation.replace(varRegex, newNameXMILE); + } + + if (variable.arrayEquations && Array.isArray(variable.arrayEquations)) { + for (const ae of variable.arrayEquations) { + if (ae.equation && varRegex.test(ae.equation)) { + ae.equation = ae.equation.replace(varRegex, newNameXMILE); + } + } + } + + if (variable.additionalProperties && typeof variable.additionalProperties === 'object') { + for (const [key, val] of Object.entries(variable.additionalProperties)) { + if (typeof val === 'string' && varRegex.test(val)) { + variable.additionalProperties[key] = val.replace(varRegex, newNameXMILE); + } + } + } + } + + update.name = newName; + delete update.newName; + } + + model.variables[index] = { ...model.variables[index], ...update }; + } else { + return `Error: Variable "${varName}" not found`; + } + } + } else if (operation === 'remove') { + if (!Array.isArray(data)) { + return 'Error: For remove operation, data must be an array of objects with name. Example: [{name: "var1"}, {name: "var2"}]'; + } + const normalizedRemoveNames = data.map(item => normSearch(item?.name)); + model.variables = model.variables.filter(v => !normalizedRemoveNames.includes(normSearch(v.name))); + } + return null; + }; +} + +function relationshipsMutator(operation, data) { + return (model) => { + model.relationships = model.relationships || []; + if (!Array.isArray(data)) { + return `Error: For ${operation} operation, data must be an array of relationship objects. Example: [{from: "var1", to: "var2", polarity: "+"}]`; + } + for (const r of data) { + r.from = normName(r.from); + r.to = normName(r.to); + if (!r.from || !r.to) { + return 'Error: Relationships must have "from" and "to" fields'; + } + } + + if (operation === 'add') { + for (const r of data) { + if (r.polarity !== undefined && !['+', '-'].includes(r.polarity)) { + return `Error: Relationship polarity must be "+" or "-", got "${r.polarity}"`; + } + } + model.relationships.push(...data); + } else if (operation === 'update') { + for (const update of data) { + const index = model.relationships.findIndex(r => normSearch(r.from) === normSearch(update.from) && normSearch(r.to) === normSearch(update.to)); + if (index >= 0) { + model.relationships[index] = { ...model.relationships[index], ...update }; + } else { + return `Error: Relationship from "${update.from}" to "${update.to}" not found`; + } + } + } else if (operation === 'remove') { + model.relationships = model.relationships.filter(r => + !data.some(rem => normSearch(rem.from) === normSearch(r.from) && normSearch(rem.to) === normSearch(r.to)) + ); + } + return null; + }; +} + +function modulesMutator(operation, data) { + return (model) => { + model.modules = model.modules || []; + if (operation === 'update') { + if (!Array.isArray(data)) { + return 'Error: For update operation, data must be an array of module objects. Example: [{name: "Module1", parentModule: null}]'; + } + for (const m of data) { + m.name = normName(m.name); + if (!m.name || m.parentModule === undefined) { + return 'Error: Modules must have "name" and "parentModule" fields'; + } + } + model.modules = data; + } else if (operation === 'add') { + if (!Array.isArray(data)) { + return 'Error: For add operation, data must be an array of module objects. Example: [{name: "Module1", parentModule: null}]'; + } + for (const m of data) { + m.name = normName(m.name); + if (!m.name || m.parentModule === undefined) { + return 'Error: Modules must have "name" and "parentModule" fields'; + } + } + model.modules.push(...data); + } else if (operation === 'remove') { + if (!Array.isArray(data)) { + return 'Error: For remove operation, data must be an array of objects with name. Example: [{name: "Module1"}, {name: "Module2"}]'; + } + const normalizedRemoveModules = data.map(item => normSearch(item?.name)); + model.modules = model.modules.filter(m => !normalizedRemoveModules.includes(normSearch(m.name))); + } + return null; + }; +} + +/** + * Edit variables: add, update (including rename), or remove. + */ +export function createEditVariablesTool(sessionManager, sessionId, sendToClient) { + return { + description: `Edit the variables section of the model. data is always an array of variable objects. Every object must include 'name'. Other fields are interpreted by operation: + +- add: every object must also include 'type' (stock|flow|variable); other fields populate the new variable +- update: 'name' locates the existing variable; the other fields you include replace those values. To rename, also pass 'newName' — the tool then rewrites ALL references to the old name across every equation, arrayEquations entry, and equation-valued additionalProperties field (processTime, capacity, leakFraction, purgeEq, etc.) in every variable across every module, matching case-insensitively in XMILE format (with underscores). To change additionalProperties, provide the COMPLETE replacement object. +- remove: only 'name' is read; all other fields are ignored + +CRITICAL EQUATION RULES: +- XMILE naming: replace spaces with underscores in variable references inside equations ("birth_rate" not "birth rate") +- Every variable MUST have either 'equation' OR 'arrayEquations' (never both, never neither). For arrayed STOCKS, always use arrayEquations to give per-element initial values. +- NEVER embed numerical constants directly in equations — create separate named variables for constants +- Stock-flow constraint: a flow can NEVER appear in BOTH inflows AND outflows of the same stock +- SUM function syntax: always use asterisk for the dimension being summed, e.g. SUM(Revenue[*]) — every SUM equation must contain at least one * + +CRITICAL MODULE RULES: +- Variable names use ONLY the immediate owning module as a prefix: "ModuleName.variableName" +- NEVER use the full hierarchy path in a variable name (WRONG: "Company.Sales.revenue", CORRECT: "Sales.revenue") +- Cross-module references require ghost variables: set crossLevelGhostOf to the source variable name, leave equation empty + +CRITICAL ARRAY RULES: +- Array dimensions must be defined in specs.arrayDimensions BEFORE any variable references them (use edit_specs first) +- For arrayed variables, set 'dimensions' to the list of dimension names that exist in specs.arrayDimensions +- If all elements share one formula, provide 'equation' only; if elements differ, provide 'arrayEquations' for every element and leave 'equation' empty + +CRITICAL SUBTYPE RULES (queue/oven/conveyor/leakage/discreteOutflow/queueOutflow/queueOverflow): +- Use sub-types ONLY when the model already has discrete-entity semantics or the user explicitly requests them — they add significant complexity +- Stock sub-types: set subType AND additionalProperties; equation is still the initial value (like a regular stock) +- Flow sub-types: set subType only and leave equation as "" — the flow is computed automatically, do NOT write an equation +- All sub-type settings (processTime, capacity, leakFraction, etc.) go in additionalProperties, NEVER embedded in equations +- Every variable referenced in an additionalProperties equation REQUIRES a relationship arrow FROM that variable TO the element +- CONVEYOR WIRING: every conveyorLeakage flow MUST appear in the outflows of its source conveyor AND in the inflows of its destination. NEVER split a conveyor outflow with auxiliary arithmetic — route directly to one destination. +- queueOverflow flows require overflow: true on the queue's additionalProperties +- Use conveyor (not plain stock) when entities must spend a minimum/fixed duration in a stage; use a plain stock when residence time is exponentially distributed (first-order delay) + +After editing, the model is validated and sent to the client for processing before the session state is updated.`, + supportedModes: ['sfd', 'cld'], + minModelTokens: config.agentTargetedEditingMinimum, + inputSchema: z.object({ + operation: z.enum(['add', 'update', 'remove']).describe('Operation to perform'), + data: z.array(z.object({ + ...variableBase, + newName: z.string().describe(LLMWrapper.SCHEMA_STRINGS.name).optional() + }).partial().required({ name: true })).describe('Array of variable objects. Each requires name; for add also requires type; for update fields you include replace those values (pass newName to rename); for remove only name is read.') + }), + handler: async ({ operation, data }) => { + try { + return await applyEdit({ + sessionManager, sessionId, sendToClient, + section: 'variables', operation, + mutate: variablesMutator(operation, data) + }); + } catch (error) { + return createErrorResponse(`Failed to edit variables: ${error.message}`, error); + } + } + }; +} + +/** + * Edit relationships: add, update, or remove. + */ +export function createEditRelationshipsTool(sessionManager, sessionId, sendToClient) { + return { + description: `Edit the relationships section of the model. A relationship is a causal arrow from one variable to another with a polarity (+ or -). data is always an array of relationship objects. Each object must include 'from' and 'to'. Other fields are interpreted by operation: + +- add: include polarity and (optionally) reasoning/polarityReasoning for each new relationship +- update: 'from' and 'to' locate the existing relationship; other fields you include replace those values +- remove: only 'from' and 'to' are read; other fields are ignored + +CRITICAL: Every variable referenced inside an additionalProperties equation on a discrete-entity element (e.g. processTime, capacity, leakFraction, purgeEq, queueOutflowPriority) REQUIRES a relationship arrow FROM that referenced variable TO the element.`, + supportedModes: ['sfd', 'cld'], + minModelTokens: config.agentTargetedEditingMinimum, + inputSchema: z.object({ + operation: z.enum(['add', 'update', 'remove']).describe('Operation to perform'), + data: z.array( + z.object(relationshipBase).partial().required({ from: true, to: true }) + ).describe('Array of relationship objects. Each requires from and to; for add also requires polarity; for update fields you include replace those values; for remove only from and to are read.') + }), + handler: async ({ operation, data }) => { + try { + return await applyEdit({ + sessionManager, sessionId, sendToClient, + section: 'relationships', operation, + mutate: relationshipsMutator(operation, data) + }); + } catch (error) { + return createErrorResponse(`Failed to edit relationships: ${error.message}`, error); + } + } + }; +} + +/** + * Edit simulation specs (startTime, stopTime, dt, timeUnits, arrayDimensions). + */ +export function createEditSpecsTool(sessionManager, sessionId, sendToClient) { + return { + description: `Update the simulation specs (startTime, stopTime, dt, timeUnits, arrayDimensions). Only fields you include in data are changed; omitted fields keep their current values. + +CRITICAL: When updating arrayDimensions, provide the COMPLETE array — it replaces the entire arrayDimensions list. Each dimension requires all four fields (type, name, size, elements) and elements.length MUST equal size. Define dimensions here BEFORE any variable references them via its 'dimensions' field.`, + supportedModes: ['sfd', 'cld'], + minModelTokens: config.agentTargetedEditingMinimum, + inputSchema: z.object({ + data: z.object(simSpecsBase).partial().describe('Spec fields to update. Only included fields are changed.') + }), + handler: async ({ data }) => { + try { + return await applyEdit({ + sessionManager, sessionId, sendToClient, + section: 'specs', operation: 'update', + mutate: specsMutator(data) + }); + } catch (error) { + return createErrorResponse(`Failed to edit specs: ${error.message}`, error); + } + } + }; +} + +/** + * Edit modules: add, update (replace entire hierarchy), or remove. + */ +export function createEditModulesTool(sessionManager, sessionId, sendToClient) { + return { + description: `Edit the module hierarchy. data is always an array of module objects. Each object must include 'name'. Other fields are interpreted by operation: + +- add: include 'parentModule' (string parent name, or null for a root module) +- update: data is the COMPLETE replacement array — every module you want kept must be present with its parentModule; modules omitted are dropped +- remove: only 'name' is read; other fields are ignored + +IMPORTANT: The modules array only defines the hierarchical structure. It does NOT control which variables belong to a module — variable membership is determined by the variable name prefix ("Finance.revenue" belongs to Finance). To move a variable between modules, edit the variable's name via edit_variables (operation: update, newName: "NewModule.variableName").`, + supportedModes: ['sfd', 'cld'], + minModelTokens: config.agentTargetedEditingMinimum, + inputSchema: z.object({ + operation: z.enum(['add', 'update', 'remove']).describe('Operation to perform'), + data: z.array( + LLMWrapper.moduleSchema().partial().required({ name: true }) + ).describe('Array of module objects. Each requires name; for add/update also include parentModule; for remove only name is read.') + }), + handler: async ({ operation, data }) => { + try { + return await applyEdit({ + sessionManager, sessionId, sendToClient, + section: 'modules', operation, + mutate: modulesMutator(operation, data) + }); + } catch (error) { + return createErrorResponse(`Failed to edit modules: ${error.message}`, error); + } + } + }; +} diff --git a/agent/tools/builtin/toolHelpers.js b/agent/tools/builtin/toolHelpers.js new file mode 100644 index 00000000..434d2acb --- /dev/null +++ b/agent/tools/builtin/toolHelpers.js @@ -0,0 +1,118 @@ +/** + * Helper utilities shared across built-in tools + */ +import { tool as sdkTool } from '@anthropic-ai/claude-agent-sdk'; +import { readdirSync, readFileSync, existsSync } from 'fs'; +import { join } from 'path'; +import logger from '../../../utilities/logger.js'; + +/** + * Wrapper for the SDK tool() function for use with Claude Agent SDK + * Note: inputSchema should be a Zod schema + * @param {Object} config - Tool configuration + * @param {string} config.name - Tool name + * @param {string} config.description - Tool description + * @param {Object} config.inputSchema - Zod schema for input validation + * @param {Function} config.execute - Tool execution function + * @returns {Object} SDK tool instance + */ +export function tool({ name, description, inputSchema, execute }) { + return sdkTool(name, description, inputSchema, execute); +} + +// Keys that are valid JSON Schema but not supported by the Gemini function-declaration schema. +const GEMINI_UNSUPPORTED_KEYS = new Set([ + '$schema', + 'additionalProperties', + 'propertyNames', + 'exclusiveMinimum', // handled below for numeric form; boolean form is dropped + 'exclusiveMaximum', +]); + +export function sanitizeSchemaForGemini(schema) { + if (!schema || typeof schema !== 'object') return schema; + if (Array.isArray(schema)) return schema.map(sanitizeSchemaForGemini); + + const out = {}; + for (const [k, v] of Object.entries(schema)) { + if (k === 'exclusiveMinimum' && typeof v === 'number') { + out.minimum = v; + } else if (k === 'exclusiveMaximum' && typeof v === 'number') { + out.maximum = v; + } else if (GEMINI_UNSUPPORTED_KEYS.has(k)) { + // drop — Gemini rejects these fields + } else { + out[k] = sanitizeSchemaForGemini(v); + } + } + return out; +} + +/** + * Generate a unique request ID for async operations + * @param {string} prefix - Prefix for the request ID (e.g., 'feedback', 'tool') + * @returns {string} Unique request ID + */ +export function generateRequestId(prefix = 'request') { + return `${prefix}_${Date.now()}_${Math.random().toString(36).substring(7)}`; +} + +/** + * Create a standardized success response + * @param {string|Object} result - The result to return (string or object to be stringified) + * @returns {Object} Standardized success response + */ +export function createSuccessResponse(result) { + const text = typeof result === 'string' ? result : JSON.stringify(result); + return { + content: [{ type: 'text', text }], + isError: false + }; +} + +/** + * Load behavior content from the most recent variable_data JSON file in the session temp dir, + * filtered to the given run IDs (or the last run ID in the file if none specified). + * Returns undefined if no variable_data file exists. + * @param {string} sessionTempDir - Path to the session temp directory + * @param {string[]} [runIds] - Optional run IDs to include; defaults to the last run in the file + * @returns {string|undefined} JSON string of filtered run data, or undefined + */ +export function loadBehaviorContent(sessionTempDir, runIds) { + if (!existsSync(sessionTempDir)) return undefined; + + const files = readdirSync(sessionTempDir) + .filter(f => f.startsWith('variable_data_') && f.endsWith('.json')) + .sort(); + + if (files.length === 0) return undefined; + + const latest = JSON.parse(readFileSync(join(sessionTempDir, files[files.length - 1]), 'utf-8')); + const allRunIds = Object.keys(latest); + if (allRunIds.length === 0) return undefined; + + const selected = (runIds && runIds.length > 0) + ? runIds.filter(id => id in latest) + : [allRunIds[allRunIds.length - 1]]; + + if (selected.length === 1) return JSON.stringify(latest[selected[0]]); + + const filtered = Object.fromEntries(selected.map(id => [id, latest[id]])); + return JSON.stringify(filtered); +} + +/** + * Create a standardized error response + * @param {string} errorMessage - The error message to return + * @param {Error} error - Optional error object for logging + * @returns {Object} Standardized error response + */ +export function createErrorResponse(errorMessage, error = null) { + if (error) { + logger.debug('Tool error:', error); + } + return { + content: [{ type: 'text', text: errorMessage }], + isError: true + }; +} diff --git a/agent/utilities/AgentConfigurationManager.js b/agent/utilities/AgentConfigurationManager.js new file mode 100644 index 00000000..4546ff77 --- /dev/null +++ b/agent/utilities/AgentConfigurationManager.js @@ -0,0 +1,314 @@ +import { readFileSync } from 'fs'; +import logger from '../../utilities/logger.js'; + +/** + * AgentConfigurationManager + * Loads and manages agent configuration from Markdown files + * + * Key Features: + * - Loads agent configuration from MD files (e.g., socrates.md, merlin.md) + * - Provides system prompts for Claude Agent SDK + * - NO filesystem writes - all modifications in memory only + */ +export class AgentConfigurationManager { + static UNIVERSAL_AGENT_INSTRUCTIONS = +`# System Dynamics Modeling Assistant + +## CRITICAL: Text Generation +- NEVER use emojis +- NEVER use LaTeX + +## ABSOLUTE RULE: NEVER mention, name, describe, or reference any specific feedback loop unless it was returned by get_feedback_information in the current session.** Do not infer loops from variable names, equations, or SD knowledge. If you have not called get_feedback_information, you have NO knowledge of the loops — treat them as completely unknown. Call get_feedback_information immediately when a user asks about loops or to understand the model. + +## CRITICAL: Model Type Enforcement +Each session works with ONE model type: either CLD (Causal Loop Diagram) or SFD (Stock Flow Diagram). +The model type is set at session initialization and CANNOT be changed. +NEVER switch between CLD and SFD during a session. + +## CRITICAL: Feedback Loop Analysis and Model Understanding +**ABSOLUTE RULE: ALWAYS call get_feedback_information before discuss_model_with_seldon, discuss_model_across_runs, or generate_ltm_narrative — no exceptions.** The model must be run first; these tools require it and will hallucinate without it. + +- When feedback data is available use discuss_model_with_seldon to explain model behavior to users. + +## CRITICAL: Never Directly Edit model.sdjson +NEVER directly modify model.sdjson on disk by any means. +All model changes MUST go through the designated model tools (generate_quantitative_model, generate_qualitative_model, edit_variables, edit_relationships, edit_specs, edit_modules, etc.). +Direct file edits bypass validation, client synchronization, and session state - they will corrupt the model. + +## CRITICAL: Automatic Model Validation +After ANY tool use that modifies the model (generate_quantitative_model, generate_qualitative_model, edit_variables, edit_relationships, edit_specs, edit_modules), you MUST: +1. Immediately use get_current_model to retrieve the updated model +2. Check that returned model for errors and warnings +3. If ERRORS are present: You MUST fix them before proceeding. Attempt to fix them yourself first. If you cannot fix them, ask the user to fix them. +4. If WARNINGS are present: You SHOULD fix them before proceeding. Attempt to fix them yourself first. If you cannot fix them, ask the user to fix them. +5. Do NOT continue with other tasks until all errors are resolved and warnings are addressed. + +## Using Seldon for Model Planning and Critique +Use discuss_model_with_seldon to critique model structure, validate approaches, understand causal mechanisms, and generate policy recommendations. Consult Seldon when facing complex modeling decisions. Always share feedback loop information with Seldon in all its forms. +`; + + static SFD_AGENT_INSTRUCTIONS = +`## CRITICAL: SFD Behavior +SFDs (Stock Flow Diagrams) are QUANTITATIVE: +- SFDs have equations and can be simulated to produce time series behavior +- Use run_model, get_variable_data, and create_visualization for SFDs only +- ALWAYS check that stocks and variables that represent physical quantities (population, inventory, resources, etc.) cannot go negative +- Add appropriate constraints to prevent negative values where they are physically impossible +- Stocks often go negative when there is no first order control on their flows. When a stock unexpectedly goes negative, add first order control structures that naturally slow outflows as the stock approaches zero (e.g., fractional outflow rates proportional to the stock level) +- AVOID using MIN/MAX functions to clamp stocks to zero - they mask the underlying structural problem. Fix the model structure instead. +- Unit warnings are NOT cosmetic, they are important and MUST be fixed +- Use // for safe division (e.g., a // b) - this divides a by b but returns 0 when b is zero, preventing model crashes when a denominator can reach zero +- Use XMILE builtin function names: SMTH1, SMTH3, DELAY1, DELAY3, etc. — NOT SMOOTH1, SMOOTH3, or other non-XMILE variants +- NEVER embed numerical constants directly in equations with other variables. ALWAYS create separate named variables for all constants. + +## CRITICAL: Unknown Run References +If the user references a run by name or ID that you have not seen in this session, call get_run_info before doing anything else. Do not assume the run does not exist and do not ask the user to clarify — check first. + +## CRITICAL: Tool Sequencing After run_model +**get_feedback_information and get_variable_data MUST always be called AFTER run_model completes - never in the same parallel batch as run_model.** +run_model produces the data these tools depend on. Always wait for run_model to finish before calling them. + +## CRITICAL: Feedback Information Recovery Protocol +When feedback analysis tools fail due to missing feedback information: +1. FIRST: Run the model again using run_model() to generate fresh feedback data +2. SECOND: Retry the feedback analysis (first: get_feedback_information, then: discuss_model_with_seldon, etc.) +3. If STILL no feedback information after running: + - Inform user that no feedback loops are currently being tracked + - Explain: "To enable feedback loop analysis, please enable it in your software" +4. NEVER give up after first failure - always attempt to run model first + +## CRITICAL: Data Inspection Before Interpretation +Before interpreting simulation results or describing variable behavior, you MUST call get_variable_data and explicitly inspect the numerical values (using read_file). Never assume behavior based on variable names or expected causal outcomes. + +## CRITICAL: Visualization Requests +When a user requests a visualization: +- ALWAYS use the current model as-is without any modifications +- NEVER modify, update, or change the existing model structure or parameters to create visualizations +- If the current model cannot produce the requested visualization, inform the user rather than modifying the model +- Visualizations should reflect the current state of the model, not an idealized or modified version + +**ABSOLUTE RULE: ALL plotting and charting MUST go through the create_visualization tool — no exceptions.** +NEVER write Python plotting code yourself. NEVER hand-author a matplotlib script and run it manually. +The create_visualization tool handles all chart types (time_series, comparison, phase_portrait, feedback_dominance) and AI-custom plots via useAICustom=true. If you think you need to write plotting code directly, you are wrong — use create_visualization instead. + +**CRITICAL: Never fabricate data files for create_visualization.** +Always pass a filePath that came from get_variable_data or get_feedback_information. +Never write, generate, or construct a data file yourself and pass it to create_visualization — the visualization must reflect real simulation output, not invented data. + +**How to plot time series, phase portraits, or comparisons:** +1. Call get_variable_data — it returns a filePath pointing to the written data file +2. Pass that filePath directly to create_visualization + +**How to plot feedback loop dominance (stacked area of loop percentages):** +1. Call get_feedback_information — it returns a filePath pointing to feedback.json +2. Pass that filePath to create_visualization with type: "feedback_dominance" + +**How to overlay dominant-loop periods on a time-series plot:** +1. Ensure get_feedback_information has already been called (feedback.json exists) +2. Pass the variable data filePath to create_visualization with options.includeFeedbackContext: true + +## Feedback Loop Dominance Visualization Style +When asked to visualize feedback loop dominance alongside a variable's behavior, use the includeFeedbackContext: true option on the create_visualization tool with a time_series type. This overlays colored background bands keyed to the dominant loop in each period automatically - **NOT** a stacked area chart of loop percentages. + +Reserve the feedback_dominance visualization type (stacked area) for when the user explicitly wants the quantitative percentage breakdown of loop contributions over time. +`; + + static CLD_AGENT_INSTRUCTIONS = +`## CRITICAL: CLD Behavior +CLDs (Causal Loop Diagrams) are QUALITATIVE ONLY: +- CLDs show causal structure and feedback loops but have NO quantitative behavior +- NEVER run simulations on CLDs (no run_model, no get_variable_data) +- NEVER create visualizations for CLDs (no create_visualization) +- CLDs are for conceptual exploration and understanding causal relationships only +- CLDs help identify feedback loop structure before building quantitative models +`; + + static REQUIRED_FRONTMATTER_FIELDS = ['name', 'agent_mode']; + + /** + * @param {{ path: string } | { markdownContent: string }} agentConfig + */ + constructor({ path, markdownContent } = {}) { + if (markdownContent !== undefined) { + this.configPath = null; + const { metadata, content } = AgentConfigurationManager.parseContent(markdownContent); + this.#validateFrontmatter(metadata); + this.#init(metadata, content); + } else { + this.configPath = path; + const { metadata, content } = this.#loadFile(path); + this.#init(metadata, content); + } + } + + #validateFrontmatter(metadata) { + const missing = AgentConfigurationManager.REQUIRED_FRONTMATTER_FIELDS.filter(f => !metadata[f]); + if (missing.length > 0) { + throw new Error(`Invalid agent configuration: missing required frontmatter fields: ${missing.join(', ')}`); + } + } + + #init(metadata, content) { + this.metadata = metadata; + this.systemPrompt = content; + this.config = { + agent: { + name: metadata.name, + description: metadata.description, + version: metadata.version, + max_iterations: metadata.max_iterations || 20, + agent_mode: metadata.agent_mode || 'anthropic-sdk', + supported_modes: metadata.supported_modes || [] + } + }; + this.baseConfig = this.config.agent; + } + + static parseContent(fileContent) { + const normalized = fileContent.replace(/\r\n/g, '\n'); + const match = normalized.match(/^---\n([\s\S]*?)\n---\n([\s\S]*)$/); + + if (match) { + const metadata = AgentConfigurationManager.#parseSimpleYAML(match[1]); + return { metadata, content: match[2] }; + } + + logger.error('Agent configuration has no frontmatter, using defaults'); + return { + metadata: { + name: 'Unknown', + description: '', + version: '1.0', + max_iterations: 20, + agent_mode: 'anthropic-sdk', + supported_modes: [] + }, + content: fileContent + }; + } + + #loadFile(path) { + try { + const fileContent = readFileSync(path, 'utf8'); + return AgentConfigurationManager.parseContent(fileContent); + } catch (err) { + logger.error(`Failed to load config from ${path}:`, err); + throw new Error(`Configuration file not found or invalid: ${path}`); + } + } + + + static #parseSimpleYAML(yamlText) { + const metadata = {}; + const lines = yamlText.split('\n'); + let currentKey = null; + let currentArray = null; + + for (const line of lines) { + const trimmed = line.trim(); + if (!trimmed) continue; + + // Check for array item + if (trimmed.startsWith('- ') && currentArray) { + currentArray.push(trimmed.substring(2).trim()); + } + // Check for key-value pair + else if (trimmed.includes(':')) { + const colonIndex = trimmed.indexOf(':'); + const key = trimmed.substring(0, colonIndex).trim(); + const value = trimmed.substring(colonIndex + 1).trim(); + + if (value === '') { + // This might be starting an array + currentKey = key; + currentArray = []; + metadata[key] = currentArray; + } else { + // Simple value - remove quotes if present + let parsedValue = value.replace(/^["']|["']$/g, ''); + // Try to parse as number + if (!isNaN(parsedValue) && parsedValue !== '') { + parsedValue = Number(parsedValue); + } + metadata[key] = parsedValue; + currentKey = null; + currentArray = null; + } + } + } + + return metadata; + } + + /** + * Build system prompt with optional model type + * Combines universal instructions with agent-specific content + */ + buildSystemPrompt(mode = null) { + // Start with universal instructions + let prompt = AgentConfigurationManager.UNIVERSAL_AGENT_INSTRUCTIONS; + + // Add mode-specific instructions + if (mode === 'sfd') { + prompt += '\n' + AgentConfigurationManager.SFD_AGENT_INSTRUCTIONS; + } else if (mode === 'cld') { + prompt += '\n' + AgentConfigurationManager.CLD_AGENT_INSTRUCTIONS; + } + + // Add model type section if specified + if (mode) { + prompt += `\n\n## SESSION MODEL TYPE: ${mode.toUpperCase()}`; + prompt += `\nThis session is working with ${mode === 'cld' ? 'Causal Loop Diagrams (CLD)' : 'Stock Flow Diagrams (SFD)'}.`; + prompt += '\nYou must work exclusively with this model type for the entire session.'; + } + + // Append agent-specific content from the MD file + // Skip the duplicate universal instructions section if present in the MD file + let agentContent = this.systemPrompt; + + // Remove the universal instructions section from agent content if it exists + const universalSectionEnd = agentContent.indexOf('## SESSION MODEL TYPE:'); + if (universalSectionEnd === -1) { + // No MODEL TYPE section, check for the end of universal instructions + const seldonEnd = agentContent.indexOf('ALWAYS share feedback loop information'); + if (seldonEnd !== -1) { + const nextSection = agentContent.indexOf('\n\n##', seldonEnd); + if (nextSection !== -1) { + agentContent = agentContent.substring(nextSection); + } + } + } else { + // Find the next section after SESSION MODEL TYPE + const nextSection = agentContent.indexOf('\n\n##', universalSectionEnd + 20); + if (nextSection !== -1) { + agentContent = agentContent.substring(nextSection); + } + } + + prompt += agentContent; + + return prompt; + } + + getAgentName() { + return (this.metadata.name || 'agent').toLowerCase().replace(/[^a-z0-9]+/g, '_'); + } + + /** + * Get maximum iterations for agent conversation loop + * @returns {number} Maximum iterations (default: 20) + */ + getMaxIterations() { + return this.baseConfig?.max_iterations || 20; + } + + /** + * Returns the loop strategy: 'sdk' | 'manual'. + * Provider is a runtime option supplied by the client, not the agent definition. + */ + getAgentMode() { + const val = this.metadata.agent_mode; + if (val === 'sdk' || val === 'manual') return val; + return 'sdk'; + } +} diff --git a/agent/utilities/AgentEvalRunner.js b/agent/utilities/AgentEvalRunner.js new file mode 100644 index 00000000..9b49edb7 --- /dev/null +++ b/agent/utilities/AgentEvalRunner.js @@ -0,0 +1,376 @@ +import { readFileSync } from 'fs'; +import { join, dirname } from 'path'; +import { fileURLToPath } from 'url'; +import { AgentOrchestrator } from '../AgentOrchestrator.js'; +import { SessionManager } from './SessionManager.js'; +import SDJsonToXMILE from '../../utilities/SDJsonToXMILE.js'; +import PySDSimulator from '../../evals/utilities/simulator/PySDSimulator.js'; +import logger from '../../utilities/logger.js'; + +const __dirname = dirname(fileURLToPath(import.meta.url)); +const AGENT_CONFIG_DIR = join(__dirname, '../config'); + +const EVAL_MODE_INSTRUCTION = ` +## EVAL MODE: No User Present +You are running in automated evaluation mode. There is NO user. You MUST: +- Never ask the user questions or for clarification +- Never stop to request input or confirmation +- Make your best judgment and proceed autonomously +- Iterate until the task is fully complete +- If you are uncertain about a requirement, make a reasonable assumption and continue +`; + +/** + * Find all simple cycles in a directed graph using DFS. + * Each cycle is found exactly once (starting from its lexicographically-smallest node). + */ +export function findFeedbackLoops(relationships) { + const adj = {}; + for (const rel of (relationships || [])) { + if (!adj[rel.from]) adj[rel.from] = []; + adj[rel.from].push({ to: rel.to, polarity: rel.polarity || '+' }); + } + + const allNodes = [...new Set([ + ...Object.keys(adj), + ...(relationships || []).map(r => r.to) + ])].sort(); + + const nodeIndex = {}; + allNodes.forEach((n, i) => { nodeIndex[n] = i; }); + + const loops = []; + let loopCounter = 0; + + for (let startIdx = 0; startIdx < allNodes.length; startIdx++) { + const startNode = allNodes[startIdx]; + const path = [startNode]; + const pathPolarities = []; + const inPath = new Set([startNode]); + + function dfs(node) { + for (const { to, polarity } of (adj[node] || [])) { + if (to === startNode && path.length > 1) { + // Found a cycle back to start — record it + const cyclePolarities = [...pathPolarities, polarity]; + const negativeCount = cyclePolarities.filter(p => p === '-').length; + const loopPolarity = negativeCount % 2 === 0 ? '+' : '-'; + loopCounter++; + const links = []; + for (let i = 0; i < path.length; i++) { + links.push({ + from: path[i], + to: i + 1 < path.length ? path[i + 1] : startNode, + polarity: cyclePolarities[i] + }); + } + loops.push({ + identifier: `L${loopCounter}`, + name: `Loop ${loopCounter}`, + links, + polarity: loopPolarity + }); + } else if (!inPath.has(to) && nodeIndex[to] > startIdx) { + inPath.add(to); + path.push(to); + pathPolarities.push(polarity); + dfs(to); + path.pop(); + pathPolarities.pop(); + inPath.delete(to); + } + } + } + + dfs(startNode); + } + + return loops; +} + +/** + * Patch a markdown string's frontmatter. + * Replaces max_iterations and optionally agent_mode, then appends eval instructions. + */ +export function patchAgentConfig(markdownContent, agentMode) { + // Patch max_iterations to effectively unlimited + let patched = markdownContent.replace( + /^max_iterations:\s*\d+/m, + 'max_iterations: 9999' + ); + + // Optionally override agent_mode + if (agentMode) { + patched = patched.replace( + /^agent_mode:\s*.+/m, + `agent_mode: ${agentMode}` + ); + } + + // Append eval-mode instruction to the body (after closing ---) + const frontmatterEnd = patched.indexOf('\n---\n'); + if (frontmatterEnd !== -1) { + const insertAt = frontmatterEnd + 5; // after '\n---\n' + patched = patched.slice(0, insertAt) + EVAL_MODE_INSTRUCTION + patched.slice(insertAt); + } else { + patched += EVAL_MODE_INSTRUCTION; + } + + return patched; +} + +/** + * Resolve a pending request stored in a Map (pendingModelRequests or pendingFeedbackRequests). + * Clears the timeout and removes the entry before resolving/rejecting. + */ +function resolvePending(map, requestId, value) { + const pending = map?.get(requestId); + if (pending) { + clearTimeout(pending.timeout); + map.delete(requestId); + pending.resolve(value); + } +} + +function rejectPending(map, requestId, error) { + const pending = map?.get(requestId); + if (pending) { + clearTimeout(pending.timeout); + map.delete(requestId); + pending.reject(error); + } +} + +/** + * Run the agent to completion for eval purposes. + * + * @param {string} prompt - The user prompt + * @param {Object} currentModel - The current SD model (sdjson) + * @param {Object} parameters - Engine parameters including agentName, agentMode, provider, mode, + * problemStatement, backgroundKnowledge, feedbackContent + * @returns {{ lastModel: Object|null, explanation: string }} + */ +export async function runAgent(prompt, currentModel, parameters) { + const { + agentName = 'merlin', + agentMode, + provider = 'anthropic', + mode = 'sfd', + problemStatement, + backgroundKnowledge, + feedbackContent + } = parameters; + + // Derive base session mode (strip -discuss suffix) + const baseMode = mode.replace(/-discuss$/, ''); + + // 1. Load and patch agent config + const configPath = join(AGENT_CONFIG_DIR, `${agentName}.md`); + let markdownContent; + try { + markdownContent = readFileSync(configPath, 'utf-8'); + } catch (err) { + throw new Error(`Agent config not found: ${configPath}`); + } + markdownContent = patchAgentConfig(markdownContent, agentMode); + + // 2. Set up session + const sessionManager = new SessionManager({ disableCleanup: true }); + const sessionId = sessionManager.createSession({ readyState: 1, send: () => {} }); + sessionManager.initializeSession( + sessionId, + baseMode, + currentModel || { variables: [], relationships: [] }, + [], + { + supportsArrays: true, + supportsModules: true, + supportsSubTypes: false + }, + 'eval-client' + ); + + // 3. In-memory run storage + const storedRuns = new Map(); + let runCounter = 0; + + const textParts = []; + let resolveComplete; + let rejectComplete; + const completionPromise = new Promise((res, rej) => { + resolveComplete = res; + rejectComplete = rej; + }); + + // 4. Mock sendToClient + const sendToClient = async (message) => { + const session = sessionManager.getSession(sessionId); + + switch (message.type) { + case 'get_current_model': { + // setImmediate: sendToClient is awaited BEFORE the tool stores its resolver in the + // pending Map, so we must defer resolution until after the current call stack unwinds. + // Read from session (not the closure) so updates pushed via update_model are visible. + const gcmReqId = message.requestId; + setImmediate(() => { + const latestModel = sessionManager.getClientModel(sessionId) || { variables: [], relationships: [] }; + resolvePending(sessionManager.getSession(sessionId)?.pendingModelRequests, gcmReqId, latestModel); + }); + break; + } + + case 'update_model': { + const modelData = message.modelData; + const umReqId = message.requestId; + setImmediate(() => resolvePending(sessionManager.getSession(sessionId)?.pendingModelRequests, umReqId, modelData)); + break; + } + + case 'run_model': { + const model = sessionManager.getClientModel(sessionId); + let runId = `eval-run-${++runCounter}`; + try { + const xmileContent = SDJsonToXMILE(model, { + modelName: 'eval-model', + vendor: 'sd-ai-evals', + product: 'sd-ai-evals', + version: '1.0' + }); + + const varNames = (model?.variables || []) + .map(v => v.name?.replace(/\s+/g, '_')) + .filter(Boolean); + + if (varNames.length > 0) { + const sim = new PySDSimulator(xmileContent); + const results = await sim.simulate(varNames); + storedRuns.set(runId, results); + } else { + storedRuns.set(runId, {}); + } + } catch (err) { + logger.warn(`[AgentEvalRunner] Simulation failed for run ${runId}: ${err.message}`); + runId = `eval-run-failed-${runCounter}`; + storedRuns.set(runId, {}); + } + // run_model awaits simulation above, so sendToClient returns after the async work. + // The tool creates its promise immediately after sendToClient returns, so + // setImmediate fires after the resolver is in the Map. + const rmRunId = runId; + const rmReqId = message.requestId; + setImmediate(() => resolvePending(sessionManager.getSession(sessionId)?.pendingModelRequests, rmReqId, { runId: rmRunId })); + break; + } + + case 'get_run_info': { + const runs = Array.from(storedRuns.entries()).map(([id, data]) => ({ + id, + name: id, + variables: Object.keys(data).filter(k => k !== 'time') + })); + const griReqId = message.requestId; + setImmediate(() => resolvePending(sessionManager.getSession(sessionId)?.pendingModelRequests, griReqId, { runs })); + break; + } + + case 'get_variable_data': { + const { variableNames = [], runIds = [], detailed = false } = message; + const targetPoints = detailed ? 200 : 50; + const result = {}; + for (const runId of runIds) { + const runData = storedRuns.get(runId); + if (runData) { + result[runId] = {}; + const timeArr = runData.time; + if (timeArr && timeArr.length > targetPoints) { + const indices = Array.from({ length: targetPoints }, (_, i) => + Math.round(i * (timeArr.length - 1) / (targetPoints - 1)) + ); + result[runId].time = indices.map(i => timeArr[i]); + for (const varName of variableNames) { + const arr = runData[varName]; + if (arr !== undefined) result[runId][varName] = indices.map(i => arr[i]); + } + } else { + if (timeArr) result[runId].time = timeArr; + for (const varName of variableNames) { + if (runData[varName] !== undefined) result[runId][varName] = runData[varName]; + } + } + } + } + const gvdReqId = message.requestId; + setImmediate(() => resolvePending(sessionManager.getSession(sessionId)?.pendingModelRequests, gvdReqId, result)); + break; + } + + case 'feedback_request': { + let resolvedFeedbackContent; + if (feedbackContent) { + resolvedFeedbackContent = feedbackContent; + } else { + const model = sessionManager.getClientModel(sessionId); + resolvedFeedbackContent = { feedbackLoops: findFeedbackLoops(model?.relationships) }; + } + const frReqId = message.requestId; + const frPayload = { feedbackContent: resolvedFeedbackContent, runIds: message.runIds }; + setImmediate(() => resolvePending(sessionManager.getSession(sessionId)?.pendingFeedbackRequests, frReqId, frPayload)); + break; + } + + case 'agent_text': { + if (!message.isThinking && message.content) { + textParts.push(message.content); + } + break; + } + + case 'agent_complete': { + resolveComplete(message.status); + break; + } + + case 'error': { + rejectComplete(new Error(message.error || 'Agent error')); + break; + } + + default: + break; + } + }; + + // 5. Compose user message (problemStatement → backgroundKnowledge → prompt) + const parts = []; + if (problemStatement) { + parts.push( + `The user has stated that they are conducting this modeling exercise to understand the following problem better.\n\n${problemStatement}` + ); + } + if (backgroundKnowledge) { + parts.push( + `Please be sure to consider the following critically important background information when you give your answer. You MUST use ONLY this background information to answer — do not draw on your own training knowledge or make assumptions beyond what is explicitly stated here. You MUST use the exact variable names as written — do not rename, paraphrase, or substitute any variable name that is explicitly referenced in this information.\n\n${backgroundKnowledge}` + ); + } + parts.push(prompt); + const userMessage = parts.join('\n\n'); + + // 6. Run the agent + const orchestrator = new AgentOrchestrator( + sessionManager, + sessionId, + sendToClient, + { markdownContent }, + provider + ); + + await Promise.all([ + orchestrator.startConversation(userMessage), + completionPromise + ]); + + return { + lastModel: sessionManager.getClientModel(sessionId), + explanation: textParts.join('\n\n') + }; +} diff --git a/agent/utilities/EngineWrapper.js b/agent/utilities/EngineWrapper.js new file mode 100644 index 00000000..fe948b72 --- /dev/null +++ b/agent/utilities/EngineWrapper.js @@ -0,0 +1,228 @@ +import logger from '../../utilities/logger.js'; +import QuantitativeEngine from '../../engines/quantitative/engine.js'; +import QualitativeEngine from '../../engines/qualitative/engine.js'; +import SeldonEngine from '../../engines/seldon/engine.js'; +import SeldonILEEngine from '../../engines/seldon-ile-user/engine.js'; +import DocumentationEngine from '../../engines/generate-documentation/engine.js'; +import SeldonMentorEngine from '../../engines/seldon-mentor/engine.js'; +import LTMEngine from '../../engines/ltm-narrative/engine.js'; +import SeldonEngineBrain from '../../engines/seldon/SeldonBrain.js'; +import SeldonILEUserBrain from '../../engines/seldon-ile-user/SeldonILEUserBrain.js'; + +/** + * EngineWrapper + * Adapts existing SD-AI engines to be called as functions + * + * Provides a unified interface to call: + * - Quantitative Engine (SFD generation) + * - Qualitative Engine (CLD generation) + * - Seldon (expert discussion) + * - Seldon-ILE-User (user-friendly discussion) + * - Generate Documentation + * - LTM Narrative + */ + +/** + * Call the Quantitative Engine + */ +export async function callQuantitativeEngine(prompt, currentModel, parameters = {}) { + try { + + // Create engine instance with parameters + const engine = new QuantitativeEngine(parameters); + + // Call generate method + const result = await engine.generate(prompt, currentModel, parameters); + + return { + success: true, + model: result.model, + supportingInfo: result.supportingInfo + }; + + } catch (error) { + logger.error('Quantitative Engine error:', error); + return { + success: false, + error: error.message + }; + } +} + +/** + * Call the Qualitative Engine + */ +export async function callQualitativeEngine(prompt, currentModel, parameters = {}) { + try { + + const engine = new QualitativeEngine(parameters); + const result = await engine.generate(prompt, currentModel, parameters); + + return { + success: true, + model: result.model, + supportingInfo: result.supportingInfo + }; + + } catch (error) { + logger.error('Qualitative Engine error:', error); + return { + success: false, + error: error.message + }; + } +} + +/** + * Call Seldon (expert discussion) + */ +export async function callSeldonEngine(prompt, model, feedbackContent, parameters = {}) { + try { + + const engine = new SeldonEngine(parameters); + + const seldonParams = { + ...parameters, + ...(feedbackContent && { feedbackContent }) + }; + + const beBrief = "\n\n**CRITICAL**\nBe brief in your response."; + seldonParams.systemPrompt = SeldonEngineBrain.DEFAULT_SYSTEM_PROMPT + beBrief + + const result = await engine.generate(prompt, model, seldonParams); + + return { + success: true, + output: result.output + }; + + } catch (error) { + logger.error('Seldon Engine error:', error); + return { + success: false, + error: error.message + }; + } +} + +/** + * Call Seldon-ILE-User (user-friendly discussion) + */ +export async function callSeldonILEEngine(prompt, model, runName, parameters = {}) { + try { + + const engine = new SeldonILEEngine(parameters); + + // Prepare parameters + const seldonParams = { + ...parameters, + currentRunName: runName + }; + + const beBrief = "\n\n**CRITICAL**\nBe brief in your response."; + seldonParams.systemPrompt = SeldonILEUserBrain.DEFAULT_SYSTEM_PROMPT + beBrief + + + const result = await engine.generate(prompt, model, seldonParams); + + return { + success: true, + output: result.output + }; + + } catch (error) { + logger.error('Seldon-ILE Engine error:', error); + return { + success: false, + error: error.message + }; + } +} + +/** + * Call Generate Documentation Engine + */ +export async function callDocumentationEngine(model, parameters = {}) { + try { + + const engine = new DocumentationEngine(parameters); + + // Documentation engine typically doesn't need a prompt + const result = await engine.generate('', model, parameters); + + return { + success: true, + model: result.model, + supportingInfo: result.supportingInfo + }; + + } catch (error) { + logger.error('Documentation Engine error:', error); + return { + success: false, + error: error.message + }; + } +} + +/** + * Call LTM Narrative Engine + */ +export async function callLTMEngine(model, feedbackContent, parameters = {}) { + try { + + const engine = new LTMEngine(parameters); + + const ltmParams = { + ...parameters, + feedbackContent + }; + + const result = await engine.generate('', model, ltmParams); + + return { + success: true, + feedbackLoops: result.feedbackLoops, + output: result.output + }; + + } catch (error) { + logger.error('LTM Engine error:', error); + return { + success: false, + error: error.message + }; + } +} + +/** + * Call Seldon Mentor Engine + */ +export async function callSeldonMentorEngine(prompt, model, feedbackContent, parameters = {}) { + try { + + const engine = new SeldonMentorEngine(parameters); + + const mentorParams = { + ...parameters, + ...(feedbackContent && { feedbackContent }) + }; + + const beBrief = "\n\n**CRITICAL**\nBe brief in your response."; + mentorParams.systemPrompt = SeldonEngineBrain.MENTOR_SYSTEM_PROMPT + beBrief + + const result = await engine.generate(prompt, model, mentorParams); + + return { + success: true, + output: result.output + }; + + } catch (error) { + logger.error('Seldon Mentor Engine error:', error); + return { + success: false, + error: error.message + }; + } +} \ No newline at end of file diff --git a/agent/utilities/MessageProtocol.js b/agent/utilities/MessageProtocol.js new file mode 100644 index 00000000..e3f9de82 --- /dev/null +++ b/agent/utilities/MessageProtocol.js @@ -0,0 +1,352 @@ +import { timeout } from 'async'; +import { z } from 'zod'; + +/** + * Message Protocol Schemas + * Defines all WebSocket message types and their validation schemas + */ + +// ============================================================================ +// SHARED SCHEMAS +// ============================================================================ + +/** + * SD-JSON Model Schema + * Accepts any model structure (CLD or SFD) with minimal validation + * Uses catchall to allow additional fields defined by LLMWrapper schemas + */ +const SDVariableSchema = z.object({ + name: z.string(), + type: z.string() +}).catchall(z.any()); + +const SDRelationshipSchema = z.object({ + from: z.string(), + to: z.string() +}).catchall(z.any()); + +const FeedbackLoopSchema = z.object({ + identifier: z.string(), + name: z.string(), + links: z.array(z.object({ + from: z.string(), + to: z.string(), + polarity: z.string() + })), + polarity: z.string(), + loopset: z.number().optional(), + 'Percent of Model Behavior Explained By Loop': z.array(z.object({ + time: z.number(), + value: z.number() + })).optional(), + loopScore: z.array(z.object({ + time: z.number(), + value: z.number() + })).optional() +}); + +export const FeedbackContentSchema = z.object({ + feedbackLoops: z.array(FeedbackLoopSchema), + dominantLoopsByPeriod: z.array(z.object({ + dominantLoops: z.array(z.string()), + startTime: z.number(), + endTime: z.number() + })).optional() +}).describe('Feedback loop analysis data'); + +const RunSchema = z.object({ + id: z.any().describe('Unique identifier for the run'), + name: z.string().describe('Display name for the run'), + isExternal: z.boolean().optional().describe('Whether the run is from an external source'), + variables: z.array(z.string()).optional().describe('Names of variables available in this run') +}).catchall(z.any()); + +export const GetRunInfoResponseSchema = z.object({ + runs: z.array(RunSchema).describe('List of simulation runs') +}).catchall(z.any()); + +export const SDModelSchema = z.object({ + variables: z.array(SDVariableSchema).optional(), + relationships: z.array(SDRelationshipSchema).optional(), + specs: z.record(z.string(), z.any()).optional(), + modules: z.array(z.any()).optional(), + unitWarnings: z.array(z.any()).optional(), + errors: z.array(z.any()).optional(), + explanation: z.string().optional(), + title: z.string().optional() +}).catchall(z.any()).describe('SD-JSON model structure (CLD or SFD)'); + +export const GetCurrentModelResponseSchema = SDModelSchema; + +export const UpdateModelResponseSchema = SDModelSchema; + +export const RunModelResponseSchema = z.object({ + runId: z.any().describe('ID of the completed simulation run') +}).catchall(z.any()).describe('Response from the client after running the model'); + +// ============================================================================ +// CLIENT → SERVER MESSAGES +// ============================================================================ + +const ToolDefinitionSchema = z.object({ + name: z.string().describe('Unique name identifier for the tool'), + description: z.string().describe('Human-readable description of what the tool does'), + timeout: z.number().optional().describe('The number of miliseconds to wait for this tool to execute'), + inputSchema: z.object({ + type: z.literal('object').describe('Schema type, must be "object"'), + properties: z.record(z.string(), z.any()).describe('Map of parameter names to their schema definitions'), + required: z.array(z.string()).optional().describe('Array of required parameter names') + }).describe('JSON Schema defining the tool input parameters') +}); + +const HistoricalMessageSchema = z.object({ + type: z.enum(['agent_text', 'visualization', 'agent_complete', 'user_text']).describe('Type of historical message'), + content: z.string().optional().describe('Text content (for agent_text, agent_complete, and user_text messages)'), + isThinking: z.boolean().optional().describe('Whether this is thinking text (for agent_text messages)'), + visualizationId: z.string().optional().describe('Unique ID for the visualization (for visualization messages)'), + visualizationTitle: z.string().optional().describe('Title of the visualization (for visualization messages)'), + visualizationDescription: z.string().optional().describe('Description of the visualization (for visualization messages)'), + svgData: z.string().optional().describe('Image data (for visualization messages)'), + status: z.string().optional().describe('Status for agent_complete messages') +}).catchall(z.any()).describe('Historical message from a previous session'); + +export const InitializeSessionMessageSchema = z.object({ + type: z.literal('initialize_session').describe('Message type identifier'), + sessionId: z.string().optional().describe('Optional session ID to resume an existing session. If not provided, a new session will be created.'), + authenticationKey: z.string().describe('Authentication key for server access'), + clientProduct: z.string().describe('Client product name (e.g., "sd-web", "sd-desktop")'), + clientVersion: z.string().describe('Client version (e.g., "1.0.0")'), + clientId: z.string().optional().describe('A unique identifier for the end user of this session. Currently un-used'), + mode: z.enum(['cld', 'sfd']).describe('Model type: CLD (Causal Loop Diagram) or SFD (Stock Flow Diagram). This cannot be changed during the session.'), + model: SDModelSchema, + tools: z.array(ToolDefinitionSchema).describe('Array of client-side tools available for the agent to call'), + supportsArrays: z.boolean().optional().describe('Whether the client supports arrayed models'), + supportsModules: z.boolean().optional().describe('Whether the client supports modular models'), + supportsSubTypes: z.boolean().optional().describe('Whether the client supports queues, conveyors, and ovens'), + historicalMessages: z.array(HistoricalMessageSchema).optional().describe('Optional array of historical messages from a previous session to provide context'), + context: z.record(z.string(), z.any()).optional().describe('Optional context information (metadata, user preferences, etc.)'), + timestamp: z.string().optional().describe('ISO 8601 timestamp of when the message was created') +}); + +const SelectAgentMessageSchema = z.object({ + type: z.literal('select_agent').describe('Message type identifier'), + sessionId: z.string().describe('Unique session identifier'), + agentId: z.string().optional().describe('Agent ID to use (e.g., "merlin", "socrates")'), + agentConfig: z.string().optional().describe('Custom agent configuration as a markdown string with YAML frontmatter (name, agent_mode, supported_modes, supported_providers) followed by agent instructions'), + provider: z.enum(['anthropic', 'google']).optional().default('anthropic').describe('LLM provider to use; ignored if agent supports only one provider'), + timestamp: z.string().optional().describe('ISO 8601 timestamp of when the message was created') +}).refine(msg => msg.agentId || msg.agentConfig, { + message: 'Either agentId or agentConfig must be provided' +}); + +export const ChatMessageSchema = z.object({ + type: z.literal('chat').describe('Message type identifier'), + sessionId: z.string().describe('Unique session identifier'), + message: z.string().describe('The user chat message text to send to the agent'), + timestamp: z.string().optional().describe('ISO 8601 timestamp of when the message was created') +}); + +const ToolCallResponseMessageSchema = z.object({ + type: z.literal('tool_call_response').describe('Message type identifier'), + sessionId: z.string().describe('Unique session identifier'), + callId: z.string().describe('The call ID from the tool_call_request being responded to'), + result: z.any().describe('The result data from executing the tool, or error message if isError is true'), + isError: z.boolean().optional().default(false).describe('Whether the tool execution resulted in an error'), + timestamp: z.string().optional().describe('ISO 8601 timestamp of when the message was created') +}); + +export const ModelUpdatedNotificationSchema = z.object({ + type: z.literal('model_updated_notification').describe('Message type identifier'), + sessionId: z.string().describe('Unique session identifier'), + model: SDModelSchema, + changeReason: z.string().describe('Human-readable explanation of why the model was updated'), + timestamp: z.string().optional().describe('ISO 8601 timestamp of when the message was created') +}); + +const StopIterationMessageSchema = z.object({ + type: z.literal('stop_iteration').describe('Message type identifier'), + sessionId: z.string().describe('Unique session identifier'), + timestamp: z.string().optional().describe('ISO 8601 timestamp of when the message was created') +}); + +const DisconnectMessageSchema = z.object({ + type: z.literal('disconnect').describe('Message type identifier'), + sessionId: z.string().describe('Unique session identifier for the session to disconnect') +}); + +const ClientMessageSchema = z.discriminatedUnion('type', [ + InitializeSessionMessageSchema, + SelectAgentMessageSchema, + ChatMessageSchema, + ToolCallResponseMessageSchema, + ModelUpdatedNotificationSchema, + StopIterationMessageSchema, + DisconnectMessageSchema +]); + +// ============================================================================ +// MESSAGE VALIDATION HELPERS +// ============================================================================ + +export function validateClientMessage(message) { + try { + return { + success: true, + data: ClientMessageSchema.parse(message) + }; + } catch (error) { + return { + success: false, + error: error.message, + details: error.errors + }; + } +} + +// ============================================================================ +// MESSAGE BUILDERS +// ============================================================================ + +export function createSessionCreatedMessage(sessionId) { + return { + type: 'session_created', + sessionId, + timestamp: new Date().toISOString() + }; +} + +export function createSessionReadyMessage(sessionId, availableAgents, defaults) { + return { + type: 'session_ready', + sessionId, + availableAgents, + defaults, + timestamp: new Date().toISOString() + }; +} + +export function createAgentSelectedMessage(sessionId, agentId, agentName, supportedProviders, currentProvider) { + return { + type: 'agent_selected', + sessionId, + agentId, + agentName, + supportedProviders, + currentProvider, + timestamp: new Date().toISOString() + }; +} + +export function createAgentTextMessage(sessionId, content, isThinking = false) { + return { + type: 'agent_text', + sessionId, + content, + isThinking, + timestamp: new Date().toISOString() + }; +} + +export function createToolCallNotificationMessage(sessionId, callId, toolName, args, isBuiltIn) { + return { + type: 'tool_call_notification', + sessionId, + callId, + toolName, + arguments: args, + isBuiltIn, + timestamp: new Date().toISOString() + }; +} + +export function createToolCallCompletedMessage(sessionId, callId, toolName, result, isError = false, responseType = null) { + return { + type: 'tool_call_completed', + sessionId, + callId, + toolName, + result, + isError, + ...(responseType && { responseType }), + timestamp: new Date().toISOString() + }; +} + +export function createAgentCompleteMessage(sessionId, status, finalMessage) { + return { + type: 'agent_complete', + sessionId, + finalMessage, + status, + timestamp: new Date().toISOString() + }; +} + +export function createErrorMessage(sessionId, error, errorCode) { + return { + type: 'error', + sessionId, + error: typeof error === 'string' ? error : error.message, + errorCode, + timestamp: new Date().toISOString() + }; +} + +export function createFeedbackRequestMessage(sessionId, requestId, runIds) { + return { + type: 'feedback_request', + sessionId, + requestId, + runIds, + timestamp: new Date().toISOString() + }; +} + +export function createGetCurrentModelMessage(sessionId, requestId) { + return { + type: 'get_current_model', + sessionId, + requestId, + timestamp: new Date().toISOString() + }; +} + +export function createUpdateModelMessage(sessionId, requestId, modelData) { + return { + type: 'update_model', + sessionId, + requestId, + modelData, + timestamp: new Date().toISOString() + }; +} + +export function createRunModelMessage(sessionId, requestId) { + return { + type: 'run_model', + sessionId, + requestId, + timestamp: new Date().toISOString() + }; +} + +export function createGetRunInfoMessage(sessionId, requestId) { + return { + type: 'get_run_info', + sessionId, + requestId, + timestamp: new Date().toISOString() + }; +} + +export function createGetVariableDataMessage(sessionId, requestId, variableNames, runIds, detailed) { + return { + type: 'get_variable_data', + sessionId, + requestId, + variableNames, + runIds, + detailed, + timestamp: new Date().toISOString() + }; +} diff --git a/agent/utilities/SessionManager.js b/agent/utilities/SessionManager.js new file mode 100644 index 00000000..c144fcc0 --- /dev/null +++ b/agent/utilities/SessionManager.js @@ -0,0 +1,823 @@ +import { randomBytes } from 'crypto'; +import { tmpdir } from 'os'; +import { dirname, join } from 'path'; +import { existsSync, mkdirSync, readdirSync, rmSync, writeFileSync } from 'fs'; +import Anthropic from '@anthropic-ai/sdk'; +import { GoogleGenAI } from '@google/genai'; +import { countTokens } from '@anthropic-ai/tokenizer'; +import logger from '../../utilities/logger.js'; +import TokenUsageReporter, { Provider } from '../../utilities/TokenUsageReporter.js'; +import config from '../../config.js'; + +/** + * SessionManager + * Manages in-memory WebSocket sessions with session-specific temp folders + * + * Key Features: + * - Pure in-memory state (no persistence) + * - Session-specific temp folders for Python visualizations + * - Automatic cleanup on disconnect + * - Stale session cleanup + * - Orphaned temp directory cleanup + */ +export class SessionManager { + static MAX_COMPRESSION_TOKENS_PER_PASS = 200_000; + + constructor(options = {}) { + this.sessions = new Map(); + + // Use explicit override (mainly for isolation in tests) > per-process + // subdirectory under the configured temp directory > OS tmpdir. + // + // The `pid-${process.pid}` segment is critical under PM2 cluster mode + // (or any multi-process deployment sharing AGENT_SESSION_TEMP_DIR): + // #cleanupOrphanedTempDirs reads `this.tempBasePath` and removes anything + // not in *its own* this.sessions map. Without per-pid namespacing each + // process would rm-rf its sibling processes' active session dirs on the + // 5-minute cleanup tick, breaking the bwrap bind mount under live workers + // (the root cause of the /session/*.json ENOENT errors). + this.tempBasePath = options.tempBasePath + || join(config.agentSessionTempDir || tmpdir(), 'sd-agent', `pid-${process.pid}`); + + // Configuration + this.maxSessions = options.maxSessions || 1000; + this.maxConversationHistory = options.maxConversationHistory || 100; + this.maxSessionAge = options.maxSessionAge || 8 * 60 * 60 * 1000; // 8 hours + this.sessionTimeout = options.sessionTimeout || 30 * 60 * 1000; // 30 minutes + this.cleanupInterval = options.cleanupInterval || 5 * 60 * 1000; // 5 minutes + + // Ensure base temp directory exists + if (!existsSync(this.tempBasePath)) { + mkdirSync(this.tempBasePath, { recursive: true }); + } + + // Flag (but don't reap) pid-* siblings whose owning process is no longer + // alive — leftovers from PM2 restarts/crashes where the dying process + // couldn't run its own shutdown cleanup. We avoid auto-reaping because + // PID reuse plus a false-positive "dead" check could rm a live sibling's + // bind-mount source out from under its worker (the exact failure mode + // the per-pid layout exists to prevent). Operators can clear stale + // pid-* dirs manually when the log surfaces them. + if (!options.tempBasePath && !options.disableCleanup) { + this.#flagDeadProcessDirs(); + } + + // Start cleanup timer (disabled in worker processes — lifetime managed by main) + if (!options.disableCleanup) { + this.#startCleanupTimer(); + } + + logger.log(`SessionManager initialized. Temp base: ${this.tempBasePath}`); + } + + /** + * Scan the parent `sd-agent` directory for `pid-*` subdirs whose owning + * process is no longer alive and log them. Operators should remove these + * manually — we intentionally do NOT auto-reap because PID reuse plus an + * imperfect liveness check could rm a live sibling's bind-mount source. + */ + #flagDeadProcessDirs() { + const parentDir = dirname(this.tempBasePath); + if (!existsSync(parentDir)) return; + + let entries; + try { + entries = readdirSync(parentDir); + } catch (err) { + logger.warn(`Could not scan ${parentDir} for dead pid dirs: ${err.message}`); + return; + } + + const stale = []; + for (const entry of entries) { + if (!entry.startsWith('pid-')) continue; + const pid = Number(entry.slice(4)); + if (!Number.isInteger(pid) || pid <= 0) continue; + if (pid === process.pid) continue; + + let alive = false; + try { + process.kill(pid, 0); + alive = true; + } catch (err) { + // EPERM = process exists but we lack permission; treat as alive. + // ESRCH = no such process — flag as stale. + if (err.code === 'EPERM') alive = true; + } + + if (!alive) stale.push({ entry, pid }); + } + + if (stale.length > 0) { + const summary = stale.map(({ entry, pid }) => `${entry} (pid=${pid})`).join(', '); + logger.warn( + `Found ${stale.length} stale pid-* temp dir(s) under ${parentDir} ` + + `whose owning process is no longer alive: ${summary}. ` + + `Remove manually once confirmed stale.` + ); + } + } + + /** + * Generate a unique session ID + */ + #generateSessionId() { + return `sess_${randomBytes(16).toString('hex')}`; + } + + /** + * Create a new session + */ + createSession(ws) { + // Enforce max sessions + if (this.sessions.size >= this.maxSessions) { + throw new Error('Server at capacity. Please try again later.'); + } + + const sessionId = this.#generateSessionId(); + const sessionTempDir = join(this.tempBasePath, sessionId); + + // Create session-specific temp folder + try { + mkdirSync(sessionTempDir, { recursive: true }); + } catch (err) { + logger.error(`Failed to create temp directory for session ${sessionId}:`, err); + throw new Error('Failed to initialize session temp directory'); + } + + const session = { + sessionId, + ws, + tempDir: sessionTempDir, + createdAt: Date.now(), + lastActivity: Date.now(), + + // Client-provided data + mode: null, // 'cld' or 'sfd' - set once at initialization, never changes + clientModel: null, + clientTools: [], + context: {}, + clientId: null, + + // Model token tracking + modelTokenCount: 0, + + // Active tool calls awaiting client response + pendingToolCalls: new Map(), + + // Agent conversation context (for Claude Agent SDK) + conversationContext: [], + + // Async hook installed by WebSocketHandler so stale-session cleanup can + // wait for the worker to exit before rmSync removes the bwrap bind-mount + // source. Null when no worker is running for this session. + workerTeardown: null, + }; + + this.sessions.set(sessionId, session); + + logger.log(`Session created: ${sessionId} (total: ${this.sessions.size})`); + + return sessionId; + } + + /** + * Register a session with a known ID and an explicit temp directory path. + * Used by worker processes where the session ID and temp dir are assigned + * by the main process and passed in via environment variables. + */ + createSessionWithId(sessionId, ws, tempDir) { + if (this.sessions.has(sessionId)) return sessionId; + if (this.sessions.size >= this.maxSessions) { + throw new Error('Server at capacity. Please try again later.'); + } + + try { + mkdirSync(tempDir, { recursive: true }); + } catch (err) { + logger.error(`Failed to ensure temp directory for session ${sessionId}:`, err); + throw new Error('Failed to initialize session temp directory'); + } + + const session = { + sessionId, + ws, + tempDir, + createdAt: Date.now(), + lastActivity: Date.now(), + mode: null, + clientModel: null, + clientTools: [], + context: {}, + clientId: null, + modelTokenCount: 0, + pendingToolCalls: new Map(), + conversationContext: [], + workerTeardown: null, + }; + + this.sessions.set(sessionId, session); + logger.log(`Session registered: ${sessionId}`); + return sessionId; + } + + /** + * Install an async teardown hook the cleanup path will await before rmSync'ing + * the session temp dir. Used to keep the worker's bwrap `--bind` source alive + * until the worker process has actually exited. + */ + setWorkerTeardown(sessionId, teardownFn) { + const session = this.sessions.get(sessionId); + if (session) { + session.workerTeardown = teardownFn; + } + } + + /** + * Get a session by ID + */ + getSession(sessionId) { + const session = this.sessions.get(sessionId); + if (session) { + session.lastActivity = Date.now(); + } + return session; + } + + /** + * Initialize a session with model and tools + */ + initializeSession(sessionId, mode, model, tools, context, clientId, capabilities = {}) { + const session = this.getSession(sessionId); + if (!session) { + throw new Error(`Session not found: ${sessionId}`); + } + + // Validate model type + if (mode !== 'cld' && mode !== 'sfd') { + throw new Error(`Invalid mode: ${mode}. Must be 'cld' or 'sfd'`); + } + + // Set model type (can only be set once) + if (session.mode && session.mode !== mode) { + throw new Error(`Cannot change model type from ${session.mode} to ${mode} during session`); + } + session.mode = mode; + + if (clientId == null) { + throw new Error('clientId is required'); + } + + session.clientTools = tools || []; + session.context = context || {}; + session.clientId = clientId; + session.supportsArrays = capabilities.supportsArrays ?? false; + session.supportsModules = capabilities.supportsModules ?? false; + session.supportsSubTypes = capabilities.supportsSubTypes ?? false; + this.updateClientModel(sessionId, model); + + logger.log(`Session initialized: ${sessionId} with mode=${mode} and ${tools.length} client tools`); + } + + /** + * Update the client model reference and persist to disk. + * Returns { modelPath, message } when the model is written. + */ + updateClientModel(sessionId, model) { + const session = this.getSession(sessionId); + if (session) { + session.clientModel = model; + if (model) { + const result = this.#writeModelToDisk(sessionId, model); + const parts = []; + if (model.errors?.length) { + parts.push(`Errors: ${model.errors.map(e => typeof e === 'string' ? e : JSON.stringify(e)).join('; ')}`); + } + if (model.unitWarnings?.length) { + parts.push(`Unit warnings: ${model.unitWarnings.map(w => typeof w === 'string' ? w : JSON.stringify(w)).join('; ')}`); + } + return { ...result, issues: parts.length ? parts.join('\n') : null }; + } + } + } + + /** + * Get the current client model + */ + getClientModel(sessionId) { + const session = this.getSession(sessionId); + return session?.clientModel; + } + + /** + * Update model token count and check if it exceeds limit + */ + updateModelTokenCount(sessionId, tokenCount) { + const session = this.getSession(sessionId); + if (session) { + session.modelTokenCount = tokenCount; + } + } + + /** + * Get model token count + */ + getModelTokenCount(sessionId) { + const session = this.getSession(sessionId); + return session?.modelTokenCount || 0; + } + + /** + * Get session temp directory + */ + getSessionTempDir(sessionId) { + const session = this.getSession(sessionId); + return session?.tempDir; + } + + /** + * Write a model to disk and return the LLM message describing where to find it. + * Returns { modelPath, message }. + */ + #writeModelToDisk(sessionId, model) { + const sessionTempDir = this.getSessionTempDir(sessionId); + const modelPath = join(sessionTempDir, 'model.sdjson'); + try { + mkdirSync(sessionTempDir, { recursive: true }); + } catch (err) { + logger.error(`[${sessionId}] Write Model to Disk... failed to create session temp directory '${sessionTempDir}':`, err); + throw new Error(`Failed to create session temp directory '${sessionTempDir}': ${err.message}`); + } + try { + writeFileSync(modelPath, JSON.stringify(model, null, 2)); + } catch (err) { + // ENOENT here on a path whose parent we just mkdir'd usually means the + // host removed the bwrap bind-mount source out from under this worker + // (e.g. WebSocket closed and triggered cleanupSessionTempDir while we + // were mid-tool-call). Capture the directory state so the post-mortem + // confirms the race rather than guessing. + const dirExists = existsSync(sessionTempDir); + logger.error(`[${sessionId}] Failed to write model to '${modelPath}' (sessionTempDir exists=${dirExists}):`, err); + throw new Error(`Failed to write model to '${modelPath}': ${err.message}`); + } + const message = `The model has been written to disk at: ${modelPath}. Other tools will load it automatically — you do not need to read this file. Use the read_model_section tool if you need to inspect specific sections.`; + return { modelPath, message }; + } + + /** + * Write arbitrary data to a named file in the session temp directory. + * Returns { filePath, message }. + */ + writeDataToDisk(sessionId, filename, data) { + const sessionTempDir = this.getSessionTempDir(sessionId); + const filePath = join(sessionTempDir, filename); + try { + mkdirSync(sessionTempDir, { recursive: true }); + } catch (err) { + logger.error(`[${sessionId}] Write Data to Disk... failed to create session temp directory '${sessionTempDir}':`, err); + throw new Error(`Failed to create session temp directory '${sessionTempDir}': ${err.message}`); + } + try { + writeFileSync(filePath, JSON.stringify(data, null, 2)); + } catch (err) { + logger.error(`[${sessionId}] Failed to write data to '${filePath}':`, err); + throw new Error(`Failed to write data to '${filePath}': ${err.message}`); + } + const message = `The data has been written to disk at: ${filePath}. Use the Read filesystem tool to load it into context.`; + return { filePath, message }; + } + + /** + * Add to conversation context + */ + addToConversationHistory(sessionId, message) { + const session = this.getSession(sessionId); + if (session) { + session.conversationContext.push(message); + + // Limit conversation history size to prevent memory bloat + if (session.conversationContext.length > this.maxConversationHistory) { + session.conversationContext = session.conversationContext.slice(-this.maxConversationHistory); + } + } + } + + /** + * Get conversation context + */ + getConversationContext(sessionId) { + const session = this.getSession(sessionId); + return session?.conversationContext || []; + } + + /** + * Summarize an array of messages using the LLM and return a single summary message object. + * Private — only called by #summarizeContextIfNeeded and cleanupContext. + */ + async #summarizeMessages(messages, sessionId) { + try { + const isGeminiFormat = messages.some(m => Array.isArray(m.parts)); + + const conversationText = messages.map((msg) => { + if (isGeminiFormat) { + const role = msg.role === 'user' ? 'User' : 'Assistant'; + if (!Array.isArray(msg.parts)) return ''; + const text = msg.parts.filter(p => p.text).map(p => p.text).join('\n'); + return text ? `${role}: ${text}` : ''; + } else { + if (msg.role === 'user') { + return `User: ${typeof msg.content === 'string' ? msg.content : JSON.stringify(msg.content)}`; + } else if (msg.role === 'assistant') { + if (Array.isArray(msg.content)) { + const textContent = msg.content + .filter(block => block.type === 'text') + .map(block => block.text || block) + .join('\n'); + return textContent ? `Assistant: ${textContent}` : ''; + } + return `Assistant: ${msg.content}`; + } + } + return ''; + }).filter(line => line).join('\n\n'); + + const summaryPrompt = `Please create a concise summary of the following conversation history. Focus on: +- The main task or goal the user requested +- Key decisions, findings, or results achieved +- Important context needed for continuing the conversation +- Current state of the work + +Keep the summary brief but informative (2-4 paragraphs maximum). + +Conversation history: +${conversationText}`; + + const clientId = this.getSession(sessionId)?.clientId ?? null; + const reporter = new TokenUsageReporter(config.tokenReporterURL, clientId); + + let summaryText; + if (isGeminiFormat) { + if (!this.gemini) { + this.gemini = new GoogleGenAI({ apiKey: process.env.GEMINI_API_KEY }); + } + const response = await this.gemini.models.generateContent({ + model: config.agentGeminiSummaryModel, + contents: [{ role: 'user', parts: [{ text: summaryPrompt }] }] + }); + reporter.report({ provider: Provider.GOOGLE, model: config.agentGeminiSummaryModel, usage: response.usageMetadata }).catch(() => {}); + summaryText = response.text || response.candidates?.[0]?.content?.parts?.[0]?.text || ''; + } else { + if (!this.anthropic) { + this.anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY }); + } + const response = await this.anthropic.messages.create({ + model: config.agentAnthropicSummaryModel, + max_tokens: 1024, + messages: [{ role: 'user', content: summaryPrompt }] + }); + reporter.report({ provider: Provider.ANTHROPIC, model: config.agentAnthropicSummaryModel, usage: response.usage }).catch(() => {}); + summaryText = response.content[0].text; + } + + logger.log(`Created message history summary: ${summaryText.substring(0, 100)}...`); + + if (isGeminiFormat) { + return { + role: 'user', + parts: [{ text: `[Previous conversation summary]\n${summaryText}\n[End of summary - continuing conversation]` }] + }; + } + return { + role: 'user', + content: `[Previous conversation summary]\n${summaryText}\n[End of summary - continuing conversation]` + }; + + } catch (error) { + logger.error('Error summarizing message history:', error); + const isGeminiFormat = messages.some(m => Array.isArray(m.parts)); + if (isGeminiFormat) { + return { role: 'user', parts: [{ text: '[Previous conversation summary: Earlier messages were condensed to save context. The conversation is continuing from this point.]' }] }; + } + return { + role: 'user', + content: '[Previous conversation summary: Earlier messages were condensed to save context. The conversation is continuing from this point.]' + }; + } + } + + /** + * If the session's conversation context exceeds maxContextTokens, summarize all messages + * and replace the context with [original_user_message, ...summaries]. Messages are split + * into chunks of MAX_COMPRESSION_TOKENS_PER_PASS before summarizing to handle large + * histories (e.g. on session initialization) that would exceed the LLM's input limit. + */ + async #summarizeContextIfNeeded(sessionId, maxContextTokens) { + const session = this.getSession(sessionId); + if (!session) return; + + const messages = session.conversationContext; + if (messages.length <= 1) return; + + const currentTokens = countTokens(JSON.stringify(messages)); + if (currentTokens <= maxContextTokens) return; + + logger.log(`Message history exceeds token limit: ${currentTokens} tokens (limit: ${maxContextTokens}), summarizing context`); + + const lastUserIdx = messages.findLastIndex(m => m.role === 'user'); + const lastMessage = lastUserIdx !== -1 ? messages[lastUserIdx] : null; + + // If the last user message contains tool results (either format), also keep the preceding + // model turn (tool_use/functionCall blocks) to avoid orphaned pairs. + let tailStart = lastUserIdx !== -1 ? lastUserIdx : messages.length; + const isClaudeToolResult = Array.isArray(lastMessage?.content) && lastMessage.content.some(b => b.type === 'tool_result'); + const isGeminiFunctionResponse = Array.isArray(lastMessage?.parts) && lastMessage.parts.some(p => p.functionResponse); + const prevRole = lastUserIdx > 0 ? messages[lastUserIdx - 1]?.role : null; + if (lastMessage && (isClaudeToolResult || isGeminiFunctionResponse) && + lastUserIdx > 0 && (prevRole === 'assistant' || prevRole === 'model')) { + tailStart = lastUserIdx - 1; + } + + const tail = messages.slice(tailStart); + const remaining = messages.slice(0, tailStart); + + // Split remaining messages into chunks that fit within the per-pass token budget + const chunks = []; + let chunk = []; + let chunkTokens = 0; + for (const msg of remaining) { + const msgTokens = countTokens(JSON.stringify(msg)); + if (chunkTokens + msgTokens > SessionManager.MAX_COMPRESSION_TOKENS_PER_PASS && chunk.length > 0) { + chunks.push(chunk); + chunk = []; + chunkTokens = 0; + } + chunk.push(msg); + chunkTokens += msgTokens; + } + if (chunk.length > 0) chunks.push(chunk); + + const summaries = await Promise.all(chunks.map(c => this.#summarizeMessages(c, sessionId))); + const replacement = [...summaries, ...tail]; + messages.splice(0, messages.length, ...replacement); + + const newTokenCount = countTokens(JSON.stringify(messages)); + logger.log(`Summarized context in ${chunks.length} chunk(s): ${messages.length} messages, ${newTokenCount} tokens (saved ${currentTokens - newTokenCount})`); + } + + /** + * Clean up the session's conversation context by summarizing if over the token limit. + */ + async cleanupContext(sessionId, maxContextTokens) { + await this.#summarizeContextIfNeeded(sessionId, maxContextTokens); + } + + /** + * Add a pending tool call + */ + addPendingToolCall(sessionId, callId, toolName, args) { + const session = this.getSession(sessionId); + if (session) { + let resolver, rejecter; + const promise = new Promise((resolve, reject) => { + resolver = resolve; + rejecter = reject; + }); + + session.pendingToolCalls.set(callId, { + toolName, + arguments: args, + timestamp: Date.now(), + promise, + resolve: resolver, + reject: rejecter + }); + + return promise; + } + return Promise.reject(new Error('Session not found')); + } + + /** + * Resolve a pending tool call + */ + resolvePendingToolCall(sessionId, callId, result, isError = false) { + const session = this.getSession(sessionId); + if (session) { + const pendingCall = session.pendingToolCalls.get(callId); + if (pendingCall) { + if (isError) { + pendingCall.reject(new Error(typeof result === 'string' ? result : (result?.error || 'Tool call failed'))); + } else { + pendingCall.resolve(result); + } + session.pendingToolCalls.delete(callId); + return true; + } + } + return false; + } + + /** + * Get pending tool call + */ + getPendingToolCall(sessionId, callId) { + const session = this.getSession(sessionId); + return session?.pendingToolCalls.get(callId); + } + + /** + * Delete a session and cleanup resources + */ + deleteSession(sessionId) { + const session = this.sessions.get(sessionId); + if (session) { + // Reject any pending tool calls + for (const [, pendingCall] of session.pendingToolCalls.entries()) { + pendingCall.reject(new Error('Session closed')); + } + session.pendingToolCalls.clear(); + + // Reject pending feedback/model requests created by builtin tools. + // Each entry owns a setTimeout handle that must be cleared so the + // session object becomes GC-eligible immediately. + for (const map of [session.pendingFeedbackRequests, session.pendingModelRequests]) { + if (!map) continue; + for (const [, pending] of map.entries()) { + clearTimeout(pending.timeout); + pending.reject(new Error('Session closed')); + } + map.clear(); + } + + // Clean up session temp folder + this.cleanupSessionTempDir(session.tempDir); + + // Clean up references + session.ws = null; + session.clientModel = null; + session.conversationContext = []; + + this.sessions.delete(sessionId); + + logger.log(`Session deleted: ${sessionId} (remaining: ${this.sessions.size})`); + } + } + + /** + * Clean up a session temp directory + */ + cleanupSessionTempDir(tempDir) { + try { + if (existsSync(tempDir)) { + // Remove directory and all its contents recursively + rmSync(tempDir, { recursive: true, force: true }); + logger.log(`Cleaned up temp directory: ${tempDir}`); + } + } catch (err) { + logger.error(`Failed to cleanup temp directory ${tempDir}:`, err); + } + } + + /** + * Start cleanup timer for stale sessions and orphaned temp dirs. + * Both sweeps are awaited together so the next interval can't fire a second + * sweep on top of a slow one (worker teardowns can take up to ~4s each). + */ + #startCleanupTimer() { + this.cleanupInProgress = false; + this.cleanupTimer = setInterval(() => { + if (this.cleanupInProgress) { + logger.log('SessionManager cleanup cycle still in progress, skipping this tick'); + return; + } + this.cleanupInProgress = true; + Promise.resolve() + .then(() => this.cleanupStaleSessions()) + .then(() => this.#cleanupOrphanedTempDirs()) + .catch((err) => logger.error('Error during cleanup cycle:', err)) + .finally(() => { this.cleanupInProgress = false; }); + }, this.cleanupInterval); + } + + /** + * Clean up stale sessions. Async because, when a worker is running, we must + * await its exit before deleteSession() rm's the bwrap `--bind` source — a + * write from inside the still-mounted sandbox after the source is gone fails + * with ENOENT (see SessionManager#writeModelToDisk error path). + */ + async cleanupStaleSessions() { + const now = Date.now(); + + // Snapshot first so deleteSession() calls (which mutate this.sessions) + // during async teardowns can't disturb iteration. + const candidates = []; + for (const [sessionId, session] of this.sessions.entries()) { + const age = now - session.createdAt; + const inactivity = now - session.lastActivity; + if (age > this.maxSessionAge || inactivity > this.sessionTimeout) { + candidates.push({ sessionId, session, age, inactivity }); + } + } + + let cleanedCount = 0; + for (const { sessionId, session, age, inactivity } of candidates) { + // A concurrent WS close may have already removed it while we were + // awaiting a previous teardown. + if (!this.sessions.has(sessionId)) continue; + + const trigger = age > this.maxSessionAge ? 'max-age' : 'inactivity'; + const hasWorker = typeof session.workerTeardown === 'function'; + logger.log( + `Cleaning up stale session: ${sessionId} (trigger=${trigger}, age=${Math.round(age/1000/60)}m, ` + + `inactive=${Math.round(inactivity/1000/60)}m, hasWorker=${hasWorker}, ` + + `wsReadyState=${session.ws?.readyState ?? 'none'})` + ); + + // Close WebSocket if still open. This will also fire #onClose on the + // handler side, which is idempotent with the teardown we're about to do. + if (session.ws && session.ws.readyState === 1) { + try { session.ws.close(1000, 'Session timeout'); } catch { /* already closing */ } + } + + // Wait for the worker to actually exit before we let deleteSession + // rmSync the temp dir. #killWorker is safe to call twice (the second + // call sees this.#worker === null and resolves immediately). + if (hasWorker) { + const teardownStart = Date.now(); + try { + await session.workerTeardown(); + logger.log(`[session:${sessionId}] Stale-cleanup worker teardown completed in ${Date.now() - teardownStart}ms`); + } catch (err) { + logger.error(`[session:${sessionId}] Worker teardown failed during stale cleanup (proceeding with delete anyway):`, err); + } + } + + this.deleteSession(sessionId); + cleanedCount++; + } + + if (cleanedCount > 0) { + logger.log(`Cleaned up ${cleanedCount} stale session(s)`); + } + } + + /** + * Clean up orphaned temp directories + */ + #cleanupOrphanedTempDirs() { + try { + if (!existsSync(this.tempBasePath)) { + return; + } + + const tempDirs = readdirSync(this.tempBasePath); + const activeSessionIds = new Set(this.sessions.keys()); + let cleanedCount = 0; + + for (const dir of tempDirs) { + // Check if this temp dir belongs to an active session + if (!activeSessionIds.has(dir)) { + const fullPath = join(this.tempBasePath, dir); + + // Additional safety check: only delete dirs that match session pattern + if (dir.startsWith('sess_')) { + this.cleanupSessionTempDir(fullPath); + cleanedCount++; + logger.log(`Cleaned up orphaned temp directory: ${dir}`); + } + } + } + + if (cleanedCount > 0) { + logger.log(`Cleaned up ${cleanedCount} orphaned temp director(ies)`); + } + } catch (err) { + logger.error('Failed to cleanup orphaned temp dirs:', err); + } + } + + /** + * Shutdown - cleanup all sessions + */ + shutdown() { + logger.log('SessionManager shutting down...'); + + // Stop cleanup timer + if (this.cleanupTimer) { + clearInterval(this.cleanupTimer); + } + + // Close all sessions + for (const [sessionId, session] of this.sessions.entries()) { + if (session.ws && session.ws.readyState === 1) { + session.ws.close(1000, 'Server shutting down'); + } + this.deleteSession(sessionId); + } + + // Final cleanup of any remaining temp directories + this.#cleanupOrphanedTempDirs(); + + logger.log('SessionManager shutdown complete'); + } +} diff --git a/agent/utilities/VisualizationEngine.js b/agent/utilities/VisualizationEngine.js new file mode 100644 index 00000000..28dca2f5 --- /dev/null +++ b/agent/utilities/VisualizationEngine.js @@ -0,0 +1,780 @@ +import { randomBytes } from 'crypto'; +import { join, resolve, normalize, dirname } from 'path'; +import { writeFileSync, readFileSync, existsSync, unlinkSync } from 'fs'; +import { spawn } from 'child_process'; +import { fileURLToPath } from 'url'; +import { LLMWrapper } from '../../utilities/LLMWrapper.js'; +import logger from '../../utilities/logger.js'; + +const __filename = fileURLToPath(import.meta.url); +const __dirname = dirname(__filename); + +/** + * VisualizationEngine + * Creates visualizations using Python/matplotlib + * + * Key Features: + * - Always returns SVG string + * - Python/matplotlib for template-based visualizations + * - AI-generated custom Python code for unique requirements + * - Session-specific temp folder management + * - Automatic cleanup after visualization creation + */ +export class VisualizationEngine { + constructor(sessionManager, sessionId) { + this.sessionManager = sessionManager; + this.sessionId = sessionId; + this.sessionTempDir = sessionManager.getSessionTempDir(sessionId); + + if (!this.sessionTempDir) { + throw new Error(`Session not found: ${sessionId}`); + } + + // Normalize and resolve the session temp directory for security checks + this.resolvedTempDir = resolve(normalize(this.sessionTempDir)); + + this.clientId = sessionManager.getSession(sessionId)?.clientId ?? null; + } + + /** + * Validate that a file path is within the session temp directory + * This prevents path traversal attacks (e.g., ../../etc/passwd) + * @param {string} filePath - The file path to validate + * @returns {string} The validated, resolved path + * @throws {Error} If the path is outside the session temp directory + */ + validatePath(filePath) { + // Resolve and normalize the path to eliminate any .. or symbolic links + const resolvedPath = resolve(normalize(filePath)); + + // Check if the resolved path starts with the session temp directory + if (!resolvedPath.startsWith(this.resolvedTempDir + '/') && resolvedPath !== this.resolvedTempDir) { + throw new Error(`Security violation: Path '${filePath}' is outside session directory`); + } + + return resolvedPath; + } + + /** + * Generate a unique visualization ID + */ + generateVizId() { + return `viz_${randomBytes(8).toString('hex')}`; + } + + /** + * Truncate all arrays in data to the shortest length among time and the requested variables. + * Prevents matplotlib errors when detailed run data and time arrays have different lengths. + */ + #normalizeRunData(runData, variables, label) { + if (!runData?.time || !Array.isArray(runData.time)) return runData; + const keys = ['time', ...variables].filter(k => Array.isArray(runData[k])); + const minLen = Math.min(...keys.map(k => runData[k].length)); + if (keys.every(k => runData[k].length === minLen)) return runData; + const trimmed = keys.filter(k => runData[k].length > minLen); + logger.log(`normalizeArrayLengths${label ? ` ${label}` : ''}: trimming to ${minLen} points. Affected keys: ${trimmed.map(k => `${k}(${runData[k].length})`).join(', ')}`); + const normalized = { ...runData }; + for (const k of trimmed) normalized[k] = normalized[k].slice(0, minLen); + return normalized; + } + + normalizeArrayLengths(data, variables) { + // Run-keyed comparison format: { runId: { time: [...], var1: [...], ... } } + // Each run is flat and normalized independently. + if (data && typeof data === 'object' && !Array.isArray(data) && !('time' in data)) { + const firstVal = data[Object.keys(data)[0]]; + if (firstVal && typeof firstVal === 'object' && !Array.isArray(firstVal) && Array.isArray(firstVal.time)) { + const normalized = {}; + for (const [runId, runData] of Object.entries(data)) { + normalized[runId] = this.#normalizeRunData(runData, variables, runId); + } + return normalized; + } + } + + // Flat format: { time, var1, var2, ... } (time_series, phase_portrait, feedback_dominance) + return this.#normalizeRunData(data, variables, null); + } + + /** + * Create visualization - always returns SVG string + */ + async createVisualization(type, data, variables, options = {}) { + const useAICustom = options.useAICustom || false; + + if (useAICustom) { + return await this.createAICustomVisualization(data, variables, options); + } else { + return await this.createVisualizationWithPython(type, data, variables, options); + } + } + + /** + * Create custom visualization using AI to write Python/matplotlib code - returns SVG string + */ + async createAICustomVisualization(data, variables, options) { + const vizId = this.generateVizId(); + const scriptPath = this.validatePath(join(this.sessionTempDir, `visualization-${vizId}.py`)); + const dataPath = this.validatePath(join(this.sessionTempDir, `data-${vizId}.json`)); + const outputPath = this.validatePath(join(this.sessionTempDir, `visualization-${vizId}.svg`)); + + let svgContent = null; + let error = null; + + try { + // 1. Write data to temp file + const normalizedData = this.normalizeArrayLengths(data, variables); + writeFileSync(dataPath, JSON.stringify(normalizedData)); + + // 2. Generate Python script using AI + const pythonScript = await this.generateAIVisualizationScript( + dataPath, outputPath, data, variables, options + ); + writeFileSync(scriptPath, pythonScript); + logger.log(`[VizEngine] AI script created: ${scriptPath} at ${new Date().toISOString()}`); + + // 3. Execute Python script + await this.executePythonScript(scriptPath); + + // 4. Read generated SVG and validate + const fileContent = readFileSync(outputPath, 'utf8'); + + if (!fileContent.includes(' 0) + ? `\n\nPRE-DEFINED VARIABLE — HIGHLIGHT_PERIODS: +A Python list named HIGHLIGHT_PERIODS is already defined in the required boilerplate. Each entry has keys: start (number), end (number), label (string), and optionally color (string). +Use axvspan(p['start'], p['end'], ...) to draw background bands and mpatches.Patch for legend entries. Do NOT read any file to get this data — it is already in the variable.` + : ''; + + const schemaPrompt = `DATA FILE SCHEMA for this request: +The following describes the structure of the JSON file on disk. Use the exact field paths to write your parsing code. Do NOT treat these values as data — read everything from disk at runtime. + +${dataDescription}${periodsSchemaNote}`; + + const periodsConstant = (options.highlightPeriods?.length > 0) + ? `5. HIGHLIGHT_PERIODS = ${JSON.stringify(options.highlightPeriods)} # server-computed dominant loop periods — use these directly, do not re-read from any file\n` + : ''; + + const userPrompt = `Generate Python code for this visualization: + +Goal: ${visualizationGoal} +Size: ${(options.width || 800)/100}x${(options.height || 600)/100} inches + +${options.customRequirements ? `Requirements: ${options.customRequirements}\n` : ''} +Required — copy these lines exactly, do not alter the paths: +1. matplotlib.use('Agg') BEFORE import matplotlib.pyplot +2. import warnings; warnings.filterwarnings('ignore') +3. with open('${actualDataPath}', 'r') as f: data = json.load(f) +4. plt.savefig('${outputPath}', format='svg', bbox_inches='tight') +${periodsConstant} +Generate ONLY working Python code, no explanations.`; + + try { + // Construct a properly-configured LLMWrapper so getLLMParameters can parse the model + // name and extract any thinking-level suffix (e.g., 'gemini-3-flash-preview low'). + const vizLLM = new LLMWrapper({ clientId: this.clientId, underlyingModel: options.underlyingModel }); + const { temperature, underlyingModel: parsedModel, reasoningEffort } = vizLLM.getLLMParameters(0.1); + + // Create messages array. + // systemPrompt is stable across requests and will be cached. + // schemaPrompt is request-specific and sent as a separate turn after the system message. + const messages = [ + { role: 'system', content: systemPrompt }, + { role: 'system', content: schemaPrompt }, + { role: 'assistant', content: 'I have reviewed the data file schema and am ready to generate the visualization code.' }, + { role: 'user', content: userPrompt } + ]; + + const response = await vizLLM.createChatCompletion( + messages, + parsedModel, + null, // no zodSchema + temperature, + reasoningEffort + ); + + // Extract Python code from response content + let pythonCode = response.content.trim(); + + // Remove markdown code blocks if present + if (pythonCode.startsWith('```python')) { + pythonCode = pythonCode.replace(/```python\r?\n/, '').replace(/\r?\n```$/, ''); + } else if (pythonCode.startsWith('```')) { + pythonCode = pythonCode.replace(/```\r?\n/, '').replace(/\r?\n```$/, ''); + } + + return pythonCode; + + } catch (err) { + // Suppress error logging - errors are thrown and handled by caller + throw new Error(`AI visualization generation failed: ${err.message}`); + } + } + + /** + * Describe data for AI to understand + */ + buildSchemaDescription(filePath, data) { + const describe = (val, depth = 0) => { + if (val === null || val === undefined) return 'null'; + + if (Array.isArray(val)) { + if (val.length === 0) return '[]'; + const first = val[0]; + if (typeof first === 'number') { + const sample = val.slice(0, Math.min(val.length, 100)); + const min = Math.min(...sample).toFixed(2); + const max = Math.max(...sample).toFixed(2); + return `[number, ...] // ${val.length} values, range ${min}–${max}`; + } + if (typeof first === 'string') { + const preview = val.slice(0, 3).map(s => JSON.stringify(s)).join(', '); + return `[${preview}${val.length > 3 ? ', ...' : ''}] // ${val.length} strings`; + } + if (typeof first === 'object' && first !== null) { + const pad = ' '.repeat(depth + 1); + return `[ // ${val.length} items\n${pad}${describe(first, depth + 1)}\n${' '.repeat(depth)}]`; + } + return JSON.stringify(val.slice(0, 3)) + (val.length > 3 ? '...' : ''); + } + + if (typeof val === 'object') { + if (depth > 4) return '{...}'; + const pad = ' '.repeat(depth + 1); + const entries = Object.entries(val) + .map(([k, v]) => `${pad}"${k}": ${describe(v, depth + 1)}`) + .join(',\n'); + return `{\n${entries}\n${' '.repeat(depth)}}`; + } + + if (typeof val === 'string') return JSON.stringify(val); + return String(val); + }; + + return `File: ${filePath}\nSchema:\n${describe(data)}`; + } + + describeData(data, variables) { + const lines = []; + + // Time series info + if (data.time) { + lines.push(`Time series data with ${data.time.length} time points`); + lines.push(`Time range: ${data.time[0]} to ${data.time[data.time.length - 1]}`); + } + + // Variables info + lines.push(`\nVariables (${variables.length}):`); + variables.forEach(varName => { + if (data[varName]) { + const values = data[varName]; + const min = Math.min(...values); + const max = Math.max(...values); + const avg = values.reduce((a, b) => a + b, 0) / values.length; + + lines.push(`- ${varName}: range [${min.toFixed(2)}, ${max.toFixed(2)}], avg ${avg.toFixed(2)}`); + + // Detect trends + const first = values[0]; + const last = values[values.length - 1]; + const change = ((last - first) / first * 100).toFixed(1); + lines.push(` Trend: ${change > 0 ? 'increasing' : 'decreasing'} by ${Math.abs(change)}%`); + } + }); + + // Feedback loops if present + if (data.feedbackLoops) { + lines.push(`\nFeedback loops: ${data.feedbackLoops.length} loops present`); + data.feedbackLoops.forEach(loop => { + lines.push(`- ${loop.name || 'Unnamed'} (${loop.polarity})`); + }); + } + + return lines.join('\n'); + } + + /** + * Create visualization using Python (matplotlib) - returns SVG string + */ + async createVisualizationWithPython(type, data, variables, options) { + const vizId = this.generateVizId(); + const scriptPath = this.validatePath(join(this.sessionTempDir, `visualization-${vizId}.py`)); + const dataPath = this.validatePath(join(this.sessionTempDir, `data-${vizId}.json`)); + const outputPath = this.validatePath(join(this.sessionTempDir, `visualization-${vizId}.svg`)); + + let svgContent = null; + let error = null; + + try { + // 1. Write data to temp file + const normalizedData = this.normalizeArrayLengths(data, variables); + writeFileSync(dataPath, JSON.stringify(normalizedData)); + + // 2. Generate Python script + const pythonScript = this.generatePythonVisualizationScript( + type, dataPath, outputPath, variables, options + ); + writeFileSync(scriptPath, pythonScript); + logger.log(`[VizEngine] Template script created: ${scriptPath} at ${new Date().toISOString()}`); + + // 3. Execute Python script + await this.executePythonScript(scriptPath); + + // 4. Read generated SVG and validate + const fileContent = readFileSync(outputPath, 'utf8'); + + if (!fileContent.includes('= ${start} and t <= ${end}] +for _key in list(data.keys()): + if isinstance(data[_key], list) and len(data[_key]) == len(_time_arr): + data[_key] = [data[_key][i] for i in _indices] +`; + } + + // Returns a Python snippet that filters a run-keyed {runId: {time, var1,...}} data dict to options.timeRange. + #timeRangeFilterRunKeyed(options) { + if (!options?.timeRange) return ''; + const { start, end } = options.timeRange; + return ` +# Apply time range filter per run +for _run_id in list(data.keys()): + _run = data[_run_id] + _time_arr = _run.get('time', []) + _indices = [i for i, t in enumerate(_time_arr) if t >= ${start} and t <= ${end}] + for _key in list(_run.keys()): + if isinstance(_run[_key], list) and len(_run[_key]) == len(_time_arr): + _run[_key] = [_run[_key][i] for i in _indices] +`; + } + + /** + * Generate Python script for visualization + */ + generatePythonVisualizationScript(type, dataPath, outputPath, variables, options) { + switch (type) { + case 'time_series': + return this.generateTimeSeriesScript(dataPath, outputPath, variables, options); + case 'phase_portrait': + return this.generatePhasePortraitScript(dataPath, outputPath, variables, options); + case 'feedback_dominance': + return this.generateFeedbackDominanceScript(dataPath, outputPath, variables, options); + case 'comparison': + return this.generateComparisonScript(dataPath, outputPath, variables, options); + default: + throw new Error(`Unknown visualization type: ${type}`); + } + } + + /** + * Generate time series plot script + */ + generateTimeSeriesScript(dataPath, outputPath, variables, options) { + const bandPalette = ['#4e79a7','#f28e2b','#59a14f','#e15759','#76b7b2','#edc948','#b07aa1','#ff9da7','#9c755f','#bab0ac']; + let paletteIdx = 0; + const labelColorMap = {}; + const periods = (options.highlightPeriods || []).map(period => { + if (!labelColorMap[period.label]) { + labelColorMap[period.label] = period.color || bandPalette[paletteIdx++ % bandPalette.length]; + } + return { ...period, color: labelColorMap[period.label] }; + }); + + const highlightPeriodsCode = periods.map(p => + `\nax.axvspan(${p.start}, ${p.end}, alpha=0.2, color='${p.color}', zorder=0, linewidth=0)` + ).join(''); + + const uniqueLabelPeriods = Object.entries(labelColorMap).map(([label, color]) => ({ label, color })); + const legendCode = uniqueLabelPeriods.length > 0 + ? `import matplotlib.patches as mpatches +band_handles = [${uniqueLabelPeriods.map(p => `mpatches.Patch(facecolor='${p.color}', alpha=0.6, label='${p.label}')`).join(', ')}] +line_handles = [l for l in ax.lines if not l.get_label().startswith('_')] +ax.legend(handles=band_handles + line_handles, loc='best')` + : `ax.legend(loc='best')`; + + const su = options.seriesUnits || {}; + const unitValues = variables.map(v => su[v]).filter(Boolean); + const sharedUnit = unitValues.length === variables.length && new Set(unitValues).size === 1 ? unitValues[0] : null; + const yAxisLabel = sharedUnit ? `Value (${sharedUnit})` : 'Value'; + + return ` +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +import json +import warnings +warnings.filterwarnings('ignore') + +# Load data +with open('${dataPath}', 'r') as f: + data = json.load(f) +${this.#timeRangeFilterFlat(options)} +fig, ax = plt.subplots(figsize=(${(options.width || 800)/100}, ${(options.height || 600)/100})) + +# Background highlight periods (drawn first so lines render on top) +${highlightPeriodsCode} + +# Plot each variable +${variables.map(v => { + const units = su[v]; + const label = units ? `${v.replaceAll('_', ' ')} (${units})` : v.replaceAll('_', ' '); + return `\nax.plot(data['time'], data['${v}'], label='${label}', linewidth=2, zorder=3)`; + }).join('')} + +# Styling +ax.set_xlabel('Time (${options.timeUnits || 'units'})', fontsize=12) +ax.set_ylabel('${yAxisLabel}', fontsize=12) +ax.set_title('${options.title || 'Time Series'}', fontsize=14, fontweight='bold') +${legendCode} +ax.grid(True, alpha=0.3) + +plt.tight_layout() +plt.savefig('${outputPath}', format='svg', bbox_inches='tight') +plt.close() +print('Visualization saved') +`.trim(); + } + + /** + * Generate phase portrait script + */ + generatePhasePortraitScript(dataPath, outputPath, variables, options) { + const [xVar, yVar] = variables; + const su = options.seriesUnits || {}; + const xLabel = su[xVar] ? `${xVar.replaceAll('_', ' ')} (${su[xVar]})` : xVar.replaceAll('_', ' '); + const yLabel = su[yVar] ? `${yVar.replaceAll('_', ' ')} (${su[yVar]})` : yVar.replaceAll('_', ' '); + const timeLabel = options.timeUnits ? `Time (${options.timeUnits})` : 'Time'; + return ` +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +import numpy as np +import json +import warnings +warnings.filterwarnings('ignore') + +with open('${dataPath}', 'r') as f: + data = json.load(f) +${this.#timeRangeFilterFlat(options)} +fig, ax = plt.subplots(figsize=(8, 6)) + +time = np.array(data['time']) +x = np.array(data['${xVar}']) +y = np.array(data['${yVar}']) + +scatter = ax.scatter(x, y, c=time, cmap='viridis', s=20, alpha=0.6) +ax.plot(x, y, 'k-', alpha=0.3, linewidth=0.5) + +ax.scatter(x[0], y[0], c='green', s=100, marker='o', label='Start', zorder=5) +ax.scatter(x[-1], y[-1], c='red', s=100, marker='s', label='End', zorder=5) + +ax.set_xlabel('${xLabel}', fontsize=12) +ax.set_ylabel('${yLabel}', fontsize=12) +ax.set_title('Phase Portrait: ${yVar.replaceAll('_', ' ')} vs ${xVar.replaceAll('_', ' ')}', fontsize=14, fontweight='bold') +ax.legend() +ax.grid(True, alpha=0.3) + +cbar = plt.colorbar(scatter, ax=ax) +cbar.set_label('${timeLabel}', fontsize=10) + +plt.tight_layout() +plt.savefig('${outputPath}', format='svg', bbox_inches='tight') +plt.close() +print('Visualization saved') +`.trim(); + } + + /** + * Generate feedback dominance script (stacked area chart) + * + * Expected format: + * - data: { time: [...], loopId1: [...], loopId2: [...], ... } + * - variables: ['loopId1', 'loopId2', ...] + * - options.highlightPeriods: [{ loopIds: [...], startTime: x, endTime: y, label: '...', color: '...' }, ...] + */ + generateFeedbackDominanceScript(dataPath, outputPath, variables, options) { + // Generate the loop variable names for Python script + const loopVarsList = variables.map(v => `'${v}'`).join(', '); + + return ` +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +import matplotlib.ticker +import numpy as np +import json +import warnings +warnings.filterwarnings('ignore') + +with open('${dataPath}', 'r') as f: + data = json.load(f) +${this.#timeRangeFilterFlat(options)} +fig, ax = plt.subplots(figsize=(${(options.width || 800)/100}, ${(options.height || 600)/100})) + +# Get time array +time = data.get('time', []) + +# Loop IDs to plot (from variables parameter) +loop_ids = [${loopVarsList}] + +# Collect loop data +loop_data = [] +loop_labels = [] + +for loop_id in loop_ids: + if loop_id in data: + loop_values = data[loop_id] + if loop_values and len(loop_values) > 0: + loop_data.append(np.array(loop_values)) + loop_labels.append(loop_id) + +# Create stacked area plot +if len(loop_data) > 0 and len(time) > 0: + time = np.array(time) + + # Add highlight periods for dominant loops (from options.highlightPeriods) + highlight_periods = ${JSON.stringify(options.highlightPeriods || [])} + + for period in highlight_periods: + start_time = period.get('startTime', 0) + end_time = period.get('endTime', 0) + dominant_loops = period.get('loopIds', []) + label = period.get('label', '') + color = period.get('color', 'yellow') + + if start_time < end_time: + # Create label from dominant loop IDs if not provided + if not label and dominant_loops: + label = ', '.join(dominant_loops[:3]) + if len(dominant_loops) > 3: + label += f' (+{len(dominant_loops)-3} more)' + + # Add background shading for this period + ax.axvspan(start_time, end_time, alpha=0.15, color=color, + label=f'Dominant: {label}' if label else 'Dominant period', zorder=0) + + # Plot the stacked areas on top of background shading + colors = plt.cm.tab10(np.linspace(0, 1, len(loop_data))) + ax.stackplot(time, *loop_data, labels=loop_labels, colors=colors, alpha=0.7) + + ax.yaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(lambda y, _: f'{y:.0f}%')) + ax.set_xlabel('Time (${options.timeUnits || 'units'})', fontsize=12) + ax.set_ylabel('Percent of Behavior Explained', fontsize=12) + ax.set_title('${options.title || 'Feedback Loop Dominance Over Time'}', fontsize=14, fontweight='bold') + ax.legend(loc='upper left', bbox_to_anchor=(1.02, 1), borderaxespad=0) + ax.grid(True, alpha=0.3) +else: + ax.text(0.5, 0.5, 'No feedback loop data available', + ha='center', va='center', transform=ax.transAxes, fontsize=12) + +plt.tight_layout() +plt.savefig('${outputPath}', format='svg', bbox_inches='tight') +plt.close() +print('Visualization saved') +`.trim(); + } + + /** + * Generate comparison script + */ + generateComparisonScript(dataPath, outputPath, variables, options) { + // For comparison, variables is expected to be a single variable name + const variable = Array.isArray(variables) ? variables[0] : variables; + + return ` +import matplotlib +matplotlib.use('Agg') +import matplotlib.pyplot as plt +import json +import warnings +warnings.filterwarnings('ignore') + +with open('${dataPath}', 'r') as f: + data = json.load(f) +${this.#timeRangeFilterRunKeyed(options)} +fig, ax = plt.subplots(figsize=(${(options.width || 800)/100}, ${(options.height || 600)/100})) + +colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'] +line_styles = ['-', '--', '-.', ':'] + +# Run-keyed format: { runId: { time: [...], varName: [...], ... } } +run_items = [] +for run_id, run_data in data.items(): + run_items.append((run_id, run_data.get('time', []), run_data.get('${variable}', []))) + +for idx, (label, time_data, values) in enumerate(run_items): + color = colors[idx % len(colors)] + line_style = line_styles[0] if idx == 0 else line_styles[(idx % (len(line_styles)-1)) + 1] + ax.plot(time_data, values, label=label, color=color, linestyle=line_style, linewidth=2) + +ax.set_xlabel('Time', fontsize=12) +ax.set_ylabel('${options.seriesUnits?.[variable] ? `${variable} (${options.seriesUnits[variable]})` : variable}', fontsize=12) +ax.set_title('${options.title || `Comparison: ${variable}`}', fontsize=14, fontweight='bold') +ax.legend(loc='best') +ax.grid(True, alpha=0.3) + +plt.tight_layout() +plt.savefig('${outputPath}', format='svg', bbox_inches='tight') +plt.close() +print('Visualization saved') +`.trim(); + } + + /** + * Execute a Python script. + * + * On Linux in production this process runs inside a bwrap container, so the + * OS-level mount namespace provides isolation — no additional Python-level + * sandbox wrapper is needed. On macOS/Windows (dev) the worker spawner has + * already emitted a prominent warning about the lack of sandboxing. + */ + async executePythonScript(scriptPath) { + const validatedPath = this.validatePath(scriptPath); + + return new Promise((resolve, reject) => { + const pythonProcess = spawn('python3', [validatedPath], { + cwd: this.resolvedTempDir, + env: { + PATH: process.env.PATH, + HOME: this.resolvedTempDir, + TMPDIR: this.resolvedTempDir, + }, + timeout: 70000, + }); + + let stdout = ''; + let stderr = ''; + + pythonProcess.stdout.on('data', (data) => { stdout += data.toString(); }); + pythonProcess.stderr.on('data', (data) => { stderr += data.toString(); }); + + pythonProcess.on('close', (code) => { + if (code !== 0) { + reject(new Error(`Python script failed (code ${code}): ${stderr}`)); + } else { + resolve(stdout); + } + }); + + pythonProcess.on('error', (err) => { + reject(new Error(`Failed to spawn Python: ${err.message}`)); + }); + }); + } +} diff --git a/app.js b/app.js index 0b196e4b..ff9873ad 100644 --- a/app.js +++ b/app.js @@ -2,6 +2,8 @@ import express from 'express' import config from './config.js' import cors from 'cors' import logger from './utilities/logger.js' +import { createServer } from 'http' +import { WebSocketServer } from 'ws' import v1Initialize from './routes/v1/initialize.js' import v1Engines from './routes/v1/engines.js' @@ -11,6 +13,11 @@ import v1EvalsList from './routes/v1/evalsList.js' import v1EvalsTestDetails from './routes/v1/evalsTestDetails.js' import v1Leaderboard from './routes/v1/leaderboard.js' +import { createHealthRouter } from './routes/health.js'; + +import { SessionManager } from './agent/utilities/SessionManager.js' +import { WebSocketHandler } from './agent/WebSocket.js' + const app = express() app.use(cors()) @@ -21,14 +28,77 @@ if (app.get('env') === 'production') { app.set('trust proxy', 1) // trust first proxy } -app.use("/api/v1/initialize", v1Initialize); -app.use("/api/v1/engines", v1Engines); -app.use("/api/v1/engines/", v1EngineParameters); //:engine/parameters -app.use("/api/v1/engines/", v1EngineGenerate); //:engine/generate -app.use("/api/v1/evals", v1EvalsList); -app.use("/api/v1/evals", v1EvalsTestDetails); -app.use("/api/v1/leaderboard", v1Leaderboard); +// Initialize Session Manager (before routes) +const sessionManager = new SessionManager(); + +app.use('/', createHealthRouter(sessionManager)); + + +const apiRouter = express.Router(); +apiRouter.use("/initialize", v1Initialize); +apiRouter.use("/engines", v1Engines); +apiRouter.use("/engines/", v1EngineParameters); //:engine/parameters +apiRouter.use("/engines/", v1EngineGenerate); //:engine/generate +apiRouter.use("/evals", v1EvalsList); +apiRouter.use("/evals", v1EvalsTestDetails); +apiRouter.use("/leaderboard", v1Leaderboard); + +app.use("/api/v1", apiRouter); + +// Create HTTP server for REST API +const server = createServer(app); + +// Determine if WebSocket should run on same or separate port +const useSamePort = config.port === config.websocketPort; + +// Create WebSocket server (either on same server or separate server) +let wsHttpServer; +let wss; + +if (useSamePort) { + // WebSocket on the same HTTP server as REST API + wss = new WebSocketServer({ + server: server, + path: '/api/v1' + }); +} else { + // WebSocket on a separate HTTP server and port + wsHttpServer = createServer(); + wss = new WebSocketServer({ + server: wsHttpServer, + path: '/api/v1' + }); +} + +wss.on('connection', (ws) => { + new WebSocketHandler(ws, sessionManager); +}); -app.listen(config.port, () => { +function shutdown(signal) { + logger.log(`${signal} received, shutting down gracefully...`); + // Kill all worker child processes first — ws.close() is async and process.exit() + // fires before #onClose can run #killWorker, so workers would otherwise be orphaned. + WebSocketHandler.killAll(); + wss.clients.forEach(ws => ws.close(1000, 'Server shutting down')); + sessionManager.shutdown(); + process.exit(0); +} + +process.on('SIGTERM', () => shutdown('SIGTERM')); +process.on('SIGINT', () => shutdown('SIGINT')); + +// Start HTTP server +server.listen(config.port, () => { logger.log(`ai-proxy-service listening on port ${config.port}`); + if (useSamePort) { + logger.log(`WebSocket server available at ws://localhost:${config.port}/api/v1`); + } }); + +// Start WebSocket server on separate port if needed +if (!useSamePort) { + wsHttpServer.listen(config.websocketPort, () => { + logger.log(`WebSocket server listening on port ${config.websocketPort}`); + logger.log(`WebSocket server available at ws://localhost:${config.websocketPort}/api/v1`); + }); +} diff --git a/config.js b/config.js index d27d9b93..81c74380 100644 --- a/config.js +++ b/config.js @@ -1,11 +1,39 @@ -/*** - * You must have a .env file which has the following keys - * OPEN_API_KEY which is your open AI access token - */ +import { ThinkingLevel } from "@google/genai"; + const config = { - "port": 3000, - "reporterURL": process.env.REPORTER_URL || null, // Optional URL to POST engine usage metrics + "port": process.env.PORT || 3000, + "websocketPort": process.env.WEBSOCKET_PORT || 3000, + + /* + * Reporting URLs + */ + "metricsReporterURL": process.env.METRICS_REPORTER_URL || null, // Optional URL to POST engine usage metrics + "tokenReporterURL": process.env.TOKEN_REPORTER_URL || null, // Optional URL to POST agent LLM token usage + + /* + * Defaults for the engines that use LLMWrapper and the agent tools that use those engines + */ + "buildDefaultModel": 'gemini-3.5-flash low', //LLMWrapper underlyingModel default for building model tools + "nonBuildDefaultModel": 'gemini-3.5-flash low', //LLMWrapper underlyingModel default for non-building model tools + + /* + * These settings control the operation of the agents + */ + "agentSessionTempDir": process.env.AGENT_SESSION_TEMP_DIR || null, // Optional custom temp directory for session files (defaults to OS tmpdir/sd-agent) + "agentMaxTokensForEngines": 32_000, // Maximum tokens before force switching to file-based editing + "agentMaxContextTokens": 32_000, // Maximum tokens for conversation history sent to Claude API + "agentTargetedEditingMinimum": 250, //Above this size, models can be edited without quantitative/qualitative engine + "agentDefaultProvider": 'anthropic', // Default LLM provider when client does not specify one ('anthropic' | 'google') + "agentAnthropicModel": 'claude-sonnet-4-6', // Model used for agent conversations MUST BE Anthropic models + "agentAnthropicSummaryModel": 'claude-haiku-4-5', // Model used for conversation history summarization MUST BE Anthropic models + "agentGeminiModel": 'gemini-3.5-flash', // Model used for agent conversations MUST BE gemini models + "agentGeminiSummaryModel": 'gemini-3.1-flash-lite-preview', // Model used for conversation history summarization MUST BE gemini models + "agentAnthropicEffort": "medium", + "agentAnthropicThinking": { type: "enabled", "budget_tokens": 10000 }, + "agentGeminiThinking": { thinkingLevel: ThinkingLevel.MEDIUM }, + "agentToolHighEffortBuildDefaultModel": 'gemini-3.5-flash high', //LLMWrapper underlyingModel default for building model tools + "agentToolHighEffortNonBuildDefaultModel": 'gemini-3.5-flash high', //LLMWrapper underlyingModel default for non-building model tools }; -export default config \ No newline at end of file +export default config diff --git a/engines/causal-chains/engine.js b/engines/causal-chains/engine.js index 77221962..77c65704 100644 --- a/engines/causal-chains/engine.js +++ b/engines/causal-chains/engine.js @@ -13,12 +13,13 @@ import logger from "../../utilities/logger.js"; const __filename = fileURLToPath(import.meta.url); // get the resolved path to the file const __dirname = path.dirname(__filename); // get the name of the directory const THIRD_PARTY_DIR = path.resolve(__dirname, '../../third-party/causal-chains'); +const BINARY_PATH = path.join(THIRD_PARTY_DIR, process.platform === 'win32' ? 'causal-chains.exe' : 'causal-chains'); class Engine { constructor() { } - static DEFAULT_MODEL = 'o4-mini'; + static DEFAULT_MODEL = 'gpt-5-mini'; static description() { return `This engine improves conformance to user instructions about feedback complexity by prompting the LLM to @@ -28,15 +29,16 @@ focus on chains of relationships, rather then individual links.` static supportedModes() { // check that the third-party/causal-chains Go binary exists try { - const stats = statSync(`${THIRD_PARTY_DIR}/causal-chains`); - const isExecutable = !!(stats.mode & (fs.constants.S_IXUSR | fs.constants.S_IXGRP | fs.constants.S_IXOTH)); + statSync(BINARY_PATH); + // on Windows all files are executable; on Unix check the execute bit + const isExecutable = process.platform === 'win32' || !!(statSync(BINARY_PATH).mode & 0o111); if (isExecutable) { return ["cld"]; } } catch (err) { - logger.log("Error checking supporting modes on causal-chains..."); - logger.log(err); + //logger.log("Error checking supporting modes on causal-chains..."); + //logger.log(err); // fine to fallthrough to the return below } @@ -127,7 +129,7 @@ focus on chains of relationships, rather then individual links.` const inputPath = path.resolve(path.join(tempDir, 'data.json')); // logger.log(`input path is ${inputPath}`); await fs.writeFile(inputPath, JSON.stringify(input)); - const { stdout, stderr } = await promiseExec(`${THIRD_PARTY_DIR}/causal-chains ${inputPath}`, {cwd: tempDir}); + const { stdout, stderr } = await promiseExec(`"${BINARY_PATH}" "${inputPath}"`, {cwd: tempDir}); return JSON.parse(stdout.toString()); } catch (err) { logger.log(`causal-chains returned non-zero exit code: ${err.status}`); diff --git a/engines/qualitative/QualitativeEngineBrain.js b/engines/qualitative/QualitativeEngineBrain.js index 8866b9e2..3a7d581a 100644 --- a/engines/qualitative/QualitativeEngineBrain.js +++ b/engines/qualitative/QualitativeEngineBrain.js @@ -199,9 +199,9 @@ You will conduct a multistep process: responseFormat = undefined; } - let messages = [{ - role: systemRole, - content: systemPrompt + let messages = [{ + role: systemRole, + content: systemPrompt }]; if (this.#data.backgroundKnowledge) { @@ -217,6 +217,7 @@ You will conduct a multistep process: }); } + // Check if lastModel has actual content (relationships) if (lastModel && lastModel.relationships && lastModel.relationships.length > 0) { messages.push({ role: "assistant", content: JSON.stringify(lastModel.relationships, null, 2) }); @@ -239,6 +240,14 @@ You will conduct a multistep process: } async generateDiagram(userPrompt, lastModel) { + // Ensure lastModel is always defined as an empty model structure if undefined or null + if (!lastModel || typeof lastModel !== 'object') { + lastModel = { relationships: [] }; + } else { + // Ensure required array exists + lastModel.relationships = lastModel.relationships || []; + } + const llmParams = this.setupLLMParameters(userPrompt, lastModel); //get what it thinks the relationships are with this information diff --git a/engines/qualitative/engine.js b/engines/qualitative/engine.js index 8d8f5b1c..0caa4c5e 100644 --- a/engines/qualitative/engine.js +++ b/engines/qualitative/engine.js @@ -21,6 +21,12 @@ class Engine { additionalParameters() { return [{ + name: "clientId", + type: "string", + required: false, + uiElement: "hidden", + description: "A unique identifier for the end user of this session" + },{ name: "googleKey", type: "string", required: false, diff --git a/engines/quantitative-experimental/engine.js b/engines/quantitative-experimental/engine.js index 31f62d06..8d94b92d 100644 --- a/engines/quantitative-experimental/engine.js +++ b/engines/quantitative-experimental/engine.js @@ -114,6 +114,12 @@ to experiment with the specific prompts passed to the LLM.`; required: false, uiElement: "hidden", description: "Whether or not your client can handle models with modules" + },{ + name: "supportsSubTypes", + type: "boolean", + required: false, + uiElement: "hidden", + description: "Whether or not your client can handle models with queues, conveyors or ovens" } ]); } diff --git a/engines/quantitative-mentor/engine.js b/engines/quantitative-mentor/engine.js index 390e7175..b976478b 100644 --- a/engines/quantitative-mentor/engine.js +++ b/engines/quantitative-mentor/engine.js @@ -21,6 +21,12 @@ Works by sending an LLM the user's request along with a set of systems thinking additionalParameters() { return [{ + name: "clientId", + type: "string", + required: false, + uiElement: "hidden", + description: "A unique identifier for the end user of this session" + },{ name: "googleKey", type: "string", required: false, @@ -59,6 +65,12 @@ Works by sending an LLM the user's request along with a set of systems thinking required: false, uiElement: "hidden", description: "Whether or not your client can handle models with modules" + },{ + name: "supportsSubTypes", + type: "boolean", + required: false, + uiElement: "hidden", + description: "Whether or not your client can handle models with queues, conveyors or ovens" }]; } diff --git a/engines/quantitative/QuantitativeEngineBrain.js b/engines/quantitative/QuantitativeEngineBrain.js index 68c9641e..e41d253d 100644 --- a/engines/quantitative/QuantitativeEngineBrain.js +++ b/engines/quantitative/QuantitativeEngineBrain.js @@ -41,6 +41,56 @@ When constructing modular models, you MUST create cross-level ghost variables fo FAILURE TO CREATE AND LINK GHOST VARIABLES WILL BREAK SIMULATION. This is non-negotiable. REFERENCING THE ORIGINAL SOURCE VARIABLE DIRECTLY FROM A CONSUMING MODULE WILL BREAK SIMULATION. Always use the ghost.` + static SUB_TYPE_REQUIREMENTS_SECTION = +`CRITICAL DISCRETE-ENTITY SUB-TYPE REQUIREMENTS: + +WHEN TO USE DISCRETE ENTITY SUB-TYPES: +- Use sub-types ONLY when the model explicitly requires discrete-event, queue, or pipeline semantics +- DO NOT use sub-types for standard continuous stocks and flows — they add significant complexity +- Only introduce sub-types when specifically requested by the user + +STOCK SUB-TYPES — set 'subType' and include 'additionalProperties': +- 'queue': Waiting line. additionalProperties: fifoEnabled, oneAtATime, splitBatches, discrete, roundRobin, queueOutflowPriority, purgeEq, overflow. +- 'oven': Batch processor; all items released together after processTime. additionalProperties: processTime (required), capacity, inflowLimit, fillTime, cleanTime, sample, arrest. +- 'conveyor': Pipeline delay; items exit after processTime. additionalProperties: processTime (required), capacity, inflowLimit, sample, arrest. + +FLOW SUB-TYPES — leave 'equation' empty; automatically computed: +- 'discreteOutflow': Output from a conveyor or oven. +- 'conveyorLeakage': Leakage from a conveyor. Set additionalProperties: leakFraction (required, units of 1/time_unit when exponential, dimensionless otherwise), exponential (default true — almost always use exponential; linear only when explicitly requested), leakZoneStart, leakZoneEnd, leakIntegers, ignorePrevZones, forceLeakFraction. +- 'queueOutflow': Output from a queue. +- 'queueOverflow': Overflow from a full queue (requires overflow: true on the queue). + +REGULAR FLOWS entering a conveyor may set additionalProperties: +- spreadFlow: how inflow distributes along the conveyor ('none', 'even', 'destination', 'distribution', 'source'). +- distribEq: required when spreadFlow is 'distribution'. + +EQUATION RULES: +- 'queue', 'oven', 'conveyor' stocks: 'equation' is the initial value, like a regular stock. +- Flow sub-types: leave 'equation' empty. +- Settings go in 'additionalProperties', not equations. + +RELATIONSHIP REQUIREMENTS: +- Any variable referenced in an additionalProperties expression requires a relationship arrow FROM that variable TO the element. +- Use XMILE syntax with underscores (e.g. 'service_time' not 'service time'). + +CONVEYOR DESIGN RULES: + +When to use conveyor vs. stock: +- Use a conveyor when entities must spend a minimum or fixed duration in a stage (pipeline delay, aging, disease duration). The conveyor transit time encodes the dwell time. +- Use a plain stock when residence time is exponentially distributed (first-order delay) or when there is no minimum dwell requirement. + +Leakage vs. outflow: +- 'conveyorLeakage': entities exit before completing transit (early exit). Configure via additionalProperties.leakFraction on the leakage flow. +- 'discreteOutflow': entities that completed the full transit. +- NEVER split the conveyor outflow via auxiliary arithmetic to route into different stages. + +Wiring leakages: +- Every conveyorLeakage flow must appear in the outflows list of its source conveyor AND in the inflows list of its destination. + +Mass conservation check: +- Sum of all population stocks at t=0 must equal sum at all t (unless the model has explicit external births/deaths). +- The conveyor's discreteOutflow is wired to exactly one destination — do not split it.` + static ARRAY_REQUIREMENTS_SECTION = `CRITICAL ARRAY REQUIREMENTS: @@ -444,7 +494,7 @@ NEVER identify feedback loops for the user in explanatory text. Let users discov static PROFESSIONAL_MODE_INTRO = `You are a System Dynamics Professional Modeler. Generate stock and flow models from user-provided text following these mandatory rules:` - static generateSystemPrompt(mentorMode, supportsArrays, supportsModules) { + static generateSystemPrompt(mentorMode, supportsArrays, supportsModules, supportsSubTypes) { let prompt = ""; // Add intro based on mode @@ -464,6 +514,11 @@ NEVER identify feedback loops for the user in explanatory text. Let users discov prompt += QuantitativeEngineBrain.ARRAY_REQUIREMENTS_SECTION + "\n\n"; } + // Add sub-type requirements if sub-types are supported + if (supportsSubTypes) { + prompt += QuantitativeEngineBrain.SUB_TYPE_REQUIREMENTS_SECTION + "\n\n"; + } + // Always add mandatory process section prompt += QuantitativeEngineBrain.MANDATORY_PROCESS_SECTION + "\n\n"; @@ -524,7 +579,8 @@ NEVER identify feedback loops for the user in explanatory text. Let users discov backgroundPrompt: QuantitativeEngineBrain.DEFAULT_BACKGROUND_PROMPT, problemStatementPrompt: QuantitativeEngineBrain.DEFAULT_PROBLEM_STATEMENT_PROMPT, supportsArrays: false, - supportsModules: false + supportsModules: false, + supportsSubTypes: false }; #llmWrapper; @@ -532,12 +588,13 @@ NEVER identify feedback loops for the user in explanatory text. Let users discov constructor(params) { Object.assign(this.#data, params); - // Generate system prompt based on mentor mode, array support, and module support if not explicitly provided + // Generate system prompt based on mentor mode, array support, module support, and sub-type support if not explicitly provided if (!this.#data.systemPrompt) { this.#data.systemPrompt = QuantitativeEngineBrain.generateSystemPrompt( this.#data.mentorMode, this.#data.supportsArrays, - this.#data.supportsModules + this.#data.supportsModules, + this.#data.supportsSubTypes ); } @@ -636,33 +693,6 @@ NEVER identify feedback loops for the user in explanatory text. Let users discov response.relationships = relationships; } - #expandGraphicalFunctionsAndArrayEquations(response) { - // Re-expand flattened graphicalFunction format from LLM - // LLM sends: graphicalFunction: [{x, y}, ...] - // We need: graphicalFunction: {points: [{x, y}, ...]} - response.variables.forEach((v) => { - if (v.graphicalFunction && Array.isArray(v.graphicalFunction)) { - if (v.graphicalFunction.length > 0) { - v.graphicalFunction = { - points: v.graphicalFunction - }; - } else { - delete v.graphicalFunction; - } - } - - // Re-expand flattened arrayEquations forElements from comma-separated string to array - // LLM sends: forElements: "North,Q1" - // We need: forElements: ["North", "Q1"] - if (v.arrayEquations && Array.isArray(v.arrayEquations)) { - v.arrayEquations.forEach((eq) => { - if (eq.forElements && typeof eq.forElements === 'string') { - eq.forElements = eq.forElements.split(',').map(s => s.trim()); - } - }); - } - }); - } #cleanStockFlows(response) { // Go through all variables -- for any stock with inflows/outflows remove dimensions from inflow/outflow names @@ -869,6 +899,19 @@ NEVER identify feedback loops for the user in explanatory text. Let users discov } }); } + + // Process additionalProperties expressions for sub-typed variables + if (v.subType && v.additionalProperties && typeof v.additionalProperties === 'object') { + for (const [key, val] of Object.entries(v.additionalProperties)) { + if (typeof val === 'string') { + const original = val; + v.additionalProperties[key] = this.#replaceVariableNamesInEquation(val, variableNameMap); + if (original !== v.additionalProperties[key]) { + logger.debug(`[XMILE Conversion] Variable "${v.name}" additionalProperties.${key}: "${original}" → "${v.additionalProperties[key]}"`); + } + } + } + } }); } @@ -908,9 +951,6 @@ NEVER identify feedback loops for the user in explanatory text. Let users discov // Filter and clean relationships this.#filterInvalidRelationships(originalResponse); - // Expand graphical functions and array equations from flattened LLM format - this.#expandGraphicalFunctionsAndArrayEquations(originalResponse); - // Clean stock inflows/outflows this.#cleanStockFlows(originalResponse); @@ -937,7 +977,8 @@ NEVER identify feedback loops for the user in explanatory text. Let users discov this.#data.systemPrompt = QuantitativeEngineBrain.generateSystemPrompt( this.#data.mentorMode, this.#data.supportsArrays, - this.#data.supportsModules + this.#data.supportsModules, + this.#data.supportsSubTypes ); } @@ -945,15 +986,15 @@ NEVER identify feedback loops for the user in explanatory text. Let users discov //start with the system prompt const { underlyingModel, systemRole, temperature, reasoningEffort } = this.#llmWrapper.getLLMParameters(); let systemPrompt = this.#data.systemPrompt; - let responseFormat = this.#llmWrapper.generateQuantitativeSDJSONResponseSchema(this.#data.mentorMode, this.#data.supportsArrays); + let responseFormat = this.#llmWrapper.generateQuantitativeSDJSONResponseSchema(this.#data.mentorMode, this.#data.supportsArrays, this.#data.supportsSubTypes); if (!this.#llmWrapper.model.hasStructuredOutput) { throw new Error("Unsupported LLM " + this.#data.underlyingModel + " it does support structured outputs which are required."); } - let messages = [{ - role: systemRole, - content: systemPrompt + let messages = [{ + role: systemRole, + content: systemPrompt }]; if (this.#data.backgroundKnowledge) { @@ -969,32 +1010,9 @@ NEVER identify feedback loops for the user in explanatory text. Let users discov }); } - if (lastModel) { - // Flatten graphicalFunction and arrayEquations for LLM schema compatibility - // Convert: graphicalFunction: {points: [{x, y}, ...]} - // To: graphicalFunction: [{x, y}, ...] - // Convert: forElements: ["North", "Q1"] - // To: forElements: "North,Q1" - const flattenedModel = JSON.parse(JSON.stringify(lastModel)); // deep clone - if (flattenedModel.variables) { - flattenedModel.variables.forEach((v) => { - // Flatten graphicalFunction - if (v.graphicalFunction && v.graphicalFunction.points && Array.isArray(v.graphicalFunction.points)) { - v.graphicalFunction = v.graphicalFunction.points; - } - - // Flatten arrayEquations forElements from array to comma-separated string - if (v.arrayEquations && Array.isArray(v.arrayEquations)) { - v.arrayEquations.forEach((eq) => { - if (eq.forElements && Array.isArray(eq.forElements)) { - eq.forElements = eq.forElements.join(','); - } - }); - } - }); - } - - messages.push({ role: "assistant", content: JSON.stringify(flattenedModel, null, 2) }); + // Check if lastModel has actual content (variables or relationships) + if (lastModel && (lastModel.variables?.length > 0 || lastModel.relationships?.length > 0)) { + messages.push({ role: "assistant", content: JSON.stringify(lastModel, null, 2) }); if (this.#data.assistantPrompt) messages.push({ role: "user", content: this.#data.assistantPrompt }); @@ -1013,6 +1031,15 @@ NEVER identify feedback loops for the user in explanatory text. Let users discov } async generateModel(userPrompt, lastModel) { + // Ensure lastModel is always defined as an empty model structure if undefined or null + if (!lastModel || typeof lastModel !== 'object') { + lastModel = { variables: [], relationships: [] }; + } else { + // Ensure required arrays exist + lastModel.variables = lastModel.variables || []; + lastModel.relationships = lastModel.relationships || []; + } + const llmParams = this.setupLLMParameters(userPrompt, lastModel); //get what it thinks the relationships are with this information @@ -1032,7 +1059,7 @@ NEVER identify feedback loops for the user in explanatory text. Let users discov try { parsedObj = JSON.parse(originalResponse.content); } catch (err) { - console.log(originalResponse); + logger.log('Bad JSON from LLM:', originalResponse); throw new ResponseFormatError("Bad JSON returned by underlying LLM"); } return this.processResponse(parsedObj); diff --git a/engines/quantitative/engine.js b/engines/quantitative/engine.js index 09f1e3c2..d8846fc3 100644 --- a/engines/quantitative/engine.js +++ b/engines/quantitative/engine.js @@ -21,6 +21,12 @@ Works by sending an LLM the user's request along with a set of systems thinking additionalParameters() { return [{ + name: "clientId", + type: "string", + required: false, + uiElement: "hidden", + description: "A unique identifier for the end user of this session" + },{ name: "googleKey", type: "string", required: false, @@ -59,6 +65,12 @@ Works by sending an LLM the user's request along with a set of systems thinking required: false, uiElement: "hidden", description: "Whether or not your client can handle models with modules" + },{ + name: "supportsSubTypes", + type: "boolean", + required: false, + uiElement: "hidden", + description: "Whether or not your client can handle models with queues, conveyors or ovens" }]; } diff --git a/engines/seldon-experimental/engine.js b/engines/seldon-experimental/engine.js index b2322056..dd6bc6dd 100644 --- a/engines/seldon-experimental/engine.js +++ b/engines/seldon-experimental/engine.js @@ -142,14 +142,12 @@ wants to experiment with the specific prompts passed to the LLM.`; let brain = new SeldonBrain(parameters); const response = await brain.converse(prompt, currentModel); return { - output: { - textContent: response - } + output: response }; } catch(err) { logger.error(err); - return { - err: err.toString() + return { + err: err.toString() }; } } diff --git a/engines/seldon-ile-user/SeldonILEUserBrain.js b/engines/seldon-ile-user/SeldonILEUserBrain.js index b024da6d..8420a9df 100644 --- a/engines/seldon-ile-user/SeldonILEUserBrain.js +++ b/engines/seldon-ile-user/SeldonILEUserBrain.js @@ -118,7 +118,10 @@ A dominant feedback process is one that drives more than 50% of the model's beha reply = "Please re-run the model to compute the information we need to answer your question.
" + reply; } - return reply; + return { + textContent: reply, + feedbackInformationRequired: originalResponse.feedbackInformationRequired + }; } #isValidFeedbackContent() { diff --git a/engines/seldon-ile-user/engine.js b/engines/seldon-ile-user/engine.js index 59326090..35112b99 100644 --- a/engines/seldon-ile-user/engine.js +++ b/engines/seldon-ile-user/engine.js @@ -22,6 +22,12 @@ class Engine { additionalParameters() { return [{ + name: "clientId", + type: "string", + required: false, + uiElement: "hidden", + description: "A unique identifier for the end user of this session" + },{ name: "googleKey", type: "string", required: false, @@ -80,9 +86,7 @@ class Engine { let brain = new SeldonILEUserBrain(parameters); const response = await brain.converse(prompt, currentModel); return { - output: { - textContent: response - } + output: response }; } catch(err) { logger.error(err); diff --git a/engines/seldon-mentor/engine.js b/engines/seldon-mentor/engine.js index b5e7a96e..75ecb418 100644 --- a/engines/seldon-mentor/engine.js +++ b/engines/seldon-mentor/engine.js @@ -22,6 +22,12 @@ class Engine { additionalParameters() { return [{ + name: "clientId", + type: "string", + required: false, + uiElement: "hidden", + description: "A unique identifier for the end user of this session" + },{ name: "googleKey", type: "string", required: false, @@ -74,9 +80,7 @@ class Engine { const response = await brain.converse(prompt, currentModel); return { - output: { - textContent: response - } + output: response }; } catch(err) { logger.error(err); diff --git a/engines/seldon/SeldonBrain.js b/engines/seldon/SeldonBrain.js index d78ffdea..a8a3f49b 100644 --- a/engines/seldon/SeldonBrain.js +++ b/engines/seldon/SeldonBrain.js @@ -151,7 +151,10 @@ As the world's best System Dynamics Modeler, you will consider and apply the Sys reply = "Please re-run the model with calculate loop dominance information turned on.
" + reply; } - return reply; + return { + textContent: reply, + feedbackInformationRequired: originalResponse.feedbackInformationRequired + }; } mentor() { diff --git a/engines/seldon/engine.js b/engines/seldon/engine.js index 7df22584..03b5b48d 100644 --- a/engines/seldon/engine.js +++ b/engines/seldon/engine.js @@ -22,6 +22,12 @@ class Engine { additionalParameters() { return [{ + name: "clientId", + type: "string", + required: false, + uiElement: "hidden", + description: "A unique identifier for the end user of this session" + },{ name: "googleKey", type: "string", required: false, @@ -72,14 +78,12 @@ class Engine { let brain = new SeldonBrain(parameters); const response = await brain.converse(prompt, currentModel); return { - output: { - textContent: response - } + output: response }; } catch(err) { logger.error(err); - return { - err: err.toString() + return { + err: err.toString() }; } } diff --git a/engines/test-agent-build/engine.js b/engines/test-agent-build/engine.js new file mode 100644 index 00000000..077ba02e --- /dev/null +++ b/engines/test-agent-build/engine.js @@ -0,0 +1,96 @@ +import { runAgent } from '../../agent/utilities/AgentEvalRunner.js'; +import logger from '../../utilities/logger.js'; + +class Engine { + static supportedModes() { + return ['sfd', 'cld']; + } + + static description() { + return 'Test engine that wraps AgentOrchestrator for model-generation evals. Never shown in the public engine list.'; + } + + additionalParameters() { + return [ + { + name: 'agentName', + type: 'string', + required: true, + uiElement: 'text', + label: 'Agent Name', + description: 'Which agent config to use (e.g. merlin, socrates)' + }, + { + name: 'agentMode', + type: 'string', + required: false, + uiElement: 'text', + label: 'Agent Mode', + description: 'Execution mode override: sdk or manual. Defaults to the agent config value.' + }, + { + name: 'provider', + type: 'string', + required: false, + uiElement: 'text', + label: 'Provider', + description: 'LLM provider: anthropic (default) or google' + }, + { + name: 'mode', + type: 'string', + required: true, + uiElement: 'text', + label: 'Mode', + description: 'Model type: sfd or cld' + }, + { + name: 'problemStatement', + type: 'string', + required: false, + uiElement: 'textarea', + saveForUser: 'local', + label: 'Problem Statement', + description: 'Description of a dynamic issue within the system you are studying that highlights an undesirable behavior over time.', + minHeight: 50, + maxHeight: 100 + }, + { + name: 'backgroundKnowledge', + type: 'string', + required: false, + uiElement: 'textarea', + saveForUser: 'local', + label: 'Background Knowledge', + description: 'Background information you want the LLM model to consider when generating a diagram for you', + minHeight: 100 + } + ]; + } + + async generate(prompt, currentModel, parameters) { + try { + const { lastModel, explanation } = await runAgent(prompt, currentModel, parameters); + if (!lastModel) { + return { err: 'Agent did not produce a model' }; + } + return { + supportingInfo: { + title: lastModel.title, + explanation + }, + model: { + relationships: lastModel.relationships || [], + variables: lastModel.variables || [], + ...(lastModel.specs && { specs: lastModel.specs }), + ...(lastModel.modules && { modules: lastModel.modules }) + } + }; + } catch (err) { + logger.error('[test-agent-build] generate error:', err); + return { err: err.toString() }; + } + } +} + +export default Engine; diff --git a/engines/test-agent-discuss/engine.js b/engines/test-agent-discuss/engine.js new file mode 100644 index 00000000..7ccc4dcd --- /dev/null +++ b/engines/test-agent-discuss/engine.js @@ -0,0 +1,90 @@ +import { runAgent } from '../../agent/utilities/AgentEvalRunner.js'; +import logger from '../../utilities/logger.js'; + +class Engine { + static supportedModes() { + return ['sfd-discuss', 'cld-discuss']; + } + + static description() { + return 'Test engine that wraps AgentOrchestrator for discussion/Q&A evals. Never shown in the public engine list.'; + } + + additionalParameters() { + return [ + { + name: 'agentName', + type: 'string', + required: true, + uiElement: 'text', + label: 'Agent Name', + description: 'Which agent config to use (e.g. merlin, socrates)' + }, + { + name: 'agentMode', + type: 'string', + required: false, + uiElement: 'text', + label: 'Agent Mode', + description: 'Execution mode override: sdk or manual. Defaults to the agent config value.' + }, + { + name: 'provider', + type: 'string', + required: false, + uiElement: 'text', + label: 'Provider', + description: 'LLM provider: anthropic (default) or google' + }, + { + name: 'mode', + type: 'string', + required: true, + uiElement: 'text', + label: 'Mode', + description: 'Discussion mode: sfd-discuss or cld-discuss' + }, + { + name: 'problemStatement', + type: 'string', + required: false, + uiElement: 'textarea', + saveForUser: 'local', + label: 'Problem Statement', + description: 'Description of a dynamic issue within the system you are studying that highlights an undesirable behavior over time.', + minHeight: 50, + maxHeight: 100 + }, + { + name: 'backgroundKnowledge', + type: 'string', + required: false, + uiElement: 'textarea', + saveForUser: 'local', + label: 'Background Knowledge', + description: 'Background information you want the LLM model to consider when generating a model for you', + minHeight: 100 + }, + { + name: 'feedbackContent', + type: 'feedbackJSON', + required: false, + uiElement: 'hidden', + label: 'JSON Description of feedback loops', + description: 'A JSON object representing all of the feedback loops in the model' + } + ]; + } + + async generate(prompt, currentModel, parameters) { + try { + const { explanation } = await runAgent(prompt, currentModel, parameters); + return { output: explanation }; + } catch (err) { + logger.error('[test-agent-discuss] generate error:', err); + return { err: err.toString() }; + } + } +} + +export default Engine; diff --git a/evals/categories/qualitativeCausalReasoning.js b/evals/categories/qualitativeCausalReasoning.js index 4ed04c4e..4a4b8fe0 100644 --- a/evals/categories/qualitativeCausalReasoning.js +++ b/evals/categories/qualitativeCausalReasoning.js @@ -194,22 +194,23 @@ export const groups = { distribution, and public acceptance. Key variables that experts agree are essential: disease transmission, policy interventions, economic impact, - public compliance, healthcare capacity, political pressure, mental health, public trust, vaccination rollout.`, + public compliance, healthcare capacity, political pressure, mental health impacts, vaccination rollout.`, [ { - name: "Core pandemic dynamics", - requiredVariables: ["disease transmission", "policy interventions"], + name: "Pandemic intervention dynamics", + requiredVariables: ["disease transmission", "political pressure", "policy interventions"], requiredRelationships: [ - { from: "disease transmission", to: "policy interventions", polarity: "+" } + { from: "disease transmission", to: "political pressure", polarity: "+" }, + { from: "political pressure", to: "policy interventions", polarity: "+" } ] }, { name: "Economic and social trade-offs", - requiredVariables: ["economic impact", "public compliance", "mental health"], + requiredVariables: ["economic impact", "public compliance", "mental health impacts"], requiredRelationships: [ { from: "policy interventions", to: "economic impact", polarity: "+" }, { from: "economic impact", to: "public compliance", polarity: "-" }, - { from: "policy interventions", to: "mental health", polarity: "-" } + { from: "policy interventions", to: "mental health impacts", polarity: "+" } ] }, { @@ -238,22 +239,22 @@ export const groups = { accommodation, and treatment access. Key variables that experts agree are essential: social isolation, economic stress, stigma, access to services, - community support, substance abuse, workplace policies, family relationships, treatment outcomes, mental health.`, + community support, treatment outcomes, mental health problems.`, [ { name: "Social isolation cycle", - requiredVariables: ["social isolation", "mental health", "community support"], + requiredVariables: ["social isolation", "mental health problems", "community support"], requiredRelationships: [ - { from: "social isolation", to: "mental health", polarity: "+" }, - { from: "mental health", to: "social isolation", polarity: "+" }, - { from: "community support", to: "mental health", polarity: "-" } + { from: "social isolation", to: "mental health problems", polarity: "+" }, + { from: "mental health problems", to: "social isolation", polarity: "+" }, + { from: "community support", to: "mental health problems", polarity: "-" } ] }, { name: "Economic and employment factors", - requiredVariables: ["economic stress", "workplace policies"], + requiredVariables: ["economic stress", "mental health problems"], requiredRelationships: [ - { from: "economic stress", to: "mental health", polarity: "+" } + { from: "economic stress", to: "mental health problems", polarity: "+" } ] }, { diff --git a/evals/categories/quantitativeCausalReasoning.js b/evals/categories/quantitativeCausalReasoning.js index 30a3c8fd..d783268c 100644 --- a/evals/categories/quantitativeCausalReasoning.js +++ b/evals/categories/quantitativeCausalReasoning.js @@ -262,7 +262,7 @@ export const groups = { Disease progression involves distinct compartments of people moving through the infection cycle. Use these variable names: - susceptible, exposed, infectious, recovered, infecting, incubating, recovering, contact rate`, + susceptible, exposed, infectious, recovered, infecting, incubating, recovering, contacts, vaccination`, [ { name: "SEIR disease progression", @@ -295,8 +295,8 @@ export const groups = { Recovery times vary - some patients recover quickly while others require long-term care. The system involves managing patient flows, workforce dynamics, and available bed resources. - Use these variable names: - patients, available beds, healthcare workers, workload, burnout.`, + Use these variable names: + patients, available beds, healthcare workers, workload, burnout, staff turnover.`, [ { name: "Hospital capacity management", diff --git a/evals/experiments/leaderboardCLD.json b/evals/experiments/leaderboardCLD.json index f6844a43..d20c4296 100644 --- a/evals/experiments/leaderboardCLD.json +++ b/evals/experiments/leaderboardCLD.json @@ -124,6 +124,36 @@ "requestsPerMinute": 10 } } + }, + "merlin-anthropic-cld": { + "engine": "test-agent-build", + "additionalParameters": { + "agentName": "merlin", + "agentMode": "sdk", + "provider": "anthropic", + "mode": "cld" + }, + "limits": { + "tokensPerMinute": 200000, + "baselineTokenUsage": 50000, + "requestsPerMinute": 3 + } + }, + + "merlin-google-cld": { + "engine": "test-agent-build", + "additionalParameters": { + "agentName": "merlin", + "agentMode": "sdk", + "provider": "google", + "mode": "cld" + }, + "limits": { + "tokensPerMinute": 200000, + "baselineTokenUsage": 50000, + "requestsPerMinute": 3 + } + } }, "categories": { "qualitativeTranslation": true, diff --git a/evals/experiments/leaderboardDiscuss.json b/evals/experiments/leaderboardDiscuss.json index 11b51da6..4da7a4a7 100644 --- a/evals/experiments/leaderboardDiscuss.json +++ b/evals/experiments/leaderboardDiscuss.json @@ -61,6 +61,36 @@ "requestsPerMinute": 10 } } + }, + "merlin-anthropic-discuss": { + "engine": "test-agent-discuss", + "additionalParameters": { + "agentName": "merlin", + "agentMode": "sdk", + "provider": "anthropic", + "mode": "sfd-discuss" + }, + "limits": { + "tokensPerMinute": 200000, + "baselineTokenUsage": 50000, + "requestsPerMinute": 3 + } + }, + + "merlin-google-discuss": { + "engine": "test-agent-discuss", + "additionalParameters": { + "agentName": "merlin", + "agentMode": "sdk", + "provider": "google", + "mode": "sfd-discuss" + }, + "limits": { + "tokensPerMinute": 200000, + "baselineTokenUsage": 50000, + "requestsPerMinute": 3 + } + } }, "categories": { "feedbackExplanation": true, diff --git a/evals/experiments/leaderboardSFD.json b/evals/experiments/leaderboardSFD.json index 440134af..260c7dc0 100644 --- a/evals/experiments/leaderboardSFD.json +++ b/evals/experiments/leaderboardSFD.json @@ -63,6 +63,36 @@ "requestsPerMinute": 10 } } + }, + "merlin-anthropic-sfd": { + "engine": "test-agent-build", + "additionalParameters": { + "agentName": "merlin", + "agentMode": "sdk", + "provider": "anthropic", + "mode": "sfd" + }, + "limits": { + "tokensPerMinute": 200000, + "baselineTokenUsage": 50000, + "requestsPerMinute": 3 + } + }, + + "merlin-google-sfd": { + "engine": "test-agent-build", + "additionalParameters": { + "agentName": "merlin", + "agentMode": "sdk", + "provider": "google", + "mode": "sfd" + }, + "limits": { + "tokensPerMinute": 200000, + "baselineTokenUsage": 50000, + "requestsPerMinute": 3 + } + } }, "categories": { "quantitativeTranslation": true, diff --git a/evals/run.js b/evals/run.js index 6f8b3d40..3d7a3a57 100644 --- a/evals/run.js +++ b/evals/run.js @@ -83,7 +83,7 @@ if (matchingFiles.length > 0) { if (isContinuing) { const previousFileName = matchingFiles[0]; - previousResults = fs.readFileSync(previousFileName, 'utf-8').split('\n').filter(Boolean).map(l => JSON.parse(l)) + previousResults = fs.readFileSync(previousFileName, 'utf-8').split(/\r?\n/).filter(Boolean).map(l => JSON.parse(l)) experimentResultsName = previousFileName.replace(inProgressFileSuffix,"") } else { const experimentId = uniqueFileId(); diff --git a/package-lock.json b/package-lock.json index 386d9dce..16730917 100644 --- a/package-lock.json +++ b/package-lock.json @@ -6,7 +6,10 @@ "": { "hasInstallScript": true, "dependencies": { + "@anthropic-ai/claude-agent-sdk": "^0.2.117", "@anthropic-ai/sdk": "^0.62.0", + "@anthropic-ai/tokenizer": "^0.0.4", + "@google/adk": "^1.1.0", "@google/genai": "^1.41.0", "async": "^3.2.6", "chalk": "^5.4.1", @@ -15,16 +18,17 @@ "cors": "^2.8.5", "data-forge": "^1.10.2", "express": "^4.21.2", + "gpt-tokenizer": "^3.4.0", "js-tiktoken": "^1.0.19", "limiter": "^3.0.0", "marked": "^15.0.12", - "openai": "^4.73.1", + "openai": "^6.34.0", "prompts": "^2.4.2", + "ws": "^8.18.0", "yargs": "^17.7.2", - "zod": "^3.24.1" + "zod": "^4.0.0" }, "devDependencies": { - "cross-env": "^10.1.0", "dotenv": "^16.4.7", "jest": "^30.0.4", "nodemon": "^3.1.7", @@ -33,6 +37,34 @@ "supertest": "^7.1.3" } }, + "node_modules/@a2a-js/sdk": { + "version": "0.3.13", + "resolved": "https://registry.npmjs.org/@a2a-js/sdk/-/sdk-0.3.13.tgz", + "integrity": "sha512-BZr0f9JVNQs3GKOM9xINWCh6OKIJWZFPyqqVqTym5mxO2Eemc6I/0zL7zWnljHzGdaf5aZQyQN5xa6PSH62q+A==", + "license": "Apache-2.0", + "dependencies": { + "uuid": "^11.1.0" + }, + "engines": { + "node": ">=18" + }, + "peerDependencies": { + "@bufbuild/protobuf": "^2.10.2", + "@grpc/grpc-js": "^1.11.0", + 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"sha512-rftlrkhHZOcjDwkGlnUtZZkvaPHCsDATp4pGpuOOMDaTdDDXF91wuVDJoWoPsKX/3YPQ5fHuF3STjcYyKr+Qhg==", "license": "MIT", "funding": { "url": "https://github.com/sponsors/colinhacks" } + }, + "node_modules/zod-to-json-schema": { + "version": "3.25.2", + "resolved": "https://registry.npmjs.org/zod-to-json-schema/-/zod-to-json-schema-3.25.2.tgz", + "integrity": "sha512-O/PgfnpT1xKSDeQYSCfRI5Gy3hPf91mKVDuYLUHZJMiDFptvP41MSnWofm8dnCm0256ZNfZIM7DSzuSMAFnjHA==", + "license": "ISC", + "peerDependencies": { + "zod": "^3.25.28 || ^4" + } } } } diff --git a/package.json b/package.json index fa0635ec..c9bef4a3 100644 --- a/package.json +++ b/package.json @@ -1,6 +1,9 @@ { "dependencies": { + "@anthropic-ai/claude-agent-sdk": "^0.2.117", "@anthropic-ai/sdk": "^0.62.0", + "@anthropic-ai/tokenizer": "^0.0.4", + "@google/adk": "^1.1.0", "@google/genai": "^1.41.0", "async": "^3.2.6", "chalk": "^5.4.1", @@ -9,16 +12,18 @@ "cors": "^2.8.5", "data-forge": "^1.10.2", "express": "^4.21.2", + "gpt-tokenizer": "^3.4.0", "js-tiktoken": "^1.0.19", "limiter": "^3.0.0", "marked": "^15.0.12", - "openai": "^4.73.1", + "openai": "^6.34.0", "prompts": "^2.4.2", + "ws": "^8.18.0", "yargs": "^17.7.2", - "zod": "^3.24.1" + "zod": "^4.0.0" }, "scripts": { - "postinstall": "bash third-party/install.sh", + "postinstall": "node third-party/install.js", "start": "nodemon --env-file=.env app.js", "evals": "node evals/run.js", "test": "NODE_OPTIONS=\"--experimental-vm-modules\" jest", diff --git a/routes/health.js b/routes/health.js new file mode 100644 index 00000000..e971c27f --- /dev/null +++ b/routes/health.js @@ -0,0 +1,14 @@ +import express from 'express'; + +export function createHealthRouter(sessionManager) { + const router = express.Router(); + + router.get('/status', (_req, res) => { + if (sessionManager.sessions.size > 0) + return res.status(226).json({status: 'ok', sessions: sessionManager.sessions.size }) + + return res.status(200).json({ status: 'ok', sessions: sessionManager.sessions.size }); + }); + + return router; +} \ No newline at end of file diff --git a/routes/v1/engineGenerate.js b/routes/v1/engineGenerate.js index 7858563a..d72efe1f 100644 --- a/routes/v1/engineGenerate.js +++ b/routes/v1/engineGenerate.js @@ -8,7 +8,7 @@ import GenerateMetricsReporter from './../../utilities/GenerateMetricsReporter.j import config from './../../config.js' const router = express.Router() -const reporter = new GenerateMetricsReporter(config.reporterURL) +const reporter = new GenerateMetricsReporter(config.metricsReporterURL) router.post("/:engine/generate", async (req, res) => { const enginePath = path.join(process.cwd(), 'engines', req.params.engine, 'engine.js'); diff --git a/routes/v1/engines.js b/routes/v1/engines.js index 05ef509a..e88363c3 100644 --- a/routes/v1/engines.js +++ b/routes/v1/engines.js @@ -6,7 +6,7 @@ const quantitativeEngines = ['quantitative']; router.get("/", async (req, res) => { const path = "engines" - const dirs = fs.readdirSync(path).filter(f => fs.lstatSync(`${path}/${f}`).isDirectory()); + const dirs = fs.readdirSync(path).filter(f => fs.lstatSync(`${path}/${f}`).isDirectory()).filter(f => !f.startsWith('test-')); const engines = []; for (const dir of dirs) { diff --git a/tests/agent/AgentConfigurationManager.test.js b/tests/agent/AgentConfigurationManager.test.js new file mode 100644 index 00000000..51e624ef --- /dev/null +++ b/tests/agent/AgentConfigurationManager.test.js @@ -0,0 +1,67 @@ +import { AgentConfigurationManager } from '../../agent/utilities/AgentConfigurationManager.js'; +import path from 'path'; +import { fileURLToPath } from 'url'; + +const __filename = fileURLToPath(import.meta.url); +const __dirname = path.dirname(__filename); + +describe('AgentConfigurationManager', () => { + let configManager; + + beforeEach(() => { + const configPath = path.join(__dirname, '../../agent/config/socrates.md'); + configManager = new AgentConfigurationManager({ path: configPath }); + }); + + describe('constructor', () => { + it('should load config from MD file via path option', () => { + expect(configManager.config).toBeDefined(); + expect(configManager.config.agent).toBeDefined(); + expect(configManager.config.agent.name).toMatch(/^Socrates/); + }); + + it('should throw error for non-existent config file', () => { + expect(() => { + new AgentConfigurationManager({ path: '/non/existent/path.md' }); + }).toThrow(); + }); + + it('should load config from markdownContent option', () => { + const md = `---\nname: "TestAgent"\nagent_mode: sdk\nsupported_modes:\n - sfd\nsupported_providers:\n - anthropic\n---\n## Instructions\nDo things.\n`; + const mgr = new AgentConfigurationManager({ markdownContent: md }); + expect(mgr.config.agent.name).toBe('TestAgent'); + expect(mgr.configPath).toBeNull(); + }); + + it('should throw for markdownContent missing required frontmatter fields', () => { + const md = `---\nname: "NoMode"\n---\n## Instructions\nDo things.\n`; + expect(() => new AgentConfigurationManager({ markdownContent: md })).toThrow(/agent_mode/); + }); + }); + + describe('buildSystemPrompt', () => { + it('should build system prompt with model type context', () => { + const mode = 'cld'; + + const prompt = configManager.buildSystemPrompt(mode); + + expect(prompt).toContain('CLD'); + expect(prompt).toContain('Causal Loop Diagram'); + }); + + it('should include SFD context when model type is sfd', () => { + const prompt = configManager.buildSystemPrompt('sfd'); + + expect(prompt).toContain('SFD'); + expect(prompt).toContain('Stock Flow Diagram'); + }); + + it('should include universal instructions', () => { + const prompt = configManager.buildSystemPrompt('sfd'); + + expect(prompt).toContain('CRITICAL: Text Generation'); + expect(prompt).toContain('NEVER use emojis'); + expect(prompt).toContain('Feedback Loop Analysis'); + }); + }); +}); diff --git a/tests/agent/AgentEvalRunner.test.js b/tests/agent/AgentEvalRunner.test.js new file mode 100644 index 00000000..5c550c38 --- /dev/null +++ b/tests/agent/AgentEvalRunner.test.js @@ -0,0 +1,327 @@ +import { describe, test, expect, jest, beforeAll, beforeEach } from '@jest/globals'; + +// ─── Module-level message sequence used by the AgentOrchestrator mock ──────── +// Tests set this before calling runAgent; the mock factory closes over it. +let messageSequence = []; + +// Mocks must be declared at the top level before any dynamic import of the +// module under test, so Jest can intercept the module registry. +jest.unstable_mockModule('../../agent/AgentOrchestrator.js', () => ({ + AgentOrchestrator: class MockOrchestrator { + constructor(_sm, _sid, sendFn, _config, _provider) { + this._send = sendFn; + } + async startConversation(_msg) { + for (const msg of messageSequence) { + await this._send(msg); + } + } + }, +})); + +jest.unstable_mockModule('../../utilities/SDJsonToXMILE.js', () => ({ + default: () => '', +})); + +jest.unstable_mockModule('../../evals/utilities/simulator/PySDSimulator.js', () => ({ + default: class MockSimulator { + async simulate() { + return { time: [0, 1, 2], Population: [100, 110, 121] }; + } + }, +})); + +// Dynamically import after mocks are registered +let findFeedbackLoops, patchAgentConfig, runAgent; + +beforeAll(async () => { + ({ findFeedbackLoops, patchAgentConfig, runAgent } = + await import('../../agent/utilities/AgentEvalRunner.js')); +}); + +// ─── findFeedbackLoops ─────────────────────────────────────────────────────── + +describe('findFeedbackLoops', () => { + test('returns empty array for null/undefined relationships', () => { + expect(findFeedbackLoops(null)).toEqual([]); + expect(findFeedbackLoops(undefined)).toEqual([]); + expect(findFeedbackLoops([])).toEqual([]); + }); + + test('returns empty array for a DAG (no cycles)', () => { + const rels = [ + { from: 'A', to: 'B', polarity: '+' }, + { from: 'B', to: 'C', polarity: '+' }, + ]; + expect(findFeedbackLoops(rels)).toEqual([]); + }); + + test('detects a simple 2-node reinforcing loop (both +)', () => { + const rels = [ + { from: 'A', to: 'B', polarity: '+' }, + { from: 'B', to: 'A', polarity: '+' }, + ]; + const loops = findFeedbackLoops(rels); + expect(loops).toHaveLength(1); + expect(loops[0].polarity).toBe('+'); + expect(loops[0].identifier).toBe('L1'); + }); + + test('detects a simple 2-node balancing loop (one - polarity)', () => { + const rels = [ + { from: 'A', to: 'B', polarity: '+' }, + { from: 'B', to: 'A', polarity: '-' }, + ]; + const loops = findFeedbackLoops(rels); + expect(loops).toHaveLength(1); + expect(loops[0].polarity).toBe('-'); + }); + + test('two negative links → reinforcing (even negatives)', () => { + const rels = [ + { from: 'A', to: 'B', polarity: '-' }, + { from: 'B', to: 'A', polarity: '-' }, + ]; + const loops = findFeedbackLoops(rels); + expect(loops[0].polarity).toBe('+'); + }); + + test('3-node cycle — correct link structure', () => { + const rels = [ + { from: 'A', to: 'B', polarity: '+' }, + { from: 'B', to: 'C', polarity: '+' }, + { from: 'C', to: 'A', polarity: '-' }, + ]; + const loops = findFeedbackLoops(rels); + expect(loops).toHaveLength(1); + expect(loops[0].links).toHaveLength(3); + expect(loops[0].polarity).toBe('-'); + + const fromNodes = loops[0].links.map(l => l.from).sort(); + expect(fromNodes).toEqual(['A', 'B', 'C']); + }); + + test('two independent cycles are both detected', () => { + const rels = [ + { from: 'A', to: 'B', polarity: '+' }, + { from: 'B', to: 'A', polarity: '+' }, + { from: 'C', to: 'D', polarity: '-' }, + { from: 'D', to: 'C', polarity: '+' }, + ]; + const loops = findFeedbackLoops(rels); + expect(loops).toHaveLength(2); + }); + + test('defaults missing polarity to "+"', () => { + const rels = [ + { from: 'A', to: 'B' }, + { from: 'B', to: 'A' }, + ]; + const loops = findFeedbackLoops(rels); + expect(loops[0].polarity).toBe('+'); + expect(loops[0].links.every(l => l.polarity === '+')).toBe(true); + }); + + test('loop identifiers are sequential L1, L2, ...', () => { + const rels = [ + { from: 'A', to: 'B', polarity: '+' }, + { from: 'B', to: 'A', polarity: '+' }, + { from: 'C', to: 'D', polarity: '+' }, + { from: 'D', to: 'C', polarity: '+' }, + ]; + const loops = findFeedbackLoops(rels); + const ids = loops.map(l => l.identifier).sort(); + expect(ids).toEqual(['L1', 'L2']); + }); + + test('each loop link connects consecutive nodes and closes back to start', () => { + const rels = [ + { from: 'X', to: 'Y', polarity: '+' }, + { from: 'Y', to: 'X', polarity: '-' }, + ]; + const [loop] = findFeedbackLoops(rels); + const nodes = loop.links.map(l => l.from); + const targets = loop.links.map(l => l.to); + for (let i = 0; i < nodes.length; i++) { + expect(targets[i]).toBe(nodes[(i + 1) % nodes.length]); + } + }); +}); + +// ─── patchAgentConfig ──────────────────────────────────────────────────────── + +const SAMPLE_MD = `--- +name: "TestAgent" +agent_mode: manual +max_iterations: 10 +supported_modes: + - sfd +supported_providers: + - anthropic +--- + +## Instructions +Do things. +`; + +describe('patchAgentConfig', () => { + test('replaces max_iterations with 9999', () => { + const result = patchAgentConfig(SAMPLE_MD); + expect(result).toMatch(/^max_iterations: 9999$/m); + expect(result).not.toMatch(/^max_iterations: 10$/m); + }); + + test('replaces agent_mode when agentMode is provided', () => { + const result = patchAgentConfig(SAMPLE_MD, 'sdk'); + expect(result).toMatch(/^agent_mode: sdk$/m); + expect(result).not.toMatch(/^agent_mode: manual$/m); + }); + + test('does not touch agent_mode when agentMode is omitted', () => { + const result = patchAgentConfig(SAMPLE_MD); + expect(result).toMatch(/^agent_mode: manual$/m); + }); + + test('appends EVAL MODE instruction block after closing ---', () => { + const result = patchAgentConfig(SAMPLE_MD); + const frontmatterEnd = result.indexOf('\n---\n'); + expect(frontmatterEnd).toBeGreaterThan(-1); + const bodyStart = result.slice(frontmatterEnd + 5); + expect(bodyStart).toMatch(/EVAL MODE/); + expect(bodyStart).toMatch(/Never ask the user questions/); + }); + + test('EVAL MODE instruction comes before original body content', () => { + const result = patchAgentConfig(SAMPLE_MD); + const evalIdx = result.indexOf('EVAL MODE'); + const doThingsIdx = result.indexOf('Do things.'); + expect(evalIdx).toBeLessThan(doThingsIdx); + }); + + test('appends EVAL MODE at end when no frontmatter separator exists', () => { + const noFrontmatter = 'Just some markdown content without frontmatter.'; + const result = patchAgentConfig(noFrontmatter); + expect(result).toMatch(/EVAL MODE/); + expect(result).toContain('Just some markdown content without frontmatter.'); + }); + + test('handles markdown with no max_iterations line gracefully', () => { + const md = `---\nname: "X"\nagent_mode: sdk\nsupported_modes:\n - sfd\nsupported_providers:\n - anthropic\n---\n## Body\n`; + const result = patchAgentConfig(md, 'manual'); + expect(result).toMatch(/^agent_mode: manual$/m); + expect(result).toMatch(/EVAL MODE/); + }); +}); + +// ─── sendToClient mock handler ─────────────────────────────────────────────── + +describe('sendToClient mock handler', () => { + beforeEach(() => { + messageSequence = []; + }); + + const baseParams = { + agentName: 'merlin', + agentMode: 'sdk', + provider: 'anthropic', + mode: 'sfd', + }; + + const currentModel = { + variables: [{ name: 'Population', type: 'stock', equation: '100' }], + relationships: [], + }; + + test('agent_complete resolves runAgent and returns collected text', async () => { + messageSequence = [ + { type: 'agent_text', isThinking: false, content: 'Hello' }, + { type: 'agent_text', isThinking: false, content: ' world' }, + { type: 'agent_complete', status: 'done' }, + ]; + + const result = await runAgent('test prompt', currentModel, baseParams); + expect(result.explanation).toBe('Hello\n\n world'); + }); + + test('agent_text with isThinking:true is excluded from explanation', async () => { + messageSequence = [ + { type: 'agent_text', isThinking: true, content: 'internal thought' }, + { type: 'agent_text', isThinking: false, content: 'visible response' }, + { type: 'agent_complete', status: 'done' }, + ]; + + const result = await runAgent('test prompt', currentModel, baseParams); + expect(result.explanation).not.toContain('internal thought'); + expect(result.explanation).toContain('visible response'); + }); + + test('update_model resolves without error and lastModel comes from SessionManager', async () => { + messageSequence = [ + { type: 'update_model', requestId: 'r1', modelData: { variables: [], relationships: [] } }, + { type: 'agent_complete', status: 'done' }, + ]; + + const result = await runAgent('test prompt', currentModel, baseParams); + // In real usage the tool calls sessionManager.updateClientModel() after resolution; + // here we verify runAgent completes and lastModel is whatever the session holds. + expect(result.lastModel).toBeDefined(); + }); + + test('error message rejects runAgent with an Error', async () => { + messageSequence = [ + { type: 'error', error: 'Something broke' }, + ]; + + await expect(runAgent('test prompt', currentModel, baseParams)) + .rejects.toThrow('Something broke'); + }); + + test('error with no message text uses fallback "Agent error"', async () => { + messageSequence = [ + { type: 'error' }, + ]; + + await expect(runAgent('test prompt', currentModel, baseParams)) + .rejects.toThrow('Agent error'); + }); + + test('feedback_request resolves using pre-computed feedbackContent', async () => { + const preComputed = { + feedbackLoops: [{ identifier: 'L1', name: 'Loop 1', links: [], polarity: '+' }], + }; + messageSequence = [ + { type: 'feedback_request', requestId: 'fr1', runIds: ['run-1'] }, + { type: 'agent_complete', status: 'done' }, + ]; + + const params = { ...baseParams, feedbackContent: preComputed }; + await expect(runAgent('test prompt', currentModel, params)).resolves.toBeDefined(); + }); + + test('feedback_request falls back to DFS when no feedbackContent provided', async () => { + const modelWithLoop = { + variables: [{ name: 'A' }, { name: 'B' }], + relationships: [ + { from: 'A', to: 'B', polarity: '+' }, + { from: 'B', to: 'A', polarity: '+' }, + ], + }; + messageSequence = [ + { type: 'feedback_request', requestId: 'fr2', runIds: [] }, + { type: 'agent_complete', status: 'done' }, + ]; + + await expect(runAgent('test prompt', modelWithLoop, baseParams)).resolves.toBeDefined(); + }); + + test('get_current_model resolves with the initial model', async () => { + let resolvedModel; + messageSequence = [ + { type: 'get_current_model', requestId: 'gcm1' }, + { type: 'agent_complete', status: 'done' }, + ]; + + // Just verify it doesn't hang (no timeout) — the session resolves the pending request + await expect(runAgent('test prompt', currentModel, baseParams)).resolves.toBeDefined(); + }); +}); diff --git a/tests/agent/AgentOrchestrator.test.js b/tests/agent/AgentOrchestrator.test.js new file mode 100644 index 00000000..4bf44dd9 --- /dev/null +++ b/tests/agent/AgentOrchestrator.test.js @@ -0,0 +1,443 @@ +import { AgentOrchestrator } from '../../agent/AgentOrchestrator.js'; +import { SessionManager } from '../../agent/utilities/SessionManager.js'; +import { jest } from '@jest/globals'; +import path from 'path'; +import { fileURLToPath } from 'url'; + +const __dirname = path.dirname(fileURLToPath(import.meta.url)); +const CONFIG = { path: path.join(__dirname, '../../agent/config/socrates.md') }; + +// Minimal tool bag accepted by #isBuiltInTool and execute helpers +const EMPTY_TOOLS = { tools: {} }; + +function makeOrchestrator(sessionManager, sessionId) { + process.env.ANTHROPIC_API_KEY = 'dummy'; + process.env.GEMINI_API_KEY = 'dummy'; + const sendToClient = jest.fn().mockResolvedValue(undefined); + const orc = new AgentOrchestrator(sessionManager, sessionId, sendToClient, CONFIG); + // Stub both execute methods so no real API calls happen + orc.anthropicManualExecuteToolCall = jest.fn().mockResolvedValue({ + content: 'tool output', + isError: false, + }); + orc.executeToolCallGeminiManual = jest.fn().mockResolvedValue({ + content: 'tool output', + isError: false, + }); + return orc; +} + +// Helper builders for Gemini response shapes +function geminiText(text) { + return { candidates: [{ content: { parts: [{ text }] } }] }; +} + +function geminiFunctionCalls(...calls) { + return { + candidates: [{ + content: { + parts: calls.map(({ name, args }) => ({ functionCall: { name, args: args ?? {} } })) + } + }] + }; +} + +function geminiTextAndFunctionCall(text, name, args = {}) { + return { + candidates: [{ + content: { + parts: [{ text }, { functionCall: { name, args } }] + } + }] + }; +} + +// ─── processAgentResponseAnthropicManual ──────────────────────────────────── + +describe('processAgentResponseAnthropicManual', () => { + let sessionManager; + let sessionId; + let orc; + + beforeEach(() => { + sessionManager = new SessionManager(); + sessionId = sessionManager.createSession(null); + sessionManager.initializeSession(sessionId, 'cld', {}, [], {}, 'test-client'); + orc = makeOrchestrator(sessionManager, sessionId); + }); + + afterEach(() => { + orc.destroy(); + sessionManager.shutdown(); + }); + + // ── text-only response ──────────────────────────────────────────────────── + + it('adds a single assistant text message for a text-only response', async () => { + const messages = []; + const response = { + content: [{ type: 'text', text: 'Hello world' }], + stop_reason: 'end_turn', + }; + + const continueLoop = await orc.processAgentResponseAnthropicManual( + response, messages, EMPTY_TOOLS, EMPTY_TOOLS + ); + + expect(continueLoop).toBe(false); + expect(messages).toHaveLength(1); + expect(messages[0].role).toBe('assistant'); + expect(messages[0].content).toEqual([{ type: 'text', text: 'Hello world' }]); + }); + + // ── single tool call ────────────────────────────────────────────────────── + + it('adds one assistant+user pair for a single tool call', async () => { + const messages = [{ role: 'user', content: 'question' }]; + const response = { + content: [{ type: 'tool_use', id: 'tu_1', name: 'my_tool', input: { x: 1 } }], + stop_reason: 'tool_use', + }; + + const continueLoop = await orc.processAgentResponseAnthropicManual( + response, messages, EMPTY_TOOLS, EMPTY_TOOLS + ); + + expect(continueLoop).toBe(true); + // Original user message plus new assistant + user pair = 3 messages + expect(messages).toHaveLength(3); + + const assistant = messages[1]; + expect(assistant.role).toBe('assistant'); + expect(assistant.content).toEqual([ + { type: 'tool_use', id: 'tu_1', name: 'my_tool', input: { x: 1 } }, + ]); + + const toolResult = messages[2]; + expect(toolResult.role).toBe('user'); + expect(toolResult.content).toHaveLength(1); + expect(toolResult.content[0].type).toBe('tool_result'); + expect(toolResult.content[0].tool_use_id).toBe('tu_1'); + }); + + // ── multiple tool calls — the core regression ───────────────────────────── + + it('batches multiple tool calls into ONE assistant message and ONE user message', async () => { + const messages = [{ role: 'user', content: 'do both' }]; + const response = { + content: [ + { type: 'tool_use', id: 'tu_A', name: 'tool_a', input: {} }, + { type: 'tool_use', id: 'tu_B', name: 'tool_b', input: {} }, + ], + stop_reason: 'tool_use', + }; + + await orc.processAgentResponseAnthropicManual( + response, messages, EMPTY_TOOLS, EMPTY_TOOLS + ); + + // Must be exactly 3 messages: original user + assistant + user-with-results + expect(messages).toHaveLength(3); + + const assistant = messages[1]; + expect(assistant.role).toBe('assistant'); + expect(assistant.content).toHaveLength(2); + expect(assistant.content[0]).toMatchObject({ type: 'tool_use', id: 'tu_A' }); + expect(assistant.content[1]).toMatchObject({ type: 'tool_use', id: 'tu_B' }); + + const results = messages[2]; + expect(results.role).toBe('user'); + expect(results.content).toHaveLength(2); + expect(results.content[0]).toMatchObject({ type: 'tool_result', tool_use_id: 'tu_A' }); + expect(results.content[1]).toMatchObject({ type: 'tool_result', tool_use_id: 'tu_B' }); + }); + + // ── text before tool calls ──────────────────────────────────────────────── + + it('places text and tool_use blocks in the same assistant message', async () => { + const messages = [{ role: 'user', content: 'go' }]; + const response = { + content: [ + { type: 'text', text: 'Thinking...' }, + { type: 'tool_use', id: 'tu_C', name: 'tool_c', input: {} }, + ], + stop_reason: 'tool_use', + }; + + await orc.processAgentResponseAnthropicManual( + response, messages, EMPTY_TOOLS, EMPTY_TOOLS + ); + + expect(messages).toHaveLength(3); + + const assistant = messages[1]; + expect(assistant.role).toBe('assistant'); + expect(assistant.content).toHaveLength(2); + expect(assistant.content[0]).toMatchObject({ type: 'text', text: 'Thinking...' }); + expect(assistant.content[1]).toMatchObject({ type: 'tool_use', id: 'tu_C' }); + + expect(messages[2].role).toBe('user'); + expect(messages[2].content[0]).toMatchObject({ type: 'tool_result', tool_use_id: 'tu_C' }); + }); + + // ── stop requested before first block ──────────────────────────────────── + + it('leaves messages untouched when stop is requested before processing', async () => { + orc.stopRequested = true; + const messages = [{ role: 'user', content: 'hello' }]; + const response = { + content: [{ type: 'tool_use', id: 'tu_D', name: 'tool_d', input: {} }], + stop_reason: 'tool_use', + }; + + const continueLoop = await orc.processAgentResponseAnthropicManual( + response, messages, EMPTY_TOOLS, EMPTY_TOOLS + ); + + expect(continueLoop).toBe(false); + expect(messages).toHaveLength(1); // unchanged + expect(orc.anthropicManualExecuteToolCall).not.toHaveBeenCalled(); + }); + + // ── stop requested during tool execution ───────────────────────────────── + + it('leaves messages untouched when stop is requested mid-tool-execution', async () => { + orc.anthropicManualExecuteToolCall = jest.fn().mockImplementation(async () => { + orc.stopRequested = true; + return { content: 'result', isError: false }; + }); + + const messages = [{ role: 'user', content: 'hello' }]; + const response = { + content: [{ type: 'tool_use', id: 'tu_E', name: 'tool_e', input: {} }], + stop_reason: 'tool_use', + }; + + const continueLoop = await orc.processAgentResponseAnthropicManual( + response, messages, EMPTY_TOOLS, EMPTY_TOOLS + ); + + expect(continueLoop).toBe(false); + // Nothing should have been committed to messages — no orphaned tool_use + expect(messages).toHaveLength(1); + }); + + // ── tool errors are included, not dropped ───────────────────────────────── + + it('records tool errors in the tool_result block', async () => { + orc.anthropicManualExecuteToolCall = jest.fn().mockResolvedValue({ + content: 'Something went wrong', + isError: true, + }); + + const messages = []; + const response = { + content: [{ type: 'tool_use', id: 'tu_F', name: 'tool_f', input: {} }], + stop_reason: 'tool_use', + }; + + await orc.processAgentResponseAnthropicManual( + response, messages, EMPTY_TOOLS, EMPTY_TOOLS + ); + + expect(messages[1].content[0].is_error).toBe(true); + expect(messages[1].content[0].content).toBe('Something went wrong'); + }); + + // ── max_tokens keeps the loop going ────────────────────────────────────── + + it('returns true to continue the loop when stop_reason is max_tokens', async () => { + const messages = []; + const response = { + content: [{ type: 'text', text: 'Partial...' }], + stop_reason: 'max_tokens', + }; + + const continueLoop = await orc.processAgentResponseAnthropicManual( + response, messages, EMPTY_TOOLS, EMPTY_TOOLS + ); + + expect(continueLoop).toBe(true); + }); +}); + +// ─── processGeminiManualResponse ──────────────────────────────────────────── + +describe('processGeminiManualResponse', () => { + let sessionManager; + let sessionId; + let orc; + + beforeEach(() => { + sessionManager = new SessionManager(); + sessionId = sessionManager.createSession(null); + sessionManager.initializeSession(sessionId, 'cld', {}, [], {}, 'test-client'); + orc = makeOrchestrator(sessionManager, sessionId); + }); + + afterEach(() => { + orc.destroy(); + sessionManager.shutdown(); + }); + + // ── missing/empty candidate ─────────────────────────────────────────────── + + it('returns false immediately when the response has no candidate', async () => { + const continueLoop = await orc.processGeminiManualResponse( + {}, [], EMPTY_TOOLS, EMPTY_TOOLS + ); + expect(continueLoop).toBe(false); + }); + + // ── text-only response ──────────────────────────────────────────────────── + + it('adds a model message and returns false for a text-only response', async () => { + const messages = []; + const continueLoop = await orc.processGeminiManualResponse( + geminiText('Hello from Gemini'), messages, EMPTY_TOOLS, EMPTY_TOOLS + ); + + expect(continueLoop).toBe(false); + expect(messages).toHaveLength(1); + expect(messages[0].role).toBe('model'); + expect(messages[0].parts[0].text).toBe('Hello from Gemini'); + }); + + // ── single function call ────────────────────────────────────────────────── + + it('adds model message then user message with functionResponse for one call', async () => { + const messages = [{ role: 'user', parts: [{ text: 'go' }] }]; + const continueLoop = await orc.processGeminiManualResponse( + geminiFunctionCalls({ name: 'my_tool', args: { x: 1 } }), + messages, EMPTY_TOOLS, EMPTY_TOOLS + ); + + expect(continueLoop).toBe(true); + expect(messages).toHaveLength(3); // original user + model + user-with-responses + + const model = messages[1]; + expect(model.role).toBe('model'); + expect(model.parts[0].functionCall.name).toBe('my_tool'); + + const userResp = messages[2]; + expect(userResp.role).toBe('user'); + expect(userResp.parts).toHaveLength(1); + expect(userResp.parts[0].functionResponse.name).toBe('my_tool'); + + expect(orc.executeToolCallGeminiManual).toHaveBeenCalledWith({ name: 'my_tool', input: { x: 1 } }); + }); + + // ── multiple function calls — all responses in ONE user message ─────────── + + it('batches multiple function call responses into ONE user message', async () => { + const messages = [{ role: 'user', parts: [{ text: 'do both' }] }]; + const continueLoop = await orc.processGeminiManualResponse( + geminiFunctionCalls({ name: 'tool_a' }, { name: 'tool_b' }), + messages, EMPTY_TOOLS, EMPTY_TOOLS + ); + + expect(continueLoop).toBe(true); + // original user + model + one user with both responses = 3 + expect(messages).toHaveLength(3); + + const model = messages[1]; + expect(model.role).toBe('model'); + expect(model.parts).toHaveLength(2); + + const userResp = messages[2]; + expect(userResp.role).toBe('user'); + expect(userResp.parts).toHaveLength(2); + expect(userResp.parts[0].functionResponse.name).toBe('tool_a'); + expect(userResp.parts[1].functionResponse.name).toBe('tool_b'); + + expect(orc.executeToolCallGeminiManual).toHaveBeenCalledWith({ name: 'tool_a', input: {} }); + expect(orc.executeToolCallGeminiManual).toHaveBeenCalledWith({ name: 'tool_b', input: {} }); + }); + + // ── thought parts are ignored by the text renderer ─────────────────────── + + it('skips thought parts when streaming text to the client', async () => { + const messages = []; + const response = { + candidates: [{ + content: { + parts: [ + { thought: true, text: 'internal reasoning' }, + { text: 'visible answer' }, + ] + } + }] + }; + + await orc.processGeminiManualResponse(response, messages, EMPTY_TOOLS, EMPTY_TOOLS); + + // The model message contains all parts (thought + text) + expect(messages[0].parts).toHaveLength(2); + + // Only the non-thought text should have been sent to the client + const sentTexts = orc.sendToClient.mock.calls.flatMap(args => { + const msg = args[0]; + return msg?.data?.text ? [msg.data.text] : []; + }); + expect(sentTexts.some(t => t.includes('internal reasoning'))).toBe(false); + }); + + // ── stop requested before tool execution ───────────────────────────────── + + it('returns false without executing tools when stop is set before the loop', async () => { + orc.stopRequested = true; + const messages = []; + + const continueLoop = await orc.processGeminiManualResponse( + geminiFunctionCalls({ name: 'tool_a' }), + messages, EMPTY_TOOLS, EMPTY_TOOLS + ); + + expect(continueLoop).toBe(false); + expect(orc.executeToolCallGeminiManual).not.toHaveBeenCalled(); + }); + + // ── stop requested during tool execution ───────────────────────────────── + + it('returns false without pushing the function response when stop fires mid-execution', async () => { + orc.executeToolCallGeminiManual = jest.fn().mockImplementation(async () => { + orc.stopRequested = true; + return { content: 'partial', isError: false }; + }); + + const messages = []; + const continueLoop = await orc.processGeminiManualResponse( + geminiFunctionCalls({ name: 'tool_a' }, { name: 'tool_b' }), + messages, EMPTY_TOOLS, EMPTY_TOOLS + ); + + expect(continueLoop).toBe(false); + // Only the model message is present; the user response was not committed + expect(messages).toHaveLength(1); + expect(messages[0].role).toBe('model'); + // Only one tool was executed before the stop + expect(orc.executeToolCallGeminiManual).toHaveBeenCalledTimes(1); + expect(orc.executeToolCallGeminiManual).toHaveBeenCalledWith({ name: 'tool_a', input: {} }); + }); + + // ── tool errors are included in the response parts ──────────────────────── + + it('records error output in the functionResponse for a failed tool', async () => { + orc.executeToolCallGeminiManual = jest.fn().mockResolvedValue({ + content: 'Something failed', + isError: true, + }); + + const messages = []; + await orc.processGeminiManualResponse( + geminiFunctionCalls({ name: 'bad_tool' }), + messages, EMPTY_TOOLS, EMPTY_TOOLS + ); + + const functionResp = messages[1].parts[0].functionResponse; + expect(functionResp.name).toBe('bad_tool'); + expect(functionResp.response.result).toBe('Something failed'); + + expect(orc.executeToolCallGeminiManual).toHaveBeenCalledWith({ name: 'bad_tool', input: {} }); + }); +}); diff --git a/tests/agent/AgentWorker.test.js b/tests/agent/AgentWorker.test.js new file mode 100644 index 00000000..c8e65c36 --- /dev/null +++ b/tests/agent/AgentWorker.test.js @@ -0,0 +1,385 @@ +/** + * Integration tests for the AgentWorker.js IPC protocol. + * + * Spawns the actual worker process via fork and exercises the message + * contract. Does NOT test AgentOrchestrator's agent loop (that requires + * the Anthropic API); focuses on the IPC plumbing that routes messages + * between the main process and the worker. + */ + +import { fork } from 'child_process'; +import { jest } from '@jest/globals'; +import { mkdirSync, rmSync } from 'fs'; +import { tmpdir } from 'os'; +import { join } from 'path'; +import { fileURLToPath } from 'url'; +import { dirname } from 'path'; + +const __dirname = dirname(fileURLToPath(import.meta.url)); +const WORKER_PATH = join(__dirname, '../../agent/AgentWorker.js'); + +const TEST_SESSION_ID = 'sess_test_ipc_worker'; + +function makeTempDir() { + const dir = join(tmpdir(), `agent-worker-test-${Date.now()}-${Math.random().toString(36).slice(2)}`); + mkdirSync(dir, { recursive: true }); + return dir; +} + +function spawnWorker(tempDir, sessionId = TEST_SESSION_ID) { + return fork(WORKER_PATH, [], { + env: { + ...process.env, + SESSION_ID: sessionId, + SESSION_TEMP_DIR: tempDir, + }, + stdio: ['pipe', 'pipe', 'pipe', 'ipc'], + }); +} + +/** + * Wait for the first IPC message from the worker that satisfies the predicate. + */ +function waitForMessage(worker, predicate, timeoutMs = 5000) { + return new Promise((resolve, reject) => { + const t = setTimeout( + () => reject(new Error(`IPC message timeout after ${timeoutMs}ms`)), + timeoutMs + ); + function handler(msg) { + if (predicate(msg)) { + clearTimeout(t); + worker.off('message', handler); + resolve(msg); + } + } + worker.on('message', handler); + }); +} + +/** Wait for the worker process to exit. */ +function waitForExit(worker, timeoutMs = 5000) { + return new Promise((resolve, reject) => { + if (worker.exitCode !== null) { resolve(worker.exitCode); return; } + const t = setTimeout(() => reject(new Error('Worker exit timeout')), timeoutMs); + worker.once('exit', (code) => { clearTimeout(t); resolve(code); }); + }); +} + +/** Send a minimal valid initialize message. */ +function sendInit(worker, extras = {}) { + worker.send({ + type: 'initialize', + mode: 'cld', + model: null, + tools: [], + context: {}, + conversationHistory: [], + isAgentSwitch: false, + clientId: 'test-client', + ...extras, + }); +} + +// ───────────────────────────────────────────────────────────────────────────── + +describe('AgentWorker IPC — get_context', () => { + let worker; + let tempDir; + + beforeEach(() => { + tempDir = makeTempDir(); + worker = spawnWorker(tempDir); + }); + + afterEach(() => { + worker.kill('SIGKILL'); + rmSync(tempDir, { recursive: true, force: true }); + }); + + it('responds to get_context even before initialize (returns empty array)', async () => { + const requestId = 'req-before-init'; + worker.send({ type: 'get_context', requestId }); + + const resp = await waitForMessage( + worker, + (m) => m.type === 'context_response' && m.requestId === requestId, + 30000 + ); + + expect(resp.context).toEqual([]); + }, 30000); + + it('get_context returns conversation history loaded during initialize', async () => { + const history = [ + { role: 'user', content: 'What is a stock?' }, + { role: 'assistant', content: 'A stock accumulates flows.' }, + ]; + + sendInit(worker, { conversationHistory: history }); + + const requestId = 'req-after-init'; + worker.send({ type: 'get_context', requestId }); + + const resp = await waitForMessage( + worker, + (m) => m.type === 'context_response' && m.requestId === requestId, + 30000 + ); + + expect(resp.context).toEqual(history); + }, 30000); + + it('multiple get_context calls return the same history', async () => { + const history = [{ role: 'user', content: 'Hello' }]; + sendInit(worker, { conversationHistory: history }); + + for (let i = 0; i < 3; i++) { + const requestId = `req-${i}`; + worker.send({ type: 'get_context', requestId }); + const resp = await waitForMessage( + worker, + (m) => m.type === 'context_response' && m.requestId === requestId + ); + expect(resp.context).toEqual(history); + } + }, 10000); +}); + +// ───────────────────────────────────────────────────────────────────────────── + +describe('AgentWorker IPC — tool_response routing', () => { + let worker; + let tempDir; + + beforeEach(() => { + tempDir = makeTempDir(); + worker = spawnWorker(tempDir); + sendInit(worker); + }); + + afterEach(() => { + worker.kill('SIGKILL'); + rmSync(tempDir, { recursive: true, force: true }); + }); + + it('handles unknown callId in tool_response without crashing', async () => { + worker.send({ + type: 'tool_response', + callId: 'call-totally-unknown', + result: 'some result', + isError: false, + }); + + // Worker should stay alive — verify it still responds to get_context + const requestId = 'alive-check'; + worker.send({ type: 'get_context', requestId }); + const resp = await waitForMessage( + worker, + (m) => m.type === 'context_response' && m.requestId === requestId + ); + expect(resp).toBeDefined(); + }, 10000); + + it('handles error-flagged tool_response with unknown callId without crashing', async () => { + worker.send({ + type: 'tool_response', + callId: 'call-error-unknown', + result: 'it broke', + isError: true, + }); + + const requestId = 'alive-check-2'; + worker.send({ type: 'get_context', requestId }); + const resp = await waitForMessage( + worker, + (m) => m.type === 'context_response' && m.requestId === requestId + ); + expect(resp).toBeDefined(); + }, 10000); +}); + +// ───────────────────────────────────────────────────────────────────────────── + +describe('AgentWorker IPC — model_updated', () => { + let worker; + let tempDir; + + beforeEach(() => { + tempDir = makeTempDir(); + worker = spawnWorker(tempDir); + sendInit(worker); + }); + + afterEach(() => { + worker.kill('SIGKILL'); + rmSync(tempDir, { recursive: true, force: true }); + }); + + it('model_updated does not crash the worker', async () => { + worker.send({ + type: 'model_updated', + model: { variables: [{ name: 'Population', type: 'stock' }] }, + }); + + const requestId = 'alive-after-model'; + worker.send({ type: 'get_context', requestId }); + const resp = await waitForMessage( + worker, + (m) => m.type === 'context_response' && m.requestId === requestId + ); + expect(resp).toBeDefined(); + }, 10000); +}); + +// ───────────────────────────────────────────────────────────────────────────── + +describe('AgentWorker IPC — shutdown', () => { + let tempDir; + + beforeEach(() => { + tempDir = makeTempDir(); + }); + + afterEach(() => { + rmSync(tempDir, { recursive: true, force: true }); + }); + + it('exits cleanly with code 0 on shutdown', async () => { + const worker = spawnWorker(tempDir); + sendInit(worker); + + // Confirm it's running first + const requestId = 'pre-shutdown-check'; + worker.send({ type: 'get_context', requestId }); + await waitForMessage( + worker, + (m) => m.type === 'context_response' && m.requestId === requestId + ); + + worker.send({ type: 'shutdown' }); + const code = await waitForExit(worker); + expect(code).toBe(0); + }, 10000); + + it('exits even without initialize', async () => { + const worker = spawnWorker(tempDir); + worker.send({ type: 'shutdown' }); + const code = await waitForExit(worker); + expect(code).toBe(0); + }, 10000); +}); + +// ───────────────────────────────────────────────────────────────────────────── + +describe('AgentWorker IPC — error handling', () => { + let worker; + let tempDir; + + beforeEach(() => { + tempDir = makeTempDir(); + worker = spawnWorker(tempDir); + }); + + afterEach(() => { + worker.kill('SIGKILL'); + rmSync(tempDir, { recursive: true, force: true }); + }); + + it('sends worker_error on bad initialize (invalid mode)', async () => { + worker.send({ + type: 'initialize', + mode: 'INVALID_MODE', + model: null, + tools: [], + context: {}, + conversationHistory: [], + isAgentSwitch: false, + }); + + const errMsg = await waitForMessage(worker, (m) => m.type === 'worker_error'); + expect(errMsg.error).toBeDefined(); + expect(typeof errMsg.error).toBe('string'); + }, 10000); + + it('unknown message type does not crash the worker', async () => { + sendInit(worker); + worker.send({ type: 'this_does_not_exist', payload: 42 }); + + // Worker should still respond to get_context + const requestId = 'unknown-msg-check'; + worker.send({ type: 'get_context', requestId }); + const resp = await waitForMessage( + worker, + (m) => m.type === 'context_response' && m.requestId === requestId + ); + expect(resp).toBeDefined(); + }, 10000); + + it('multiple sequential get_context requests have unique requestIds', async () => { + sendInit(worker); + + const ids = ['r1', 'r2', 'r3']; + const responses = await Promise.all( + ids.map((requestId) => { + worker.send({ type: 'get_context', requestId }); + return waitForMessage( + worker, + (m) => m.type === 'context_response' && m.requestId === requestId + ); + }) + ); + + expect(responses.map((r) => r.requestId)).toEqual(ids); + }, 10000); +}); + +// ───────────────────────────────────────────────────────────────────────────── + +describe('AgentWorker IPC — isAgentSwitch flag', () => { + let worker; + let tempDir; + + beforeEach(() => { + tempDir = makeTempDir(); + worker = spawnWorker(tempDir); + }); + + afterEach(() => { + worker.kill('SIGKILL'); + rmSync(tempDir, { recursive: true, force: true }); + }); + + it('initializing with isAgentSwitch=true still loads history correctly', async () => { + const history = [ + { role: 'user', content: 'Prior question' }, + { role: 'assistant', content: 'Prior answer' }, + ]; + + sendInit(worker, { conversationHistory: history, isAgentSwitch: true }); + + const requestId = 'switch-context-check'; + worker.send({ type: 'get_context', requestId }); + const resp = await waitForMessage( + worker, + (m) => m.type === 'context_response' && m.requestId === requestId + ); + + expect(resp.context).toEqual(history); + }, 10000); + + it('initializing with isAgentSwitch=false loads history correctly', async () => { + const history = [{ role: 'user', content: 'Fresh session question' }]; + + sendInit(worker, { conversationHistory: history, isAgentSwitch: false }); + + const requestId = 'no-switch-context-check'; + worker.send({ type: 'get_context', requestId }); + const resp = await waitForMessage( + worker, + (m) => m.type === 'context_response' && m.requestId === requestId + ); + + expect(resp.context).toEqual(history); + }, 10000); +}); diff --git a/tests/agent/MessageProtocol.test.js b/tests/agent/MessageProtocol.test.js new file mode 100644 index 00000000..0ebd940a --- /dev/null +++ b/tests/agent/MessageProtocol.test.js @@ -0,0 +1,204 @@ +import { + SDModelSchema, + InitializeSessionMessageSchema, + ChatMessageSchema, + ModelUpdatedNotificationSchema, + createAgentTextMessage, + createToolCallNotificationMessage, + createToolCallCompletedMessage, + createAgentCompleteMessage, + createErrorMessage, + createSessionReadyMessage +} from '../../agent/utilities/MessageProtocol.js'; + +describe('MessageProtocol', () => { + describe('SDModelSchema', () => { + it('should validate valid CLD model', () => { + const model = { + variables: [{ name: 'Population', type: 'variable' }], + relationships: [{ from: 'Population', to: 'Births', polarity: '+' }] + }; + + const result = SDModelSchema.safeParse(model); + expect(result.success).toBe(true); + }); + + it('should validate valid SFD model', () => { + const model = { + variables: [ + { name: 'Stock1', type: 'stock', equation: '100' }, + { name: 'Flow1', type: 'flow', equation: '5' } + ] + }; + + const result = SDModelSchema.safeParse(model); + expect(result.success).toBe(true); + }); + + it('should accept additional properties with passthrough', () => { + const model = { + variables: [], + customField: 'custom value', + anotherField: 123 + }; + + const result = SDModelSchema.safeParse(model); + expect(result.success).toBe(true); + expect(result.data.customField).toBe('custom value'); + }); + }); + + describe('InitializeSessionMessageSchema', () => { + it('should validate valid initialization message', () => { + const message = { + type: 'initialize_session', + authenticationKey: 'test-key', + clientProduct: 'sd-web', + clientVersion: '1.0.0', + mode: 'cld', + model: { variables: [] }, + tools: [] + }; + + const result = InitializeSessionMessageSchema.safeParse(message); + expect(result.success).toBe(true); + }); + + it('should require mode to be cld or sfd', () => { + const message = { + type: 'initialize_session', + authenticationKey: 'test-key', + clientProduct: 'sd-web', + clientVersion: '1.0.0', + mode: 'invalid', + model: {}, + tools: [] + }; + + const result = InitializeSessionMessageSchema.safeParse(message); + expect(result.success).toBe(false); + }); + + it('should allow optional context', () => { + const message = { + type: 'initialize_session', + authenticationKey: 'test-key', + clientProduct: 'sd-web', + clientVersion: '1.0.0', + mode: 'sfd', + model: {}, + tools: [], + context: { description: 'This is test context' } + }; + + const result = InitializeSessionMessageSchema.safeParse(message); + expect(result.success).toBe(true); + }); + }); + + describe('ChatMessageSchema', () => { + it('should validate valid chat message', () => { + const message = { + type: 'chat', + sessionId: 'test-123', + message: 'Build me a population model' + }; + + const result = ChatMessageSchema.safeParse(message); + expect(result.success).toBe(true); + }); + + it('should require message field', () => { + const message = { + type: 'chat', + sessionId: 'test-123' + }; + + const result = ChatMessageSchema.safeParse(message); + expect(result.success).toBe(false); + }); + }); + + describe('ModelUpdatedNotificationSchema', () => { + it('should validate model update notification', () => { + const message = { + type: 'model_updated_notification', + sessionId: 'test-123', + model: { variables: [{ name: 'X', type: 'stock' }] }, + changeReason: 'User requested change' + }; + + const result = ModelUpdatedNotificationSchema.safeParse(message); + expect(result.success).toBe(true); + }); + }); + + describe('message creation helpers', () => { + it('should create agent text message', () => { + const message = createAgentTextMessage('session-1', 'Hello user', false); + + expect(message.type).toBe('agent_text'); + expect(message.sessionId).toBe('session-1'); + expect(message.content).toBe('Hello user'); + expect(message.isThinking).toBe(false); + }); + + it('should create tool call notification message', () => { + const message = createToolCallNotificationMessage( + 'session-1', + 'call-123', + 'generate_quantitative_model', + { prompt: 'Build model' }, + true + ); + + expect(message.type).toBe('tool_call_notification'); + expect(message.callId).toBe('call-123'); + expect(message.toolName).toBe('generate_quantitative_model'); + expect(message.isBuiltIn).toBe(true); + }); + + it('should create tool call completed message', () => { + const message = createToolCallCompletedMessage( + 'session-1', + 'call-123', + 'generate_quantitative_model', + { model: {} }, + false + ); + + expect(message.type).toBe('tool_call_completed'); + expect(message.callId).toBe('call-123'); + expect(message.isError).toBe(false); + }); + + it('should create agent complete message', () => { + const message = createAgentCompleteMessage('session-1', 'success', 'Done'); + + expect(message.type).toBe('agent_complete'); + expect(message.status).toBe('success'); + expect(message.finalMessage).toBe('Done'); + }); + + it('should create error message', () => { + const message = createErrorMessage('session-1', 'Something went wrong', 'GENERIC', true); + + expect(message.type).toBe('error'); + expect(message.error).toBe('Something went wrong'); + expect(message.errorCode).toBe('GENERIC'); + }); + + it('should create session ready message', () => { + const availableAgents = [ + { id: 'socrates', name: 'Socrates', description: 'Helpful mentor' }, + { id: 'merlin', name: 'Merlin', description: 'Expert modeler' } + ]; + const message = createSessionReadyMessage('session-1', availableAgents); + + expect(message.type).toBe('session_ready'); + expect(message.sessionId).toBe('session-1'); + expect(message.availableAgents).toHaveLength(2); + expect(message.availableAgents[0].id).toBe('socrates'); + }); + }); +}); diff --git a/tests/agent/SessionManager.test.js b/tests/agent/SessionManager.test.js new file mode 100644 index 00000000..aaf81bcc --- /dev/null +++ b/tests/agent/SessionManager.test.js @@ -0,0 +1,378 @@ +import { SessionManager } from '../../agent/utilities/SessionManager.js'; +import { jest } from '@jest/globals'; +import fs from 'fs'; +import path from 'path'; +import os from 'os'; +import { randomBytes } from 'crypto'; + +describe('SessionManager', () => { + let sessionManager; + + beforeEach(() => { + sessionManager = new SessionManager(); + }); + + afterEach(() => { + sessionManager.shutdown(); + }); + + describe('initializeSession', () => { + it('should create a new session with CLD model type', () => { + const mode = 'cld'; + const model = { variables: [], relationships: [] }; + const tools = []; + const context = { description: 'Test context' }; + + const sessionId = sessionManager.createSession(null); // null WebSocket for testing + sessionManager.initializeSession(sessionId, mode, model, tools, context, 'test-client'); + + const session = sessionManager.getSession(sessionId); + expect(session).toBeDefined(); + expect(session.mode).toBe('cld'); + expect(session.clientModel).toEqual(model); + expect(session.context).toEqual(context); + expect(session.conversationContext).toEqual([]); + }); + + it('should create a new session with SFD model type', () => { + const mode = 'sfd'; + const model = { variables: [] }; + + const sessionId = sessionManager.createSession(null); + sessionManager.initializeSession(sessionId, mode, model, [], {}, ''); + + const session = sessionManager.getSession(sessionId); + expect(session.mode).toBe('sfd'); + }); + + it('should create temp folder for session', () => { + const sessionId = sessionManager.createSession(null); + sessionManager.initializeSession(sessionId, 'cld', {}, [], {}, ''); + + const session = sessionManager.getSession(sessionId); + expect(session.tempDir).toBeDefined(); + expect(fs.existsSync(session.tempDir)).toBe(true); + }); + + it('should throw error for invalid model type', () => { + const sessionId = sessionManager.createSession(null); + expect(() => { + sessionManager.initializeSession(sessionId, 'invalid', {}, [], {}, ''); + }).toThrow(); + }); + }); + + describe('getSession', () => { + it('should return session if exists', () => { + const sessionId = sessionManager.createSession(null); + sessionManager.initializeSession(sessionId, 'cld', {}, [], {}, ''); + + const session = sessionManager.getSession(sessionId); + expect(session).toBeDefined(); + expect(session.mode).toBe('cld'); + }); + + it('should return undefined for non-existent session', () => { + const session = sessionManager.getSession('non-existent'); + expect(session).toBeUndefined(); + }); + }); + + describe('updateClientModel', () => { + it('should update the client model', () => { + const sessionId = sessionManager.createSession(null); + sessionManager.initializeSession(sessionId, 'sfd', {}, [], {}, ''); + + const newModel = { variables: [{ name: 'Stock1', type: 'stock' }] }; + sessionManager.updateClientModel(sessionId, newModel); + + const session = sessionManager.getSession(sessionId); + expect(session.clientModel).toEqual(newModel); + }); + + it('should not throw error for non-existent session', () => { + expect(() => { + sessionManager.updateClientModel('non-existent', {}); + }).not.toThrow(); + }); + }); + + describe('conversation history', () => { + let testSessionId; + + beforeEach(() => { + testSessionId = sessionManager.createSession(null); + sessionManager.initializeSession(testSessionId, 'cld', {}, [], {}, ''); + }); + + it('should add messages to conversation history', () => { + sessionManager.addToConversationHistory(testSessionId, { + role: 'user', + content: 'Hello' + }); + + const history = sessionManager.getConversationContext(testSessionId); + expect(history).toHaveLength(1); + expect(history[0].role).toBe('user'); + expect(history[0].content).toBe('Hello'); + }); + + it('should maintain conversation order', () => { + sessionManager.addToConversationHistory(testSessionId, { + role: 'user', + content: 'First' + }); + sessionManager.addToConversationHistory(testSessionId, { + role: 'assistant', + content: 'Second' + }); + + const history = sessionManager.getConversationContext(testSessionId); + expect(history).toHaveLength(2); + expect(history[0].content).toBe('First'); + expect(history[1].content).toBe('Second'); + }); + }); + + describe('deleteSession', () => { + it('should remove session and clean up temp folder', () => { + const sessionId = sessionManager.createSession(null); + sessionManager.initializeSession(sessionId, 'cld', {}, [], {}, ''); + + const session = sessionManager.getSession(sessionId); + const tempFolder = session.tempDir; + expect(fs.existsSync(tempFolder)).toBe(true); + + sessionManager.deleteSession(sessionId); + + expect(sessionManager.getSession(sessionId)).toBeUndefined(); + expect(fs.existsSync(tempFolder)).toBe(false); + }); + + it('should not throw error for non-existent session', () => { + expect(() => { + sessionManager.deleteSession('non-existent'); + }).not.toThrow(); + }); + }); + + describe('shutdown', () => { + it('should clean up all sessions', () => { + const sessionId1 = sessionManager.createSession(null); + sessionManager.initializeSession(sessionId1, 'cld', {}, [], {}, ''); + + const sessionId2 = sessionManager.createSession(null); + sessionManager.initializeSession(sessionId2, 'sfd', {}, [], {}, ''); + + const session1 = sessionManager.getSession(sessionId1); + const session2 = sessionManager.getSession(sessionId2); + const temp1 = session1.tempDir; + const temp2 = session2.tempDir; + + sessionManager.shutdown(); + + expect(sessionManager.getSession(sessionId1)).toBeUndefined(); + expect(sessionManager.getSession(sessionId2)).toBeUndefined(); + expect(fs.existsSync(temp1)).toBe(false); + expect(fs.existsSync(temp2)).toBe(false); + }); + }); + + describe('getSessionTempDir', () => { + it('should return temp folder path for session', () => { + const sessionId = sessionManager.createSession(null); + sessionManager.initializeSession(sessionId, 'cld', {}, [], {}, ''); + + const tempFolder = sessionManager.getSessionTempDir(sessionId); + expect(tempFolder).toBeDefined(); + expect(fs.existsSync(tempFolder)).toBe(true); + }); + + it('should return undefined for non-existent session', () => { + const tempFolder = sessionManager.getSessionTempDir('non-existent'); + expect(tempFolder).toBeUndefined(); + }); + }); + + describe('setWorkerTeardown', () => { + it('initializes workerTeardown to null on new sessions', () => { + const sessionId = sessionManager.createSession(null); + // Bypass getSession() so we don't touch lastActivity in assertions + // that other tests might extend. + expect(sessionManager.sessions.get(sessionId).workerTeardown).toBeNull(); + }); + + it('installs a teardown hook on the session', () => { + const sessionId = sessionManager.createSession(null); + const teardown = () => Promise.resolve(); + sessionManager.setWorkerTeardown(sessionId, teardown); + expect(sessionManager.sessions.get(sessionId).workerTeardown).toBe(teardown); + }); + + it('is a no-op for an unknown session id', () => { + expect(() => sessionManager.setWorkerTeardown('nope', () => Promise.resolve())).not.toThrow(); + }); + }); + + describe('cleanupStaleSessions', () => { + // Drive cleanup manually with tight timeouts so we don't depend on the + // 5-minute interval timer. Isolate the temp base so other parallel test + // suites' SessionManager.shutdown() (which calls cleanupOrphanedTempDirs) + // can't reap our session dir as an "orphan". + let sm; + let tempBasePath; + + beforeEach(() => { + tempBasePath = path.join(os.tmpdir(), `sm-cleanup-${randomBytes(8).toString('hex')}`); + sm = new SessionManager({ + maxSessionAge: 50, + sessionTimeout: 50, + disableCleanup: true, + tempBasePath, + }); + }); + + afterEach(() => { + sm.shutdown(); + try { fs.rmSync(tempBasePath, { recursive: true, force: true }); } catch { /* already gone */ } + }); + + it('leaves fresh sessions alone', async () => { + const sessionId = sm.createSession(null); + sm.initializeSession(sessionId, 'cld', {}, [], {}, ''); + + await sm.cleanupStaleSessions(); + + expect(sm.sessions.has(sessionId)).toBe(true); + }); + + it('removes sessions that have exceeded the inactivity timeout', async () => { + const sessionId = sm.createSession(null); + sm.initializeSession(sessionId, 'cld', {}, [], {}, ''); + const tempDir = sm.sessions.get(sessionId).tempDir; + + await new Promise((r) => setTimeout(r, 80)); + await sm.cleanupStaleSessions(); + + expect(sm.sessions.has(sessionId)).toBe(false); + expect(fs.existsSync(tempDir)).toBe(false); + }); + + it('awaits workerTeardown before deleting the session or its temp dir', async () => { + // This is the bug-fix invariant: when a worker is running, the host must + // keep the bind-mount source alive until the worker has actually exited. + const sessionId = sm.createSession(null); + sm.initializeSession(sessionId, 'cld', {}, [], {}, ''); + const tempDir = sm.sessions.get(sessionId).tempDir; + + let dirExistedWhenTeardownCalled = null; + let sessionStillRegisteredAtTeardown = null; + let releaseTeardown; + const teardownGate = new Promise((resolve) => { releaseTeardown = resolve; }); + + sm.setWorkerTeardown(sessionId, () => { + dirExistedWhenTeardownCalled = fs.existsSync(tempDir); + sessionStillRegisteredAtTeardown = sm.sessions.has(sessionId); + return teardownGate; + }); + + await new Promise((r) => setTimeout(r, 80)); + + const cleanupPromise = sm.cleanupStaleSessions(); + + // Let the cleanup loop reach the await on our teardown gate. + await new Promise((r) => setImmediate(r)); + + // Mid-teardown: dir + session must still be present, otherwise a live + // worker would observe its `/session` bind mount yanked. + expect(sm.sessions.has(sessionId)).toBe(true); + expect(fs.existsSync(tempDir)).toBe(true); + + releaseTeardown(); + await cleanupPromise; + + expect(dirExistedWhenTeardownCalled).toBe(true); + expect(sessionStillRegisteredAtTeardown).toBe(true); + expect(sm.sessions.has(sessionId)).toBe(false); + expect(fs.existsSync(tempDir)).toBe(false); + }); + + it('still deletes the session if workerTeardown rejects', async () => { + const sessionId = sm.createSession(null); + sm.initializeSession(sessionId, 'cld', {}, [], {}, ''); + const tempDir = sm.sessions.get(sessionId).tempDir; + + sm.setWorkerTeardown(sessionId, () => Promise.reject(new Error('worker exit failed'))); + + await new Promise((r) => setTimeout(r, 80)); + await sm.cleanupStaleSessions(); + + expect(sm.sessions.has(sessionId)).toBe(false); + expect(fs.existsSync(tempDir)).toBe(false); + }); + + it('closes the WebSocket if it is still open', async () => { + const ws = { readyState: 1, close: jest.fn() }; + const sessionId = sm.createSession(ws); + sm.initializeSession(sessionId, 'cld', {}, [], {}, ''); + + await new Promise((r) => setTimeout(r, 80)); + await sm.cleanupStaleSessions(); + + expect(ws.close).toHaveBeenCalledWith(1000, 'Session timeout'); + }); + + it('does not call ws.close if the WebSocket is already closed', async () => { + const ws = { readyState: 3, close: jest.fn() }; + const sessionId = sm.createSession(ws); + sm.initializeSession(sessionId, 'cld', {}, [], {}, ''); + + await new Promise((r) => setTimeout(r, 80)); + await sm.cleanupStaleSessions(); + + expect(ws.close).not.toHaveBeenCalled(); + // Session should still be removed. + expect(sm.sessions.has(sessionId)).toBe(false); + }); + + it('skips sessions removed concurrently while awaiting another teardown', async () => { + // If a session gets deleted out from under us (e.g. WS close handler + // fires while we are awaiting a slow teardown for a different session), + // cleanupStaleSessions must not call deleteSession on it again. + const sessionA = sm.createSession(null); + sm.initializeSession(sessionA, 'cld', {}, [], {}, ''); + const sessionB = sm.createSession(null); + sm.initializeSession(sessionB, 'cld', {}, [], {}, ''); + const tempA = sm.sessions.get(sessionA).tempDir; + const tempB = sm.sessions.get(sessionB).tempDir; + + let releaseA; + const aGate = new Promise((resolve) => { releaseA = resolve; }); + sm.setWorkerTeardown(sessionA, () => aGate); + + const deleteSpy = jest.spyOn(sm, 'deleteSession'); + + await new Promise((r) => setTimeout(r, 80)); + const cleanupPromise = sm.cleanupStaleSessions(); + + // Drop into the await on sessionA's teardown. + await new Promise((r) => setImmediate(r)); + + // Simulate a concurrent WS close removing session B. + sm.deleteSession(sessionB); + expect(fs.existsSync(tempB)).toBe(false); + + releaseA(); + await cleanupPromise; + + // sessionB should have only been deleted once (the concurrent removal). + const bDeletes = deleteSpy.mock.calls.filter(([id]) => id === sessionB).length; + expect(bDeletes).toBe(1); + // sessionA still got cleaned up after its teardown resolved. + expect(sm.sessions.has(sessionA)).toBe(false); + expect(fs.existsSync(tempA)).toBe(false); + + deleteSpy.mockRestore(); + }); + }); +}); diff --git a/tests/agent/SessionManagerSummarization.test.js b/tests/agent/SessionManagerSummarization.test.js new file mode 100644 index 00000000..91245886 --- /dev/null +++ b/tests/agent/SessionManagerSummarization.test.js @@ -0,0 +1,250 @@ +import { SessionManager } from '../../agent/utilities/SessionManager.js'; +import { AgentOrchestrator } from '../../agent/AgentOrchestrator.js'; +import { jest } from '@jest/globals'; +import path from 'path'; +import { fileURLToPath } from 'url'; + +const __filename = fileURLToPath(import.meta.url); +const __dirname = path.dirname(__filename); + +const AGENT_A_CONFIG = { path: path.join(__dirname, '../../agent/config/socrates.md') }; +const AGENT_B_CONFIG = { path: path.join(__dirname, '../../agent/config/merlin.md') }; + +function makeGeminiMock(summaryText = 'Mocked summary.') { + return { + models: { + generateContent: jest.fn().mockResolvedValue({ + text: summaryText + }) + } + }; +} + +function userMsg(text) { + return { role: 'user', parts: [{ text }] }; +} + +function modelMsg(text) { + return { role: 'model', parts: [{ text }] }; +} + +function modelResultMessage(id) { + return { + role: 'user', + parts: [{ + functionResponse: { + name: 'generate_model', + response: { result: JSON.stringify({ model: { variables: [] }, resultId: id }) } + } + }] + }; +} + +function modelToolCallMessage(id) { + return { + role: 'model', + parts: [{ + functionCall: { + name: 'generate_model', + args: { id } + } + }] + }; +} + +// ─── SessionManager.cleanupContext ───────────────────────────────── + +describe('SessionManager.cleanupContext', () => { + let sessionManager; + let sessionId; + + beforeEach(() => { + sessionManager = new SessionManager(); + sessionId = sessionManager.createSession(null); + sessionManager.initializeSession(sessionId, 'cld', {}, [], {}, 'test-client'); + sessionManager.gemini = makeGeminiMock(); + }); + + afterEach(() => { sessionManager.shutdown(); }); + + it('does nothing when context is under the token limit', async () => { + sessionManager.addToConversationHistory(sessionId, userMsg('Hello')); + sessionManager.addToConversationHistory(sessionId, modelMsg('Hi there')); + + const contextBefore = [...sessionManager.getConversationContext(sessionId)]; + await sessionManager.cleanupContext(sessionId, 100_000); + + expect(sessionManager.getConversationContext(sessionId)).toEqual(contextBefore); + expect(sessionManager.gemini.models.generateContent).not.toHaveBeenCalled(); + }); + + it('replaces old messages with a summary when over the token limit', async () => { + for (let i = 0; i < 10; i++) { + sessionManager.addToConversationHistory(sessionId, userMsg(`Message ${i}`)); + sessionManager.addToConversationHistory(sessionId, modelMsg(`Response ${i}`)); + } + + await sessionManager.cleanupContext(sessionId, 1); + + const context = sessionManager.getConversationContext(sessionId); + expect(context[0].role).toBe('user'); + expect(context[0].parts[0].text).toMatch(/\[Previous conversation summary\]/); + expect(sessionManager.gemini.models.generateContent).toHaveBeenCalled(); + }); + + it('modifies the session context in-place so the live reference reflects the change', async () => { + for (let i = 0; i < 8; i++) { + sessionManager.addToConversationHistory(sessionId, userMsg(`Message ${i}`)); + sessionManager.addToConversationHistory(sessionId, modelMsg(`Response ${i}`)); + } + + const liveRef = sessionManager.getConversationContext(sessionId); + const originalLength = liveRef.length; + + await sessionManager.cleanupContext(sessionId, 1); + + // splice is in-place: the same array object must be updated, not replaced + expect(liveRef).toBe(sessionManager.getConversationContext(sessionId)); + expect(liveRef.length).toBeLessThan(originalLength); + expect(liveRef[0].parts[0].text).toMatch(/\[Previous conversation summary\]/); + }); + + it('uses a fallback summary message when the LLM call fails', async () => { + sessionManager.gemini.models.generateContent.mockRejectedValue(new Error('API error')); + + for (let i = 0; i < 5; i++) { + sessionManager.addToConversationHistory(sessionId, userMsg(`Message ${i}`)); + sessionManager.addToConversationHistory(sessionId, modelMsg(`Response ${i}`)); + } + + await sessionManager.cleanupContext(sessionId, 1); + + const context = sessionManager.getConversationContext(sessionId); + expect(context[0].parts[0].text).toMatch(/condensed/); + }); + + it('does nothing for a non-existent session ID', async () => { + await expect( + sessionManager.cleanupContext('non-existent-id', 1) + ).resolves.toBeUndefined(); + }); +}); + +// ─── SessionManager.cleanupContext ─────────────────────────────────────────── + +describe('SessionManager.cleanupContext', () => { + let sessionManager; + let sessionId; + + beforeEach(() => { + sessionManager = new SessionManager(); + sessionId = sessionManager.createSession(null); + sessionManager.initializeSession(sessionId, 'cld', {}, [], {}, 'test-client'); + sessionManager.gemini = makeGeminiMock(); + }); + + afterEach(() => { sessionManager.shutdown(); }); + + it('does nothing when context is empty', async () => { + await expect( + sessionManager.cleanupContext(sessionId, 100_000) + ).resolves.toBeUndefined(); + expect(sessionManager.getConversationContext(sessionId)).toHaveLength(0); + }); + + it('summarizes after removing stale models when still over the token limit', async () => { + for (let i = 0; i < 5; i++) { + sessionManager.addToConversationHistory(sessionId, userMsg(`request ${i}`)); + sessionManager.addToConversationHistory(sessionId, modelToolCallMessage(String(i))); + sessionManager.addToConversationHistory(sessionId, modelResultMessage(String(i))); + } + + await sessionManager.cleanupContext(sessionId, 1); + + const context = sessionManager.getConversationContext(sessionId); + const hasSummary = context.some( + msg => Array.isArray(msg.parts) && msg.parts[0]?.text?.includes('[Previous conversation summary]') + ); + expect(hasSummary).toBe(true); + expect(sessionManager.gemini.models.generateContent).toHaveBeenCalled(); + }); +}); + +// ─── Agent switch context continuity ───────────────────────────────────────── + +describe('Agent switch - context continuity between orchestrators', () => { + let sessionManager; + let sessionId; + const sendToClient = jest.fn(); + + beforeEach(() => { + sessionManager = new SessionManager(); + sessionId = sessionManager.createSession(null); + sessionManager.initializeSession(sessionId, 'cld', {}, [], {}, 'test-client'); + process.env.GEMINI_API_KEY = 'dummy_key'; + process.env.ANTHROPIC_API_KEY = 'dummy_key'; + }); + + afterEach(() => { + sessionManager.shutdown(); + sendToClient.mockClear(); + }); + + it('second orchestrator sees context accumulated by the first orchestrator', () => { + const orchestratorA = new AgentOrchestrator(sessionManager, sessionId, sendToClient, AGENT_A_CONFIG); + + // Simulate agent A processing a conversation turn (manual mode pushes to live context) + sessionManager.addToConversationHistory(sessionId, userMsg('Build a causal loop diagram')); + const context = sessionManager.getConversationContext(sessionId); + context.push(modelMsg('Here is the CLD.')); + + // websocket.js captures the context on switch, then creates a new orchestrator + const capturedOnSwitch = sessionManager.getConversationContext(sessionId); + + const orchestratorB = new AgentOrchestrator(sessionManager, sessionId, sendToClient, AGENT_B_CONFIG); + + // Agent B reads the session context — must see what agent A built + const agentBContext = sessionManager.getConversationContext(sessionId); + expect(agentBContext).toBe(capturedOnSwitch); + expect(agentBContext).toHaveLength(2); + expect(agentBContext[0].parts[0].text).toBe('Build a causal loop diagram'); + expect(agentBContext[1].parts[0].text).toBe('Here is the CLD.'); + + orchestratorA.destroy(); + orchestratorB.destroy(); + }); + + it('second orchestrator sees the summarized context after summarization by the first', async () => { + sessionManager.gemini = makeGeminiMock( + 'Agent A built a CLD with 5 variables and 3 feedback loops.' + ); + + const orchestratorA = new AgentOrchestrator(sessionManager, sessionId, sendToClient, AGENT_A_CONFIG); + + // Agent A accumulates a large context + for (let i = 0; i < 10; i++) { + sessionManager.addToConversationHistory(sessionId, userMsg(`Step ${i}`)); + sessionManager.addToConversationHistory(sessionId, modelMsg(`Done ${i}`)); + } + const fullLength = sessionManager.getConversationContext(sessionId).length; + + // Summarization fires during agent A's last turn + await sessionManager.cleanupContext(sessionId, 1); + + // websocket.js captures context and creates agent B + const capturedOnSwitch = sessionManager.getConversationContext(sessionId); + const orchestratorB = new AgentOrchestrator(sessionManager, sessionId, sendToClient, AGENT_B_CONFIG); + + const agentBContext = sessionManager.getConversationContext(sessionId); + + // Agent B sees the summarized (shorter) context, not the full bloated one + expect(agentBContext).toBe(capturedOnSwitch); + expect(agentBContext.length).toBeLessThan(fullLength); + expect( + agentBContext.some(m => Array.isArray(m.parts) && m.parts[0]?.text?.includes('[Previous conversation summary]')) + ).toBe(true); + + orchestratorA.destroy(); + orchestratorB.destroy(); + }); +}); diff --git a/tests/agent/WorkerSpawner.test.js b/tests/agent/WorkerSpawner.test.js new file mode 100644 index 00000000..0d5e1760 --- /dev/null +++ b/tests/agent/WorkerSpawner.test.js @@ -0,0 +1,195 @@ +/** + * Tests for agent/WorkerSpawner.js + * + * Covers: + * - WorkerSpawner.CONTAINER_SESSION_PATH value + * - WorkerSpawner.spawn returns a live ChildProcess with an IPC channel + * - The spawned process terminates cleanly when sent SIGKILL + * - SessionManager.createSessionWithId (the companion addition) + */ + +import { jest } from '@jest/globals'; +import { mkdirSync, rmSync } from 'fs'; +import { tmpdir } from 'os'; +import { join } from 'path'; +import { WorkerSpawner } from '../../agent/WorkerSpawner.js'; +import { SessionManager } from '../../agent/utilities/SessionManager.js'; + +function makeTempDir() { + const dir = join(tmpdir(), `spawner-test-${Date.now()}-${Math.random().toString(36).slice(2)}`); + mkdirSync(dir, { recursive: true }); + return dir; +} + +// ───────────────────────────────────────────────────────────────────────────── + +describe('WorkerSpawner.CONTAINER_SESSION_PATH', () => { + it('is /session', () => { + expect(WorkerSpawner.CONTAINER_SESSION_PATH).toBe('/session'); + }); +}); + +// ───────────────────────────────────────────────────────────────────────────── + +describe('WorkerSpawner.spawn', () => { + const workers = []; + + afterEach(() => { + // Kill any workers that leaked out of tests + for (const { worker, tempDir } of workers.splice(0)) { + worker.kill('SIGKILL'); + rmSync(tempDir, { recursive: true, force: true }); + } + }); + + async function spawn(sessionId = 'sess_spawner_test') { + const tempDir = makeTempDir(); + const worker = await WorkerSpawner.spawn(sessionId, tempDir); + workers.push({ worker, tempDir }); + return { worker, tempDir }; + } + + it('returns an object with a send() method (ChildProcess IPC interface)', async () => { + const { worker } = await spawn(); + expect(typeof worker.send).toBe('function'); + }); + + it('returns an object with a kill() method', async () => { + const { worker } = await spawn(); + expect(typeof worker.kill).toBe('function'); + }); + + it('returned process has a pid', async () => { + const { worker } = await spawn(); + expect(typeof worker.pid).toBe('number'); + expect(worker.pid).toBeGreaterThan(0); + }); + + it('returned process is initially alive (exitCode is null)', async () => { + const { worker } = await spawn(); + expect(worker.exitCode).toBeNull(); + }); + + it('can send IPC messages without throwing', async () => { + const { worker } = await spawn(); + expect(() => { + worker.send({ type: 'get_context', requestId: 'probe' }); + }).not.toThrow(); + }); + + it('IPC channel is active — worker responds to get_context', async () => { + const { worker } = await spawn(); + + const response = await new Promise((resolve, reject) => { + const t = setTimeout(() => reject(new Error('IPC timeout')), 8000); + worker.on('message', (msg) => { + if (msg.type === 'context_response' && msg.requestId === 'probe') { + clearTimeout(t); + resolve(msg); + } + }); + // get_context works even before initialize (returns []) + worker.send({ type: 'get_context', requestId: 'probe' }); + }); + + expect(response.context).toEqual([]); + }, 10000); + + it('process exits after SIGKILL', async () => { + const { worker } = await spawn(); + + const exitCode = await new Promise((resolve, reject) => { + const t = setTimeout(() => reject(new Error('Kill timeout')), 5000); + worker.once('exit', (code, signal) => { + clearTimeout(t); + resolve({ code, signal }); + }); + worker.kill('SIGKILL'); + }); + + // exitCode may be null on SIGKILL (signal-terminated), signal will be SIGKILL + expect(exitCode.signal === 'SIGKILL' || exitCode.code !== undefined).toBe(true); + }, 8000); + + it('each spawned worker gets its own process (distinct pids)', async () => { + const { worker: w1 } = await spawn('sess_a'); + const { worker: w2 } = await spawn('sess_b'); + expect(w1.pid).not.toBe(w2.pid); + }); +}); + +// ───────────────────────────────────────────────────────────────────────────── + +describe('SessionManager.createSessionWithId', () => { + let sm; + + beforeEach(() => { + sm = new SessionManager({ disableCleanup: true }); + }); + + afterEach(() => { + sm.shutdown(); + }); + + it('creates a session with the provided ID', () => { + const tempDir = makeTempDir(); + try { + sm.createSessionWithId('test-id-1', null, tempDir); + expect(sm.getSession('test-id-1')).toBeDefined(); + } finally { + rmSync(tempDir, { recursive: true, force: true }); + } + }); + + it('session has the correct tempDir', () => { + const tempDir = makeTempDir(); + try { + sm.createSessionWithId('test-id-2', null, tempDir); + expect(sm.getSession('test-id-2').tempDir).toBe(tempDir); + } finally { + rmSync(tempDir, { recursive: true, force: true }); + } + }); + + it('session starts with empty conversationContext', () => { + const tempDir = makeTempDir(); + try { + sm.createSessionWithId('test-id-3', null, tempDir); + expect(sm.getConversationContext('test-id-3')).toEqual([]); + } finally { + rmSync(tempDir, { recursive: true, force: true }); + } + }); + + it('is idempotent — second call with same ID returns existing session', () => { + const tempDir = makeTempDir(); + try { + sm.createSessionWithId('test-id-4', null, tempDir); + sm.addToConversationHistory('test-id-4', { role: 'user', content: 'hello' }); + sm.createSessionWithId('test-id-4', null, tempDir); // second call + // History should be preserved — session was not replaced + expect(sm.getConversationContext('test-id-4')).toHaveLength(1); + } finally { + rmSync(tempDir, { recursive: true, force: true }); + } + }); + + it('can be initialized with initializeSession after creation', () => { + const tempDir = makeTempDir(); + try { + sm.createSessionWithId('test-id-5', null, tempDir); + expect(() => { + sm.initializeSession('test-id-5', 'sfd', null, [], {}, 'test-client'); + }).not.toThrow(); + expect(sm.getSession('test-id-5').mode).toBe('sfd'); + } finally { + rmSync(tempDir, { recursive: true, force: true }); + } + }); + + it('disableCleanup prevents the cleanup timer from running', () => { + // If cleanup were running, it would call cleanupStaleSessions every 5 minutes. + // Just verify the timer is not set — SessionManager.cleanupTimer should be undefined. + expect(sm.cleanupTimer).toBeUndefined(); + }); +}); diff --git a/tests/agent/createVisualizationHelpers.test.js b/tests/agent/createVisualizationHelpers.test.js new file mode 100644 index 00000000..55640ddc --- /dev/null +++ b/tests/agent/createVisualizationHelpers.test.js @@ -0,0 +1,68 @@ +import { isRunKeyedFormat, extractRunFeedback } from '../../agent/tools/builtin/createVisualization.js'; + +describe('isRunKeyedFormat', () => { + it('returns true for run-keyed variable data', () => { + const data = { + run_abc: { time: [0, 1], Population: [1000, 1020] }, + run_def: { time: [0, 1], Population: [1000, 980] } + }; + expect(isRunKeyedFormat(data)).toBe(true); + }); + + it('returns true for single run', () => { + expect(isRunKeyedFormat({ run_abc: { time: [0, 1], Population: [1000, 1020] } })).toBe(true); + }); + + it('returns false for flat format (has time key at top level)', () => { + expect(isRunKeyedFormat({ time: [0, 1], Population: [1000, 1020] })).toBe(false); + }); + + it('returns false for feedback format (has feedbackContent key)', () => { + expect(isRunKeyedFormat({ feedbackContent: {} })).toBe(false); + }); + + it('returns false for empty object', () => { + expect(isRunKeyedFormat({})).toBe(false); + }); + + it('returns false when run values are not objects with a time array', () => { + expect(isRunKeyedFormat({ run_abc: [1000, 1020] })).toBe(false); + }); +}); + +describe('extractRunFeedback', () => { + const flatFeedback = { feedbackLoops: [{ identifier: 'R1' }], dominantLoopsByPeriod: [] }; + + it('returns flat feedbackContent unchanged', () => { + expect(extractRunFeedback(flatFeedback)).toBe(flatFeedback); + }); + + it('returns the preferred run when specified', () => { + const content = { + run_abc: { feedbackLoops: [{ identifier: 'R1' }] }, + run_def: { feedbackLoops: [{ identifier: 'R2' }] } + }; + expect(extractRunFeedback(content, 'run_def')).toBe(content.run_def); + }); + + it('falls back to last run when preferredRunId is absent', () => { + const content = { + run_abc: { feedbackLoops: [{ identifier: 'R1' }] }, + run_def: { feedbackLoops: [{ identifier: 'R2' }] } + }; + expect(extractRunFeedback(content)).toBe(content.run_def); + }); + + it('falls back to last run when preferredRunId is not in content', () => { + const content = { + run_abc: { feedbackLoops: [] }, + run_def: { feedbackLoops: [] } + }; + expect(extractRunFeedback(content, 'run_missing')).toBe(content.run_def); + }); + + it('returns input unchanged for null/undefined', () => { + expect(extractRunFeedback(null)).toBe(null); + expect(extractRunFeedback(undefined)).toBe(undefined); + }); +}); diff --git a/tests/agent/tools/largeModelTools.test.js b/tests/agent/tools/largeModelTools.test.js new file mode 100644 index 00000000..861e2740 --- /dev/null +++ b/tests/agent/tools/largeModelTools.test.js @@ -0,0 +1,613 @@ +import { + createReadModelSectionTool, + createEditVariablesTool, + createEditRelationshipsTool, + createEditModulesTool +} from '../../../agent/tools/builtin/largeModelTools.js'; +import { mkdtempSync, writeFileSync, rmSync } from 'fs'; +import { join } from 'path'; +import { tmpdir } from 'os'; + +const SESSION_ID = 'test-session'; + +// Canonical storage format: variable names use spaces, equations use underscores +const BASE_MODEL = { + specs: { startTime: 0, stopTime: 100, dt: 1, timeUnits: 'Year' }, + variables: [ + { name: 'birth rate', type: 'variable', equation: 'Population * birth_fraction' }, + { name: 'death rate', type: 'variable', equation: 'Population * death_fraction' }, + { name: 'Population', type: 'stock', equation: '1000' }, + { name: 'Finance.net revenue', type: 'variable', equation: 'total_revenue - total_costs' }, + { name: 'Finance.total costs', type: 'variable', equation: 'fixed_costs + variable_costs' }, + ], + relationships: [ + { from: 'birth rate', to: 'Population', polarity: '+' }, + { from: 'death rate', to: 'Population', polarity: '-' }, + ], + modules: [ + { name: 'Finance', parentModule: null }, + { name: 'My Module', parentModule: null }, + ], +}; + +// ─── helpers ────────────────────────────────────────────────────────────────── + +function makeTempDir(model = BASE_MODEL) { + const dir = mkdtempSync(join(tmpdir(), 'sd-ai-test-')); + writeFileSync(join(dir, 'model.sdjson'), JSON.stringify(model)); + return dir; +} + +function makeReadTool(tempDir) { + const sessionManager = { getSessionTempDir: () => tempDir }; + return createReadModelSectionTool(sessionManager, SESSION_ID); +} + +function parseResult(result) { + expect(result.isError).toBeFalsy(); + return JSON.parse(result.content[0].text); +} + +// ─── createReadModelSectionTool ─────────────────────────────────────────────── + +describe('createReadModelSectionTool normalization', () => { + let tempDir; + + beforeEach(() => { tempDir = makeTempDir(); }); + afterEach(() => { rmSync(tempDir, { recursive: true, force: true }); }); + + describe('variableNames filter', () => { + it('matches underscore query against space-named variables', async () => { + const tool = makeReadTool(tempDir); + const { variables } = parseResult( + await tool.handler({ section: 'variables', filter: { variableNames: ['birth_rate'] } }) + ); + expect(variables).toHaveLength(1); + expect(variables[0].name).toBe('birth_rate'); // read tool outputs underscores + }); + + it('matches space query against space-named variables', async () => { + const tool = makeReadTool(tempDir); + const { variables } = parseResult( + await tool.handler({ section: 'variables', filter: { variableNames: ['birth rate'] } }) + ); + expect(variables).toHaveLength(1); + }); + + it('is case-insensitive', async () => { + const tool = makeReadTool(tempDir); + const { variables } = parseResult( + await tool.handler({ section: 'variables', filter: { variableNames: ['BIRTH_RATE'] } }) + ); + expect(variables).toHaveLength(1); + }); + + it('matches base name (without module prefix) using underscores', async () => { + const tool = makeReadTool(tempDir); + const { variables } = parseResult( + await tool.handler({ section: 'variables', filter: { variableNames: ['net_revenue'] } }) + ); + expect(variables).toHaveLength(1); + expect(variables[0].name).toBe('Finance.net_revenue'); + }); + }); + + describe('moduleName filter (variables section)', () => { + it('matches exact module name', async () => { + const tool = makeReadTool(tempDir); + const { variables } = parseResult( + await tool.handler({ section: 'variables', filter: { moduleName: 'Finance' } }) + ); + expect(variables).toHaveLength(2); + }); + + it('is case-insensitive', async () => { + const tool = makeReadTool(tempDir); + const { variables } = parseResult( + await tool.handler({ section: 'variables', filter: { moduleName: 'FINANCE' } }) + ); + expect(variables).toHaveLength(2); + }); + + it('treats underscores and spaces as equivalent', async () => { + const dir = makeTempDir({ + ...BASE_MODEL, + variables: [{ name: 'My Module.revenue', type: 'variable', equation: '100' }], + }); + const tool = makeReadTool(dir); + const { variables } = parseResult( + await tool.handler({ section: 'variables', filter: { moduleName: 'My_Module' } }) + ); + expect(variables).toHaveLength(1); + rmSync(dir, { recursive: true, force: true }); + }); + }); + + describe('usedInEquation filter', () => { + it('finds variables when searching with spaces (equation uses underscores)', async () => { + const tool = makeReadTool(tempDir); + const { variables } = parseResult( + await tool.handler({ section: 'variables', filter: { usedInEquation: 'birth fraction' } }) + ); + expect(variables).toHaveLength(1); + expect(variables[0].name).toBe('birth_rate'); + }); + + it('finds variables when searching with underscores', async () => { + const tool = makeReadTool(tempDir); + const { variables } = parseResult( + await tool.handler({ section: 'variables', filter: { usedInEquation: 'birth_fraction' } }) + ); + expect(variables).toHaveLength(1); + }); + + it('is case-insensitive', async () => { + const tool = makeReadTool(tempDir); + const { variables } = parseResult( + await tool.handler({ section: 'variables', filter: { usedInEquation: 'BIRTH_FRACTION' } }) + ); + expect(variables).toHaveLength(1); + }); + + it('searches arrayEquations with normalization', async () => { + const dir = makeTempDir({ + ...BASE_MODEL, + variables: [{ + name: 'arrayed var', type: 'variable', + arrayEquations: [{ index: '1', equation: 'base_rate * scale' }], + }], + }); + const tool = makeReadTool(dir); + const { variables } = parseResult( + await tool.handler({ section: 'variables', filter: { usedInEquation: 'base rate' } }) + ); + expect(variables).toHaveLength(1); + rmSync(dir, { recursive: true, force: true }); + }); + }); + + describe('relationshipFrom filter', () => { + it('matches underscore query against space-stored from field', async () => { + const tool = makeReadTool(tempDir); + const { relationships } = parseResult( + await tool.handler({ section: 'relationships', filter: { relationshipFrom: 'birth_rate' } }) + ); + expect(relationships).toHaveLength(1); + expect(relationships[0].to).toBe('Population'); + }); + + it('is case-insensitive', async () => { + const tool = makeReadTool(tempDir); + const { relationships } = parseResult( + await tool.handler({ section: 'relationships', filter: { relationshipFrom: 'Birth Rate' } }) + ); + expect(relationships).toHaveLength(1); + }); + }); + + describe('relationshipTo filter', () => { + it('matches underscore query against space-stored to field', async () => { + const tool = makeReadTool(tempDir); + const { relationships } = parseResult( + await tool.handler({ section: 'relationships', filter: { relationshipTo: 'population' } }) + ); + expect(relationships).toHaveLength(2); + }); + }); + + describe('moduleName filter (modules section)', () => { + it('is case-insensitive', async () => { + const tool = makeReadTool(tempDir); + const { modules } = parseResult( + await tool.handler({ section: 'modules', filter: { moduleName: 'finance' } }) + ); + expect(modules).toHaveLength(1); + expect(modules[0].name).toBe('Finance'); + }); + + it('treats underscores and spaces as equivalent', async () => { + const tool = makeReadTool(tempDir); + const { modules } = parseResult( + await tool.handler({ section: 'modules', filter: { moduleName: 'My_Module' } }) + ); + expect(modules).toHaveLength(1); + expect(modules[0].name).toBe('My Module'); + }); + }); + + describe('subType filter', () => { + it('returns only variables matching the given subType', async () => { + const dir = makeTempDir({ + ...BASE_MODEL, + variables: [ + { name: 'work queue', type: 'stock', subType: 'queue', additionalProperties: { fifoEnabled: true } }, + { name: 'pipeline', type: 'stock', subType: 'conveyor', additionalProperties: { processTime: '5' } }, + { name: 'regular stock', type: 'stock', equation: '0' }, + ], + }); + const tool = makeReadTool(dir); + const { variables } = parseResult( + await tool.handler({ section: 'variables', filter: { subType: 'queue' } }) + ); + expect(variables).toHaveLength(1); + expect(variables[0].name).toBe('work_queue'); + rmSync(dir, { recursive: true, force: true }); + }); + + it('returns empty array when no variables match the subType', async () => { + const tool = makeReadTool(tempDir); + const { variables } = parseResult( + await tool.handler({ section: 'variables', filter: { subType: 'oven' } }) + ); + expect(variables).toHaveLength(0); + }); + + it('can be combined with moduleName filter', async () => { + const dir = makeTempDir({ + ...BASE_MODEL, + variables: [ + { name: 'Ops.work queue', type: 'stock', subType: 'queue', additionalProperties: {} }, + { name: 'Finance.budget queue', type: 'stock', subType: 'queue', additionalProperties: {} }, + ], + }); + const tool = makeReadTool(dir); + const { variables } = parseResult( + await tool.handler({ section: 'variables', filter: { subType: 'queue', moduleName: 'Ops' } }) + ); + expect(variables).toHaveLength(1); + expect(variables[0].name).toBe('Ops.work_queue'); + rmSync(dir, { recursive: true, force: true }); + }); + }); +}); + +// ─── per-section edit tools ───────────────────────────────────────────────── + +describe('per-section edit tools normalization', () => { + let tempDir; + let session; + + // sendToClient mock: captures the sent model and resolves the pending request + // via setTimeout so the promise set up after sendToClient can be resolved. + function makeSendToClient() { + let capturedModel = null; + const sendToClient = async (msg) => { + if (msg.type === 'update_model') { + capturedModel = JSON.parse(JSON.stringify(msg.modelData)); + setTimeout(() => { + const pending = session.pendingModelRequests?.get(msg.requestId); + if (pending) { + clearTimeout(pending.timeout); + pending.resolve('ok'); + } + }, 0); + } + }; + return { sendToClient, getModel: () => capturedModel }; + } + + function makeEditTools(sendToClient) { + session = { + mode: 'sfd', + context: { supportsArrays: false, supportsModules: true, supportsSubTypes: true }, + pendingModelRequests: new Map(), + }; + const sessionManager = { + getSession: () => session, + getSessionTempDir: () => tempDir, + updateClientModel: () => {}, + }; + return { + variables: createEditVariablesTool(sessionManager, SESSION_ID, sendToClient), + relationships: createEditRelationshipsTool(sessionManager, SESSION_ID, sendToClient), + modules: createEditModulesTool(sessionManager, SESSION_ID, sendToClient), + }; + } + + function resetModel(model) { + writeFileSync(join(tempDir, 'model.sdjson'), JSON.stringify(model)); + } + + beforeEach(() => { tempDir = mkdtempSync(join(tmpdir(), 'sd-ai-test-')); }); + afterEach(() => { rmSync(tempDir, { recursive: true, force: true }); }); + + describe('variables add', () => { + it('normalizes underscore names to spaces', async () => { + resetModel({ variables: [], relationships: [], modules: [] }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.variables.handler({ operation: 'add', data: [ + { name: 'birth_rate', type: 'variable', equation: '0.1' } + ]}); + + expect(getModel().variables[0].name).toBe('birth rate'); + }); + + it('normalizes module-qualified names', async () => { + resetModel({ variables: [], relationships: [], modules: [] }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.variables.handler({ operation: 'add', data: [ + { name: 'Finance.net_revenue', type: 'variable', equation: '100' } + ]}); + + expect(getModel().variables[0].name).toBe('Finance.net revenue'); + }); + }); + + describe('variables update', () => { + it('finds variable by underscore name', async () => { + resetModel({ variables: [{ name: 'birth rate', type: 'variable', equation: '0.1' }], relationships: [], modules: [] }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.variables.handler({ operation: 'update', data: [ + { name: 'birth_rate', equation: '0.2' } + ]}); + + expect(getModel().variables[0].equation).toBe('0.2'); + }); + + it('finds variable case-insensitively', async () => { + resetModel({ variables: [{ name: 'birth rate', type: 'variable', equation: '0.1' }], relationships: [], modules: [] }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.variables.handler({ operation: 'update', data: [ + { name: 'Birth Rate', equation: '0.2' } + ]}); + + expect(getModel().variables[0].equation).toBe('0.2'); + }); + + it('finds variable with mixed case and underscores', async () => { + resetModel({ variables: [{ name: 'birth rate', type: 'variable', equation: '0.1' }], relationships: [], modules: [] }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.variables.handler({ operation: 'update', data: [ + { name: 'BIRTH_RATE', equation: '0.2' } + ]}); + + expect(getModel().variables[0].equation).toBe('0.2'); + }); + + it('normalizes newName to spaces', async () => { + resetModel({ variables: [{ name: 'birth rate', type: 'variable', equation: '0.1' }], relationships: [], modules: [] }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.variables.handler({ operation: 'update', data: [ + { name: 'birth_rate', newName: 'birth_fraction' } + ]}); + + expect(getModel().variables[0].name).toBe('birth fraction'); + }); + + it('updates equation-valued additionalProperties fields on rename', async () => { + resetModel({ + variables: [ + { name: 'process time', type: 'variable', equation: '10' }, + { name: 'pipeline', type: 'stock', subType: 'conveyor', additionalProperties: { processTime: 'process_time', capacity: '100' } }, + ], + relationships: [], + modules: [], + }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.variables.handler({ operation: 'update', data: [ + { name: 'process_time', newName: 'transit_time' } + ]}); + + const conveyor = getModel().variables.find(v => v.name === 'pipeline'); + expect(conveyor.additionalProperties.processTime).toBe('transit_time'); + }); + + it('leaves boolean additionalProperties fields untouched on rename', async () => { + resetModel({ + variables: [ + { name: 'flag', type: 'variable', equation: '1' }, + { name: 'work queue', type: 'stock', subType: 'queue', additionalProperties: { fifoEnabled: true, overflow: false } }, + ], + relationships: [], + modules: [], + }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.variables.handler({ operation: 'update', data: [ + { name: 'flag', newName: 'signal' } + ]}); + + const queue = getModel().variables.find(v => v.name === 'work queue'); + expect(queue.additionalProperties.fifoEnabled).toBe(true); + expect(queue.additionalProperties.overflow).toBe(false); + }); + }); + + describe('variables remove', () => { + it('removes variable found by underscore name', async () => { + resetModel({ variables: [{ name: 'birth rate', type: 'variable', equation: '0.1' }], relationships: [], modules: [] }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.variables.handler({ operation: 'remove', data: [{name: 'birth_rate'}] }); + + expect(getModel().variables).toHaveLength(0); + }); + + it('removes variable case-insensitively', async () => { + resetModel({ variables: [{ name: 'birth rate', type: 'variable', equation: '0.1' }], relationships: [], modules: [] }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.variables.handler({ operation: 'remove', data: [{name: 'BIRTH RATE'}] }); + + expect(getModel().variables).toHaveLength(0); + }); + + it('removes variable with mixed case and underscores', async () => { + resetModel({ variables: [{ name: 'birth rate', type: 'variable', equation: '0.1' }], relationships: [], modules: [] }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.variables.handler({ operation: 'remove', data: [{name: 'Birth_Rate'}] }); + + expect(getModel().variables).toHaveLength(0); + }); + }); + + describe('relationships add', () => { + it('normalizes from and to to spaces', async () => { + resetModel({ variables: [], relationships: [], modules: [] }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.relationships.handler({ operation: 'add', data: [ + { from: 'birth_rate', to: 'Population', polarity: '+' } + ]}); + + expect(getModel().relationships[0].from).toBe('birth rate'); + expect(getModel().relationships[0].to).toBe('Population'); + }); + }); + + describe('relationships update', () => { + it('finds relationship by underscore from/to', async () => { + resetModel({ variables: [], relationships: [{ from: 'birth rate', to: 'Population', polarity: '+' }], modules: [] }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.relationships.handler({ operation: 'update', data: [{ + from: 'birth_rate', to: 'Population', polarity: '-' + }]}); + + expect(getModel().relationships[0].polarity).toBe('-'); + }); + + it('finds relationship case-insensitively', async () => { + resetModel({ variables: [], relationships: [{ from: 'birth rate', to: 'Population', polarity: '+' }], modules: [] }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.relationships.handler({ operation: 'update', data: [{ + from: 'BIRTH RATE', to: 'population', polarity: '-' + }]}); + + expect(getModel().relationships[0].polarity).toBe('-'); + }); + + it('finds relationship with mixed case and underscores', async () => { + resetModel({ variables: [], relationships: [{ from: 'birth rate', to: 'Population', polarity: '+' }], modules: [] }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.relationships.handler({ operation: 'update', data: [{ + from: 'Birth_Rate', to: 'POPULATION', polarity: '-' + }]}); + + expect(getModel().relationships[0].polarity).toBe('-'); + }); + }); + + describe('relationships remove', () => { + it('removes relationship found by underscore from/to', async () => { + resetModel({ variables: [], relationships: [{ from: 'birth rate', to: 'Population', polarity: '+' }], modules: [] }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.relationships.handler({ operation: 'remove', data: [ + { from: 'birth_rate', to: 'Population' } + ]}); + + expect(getModel().relationships).toHaveLength(0); + }); + + it('removes relationship case-insensitively', async () => { + resetModel({ variables: [], relationships: [{ from: 'birth rate', to: 'Population', polarity: '+' }], modules: [] }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.relationships.handler({ operation: 'remove', data: [ + { from: 'BIRTH RATE', to: 'POPULATION' } + ]}); + + expect(getModel().relationships).toHaveLength(0); + }); + + it('removes relationship with mixed case and underscores', async () => { + resetModel({ variables: [], relationships: [{ from: 'birth rate', to: 'Population', polarity: '+' }], modules: [] }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.relationships.handler({ operation: 'remove', data: [ + { from: 'Birth_Rate', to: 'population' } + ]}); + + expect(getModel().relationships).toHaveLength(0); + }); + }); + + describe('modules add', () => { + it('normalizes module name underscores to spaces', async () => { + resetModel({ variables: [], relationships: [], modules: [] }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.modules.handler({ operation: 'add', data: [ + { name: 'My_Module', parentModule: null } + ]}); + + expect(getModel().modules[0].name).toBe('My Module'); + }); + }); + + describe('modules update', () => { + it('normalizes module names in replacement array', async () => { + resetModel({ variables: [], relationships: [], modules: [{ name: 'Finance', parentModule: null }] }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.modules.handler({ operation: 'update', data: [ + { name: 'Finance_Sub', parentModule: 'Finance' } + ]}); + + expect(getModel().modules[0].name).toBe('Finance Sub'); + }); + }); + + describe('modules remove', () => { + it('removes module found by underscore name', async () => { + resetModel({ variables: [], relationships: [], modules: [{ name: 'My Module', parentModule: null }] }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.modules.handler({ operation: 'remove', data: [{name: 'My_Module'}] }); + + expect(getModel().modules).toHaveLength(0); + }); + + it('removes module case-insensitively', async () => { + resetModel({ variables: [], relationships: [], modules: [{ name: 'Finance', parentModule: null }] }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.modules.handler({ operation: 'remove', data: [{name: 'FINANCE'}] }); + + expect(getModel().modules).toHaveLength(0); + }); + + it('removes module with mixed case and underscores', async () => { + resetModel({ variables: [], relationships: [], modules: [{ name: 'My Module', parentModule: null }] }); + const { sendToClient, getModel } = makeSendToClient(); + const tools = makeEditTools(sendToClient); + + await tools.modules.handler({ operation: 'remove', data: [{name: 'MY_MODULE'}] }); + + expect(getModel().modules).toHaveLength(0); + }); + }); +}); diff --git a/tests/evals/behavioralPattern.test.js b/tests/evals/behavioralPattern.test.js index ff0e8a51..24430e26 100644 --- a/tests/evals/behavioralPattern.test.js +++ b/tests/evals/behavioralPattern.test.js @@ -2,7 +2,7 @@ import { describe, test, expect } from '@jest/globals'; import * as behavioralPattern from '../../evals/categories/behavioralPattern.js'; describe('Behavioral Pattern Evaluation', () => { - + describe('Evaluate Function', () => { test('should fail when model is missing', async () => { const response = {}; @@ -47,6 +47,6 @@ describe('Behavioral Pattern Evaluation', () => { // Should not fail for missing output variable (will fail later in conversion/simulation) const missingOutputError = result.find(f => f.type === 'Missing output variable'); expect(missingOutputError).toBeUndefined(); - }); + }, 30000); }); }); diff --git a/tests/utilities/ZodToStructuredOutputConverter.test.js b/tests/utilities/ZodToStructuredOutputConverter.test.js deleted file mode 100644 index 7934195b..00000000 --- a/tests/utilities/ZodToStructuredOutputConverter.test.js +++ /dev/null @@ -1,445 +0,0 @@ -import { z } from 'zod'; -import { ZodToStructuredOutputConverter } from '../../utilities/ZodToStructuredOutputConverter.js'; -import { LLMWrapper } from '../../utilities/LLMWrapper.js'; - -describe('ZodToStructuredOutputConverter', () => { - let converter; - let llmWrapper; - - beforeEach(() => { - converter = new ZodToStructuredOutputConverter(); - // Still need LLMWrapper for testing actual schema generation - llmWrapper = new LLMWrapper({ - openAIKey: 'test-key', - anthropicKey: 'test-claude-key', - googleKey: 'test-google-key' - }); - }); - - describe('basic type conversion', () => { - it('should convert ZodString to Gemini string schema', () => { - const zodSchema = z.string(); - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'string' - }); - }); - - it('should convert ZodString with description', () => { - const zodSchema = z.string().describe('Test description'); - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'string', - description: 'Test description' - }); - }); - - it('should convert ZodNumber to Gemini number schema', () => { - const zodSchema = z.number(); - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'number' - }); - }); - - it('should convert ZodNumber with description', () => { - const zodSchema = z.number().describe('A test number'); - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'number', - description: 'A test number' - }); - }); - - it('should convert ZodBoolean to Gemini boolean schema', () => { - const zodSchema = z.boolean(); - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'boolean' - }); - }); - }); - - describe('array conversion', () => { - it('should convert ZodArray to Gemini array schema', () => { - const zodSchema = z.array(z.string()); - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'array', - items: { - type: 'string' - } - }); - }); - - it('should convert ZodArray with description and constraints', () => { - const zodSchema = z.array(z.number()).min(1).max(10).describe('Array of numbers'); - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'array', - items: { - type: 'number' - }, - description: 'Array of numbers', - minItems: 1, - maxItems: 10 - }); - }); - - it('should convert nested arrays', () => { - const zodSchema = z.array(z.array(z.string())); - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'array', - items: { - type: 'array', - items: { - type: 'string' - } - } - }); - }); - }); - - describe('object conversion', () => { - it('should convert simple ZodObject to structured output schema', () => { - const zodSchema = z.object({ - name: z.string(), - age: z.number() - }); - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'object', - properties: { - name: { type: 'string' }, - age: { type: 'number' } - }, - required: ['name', 'age'], - additionalProperties: false - }); - }); - - it('should convert ZodObject with optional properties', () => { - const zodSchema = z.object({ - name: z.string(), - age: z.number().optional(), - email: z.string().optional() - }); - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'object', - properties: { - name: { type: 'string' }, - age: { type: 'number' }, - email: { type: 'string' } - }, - required: ['name'], - additionalProperties: false - }); - }); - - it('should convert ZodObject with description', () => { - const zodSchema = z.object({ - id: z.string() - }).describe('Test object'); - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'object', - properties: { - id: { type: 'string' } - }, - required: ['id'], - additionalProperties: false, - description: 'Test object' - }); - }); - - it('should convert nested objects', () => { - const zodSchema = z.object({ - user: z.object({ - name: z.string(), - contact: z.object({ - email: z.string(), - phone: z.string().optional() - }) - }), - metadata: z.object({ - created: z.string(), - updated: z.string().optional() - }) - }); - - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'object', - properties: { - user: { - type: 'object', - properties: { - name: { type: 'string' }, - contact: { - type: 'object', - properties: { - email: { type: 'string' }, - phone: { type: 'string' } - }, - required: ['email'], - additionalProperties: false - } - }, - required: ['name', 'contact'], - additionalProperties: false - }, - metadata: { - type: 'object', - properties: { - created: { type: 'string' }, - updated: { type: 'string' } - }, - required: ['created'], - additionalProperties: false - } - }, - required: ['user', 'metadata'], - additionalProperties: false - }); - }); - }); - - describe('enum conversion', () => { - it('should convert ZodEnum to Gemini enum schema', () => { - const zodSchema = z.enum(['red', 'green', 'blue']); - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'string', - enum: ['red', 'green', 'blue'] - }); - }); - - it('should convert ZodEnum with description', () => { - const zodSchema = z.enum(['+', '-']).describe('Polarity enum'); - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'string', - enum: ['+', '-'], - description: 'Polarity enum' - }); - }); - }); - - describe('union and optional conversion', () => { - it('should convert ZodOptional by unwrapping to inner type', () => { - const zodSchema = z.string().optional(); - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'string' - }); - }); - - it('should convert ZodUnion with null by unwrapping to non-null type', () => { - const zodSchema = z.union([z.string(), z.null()]); - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'string' - }); - }); - - it('should convert ZodUnion of literals to enum', () => { - const zodSchema = z.union([z.literal('small'), z.literal('medium'), z.literal('large')]); - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'string', - enum: ['small', 'medium', 'large'] - }); - }); - - it('should convert ZodUnion of number literals to number enum', () => { - const zodSchema = z.union([z.literal(1), z.literal(2), z.literal(3)]); - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'number', - enum: [1, 2, 3] - }); - }); - - it('should handle complex unions by defaulting to string', () => { - const zodSchema = z.union([z.string(), z.number()]); - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'string' - }); - }); - }); - - describe('literal conversion', () => { - it('should convert ZodLiteral string to enum with single value', () => { - const zodSchema = z.literal('fixed-value'); - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'string', - enum: ['fixed-value'] - }); - }); - - it('should convert ZodLiteral number to enum with single value', () => { - const zodSchema = z.literal(42); - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'number', - enum: [42] - }); - }); - }); - - describe('actual schema conversion tests', () => { - it('should convert generateQualitativeSDJSONResponseSchema', () => { - const zodSchema = llmWrapper.generateQualitativeSDJSONResponseSchema(); - const result = converter.convert(zodSchema); - - expect(result.type).toBe('object'); - expect(result.properties).toBeDefined(); - expect(result.properties.relationships).toBeDefined(); - expect(result.properties.relationships.type).toBe('array'); - expect(result.properties.relationships.items.type).toBe('object'); - expect(result.properties.relationships.items.properties.from.type).toBe('string'); - expect(result.properties.relationships.items.properties.to.type).toBe('string'); - expect(result.properties.relationships.items.properties.polarity).toEqual({ - type: 'string', - enum: ['+', '-'], - description: "There are two possible kinds of relationships. The first are relationships with positive polarity that are represented with a + symbol. In relationships with positive polarity (+) a change in the from variable causes a change in the same direction in the to variable. For example, in a relationship with positive polarity (+), a decrease in the from variable, would lead to a decrease in the to variable. The second kind of relationship are those with negative polarity that are represented with a - symbol. In relationships with negative polarity (-) a change in the from variable causes a change in the opposite direction in the to variable. For example, in a relationship with negative polarity (-) an increase in the from variable, would lead to a decrease in the to variable." - }); - expect(result.properties.explanation.type).toBe('string'); - expect(result.properties.title.type).toBe('string'); - expect(result.required).toContain('relationships'); - expect(result.required).toContain('explanation'); - expect(result.required).toContain('title'); - expect(result.additionalProperties).toBe(false); - }); - - it('should convert generateQuantitativeSDJSONResponseSchema', () => { - const zodSchema = llmWrapper.generateQuantitativeSDJSONResponseSchema(false); - const result = converter.convert(zodSchema); - - expect(result.type).toBe('object'); - expect(result.properties).toBeDefined(); - expect(result.properties.variables).toBeDefined(); - expect(result.properties.variables.type).toBe('array'); - expect(result.properties.variables.items.type).toBe('object'); - expect(result.properties.variables.items.properties.type.type).toBe('string'); - expect(result.properties.variables.items.properties.type.enum).toEqual(['stock', 'flow', 'variable']); - expect(result.properties.relationships).toBeDefined(); - expect(result.properties.specs).toBeDefined(); - expect(result.properties.specs.type).toBe('object'); - expect(result.properties.specs.properties.startTime.type).toBe('number'); - expect(result.properties.specs.properties.stopTime.type).toBe('number'); - expect(result.properties.specs.properties.dt.type).toBe('number'); - expect(result.properties.specs.properties.timeUnits.type).toBe('string'); - expect(result.required).toContain('variables'); - expect(result.required).toContain('relationships'); - expect(result.required).toContain('specs'); - expect(result.additionalProperties).toBe(false); - }); - - it('should convert generateLTMNarrativeResponseSchema', () => { - const zodSchema = llmWrapper.generateLTMNarrativeResponseSchema(); - const result = converter.convert(zodSchema); - - expect(result.type).toBe('object'); - expect(result.properties).toBeDefined(); - expect(result.properties.feedbackLoops).toBeDefined(); - expect(result.properties.feedbackLoops.type).toBe('array'); - expect(result.properties.feedbackLoops.items.type).toBe('object'); - expect(result.properties.feedbackLoops.items.properties.identifier.type).toBe('string'); - expect(result.properties.feedbackLoops.items.properties.name.type).toBe('string'); - expect(result.properties.feedbackLoops.items.properties.description.type).toBe('string'); - expect(result.properties.narrativeMarkdown.type).toBe('string'); - expect(result.required).toContain('feedbackLoops'); - expect(result.required).toContain('narrativeMarkdown'); - expect(result.additionalProperties).toBe(false); - }); - }); - - describe('edge cases', () => { - it('should handle null or undefined schema', () => { - expect(converter.convert(null)).toEqual({}); - expect(converter.convert(undefined)).toEqual({}); - }); - - it('should handle schema without _def', () => { - const invalidSchema = {}; - expect(converter.convert(invalidSchema)).toEqual({}); - }); - - it('should handle unsupported Zod types by defaulting to string', () => { - const mockSchema = { - _def: { - typeName: 'ZodUnsupported' - } - }; - - const result = converter.convert(mockSchema); - - expect(result).toEqual({ type: 'string' }); - }); - - it('should handle empty objects', () => { - const zodSchema = z.object({}); - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'object', - properties: {}, - required: [], - additionalProperties: false - }); - }); - - it('should handle arrays with constraints set to null', () => { - const zodSchema = z.array(z.string()); - // Mock the internal structure to simulate null constraints - zodSchema._def.minLength = null; - zodSchema._def.maxLength = null; - - const result = converter.convert(zodSchema); - - expect(result).toEqual({ - type: 'array', - items: { - type: 'string' - } - }); - }); - }); - - describe('converter functionality', () => { - it('should expose convert method as public API', () => { - expect(typeof converter.convert).toBe('function'); - }); - - it('should be a separate class from LLMWrapper', () => { - expect(converter).toBeInstanceOf(ZodToStructuredOutputConverter); - expect(converter).not.toBeInstanceOf(LLMWrapper); - }); - }); -}); \ No newline at end of file diff --git a/third-party/PySD-simulator/install.bat b/third-party/PySD-simulator/install.bat new file mode 100644 index 00000000..21cf08a3 --- /dev/null +++ b/third-party/PySD-simulator/install.bat @@ -0,0 +1,27 @@ +@echo off +setlocal + +set "SCRIPT_DIR=%~dp0" + +echo Installing PySD simulator dependencies... + +where python3 >nul 2>&1 +if %errorlevel% equ 0 ( + set "PYTHON_CMD=python3" +) else ( + where python >nul 2>&1 + if %errorlevel% equ 0 ( + set "PYTHON_CMD=python" + ) else ( + echo Error: Python not found. Please install Python 3 to use the PySD simulator. + exit /b 1 + ) +) + +echo Using Python: %PYTHON_CMD% + +cd /d "%SCRIPT_DIR%" +%PYTHON_CMD% -m pip install -r requirements.txt + +echo Successfully installed PySD simulator dependencies +exit /b 0 diff --git a/third-party/PySD-simulator/install.sh b/third-party/PySD-simulator/install.sh index 1c1d1d2d..77d91564 100755 --- a/third-party/PySD-simulator/install.sh +++ b/third-party/PySD-simulator/install.sh @@ -23,6 +23,6 @@ echo "Using Python: $PYTHON_CMD" # Install dependencies cd "$SCRIPT_DIR" -$PYTHON_CMD -m pip install -r requirements.txt +$PYTHON_CMD -m pip install -r requirements.txt echo "Successfully installed PySD simulator dependencies" diff --git a/third-party/causal-chains/install.bat b/third-party/causal-chains/install.bat new file mode 100644 index 00000000..949a5943 --- /dev/null +++ b/third-party/causal-chains/install.bat @@ -0,0 +1,19 @@ +@echo off +setlocal + +set "SCRIPT_DIR=%~dp0" + +echo Building causal-chains engine... + +where go >nul 2>&1 +if %errorlevel% neq 0 ( + echo Error: Go toolchain not found. Please install Go to build causal-chains engine. + exit /b 1 +) + +cd /d "%SCRIPT_DIR%" +echo Running: go build -o "%SCRIPT_DIR%causal-chains.exe" main.go +go build -o "%SCRIPT_DIR%causal-chains.exe" main.go + +echo Successfully built causal-chains binary at %SCRIPT_DIR%causal-chains.exe +exit /b 0 diff --git a/third-party/causal-chains/llm/provider/factory.go b/third-party/causal-chains/llm/provider/factory.go index 4a5e08b4..81ce255d 100644 --- a/third-party/causal-chains/llm/provider/factory.go +++ b/third-party/causal-chains/llm/provider/factory.go @@ -56,7 +56,7 @@ func NewClient(cfg Config) (chat.Client, string, error) { if isGeminiModel(modelLower) { apiKey := cfg.APIKey if apiKey == "" { - apiKey = os.Getenv("GOOGLE_API_KEY") + apiKey = os.Getenv("GEMINI_API_KEY") } if apiKey == "" { return nil, "", fmt.Errorf("Google API key required for model %s", model) diff --git a/third-party/causal-chains/llm/provider/factory_test.go b/third-party/causal-chains/llm/provider/factory_test.go index c13c807f..5605a8bb 100644 --- a/third-party/causal-chains/llm/provider/factory_test.go +++ b/third-party/causal-chains/llm/provider/factory_test.go @@ -107,7 +107,7 @@ func TestNewClientGeminiModels(t *testing.T) { for _, tt := range tests { t.Run(tt.name, func(t *testing.T) { - t.Setenv("GOOGLE_API_KEY", tt.envKey) + t.Setenv("GEMINI_API_KEY", tt.envKey) _, _, err := NewClient(Config{ Model: tt.model, @@ -192,7 +192,7 @@ func TestNewClientDebugMode(t *testing.T) { // Set appropriate API keys t.Setenv("OPENAI_API_KEY", "test-openai") t.Setenv("ANTHROPIC_API_KEY", "test-anthropic") - t.Setenv("GOOGLE_API_KEY", "test-google") + t.Setenv("GEMINI_API_KEY", "test-google") _, _, err := NewClient(Config{ Model: tt.model, @@ -345,7 +345,7 @@ func TestNewClientWithThinkingLevel(t *testing.T) { for _, tt := range tests { t.Run(tt.name, func(t *testing.T) { - t.Setenv("GOOGLE_API_KEY", "test-google") + t.Setenv("GEMINI_API_KEY", "test-google") t.Setenv("ANTHROPIC_API_KEY", "test-anthropic") t.Setenv("OPENAI_API_KEY", "test-openai") diff --git a/third-party/causal-chains/main.go b/third-party/causal-chains/main.go index 14f00f2f..7260ffb9 100644 --- a/third-party/causal-chains/main.go +++ b/third-party/causal-chains/main.go @@ -71,7 +71,7 @@ func main() { input.Parameters.ApiKey = os.Getenv("OPENAI_API_KEY") } if input.Parameters.GoogleKey == "" { - input.Parameters.GoogleKey = os.Getenv("GOOGLE_API_KEY") + input.Parameters.GoogleKey = os.Getenv("GEMINI_API_KEY") } if input.Parameters.AnthropicKey == "" { input.Parameters.AnthropicKey = os.Getenv("ANTHROPIC_API_KEY") diff --git a/third-party/causal-decoder/install.bat b/third-party/causal-decoder/install.bat new file mode 100644 index 00000000..4acdbff0 --- /dev/null +++ b/third-party/causal-decoder/install.bat @@ -0,0 +1,27 @@ +@echo off +setlocal + +set "SCRIPT_DIR=%~dp0" + +echo Installing causal-decoder dependencies... + +where python3 >nul 2>&1 +if %errorlevel% equ 0 ( + set "PYTHON_CMD=python3" +) else ( + where python >nul 2>&1 + if %errorlevel% equ 0 ( + set "PYTHON_CMD=python" + ) else ( + echo Error: Python not found. Please install Python 3 to use the causal-decoder engine. + exit /b 1 + ) +) + +echo Using Python: %PYTHON_CMD% + +cd /d "%SCRIPT_DIR%" +%PYTHON_CMD% -m pip install -r requirements.txt + +echo Successfully installed causal-decoder dependencies +exit /b 0 diff --git a/third-party/causal-decoder/install.sh b/third-party/causal-decoder/install.sh index e83117d3..a6e813b2 100755 --- a/third-party/causal-decoder/install.sh +++ b/third-party/causal-decoder/install.sh @@ -23,6 +23,6 @@ echo "Using Python: $PYTHON_CMD" # Install dependencies cd "$SCRIPT_DIR" -$PYTHON_CMD -m pip install -r requirements.txt +$PYTHON_CMD -m pip install -r requirements.txt echo "Successfully installed causal-decoder dependencies" diff --git a/third-party/install.bat b/third-party/install.bat new file mode 100644 index 00000000..1ef8af4b --- /dev/null +++ b/third-party/install.bat @@ -0,0 +1,59 @@ +@echo off +setlocal enabledelayedexpansion + +rem Set SKIP_THIRD_PARTY_COMPONENTS to a comma-separated list of component names to skip. +rem Example: set SKIP_THIRD_PARTY_COMPONENTS=causal-decoder,PySD-simulator,time-series-behavior-analysis && npm install + +set "SCRIPT_DIR=%~dp0" +set "FAILED_COMPONENTS=" + +echo Installing third-party components... +echo. + +for /d %%D in ("%SCRIPT_DIR%*") do ( + if exist "%%D\install.bat" ( + set "COMPONENT_NAME=%%~nxD" + set "SKIP_THIS=" + + for %%S in ("%SKIP_THIRD_PARTY_COMPONENTS:,=" "%") do ( + if /i "%%~S"=="!COMPONENT_NAME!" set "SKIP_THIS=1" + ) + + if defined SKIP_THIS ( + echo ================================================ + echo Skipping: !COMPONENT_NAME! + echo ================================================ + echo. + ) else ( + echo ================================================ + echo Installing: %%~nxD + echo ================================================ + + call "%%D\install.bat" + if !errorlevel! equ 0 ( + echo + Successfully installed %%~nxD + echo. + ) else ( + echo - Failed to install %%~nxD + echo. + set "FAILED_COMPONENTS=!FAILED_COMPONENTS!%%~nxD " + ) + ) + ) +) + +echo ================================================ +echo Installation Summary +echo ================================================ + +if "!FAILED_COMPONENTS!"=="" ( + echo + All third-party components installed successfully! + exit /b 0 +) else ( + echo - Failed to install the following components: + for %%C in (!FAILED_COMPONENTS!) do echo - %%C + echo. + echo Note: Some components may have failed due to missing dependencies. + echo Check the output above for details. + exit /b 0 +) diff --git a/third-party/install.js b/third-party/install.js new file mode 100644 index 00000000..c35a988c --- /dev/null +++ b/third-party/install.js @@ -0,0 +1,11 @@ +import { execSync } from 'child_process'; +import { dirname, join } from 'path'; +import { fileURLToPath } from 'url'; + +const __dirname = dirname(fileURLToPath(import.meta.url)); + +if (process.platform === 'win32') { + execSync(`"${join(__dirname, 'install.bat')}"`, { stdio: 'inherit', shell: true }); +} else { + execSync(`bash "${join(__dirname, 'install.sh')}"`, { stdio: 'inherit' }); +} diff --git a/third-party/install.sh b/third-party/install.sh index ce5f4b48..ccddc9fe 100755 --- a/third-party/install.sh +++ b/third-party/install.sh @@ -2,6 +2,9 @@ # Master installation script for all third-party components # This script iterates through all subdirectories and runs their install.sh scripts +# +# Set SKIP_THIRD_PARTY_COMPONENTS to a comma-separated list of component names to skip. +# Example: SKIP_THIRD_PARTY_COMPONENTS=causal-decoder,PySD-simulator,time-series-behavior-analysis npm install set -e @@ -13,6 +16,17 @@ echo "" # Track overall success FAILED_COMPONENTS=() +should_skip() { + local name="$1" + IFS=',' read -ra SKIP_LIST <<< "${SKIP_THIRD_PARTY_COMPONENTS:-}" + for skip in "${SKIP_LIST[@]}"; do + if [ "$skip" = "$name" ]; then + return 0 + fi + done + return 1 +} + # Iterate through all subdirectories that have an install.sh script for component_dir in "$SCRIPT_DIR"/*/; do # Remove trailing slash and get component name @@ -20,6 +34,14 @@ for component_dir in "$SCRIPT_DIR"/*/; do install_script="$component_dir/install.sh" if [ -f "$install_script" ] && [ -x "$install_script" ]; then + if should_skip "$component_name"; then + echo "================================================" + echo "Skipping: $component_name" + echo "================================================" + echo "" + continue + fi + echo "================================================" echo "Installing: $component_name" echo "================================================" diff --git a/third-party/time-series-behavior-analysis/install.bat b/third-party/time-series-behavior-analysis/install.bat new file mode 100644 index 00000000..81afe1e2 --- /dev/null +++ b/third-party/time-series-behavior-analysis/install.bat @@ -0,0 +1,27 @@ +@echo off +setlocal + +set "SCRIPT_DIR=%~dp0" + +echo Installing time-series-behavior-analysis dependencies... + +where python3 >nul 2>&1 +if %errorlevel% equ 0 ( + set "PYTHON_CMD=python3" +) else ( + where python >nul 2>&1 + if %errorlevel% equ 0 ( + set "PYTHON_CMD=python" + ) else ( + echo Error: Python not found. Please install Python 3 to use the time-series-behavior-analysis module. + exit /b 1 + ) +) + +echo Using Python: %PYTHON_CMD% + +cd /d "%SCRIPT_DIR%" +%PYTHON_CMD% -m pip install -r requirements.txt + +echo Successfully installed time-series-behavior-analysis dependencies +exit /b 0 diff --git a/third-party/time-series-behavior-analysis/install.sh b/third-party/time-series-behavior-analysis/install.sh index db7fd872..4dd6a462 100755 --- a/third-party/time-series-behavior-analysis/install.sh +++ b/third-party/time-series-behavior-analysis/install.sh @@ -23,6 +23,6 @@ echo "Using Python: $PYTHON_CMD" # Install dependencies cd "$SCRIPT_DIR" -$PYTHON_CMD -m pip install -r requirements.txt +$PYTHON_CMD -m pip install -r requirements.txt echo "Successfully installed time-series-behavior-analysis dependencies" diff --git a/third-party/visualization-engine/install.bat b/third-party/visualization-engine/install.bat new file mode 100644 index 00000000..4db14786 --- /dev/null +++ b/third-party/visualization-engine/install.bat @@ -0,0 +1,27 @@ +@echo off +setlocal + +set "SCRIPT_DIR=%~dp0" + +echo Installing visualization-engine dependencies... + +where python3 >nul 2>&1 +if %errorlevel% equ 0 ( + set "PYTHON_CMD=python3" +) else ( + where python >nul 2>&1 + if %errorlevel% equ 0 ( + set "PYTHON_CMD=python" + ) else ( + echo Error: Python not found. Please install Python 3 to use the visualization engine. + exit /b 1 + ) +) + +echo Using Python: %PYTHON_CMD% + +cd /d "%SCRIPT_DIR%" +%PYTHON_CMD% -m pip install -r requirements.txt + +echo Successfully installed visualization-engine dependencies +exit /b 0 diff --git a/third-party/visualization-engine/install.sh b/third-party/visualization-engine/install.sh new file mode 100755 index 00000000..08597425 --- /dev/null +++ b/third-party/visualization-engine/install.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +# Install script for visualization-engine +# Installs Python dependencies required by VisualizationEngine.js (matplotlib, numpy) + +set -e + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" + +echo "Installing visualization-engine dependencies..." + +if command -v python3 &> /dev/null; then + PYTHON_CMD="python3" +elif command -v python &> /dev/null; then + PYTHON_CMD="python" +else + echo "Error: Python not found. Please install Python 3 to use the visualization engine." + exit 1 +fi + +echo "Using Python: $PYTHON_CMD" + +cd "$SCRIPT_DIR" +$PYTHON_CMD -m pip install -r requirements.txt + +echo "Successfully installed visualization-engine dependencies" diff --git a/third-party/visualization-engine/requirements.txt b/third-party/visualization-engine/requirements.txt new file mode 100644 index 00000000..180ac118 --- /dev/null +++ b/third-party/visualization-engine/requirements.txt @@ -0,0 +1,4 @@ +matplotlib>=3.4.0 +numpy>=1.20.0 +pandas>=1.3.0 +scipy>=1.7.0 diff --git a/utilities/LLMWrapper.js b/utilities/LLMWrapper.js index 0d57e0b1..bee78376 100644 --- a/utilities/LLMWrapper.js +++ b/utilities/LLMWrapper.js @@ -3,8 +3,9 @@ import { GoogleGenAI } from "@google/genai"; import Anthropic from "@anthropic-ai/sdk"; import { z } from "zod"; import { zodResponseFormat } from "openai/helpers/zod"; -import { ZodToStructuredOutputConverter } from "./ZodToStructuredOutputConverter.js"; import { extractJsonFromContent } from "./jsonUtils.js"; +import TokenUsageReporter, { Provider } from "./TokenUsageReporter.js"; +import config from "../config.js"; export const ModelType = Object.freeze({ GEMINI: Symbol("Gemini"), @@ -69,11 +70,11 @@ export class LLMWrapper { #openAIAPI = null; #geminiAPI = null; #anthropicAPI = null; - #zodToStructuredOutputConverter = new ZodToStructuredOutputConverter(); + #tokenReporter = null; model = new ModelCapabilities(LLMWrapper.BUILD_DEFAULT_MODEL); - constructor(parameters) { + constructor(parameters = {}) { if (!parameters.openAIKey) { this.#openAIKey = process.env.OPENAI_API_KEY } else { @@ -81,7 +82,7 @@ export class LLMWrapper { } if (!parameters.googleKey) { - this.#googleKey = process.env.GOOGLE_API_KEY + this.#googleKey = process.env.GEMINI_API_KEY || process.env.GOOGLE_API_KEY; } else { this.#googleKey = parameters.googleKey; } @@ -109,6 +110,8 @@ export class LLMWrapper { if (parameters.jsonObjectMode === true) this.#jsonObjectMode = true; + this.#tokenReporter = new TokenUsageReporter(config.tokenReporterURL, parameters.clientId ?? null); + switch (this.model.kind) { case ModelType.GEMINI: if (!this.#googleKey) { @@ -147,39 +150,25 @@ export class LLMWrapper { } static MODELS = [ - {label: "GPT-5.4", value: 'gpt-5.4 medium'}, - {label: "GPT-5.3", value: 'gpt-5.3 medium'}, - {label: "GPT-5.2", value: 'gpt-5.2 medium'}, - {label: "GPT-5.1", value: 'gpt-5.1 medium'}, - {label: "GPT-5", value: 'gpt-5'}, - {label: "GPT-5-mini", value: 'gpt-5-mini'}, - {label: "GPT-5-nano", value: 'gpt-5-nano'}, - {label: "GPT-4.1", value: 'gpt-4.1'}, - {label: "GPT-4.1-mini", value: 'gpt-4.1-mini'}, - {label: "GPT-4.1-nano", value: 'gpt-4.1-nano'}, - {label: "GPT-4o", value: 'gpt-4o'}, - {label: "GPT-4o-mini", value: 'gpt-4o-mini'}, + {label: "GPT-5 latest", value: 'gpt-5'}, + {label: "GPT-5-mini latest", value: 'gpt-5-mini'}, {label: "Gemini 3.1-pro-preview", value: 'gemini-3.1-pro-preview'}, + {label: "Gemini 3.5-flash", value: 'gemini-3.5-flash'}, + {label: "Gemini 3.5-flash high", value: 'gemini-3.5-flash high'}, + {label: "Gemini 3.5-flash medium", value: 'gemini-3.5-flash medium'}, + {label: "Gemini 3.5-flash low", value: 'gemini-3.5-flash low'}, {label: "Gemini 3-flash-preview", value: 'gemini-3-flash-preview'}, - {label: "Gemini 3-flash-preview high", value: 'gemini-3-flash-preview high'}, - {label: "Gemini 3-flash-preview medium", value: 'gemini-3-flash-preview medium'}, - {label: "Gemini 3-flash-preview low", value: 'gemini-3-flash-preview low'}, - {label: "Gemini 3-flash-preview minimal", value: 'gemini-3-flash-preview minimal'}, {label: "Gemini 2.5-flash", value: 'gemini-2.5-flash'}, - {label: "Gemini 2.5-flash-lite", value: 'gemini-2.5-flash-lite'}, {label: "Gemini 2.5-pro", value: 'gemini-2.5-pro'}, + {label: "Claude Opus 4.7", value: 'claude-opus-4-7'}, {label: "Claude Opus 4.6", value: 'claude-opus-4-6'}, {label: "Claude Sonnet 4.6", value: 'claude-sonnet-4-6'}, - {label: "Claude Haiku 4.5", value: 'claude-haiku-4-5'}, - {label: "Claude Opus 4.5", value: 'claude-opus-4-5'}, {label: "Claude Sonnet 4.5", value: 'claude-sonnet-4-5'}, - {label: "o1", value: 'o1'}, - {label: "o3", value: 'o3'}, - {label: "o4-mini", value: 'o4-mini'} + {label: "Claude Haiku 4.5", value: 'claude-haiku-4-5'}, ]; - static BUILD_DEFAULT_MODEL = 'gemini-3-flash-preview low'; //'claude-opus-4-6'; - static NON_BUILD_DEFAULT_MODEL = 'gemini-3-flash-preview low'; //'claude-opus-4-6'; + static BUILD_DEFAULT_MODEL = config.buildDefaultModel; + static NON_BUILD_DEFAULT_MODEL = config.nonBuildDefaultModel; static EVAL_MODEL = process.env.EVAL_MODEL ?? 'gemini-2.5-flash'; static SCHEMA_STRINGS = { @@ -221,7 +210,8 @@ export class LLMWrapper { "stopTime": "The time at which this model stops calculating. It is measured in the units of \"timeUnits\".", "dt": "The time step for the model, how often is it calculated. The most common dt is 0.25. It is measured in the units of \"timeUnits\".", "timeUnits": "The unit of time for this model. This should match with the equations that you generate.", - + "integrationMethod": "The method used to solve this model. Euler (Default), RK4, is an optional method for systems with oscillations.", + "loopIdentifier": "The globally unique identifer for this feedback loop. You will take this value from the feedback loop identifier given to you.", "loopName": "A short, but unique name, for the process this feedback loop represents. This name must be distinct for each loop you give a name to. This name should not refer directly to the polarity of the loop. Don't use the words: growth, decline, stablizing, dampening, balancing, reinforcing, positive or negative in the name.", "loopDescription": "A description of what the process this feedback loop represents. This description should discusses the purpose of this feedback loop. It should not be longer then 3 paragraphs", @@ -241,12 +231,41 @@ export class LLMWrapper { "arrayDimensions": "The complete list of all array dimension definitions used anywhere in this model. Each dimension must be fully defined here in the simulation specs before it can be referenced by variables in their 'dimensions' field. All dimensions must have all four required fields: type, name, size, and elements.", "variableDimensions": "An ordered list of dimension names that define the subscript structure for this arrayed variable. The order matters: each element in the forElements arrays must correspond positionally to the dimensions listed here (first element matches first dimension, second element matches second dimension, etc.). If empty or omitted, this is a scalar (non-arrayed) variable.", "arrayElementEquation": "Specifies the equation for a specific subset of array elements in an arrayed variable. The 'equation' field contains the XMILE equation, and the 'forElements' field specifies which array elements this equation applies to (ordered to match the variable's dimensions list).", - "arrayEquationForElements": "A comma-separated string of array element names that identifies which specific array element(s) use this equation. Each element name corresponds positionally to the dimensions in the variable's 'dimensions' field (first element name matches first dimension, second matches second, etc.). For single-dimension arrays, this has one element name. For multi-dimensional arrays, this has multiple element names separated by commas in the same order as the dimensions. Example: 'North,Q1' or 'South,Q2'.", + "arrayEquationForElements": "An array of element names that identifies which specific array element(s) use this equation. Each element name corresponds positionally to the dimensions in the variable's 'dimensions' field (first element name matches first dimension, second matches second, etc.). For single-dimension arrays, this has one element name. For multi-dimensional arrays, this has multiple element names in the same order as the dimensions. Example: ['North','Q1'] or ['South','Q2'].", "variableArrayEquation": "CRITICAL: Used for arrayed variables when elements need different equations OR for arrayed stocks to specify initial values. Every variable MUST have either this array non-empty OR the 'equation' field non-empty - never both non-empty, never both empty. For arrayed variables: if elements have DIFFERENT formulas, you MUST populate this array with equation objects and leave 'equation' empty (empty string). This is a list of equation objects, where each object specifies an equation and the array elements it applies to (via the forElements field). You MUST provide equations that cover EVERY valid combination of array elements across all dimensions. For arrayed STOCKS, you MUST use this field to provide initial values for each stock element.", "moduleName": "The name of a module. Must follow variable naming rules: contains only alphanumeric characters and underscores, no spaces or special characters. Should never be module-qualified (do not include parent module names with dots). This is a simple identifier for the module itself.", "parentModule": "The name of the module that contains this module. If this module is at the top level (not nested within another module), this should be an empty string. If nested, this should be the simple name (not module-qualified) of the parent module.", - "modules": "A list of module definitions that exist within this model. Each module represents a logical grouping or subsystem within the model hierarchy. Modules can contain variables and can be nested within other modules to create hierarchical model structures." + "modules": "A list of module definitions that exist within this model. Each module represents a logical grouping or subsystem within the model hierarchy. Modules can contain variables and can be nested within other modules to create hierarchical model structures.", + + "subType": "The sub-type of this stock, flow, or variable. Stock sub-types (also set additionalProperties): 'queue' (a waiting line that holds items until they can be processed), 'oven' (a batch processor where items are held for a fixed cook time then released together), 'conveyor' (a pipeline delay where items travel a fixed transit time before exiting). Flow sub-types — automatically managed flows you name but do NOT write equations for: 'discreteOutflow' (output from a conveyor or oven), 'conveyorLeakage' (leakage from a conveyor — set additionalProperties to configure leakage behavior), 'queueOutflow' (output from a queue), 'queueOverflow' (overflow when a queue is full). Variable sub-types: 'delayVariable' (a plain variable whose equation contains a DELAY or SMTH builtin function — set this whenever the variable equation uses DELAY1, DELAY3, DELAY N, SMTH1, SMTH3, or any other DELAY/SMTH variant). Omit this field for all other stocks, flows, and variables.", + + "additionalProperties": "Sub-type-specific configuration for queue, oven, conveyor, conveyorLeakage, and any regular flow that uses spreadFlow. Include this object when subType is 'queue', 'oven', 'conveyor', or 'conveyorLeakage', or when the variable is a regular flow that sets spreadFlow. Omit entirely for all other variable types.", + + "processTime": "CONVEYOR/OVEN: Equation string for the transit time (conveyor) or cook time (oven) — how long items spend inside. Required for conveyor and oven sub-types.", + "capacity": "CONVEYOR/OVEN: Equation string for the maximum number of items the element can hold. Leave empty for unlimited capacity.", + "inflowLimit": "CONVEYOR/OVEN: Equation string for the maximum inflow rate per time step. Leave empty for no inflow limit.", + "fillTime": "OVEN only: Equation string for the time required to fill the element before it begins processing. Leave empty to use the default.", + "cleanTime": "OVEN only: Equation string for the clean-up time after the element empties before it can accept new items. Leave empty if no clean time is needed.", + "leakFraction": "CONVEYOR LEAKAGE: Equation string for the leak fraction. When exponential (default), this is a rate in units of 1/time_unit (e.g. 0.1 means 10% per time unit). When not exponential, this is a dimensionless fraction of contents that leaks per time step. Leave empty for no leakage.", + "exponential": "CONVEYOR LEAKAGE: If true (STRONG default — almost always use exponential), leakage is exponential (a constant fraction of remaining contents leaks each step, leak fraction in 1/time_unit). If false, leakage is linear (a fixed absolute amount, leak fraction is dimensionless). Only set false when the user explicitly requests linear leakage.", + "leakZoneStart": "CONVEYOR LEAKAGE: Equation string for the starting position (as a percentage 0–100) along the conveyor where leakage begins. Leave empty to apply leakage across the entire length.", + "leakZoneEnd": "CONVEYOR LEAKAGE: Equation string for the ending position (as a percentage 0–100) along the conveyor where leakage ends. Leave empty to apply leakage across the entire length.", + "leakIntegers": "CONVEYOR LEAKAGE: If true, leakage amounts are rounded to whole integers.", + "sample": "CONVEYOR/OVEN: Equation string — re-samples the transit or cook time when this expression evaluates to non-zero.", + "arrest": "CONVEYOR/OVEN: Equation string — halts movement through the conveyor or oven when this expression evaluates to non-zero.", + "spreadFlow": "Controls how inflows are distributed when they enter a CONVEYOR. 'none' (default): all inflow enters at the front. 'even': spread evenly across all positions. 'destination': spread proportional to existing content volume at each position. 'distribution': spread according to a user-defined distribution table (requires distribEq). 'source': spread based on the source's material profile.", + "distribEq": "Required when spreadFlow is 'distribution': Equation string specifying the distribution table. Leave empty when spreadFlow is not 'distribution'.", + "ignorePrevZones": "CONVEYOR LEAKAGE: If true, each leak zone operates independently without accounting for losses from earlier zones in the same conveyor.", + "forceLeakFraction": "CONVEYOR LEAKAGE: If true, the same leak fraction is applied regardless of how long items have been in transit.", + "fifoEnabled": "QUEUE only: If true, the queue dispatches items in FIFO (first-in, first-out) order. If false (default), items are dispatched in LIFO (last-in, first-out) order.", + "oneAtATime": "QUEUE only: If true, the queue accepts only one batch of items per time step.", + "splitBatches": "QUEUE only: If true, incoming batches may be split when entering the queue (partial batches are allowed).", + "discrete": "QUEUE only: If true, the queue operates in discrete mode (integer item quantities only). If false (default), the queue operates continuously.", + "roundRobin": "QUEUE only: If true, the queue uses round-robin selection when dispatching items to competing outflows.", + "queueOutflowPriority": "QUEUE only: Equation string setting the dispatch priority for the queue outflow. Leave empty to use the default priority.", + "purgeEq": "QUEUE only: Equation string specifying a maximum age (in time units) — items older than this value are automatically removed from the queue.", + "overflow": "QUEUE only: If true, an automatic queue overflow flow is created to handle items that cannot enter because the queue is full." }; generateSeldonResponseSchema() { @@ -355,78 +374,52 @@ export class LLMWrapper { return Relationships; } - generateQuantitativeSDJSONResponseSchema(mentorMode, supportsArrays) { + generateQuantitativeSDJSONResponseSchema(mentorMode, supportsArrays, supportsSubTypes) { const TypeEnum = z.enum(["stock", "flow", "variable"]).describe(LLMWrapper.SCHEMA_STRINGS.type); const PolarityEnum = z.enum(["+", "-"]).describe(LLMWrapper.SCHEMA_STRINGS.polarity); - const Dimension = z.object({ - type: z.enum(["labels", "numeric"]).describe(LLMWrapper.SCHEMA_STRINGS.dimensionType), - name: z.string().describe(LLMWrapper.SCHEMA_STRINGS.dimensionName), - size: z.number().describe(LLMWrapper.SCHEMA_STRINGS.dimensionSize), - elements: z.array(z.string()).describe(LLMWrapper.SCHEMA_STRINGS.dimensionElements) - }).describe(LLMWrapper.SCHEMA_STRINGS.dimension); + const Dimension = LLMWrapper.dimensionSchema(); - const GFPoint = z.object({ - x: z.number().describe(LLMWrapper.SCHEMA_STRINGS.gfPointX), - y: z.number().describe(LLMWrapper.SCHEMA_STRINGS.gfPointY) - }).describe(LLMWrapper.SCHEMA_STRINGS.gfPoint); + const GraphicalFunction = LLMWrapper.graphicalFunctionSchema().describe(LLMWrapper.SCHEMA_STRINGS.gfEquation); - const Relationship = z.object({ - from: z.string().describe(LLMWrapper.SCHEMA_STRINGS.from), - to: z.string().describe(LLMWrapper.SCHEMA_STRINGS.to), - polarity: PolarityEnum, - reasoning: z.string().describe(LLMWrapper.SCHEMA_STRINGS.reasoning), - polarityReasoning: z.string().describe(LLMWrapper.SCHEMA_STRINGS.polarityReasoning) - }).describe(LLMWrapper.SCHEMA_STRINGS.relationship); + const Relationship = z.object(LLMWrapper.relationshipSchemaBase()).describe(LLMWrapper.SCHEMA_STRINGS.relationship); const Relationships = z.array(Relationship).describe(LLMWrapper.SCHEMA_STRINGS.relationships); - // Flattened: forElements is a comma-separated string instead of array of strings - // This reduces nesting depth from 6 to 5 levels for array support - const ArrayElementEquation = z.object({ - equation: z.string().describe(LLMWrapper.SCHEMA_STRINGS.equation), - forElements: z.string().describe(LLMWrapper.SCHEMA_STRINGS.arrayEquationForElements) - }).describe(LLMWrapper.SCHEMA_STRINGS.arrayElementEquation); + const ArrayElementEquation = LLMWrapper.arrayElementEquationSchema().describe(LLMWrapper.SCHEMA_STRINGS.arrayElementEquation); const variableObj = { name: z.string().describe(LLMWrapper.SCHEMA_STRINGS.name), equation: z.string().describe(LLMWrapper.SCHEMA_STRINGS.equation), inflows: z.array(z.string()).describe(LLMWrapper.SCHEMA_STRINGS.inflows), outflows: z.array(z.string()).describe(LLMWrapper.SCHEMA_STRINGS.outflows), - graphicalFunction: z.array(GFPoint).describe(LLMWrapper.SCHEMA_STRINGS.gfEquation), + graphicalFunction: GraphicalFunction, type: TypeEnum, uniflow: z.boolean().describe(LLMWrapper.SCHEMA_STRINGS.uniflow), crossLevelGhostOf: z.string().describe(LLMWrapper.SCHEMA_STRINGS.crossLevelGhostOf), documentation: z.string().describe(LLMWrapper.SCHEMA_STRINGS.documentation), units: z.string().describe(LLMWrapper.SCHEMA_STRINGS.units) }; - + if (supportsArrays) { variableObj.dimensions = z.array(z.string()).describe(LLMWrapper.SCHEMA_STRINGS.variableDimensions); variableObj.arrayEquations = z.array(ArrayElementEquation).describe(LLMWrapper.SCHEMA_STRINGS.variableArrayEquation); } + if (supportsSubTypes) { + variableObj.subType = LLMWrapper.subTypeSchema().optional(); + variableObj.additionalProperties = LLMWrapper.additionalPropertiesSchema().describe(LLMWrapper.SCHEMA_STRINGS.additionalProperties).optional(); + } + const Variable = z.object(variableObj); const Variables = z.array(Variable).describe(LLMWrapper.SCHEMA_STRINGS.variables); - const simSpecsObj = { - startTime: z.number().describe(LLMWrapper.SCHEMA_STRINGS.startTime), - stopTime: z.number().describe(LLMWrapper.SCHEMA_STRINGS.stopTime), - dt: z.number().describe(LLMWrapper.SCHEMA_STRINGS.dt), - timeUnits: z.string().describe(LLMWrapper.SCHEMA_STRINGS.timeUnits) - }; - - if (supportsArrays) { - simSpecsObj.arrayDimensions = z.array(Dimension).describe(LLMWrapper.SCHEMA_STRINGS.arrayDimensions); - } - + const simSpecsObj = LLMWrapper.simSpecsSchemaBase(); + if (!supportsArrays) delete simSpecsObj.arrayDimensions; const SimSpecs = z.object(simSpecsObj).describe(LLMWrapper.SCHEMA_STRINGS.simSpecs); - const Module = z.object({ - name: z.string().describe(LLMWrapper.SCHEMA_STRINGS.moduleName), - parentModule: z.string().describe(LLMWrapper.SCHEMA_STRINGS.parentModule) - }); + const Module = LLMWrapper.moduleSchema(); const Model = z.object({ variables: Variables, @@ -615,6 +608,7 @@ export class LLMWrapper { } const completion = await this.#openAIAPI.chat.completions.create(completionParams); + this.#tokenReporter.report({ provider: Provider.OPENAI, model, usage: completion.usage }); const message = completion.choices[0].message; // Reasoning models (e.g. GLM-5) emit chain-of-thought in reasoning_content and // leave content null. Try to extract a valid JSON block from the reasoning text @@ -654,12 +648,8 @@ export class LLMWrapper { } if (zodSchema) { - this.#zodToStructuredOutputConverter.setOptions({ - emitOptionalProperties: false - }); - config.responseMimeType = "application/json"; - config.responseJsonSchema = this.#zodToStructuredOutputConverter.convert(zodSchema); + config.responseJsonSchema = zodSchema.toJSONSchema(); } if (Object.keys(config).length > 0) { @@ -667,6 +657,7 @@ export class LLMWrapper { } const result = await this.#geminiAPI.models.generateContent(requestConfig); + this.#tokenReporter.report({ provider: Provider.GOOGLE, model, usage: result.usageMetadata }); // Convert Gemini response to OpenAI format return { @@ -695,7 +686,7 @@ export class LLMWrapper { if (zodSchema) { completionParams.output_format = { type: "json_schema", - schema: this.#zodToStructuredOutputConverter.convert(zodSchema) + schema: zodSchema.toJSONSchema() }; } @@ -708,6 +699,7 @@ export class LLMWrapper { completionParams, { headers } ); + this.#tokenReporter.report({ provider: Provider.ANTHROPIC, model, usage: completion.usage }); // With output_format, the response is always in content[0].text as JSON if (zodSchema) { @@ -790,8 +782,126 @@ export class LLMWrapper { return claudeMessages; } + static moduleSchema() { + return z.object({ + name: z.string().describe(LLMWrapper.SCHEMA_STRINGS.moduleName), + parentModule: z.string().describe(LLMWrapper.SCHEMA_STRINGS.parentModule) + }); + } + + static relationshipSchemaBase() { + return { + from: z.string().describe(LLMWrapper.SCHEMA_STRINGS.from), + to: z.string().describe(LLMWrapper.SCHEMA_STRINGS.to), + polarity: z.enum(["+", "-"]).describe(LLMWrapper.SCHEMA_STRINGS.polarity), + reasoning: z.string().describe(LLMWrapper.SCHEMA_STRINGS.reasoning), + polarityReasoning: z.string().describe(LLMWrapper.SCHEMA_STRINGS.polarityReasoning) + }; + } + + static dimensionSchema() { + return z.object({ + type: z.enum(["labels", "numeric"]).describe(LLMWrapper.SCHEMA_STRINGS.dimensionType), + name: z.string().describe(LLMWrapper.SCHEMA_STRINGS.dimensionName), + size: z.number().describe(LLMWrapper.SCHEMA_STRINGS.dimensionSize), + elements: z.array(z.string()).describe(LLMWrapper.SCHEMA_STRINGS.dimensionElements) + }).describe(LLMWrapper.SCHEMA_STRINGS.dimension); + } + + static simSpecsSchemaBase() { + return { + startTime: z.number().describe(LLMWrapper.SCHEMA_STRINGS.startTime), + stopTime: z.number().describe(LLMWrapper.SCHEMA_STRINGS.stopTime), + dt: z.number().describe(LLMWrapper.SCHEMA_STRINGS.dt), + timeUnits: z.string().describe(LLMWrapper.SCHEMA_STRINGS.timeUnits), + integrationMethod: z.enum(["Euler", "RK4"]).describe(LLMWrapper.SCHEMA_STRINGS.integrationMethod), + arrayDimensions: z.array(LLMWrapper.dimensionSchema()).describe(LLMWrapper.SCHEMA_STRINGS.arrayDimensions) + }; + } + + static graphicalFunctionSchema() { + return z.object({ + points: z.array(z.object({ + x: z.number().describe(LLMWrapper.SCHEMA_STRINGS.gfPointX), + y: z.number().describe(LLMWrapper.SCHEMA_STRINGS.gfPointY) + }).describe(LLMWrapper.SCHEMA_STRINGS.gfPoint)) + }); + } + + static arrayElementEquationSchema() { + return z.object({ + equation: z.string().describe(LLMWrapper.SCHEMA_STRINGS.equation), + forElements: z.array(z.string()).describe(LLMWrapper.SCHEMA_STRINGS.arrayEquationForElements) + }); + } + + static subTypeSchema() { + return z.enum([ + "queue", "oven", "conveyor", + "discreteOutflow", "conveyorLeakage", "queueOutflow", "queueOverflow", + "delayVariable" + ]).describe(LLMWrapper.SCHEMA_STRINGS.subType); + } + + static additionalPropertiesSchema() { + return z.object({ + // CONVEYOR + OVEN + processTime: z.string().describe(LLMWrapper.SCHEMA_STRINGS.processTime).optional(), + capacity: z.string().describe(LLMWrapper.SCHEMA_STRINGS.capacity).optional(), + inflowLimit: z.string().describe(LLMWrapper.SCHEMA_STRINGS.inflowLimit).optional(), + fillTime: z.string().describe(LLMWrapper.SCHEMA_STRINGS.fillTime).optional(), + cleanTime: z.string().describe(LLMWrapper.SCHEMA_STRINGS.cleanTime).optional(), + leakFraction: z.string().describe(LLMWrapper.SCHEMA_STRINGS.leakFraction).optional(), + exponential: z.boolean().describe(LLMWrapper.SCHEMA_STRINGS.exponential).optional(), + leakZoneStart: z.string().describe(LLMWrapper.SCHEMA_STRINGS.leakZoneStart).optional(), + leakZoneEnd: z.string().describe(LLMWrapper.SCHEMA_STRINGS.leakZoneEnd).optional(), + leakIntegers: z.boolean().describe(LLMWrapper.SCHEMA_STRINGS.leakIntegers).optional(), + sample: z.string().describe(LLMWrapper.SCHEMA_STRINGS.sample).optional(), + arrest: z.string().describe(LLMWrapper.SCHEMA_STRINGS.arrest).optional(), + // CONVEYOR-only + spreadFlow: z.enum(["none", "even", "destination", "distribution", "source"]).describe(LLMWrapper.SCHEMA_STRINGS.spreadFlow).optional(), + distribEq: z.string().describe(LLMWrapper.SCHEMA_STRINGS.distribEq).optional(), + ignorePrevZones: z.boolean().describe(LLMWrapper.SCHEMA_STRINGS.ignorePrevZones).optional(), + forceLeakFraction: z.boolean().describe(LLMWrapper.SCHEMA_STRINGS.forceLeakFraction).optional(), + // QUEUE + fifoEnabled: z.boolean().describe(LLMWrapper.SCHEMA_STRINGS.fifoEnabled).optional(), + oneAtATime: z.boolean().describe(LLMWrapper.SCHEMA_STRINGS.oneAtATime).optional(), + splitBatches: z.boolean().describe(LLMWrapper.SCHEMA_STRINGS.splitBatches).optional(), + discrete: z.boolean().describe(LLMWrapper.SCHEMA_STRINGS.discrete).optional(), + roundRobin: z.boolean().describe(LLMWrapper.SCHEMA_STRINGS.roundRobin).optional(), + queueOutflowPriority: z.string().describe(LLMWrapper.SCHEMA_STRINGS.queueOutflowPriority).optional(), + purgeEq: z.string().describe(LLMWrapper.SCHEMA_STRINGS.purgeEq).optional(), + overflow: z.boolean().describe(LLMWrapper.SCHEMA_STRINGS.overflow).optional() + }); + } + + static variableSchemaBase() { + return { + name: z.string().describe(LLMWrapper.SCHEMA_STRINGS.name), + type: z.enum(["stock", "flow", "variable"]).describe(LLMWrapper.SCHEMA_STRINGS.type), + equation: z.string().describe(LLMWrapper.SCHEMA_STRINGS.equation).optional(), + documentation: z.string().describe(LLMWrapper.SCHEMA_STRINGS.documentation).optional(), + units: z.string().describe(LLMWrapper.SCHEMA_STRINGS.units).optional(), + uniflow: z.boolean().describe(LLMWrapper.SCHEMA_STRINGS.uniflow).optional(), + inflows: z.array(z.string()).describe(LLMWrapper.SCHEMA_STRINGS.inflows).optional(), + outflows: z.array(z.string()).describe(LLMWrapper.SCHEMA_STRINGS.outflows).optional(), + dimensions: z.array(z.string()).describe(LLMWrapper.SCHEMA_STRINGS.variableDimensions).optional(), + arrayEquations: z.array(LLMWrapper.arrayElementEquationSchema()).describe(LLMWrapper.SCHEMA_STRINGS.variableArrayEquation).optional(), + crossLevelGhostOf: z.string().describe(LLMWrapper.SCHEMA_STRINGS.crossLevelGhostOf).optional(), + graphicalFunction: LLMWrapper.graphicalFunctionSchema().describe(LLMWrapper.SCHEMA_STRINGS.gfEquation).optional(), + subType: LLMWrapper.subTypeSchema().optional(), + additionalProperties: LLMWrapper.additionalPropertiesSchema().describe(LLMWrapper.SCHEMA_STRINGS.additionalProperties).optional() + }; + } + static additionalParameters(defaultModel) { return [{ + name: "clientId", + type: "string", + required: false, + uiElement: "hidden", + description: "A unique identifier for the end user of this session" + },{ name: "openAIKey", type: "string", required: false, diff --git a/utilities/SDJsonToXMILE.js b/utilities/SDJsonToXMILE.js index 218a6c15..1327eea1 100644 --- a/utilities/SDJsonToXMILE.js +++ b/utilities/SDJsonToXMILE.js @@ -607,7 +607,7 @@ function buildAuxiliary(aux, model, currentModule = '') { } // Handle graphical functions - if (aux.graphicalFunction && aux.graphicalFunction.points) { + if (aux.graphicalFunction && aux.graphicalFunction.points && aux.graphicalFunction.points.length > 1) { lines.push(...buildGraphicalFunction(aux.graphicalFunction, equation)); } else if (equation) { // Regular equation diff --git a/utilities/StructuredOutputToZodConverter.js b/utilities/StructuredOutputToZodConverter.js new file mode 100644 index 00000000..e1dc775a --- /dev/null +++ b/utilities/StructuredOutputToZodConverter.js @@ -0,0 +1,155 @@ +import { z } from 'zod'; +import logger from './logger.js'; + +/** + * StructuredOutputToZodConverter + * Converts JSON Schema (structured output format) to Zod schemas + * + * This is the inverse of Zod's toJSONSchema() method. + * Used primarily for converting client-registered tool schemas + * (which come in JSON Schema format) to Zod schemas for validation. + */ +export class StructuredOutputToZodConverter { + /** + * Convert JSON schema to Zod schema + * @param {Object} jsonSchema - JSON Schema object + * @returns {import('zod').ZodTypeAny} Zod schema + */ + convert(jsonSchema) { + if (!jsonSchema || !jsonSchema.type) { + logger.warn('Invalid JSON Schema provided'); + return z.any(); + } + + // Handle object schema + if (jsonSchema.type === 'object') { + return this.convertObjectSchema(jsonSchema); + } + + // Handle primitive or array schema + return this.convertTypeToZod(jsonSchema); + } + + /** + * Convert JSON Schema object to Zod object schema + * @param {Object} jsonSchema - JSON Schema object with properties + * @returns {import('zod').ZodObject} Zod object schema + */ + convertObjectSchema(jsonSchema) { + const properties = jsonSchema.properties || {}; + const required = jsonSchema.required || []; + + const zodSchema = {}; + + for (const [propName, propDef] of Object.entries(properties)) { + let zodField = this.convertTypeToZod(propDef); + + // Make optional if not required + if (!required.includes(propName)) { + zodField = zodField.optional(); + } + + // Add description if present + if (propDef.description) { + zodField = zodField.describe(propDef.description); + } + + zodSchema[propName] = zodField; + } + + return z.object(zodSchema); + } + + /** + * Convert JSON Schema type to Zod type + * @param {Object} propDef - JSON Schema property definition + * @returns {import('zod').ZodTypeAny} Zod type + */ + convertTypeToZod(propDef) { + // Handle anyOf / oneOf as union + if (propDef.anyOf || propDef.oneOf) { + const items = propDef.anyOf || propDef.oneOf; + const nullItems = items.filter(v => v.type === 'null'); + const nonNullItems = items.filter(v => v.type !== 'null'); + if (nonNullItems.length === 0) return z.null(); + const variants = nonNullItems.map(v => this.convertTypeToZod(v)); + let base = variants.length === 1 ? variants[0] : z.union(variants); + return nullItems.length > 0 ? base.nullable() : base; + } + + // No type field — infer from shape + if (propDef.type === undefined) { + if (propDef.properties || propDef.additionalProperties) { + return this.convertNestedObject(propDef); + } + if (propDef.items) { + return this.convertArrayType(propDef); + } + if (propDef.enum) { + return this.convertStringType(propDef); + } + return z.any(); + } + + switch (propDef.type) { + case 'string': + return this.convertStringType(propDef); + case 'number': + return z.number(); + case 'integer': + return z.number().int(); + case 'boolean': + return z.boolean(); + case 'null': + return z.null(); + case 'array': + return this.convertArrayType(propDef); + case 'object': + return this.convertNestedObject(propDef); + default: + logger.warn(`Unknown JSON Schema type: ${propDef.type}`); + return z.any(); + } + } + + /** + * Convert string type with enum support + * @param {Object} propDef - JSON Schema string property + * @returns {import('zod').ZodString|import('zod').ZodEnum} Zod string or enum + */ + convertStringType(propDef) { + if (propDef.enum && Array.isArray(propDef.enum) && propDef.enum.length > 0) { + // Zod v4 z.enum requires at least one value + // For safety, ensure we have at least one string value + const enumValues = propDef.enum.filter(v => typeof v === 'string'); + if (enumValues.length > 0) { + return z.enum(enumValues); + } + } + return z.string(); + } + + /** + * Convert array type + * @param {Object} propDef - JSON Schema array property + * @returns {import('zod').ZodArray} Zod array + */ + convertArrayType(propDef) { + if (propDef.items) { + return z.array(this.convertTypeToZod(propDef.items)); + } + return z.array(z.any()); + } + + /** + * Convert nested object + * @param {Object} propDef - JSON Schema nested object property + * @returns {import('zod').ZodObject} Zod object + */ + convertNestedObject(propDef) { + if (propDef.properties) { + return this.convertObjectSchema(propDef); + } + return z.object({}).catchall(z.any()); + } +} diff --git a/utilities/TokenUsageReporter.js b/utilities/TokenUsageReporter.js new file mode 100644 index 00000000..05847734 --- /dev/null +++ b/utilities/TokenUsageReporter.js @@ -0,0 +1,199 @@ +import logger from './logger.js'; +import { getPricing } from './pricing.js'; + +export const Provider = Object.freeze({ + ANTHROPIC: 'anthropic', + OPENAI: 'openai', + GOOGLE: 'google', +}); + +export const ProviderDisplayNames = Object.freeze({ + [Provider.ANTHROPIC]: 'Claude', + [Provider.GOOGLE]: 'Gemini', + [Provider.OPENAI]: 'OpenAI', +}); + +class TokenUsageReporter { + /** + * @param {string|null} url - Optional URL to POST token usage to. If null, reporting is disabled. + * @param {string|null} clientId - The clientId from the InitializeSessionMessage. + */ + constructor(url = null, clientId = null) { + this.url = url; + this.clientId = clientId; + this.enabled = url !== null && url !== undefined && url !== '' && clientId !== null && clientId !== undefined && clientId !== ''; + } + + /** + * Reports token usage for an agent LLM call. + * @param {Object} params + * @param {string} params.provider - LLM provider: use Provider.ANTHROPIC | Provider.OPENAI | Provider.GOOGLE + * @param {string} params.model - Specific model name, e.g. 'claude-sonnet-4-6' or 'gemini-3-flash-preview' + * @param {Object} params.usage - Raw usage object from the LLM provider + * @param {boolean} params.potentialDuplicate - 'true' if we think this is likely a duplicate cost from either the claude sdk or the google adk + */ + async report({ provider, model, usage, potentialDuplicate }) { + if (!usage) return; + + const isAnthropic = provider === Provider.ANTHROPIC; + const isOpenAI = provider === Provider.OPENAI; + const isGemini = provider === Provider.GOOGLE; + + let tokens; + if (isAnthropic) { + tokens = { + inputTokens: usage.input_tokens ?? 0, + outputTokens: usage.output_tokens ?? 0, + cacheCreation5mInputTokens: usage.cache_creation?.ephemeral_5m_input_tokens ?? 0, + cacheCreation1hInputTokens: usage.cache_creation?.ephemeral_1h_input_tokens ?? 0, + cacheReadInputTokens: usage.cache_read_input_tokens ?? 0, + }; + } else if (isOpenAI) { + tokens = { + inputTokens: usage.prompt_tokens ?? 0, + outputTokens: usage.completion_tokens ?? 0, + cachedTokens: usage.prompt_tokens_details?.cached_tokens ?? 0, + reasoningTokens: usage.completion_tokens_details?.reasoning_tokens ?? 0, + }; + } else if (isGemini) { + tokens = { + inputTokens: usage.promptTokenCount ?? 0, + outputTokens: usage.candidatesTokenCount ?? 0, + cachedTokens: usage.cachedContentTokenCount ?? 0, + thoughtsTokens: usage.thoughtsTokenCount ?? 0, + }; + } else { + throw new Error('Unknown provider: "' + provider + '"'); + } + + const costs = this.#calculateCost(provider, model, tokens); + const fmt = (n, cost) => cost != null ? `${n}($${cost.toFixed(6)})` : `${n}`; + + const duplicateTag = potentialDuplicate ? ' [duplicate?]' : ''; + const clientTag = this.clientId ? ` client=${this.clientId}` : ''; + + if (isAnthropic) { + logger.log( + `[usage:${provider}]` + + clientTag + + duplicateTag + + ` input=${fmt(tokens.inputTokens, costs?.inputTokens)}` + + ` output=${fmt(tokens.outputTokens, costs?.outputTokens)}` + + ` cache_write_5m=${fmt(tokens.cacheCreation5mInputTokens, costs?.cacheCreation5mInputTokens)}` + + ` cache_write_1h=${fmt(tokens.cacheCreation1hInputTokens, costs?.cacheCreation1hInputTokens)}` + + ` cache_read=${fmt(tokens.cacheReadInputTokens, costs?.cacheReadInputTokens)}` + + (costs ? ` total=$${costs.total.toFixed(6)}` : '') + ); + } else if (isOpenAI) { + logger.log( + `[usage:${provider}]` + + clientTag + + duplicateTag + + ` input=${fmt(tokens.inputTokens, costs?.inputTokens)}` + + ` output=${fmt(tokens.outputTokens, costs?.outputTokens)}` + + ` cached=${fmt(tokens.cachedTokens, costs?.cachedTokens)}` + + ` reasoning=${tokens.reasoningTokens}` + + (costs ? ` total=$${costs.total.toFixed(6)}` : '') + ); + } else { + logger.log( + `[usage:${provider}]` + + clientTag + + duplicateTag + + ` input=${fmt(tokens.inputTokens, costs?.inputTokens)}` + + ` output=${fmt(tokens.outputTokens, costs?.outputTokens)}` + + ` cached=${fmt(tokens.cachedTokens, costs?.cachedTokens)}` + + ` thoughts=${fmt(tokens.thoughtsTokens, costs?.thoughtsTokens)}` + + (costs ? ` total=$${costs.total.toFixed(6)}` : '') + ); + } + + if (!this.enabled) return; + + const reportData = { + clientId: this.clientId, + provider, + model, + tokens, + cost: costs?.total ?? null, + timestamp: new Date().toISOString(), + potentialDuplicate + }; + + try { + const response = await fetch(this.url, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(reportData), + }); + + if (!response.ok) { + console.error(`TokenUsageReporter: Failed to POST to ${this.url}. Status: ${response.status}`); + } + } catch (error) { + console.error(`TokenUsageReporter: Error posting to ${this.url}:`, error.message); + } + } + + /** + * @param {string} provider - use Provider enum + * @param {string} model + * @param {Object} tokens + * @returns {{ total: number, [key: string]: number }|null} + */ + #calculateCost(provider, model, tokens) { + const pricing = getPricing(provider, model, tokens.inputTokens); + if (!pricing) return null; + + const per = (count, rate) => (count / 1_000_000) * rate; + + if (provider === Provider.ANTHROPIC) { + const inputTokens = per(tokens.inputTokens, pricing.inputTokens); + const outputTokens = per(tokens.outputTokens, pricing.outputTokens); + const cacheCreation5mInputTokens = per(tokens.cacheCreation5mInputTokens, pricing.cacheCreation5mInputTokens); + const cacheCreation1hInputTokens = per(tokens.cacheCreation1hInputTokens, pricing.cacheCreation1hInputTokens); + const cacheReadInputTokens = per(tokens.cacheReadInputTokens, pricing.cacheReadInputTokens); + return { + inputTokens, + outputTokens, + cacheCreation5mInputTokens, + cacheCreation1hInputTokens, + cacheReadInputTokens, + total: inputTokens + outputTokens + cacheCreation5mInputTokens + cacheCreation1hInputTokens + cacheReadInputTokens, + }; + } + + if (provider === Provider.GOOGLE) { + // cachedTokens are a subset of inputTokens; bill non-cached at full rate, cached at reduced rate + // thoughtsTokens are separate from outputTokens and billed at the output rate + const nonCached = tokens.inputTokens - tokens.cachedTokens; + const inputTokens = per(nonCached, pricing.inputTokens); + const cachedTokens = per(tokens.cachedTokens, pricing.cachedTokens); + const outputTokens = per(tokens.outputTokens, pricing.outputTokens); + const thoughtsTokens = per(tokens.thoughtsTokens, pricing.outputTokens); + return { + inputTokens, + cachedTokens, + outputTokens, + thoughtsTokens, + total: inputTokens + cachedTokens + outputTokens + thoughtsTokens, + }; + } + + // openai (and unknown providers, which fall back to openai pricing) + // cachedTokens are a subset of inputTokens; bill non-cached at full rate, cached at reduced rate + // reasoningTokens are already included in outputTokens (completion_tokens), so not billed separately + const nonCached = tokens.inputTokens - tokens.cachedTokens; + const inputTokens = per(nonCached, pricing.inputTokens); + const cachedTokens = per(tokens.cachedTokens, pricing.cachedTokens); + const outputTokens = per(tokens.outputTokens, pricing.outputTokens); + return { + inputTokens, + cachedTokens, + outputTokens, + total: inputTokens + cachedTokens + outputTokens, + }; + } +} + +export default TokenUsageReporter; diff --git a/utilities/ZodToStructuredOutputConverter.js b/utilities/ZodToStructuredOutputConverter.js deleted file mode 100644 index 48017b6b..00000000 --- a/utilities/ZodToStructuredOutputConverter.js +++ /dev/null @@ -1,165 +0,0 @@ -import logger from "./logger.js" - -export class ZodToStructuredOutputConverter { - #emitOptionalProperties = true; - - setOptions(parameters) { - this.#emitOptionalProperties = parameters?.emitOptionalProperties || true; - } - - convert(zodSchema) { - if (!zodSchema || !zodSchema._def) { - return {}; - } - - const zodType = zodSchema._def.typeName; - - switch (zodType) { - case 'ZodString': - return this.convertZodStringToStructuredOutput(zodSchema._def); - case 'ZodNumber': - return this.convertZodNumberToStructuredOutput(zodSchema._def); - case 'ZodBoolean': - return { type: 'boolean' }; - case 'ZodArray': - return this.convertZodArrayToStructuredOutput(zodSchema._def); - case 'ZodObject': - return this.convertZodObjectToStructuredOutput(zodSchema._def); - case 'ZodEnum': - return this.convertZodEnumToStructuredOutput(zodSchema._def); - case 'ZodOptional': - // For Claude's structured outputs, optional fields are handled via the 'required' array - // in the parent object, not via a 'nullable' property - return this.convert(zodSchema._def.innerType); - case 'ZodUnion': - return this.convertZodUnionToStructuredOutput(zodSchema._def); - case 'ZodLiteral': - return this.convertZodLiteralToStructuredOutput(zodSchema._def); - default: - logger.warn(`Unsupported Zod type: ${zodType}`); - return { type: 'string' }; - } - } - - convertZodStringToStructuredOutput(def) { - const schema = { type: 'string' }; - - if (def.description) { - schema.description = def.description; - } - - return schema; - } - - convertZodNumberToStructuredOutput(def) { - const schema = { type: 'number' }; - - if (def.description) { - schema.description = def.description; - } - - return schema; - } - - convertZodArrayToStructuredOutput(def) { - const schema = { - type: 'array', - items: this.convert(def.type) - }; - - if (def.description) { - schema.description = def.description; - } - - if (def.minLength !== null) { - schema.minItems = def.minLength.value; - } - - if (def.maxLength !== null) { - schema.maxItems = def.maxLength.value; - } - - return schema; - } - - convertZodObjectToStructuredOutput(def) { - const schema = { - type: 'object', - properties: {}, - required: [], - additionalProperties: false - }; - - if (def.description) { - schema.description = def.description; - } - - for (const [key, zodSchema] of Object.entries(def.shape())) { - schema.properties[key] = this.convert(zodSchema); - - if (this.#emitOptionalProperties) { - if (!zodSchema.isOptional()) { - schema.required.push(key); - } - } else { - // Make all fields required (optional fields will be nullable) - schema.required.push(key); - } - } - - return schema; - } - - convertZodEnumToStructuredOutput(def) { - const schema = { - type: 'string', - enum: def.values - }; - - if (def.description) { - schema.description = def.description; - } - - return schema; - } - - convertZodUnionToStructuredOutput(def) { - const options = def.options; - - // For nullable unions (T | null), just return the non-null type - // Claude handles nullability through the 'required' array in parent objects - if (options.length === 2 && options.some(opt => opt._def.typeName === 'ZodNull')) { - const nonNullOption = options.find(opt => opt._def.typeName !== 'ZodNull'); - return this.convert(nonNullOption); - } - - const enumValues = []; - let allLiterals = true; - - for (const option of options) { - if (option._def.typeName === 'ZodLiteral') { - enumValues.push(option._def.value); - } else { - allLiterals = false; - break; - } - } - - if (allLiterals && enumValues.length > 0) { - return { - type: typeof enumValues[0] === 'string' ? 'string' : 'number', - enum: enumValues - }; - } - - logger.warn('Complex union types not fully supported, defaulting to string'); - return { type: 'string' }; - } - - convertZodLiteralToStructuredOutput(def) { - return { - type: typeof def.value === 'string' ? 'string' : 'number', - enum: [def.value] - }; - } -} \ No newline at end of file diff --git a/utilities/pricing.js b/utilities/pricing.js new file mode 100644 index 00000000..21646c94 --- /dev/null +++ b/utilities/pricing.js @@ -0,0 +1,175 @@ +import logger from './logger.js'; +import { Provider } from './TokenUsageReporter.js'; + +// LLM pricing — USD per 1 million tokens +// Each provider section has a 'default' fallback for unknown models. +// Models with tiered pricing use an array of tiers; the first matching tier wins. +// A tier matches when inputTokens <= maxInputTokens, or when maxInputTokens is absent (catch-all). + +// ─── Anthropic ─────────────────────────────────────────────────────────────── +// Source: https://platform.claude.com/docs/en/about-claude/pricing +export const anthropic = { + 'claude-opus-4-7': { + inputTokens: 5.00, + cacheCreation5mInputTokens: 6.25, + cacheCreation1hInputTokens: 10.00, + cacheReadInputTokens: 0.50, + outputTokens: 25.00, + }, + 'claude-opus-4-6': { + inputTokens: 5.00, + cacheCreation5mInputTokens: 6.25, + cacheCreation1hInputTokens: 10.00, + cacheReadInputTokens: 0.50, + outputTokens: 25.00, + }, + 'claude-sonnet-4-6': { + inputTokens: 3.00, + cacheCreation5mInputTokens: 3.75, + cacheCreation1hInputTokens: 6.00, + cacheReadInputTokens: 0.30, + outputTokens: 15.00, + }, + 'claude-sonnet-4-5': { + inputTokens: 3.00, + cacheCreation5mInputTokens: 3.75, + cacheCreation1hInputTokens: 6.00, + cacheReadInputTokens: 0.30, + outputTokens: 15.00, + }, + 'claude-haiku-4-5': { + inputTokens: 1.00, + cacheCreation5mInputTokens: 1.25, + cacheCreation1hInputTokens: 2.00, + cacheReadInputTokens: 0.10, + outputTokens: 5.00, + }, + default: { + inputTokens: 5.00, + cacheCreation5mInputTokens: 6.25, + cacheCreation1hInputTokens: 10.00, + cacheReadInputTokens: 0.50, + outputTokens: 25.00, + }, +}; + +// ─── Gemini ────────────────────────────────────────────────────────────────── +// Source: https://ai.google.dev/gemini-api/docs/pricing +// Thinking/reasoning tokens are billed at the output token rate. +// cachedTokens are a subset of inputTokens and billed at the cached rate instead. +export const gemini = { + 'gemini-3.1-pro-preview': [ + { maxInputTokens: 200000, inputTokens: 2.00, cachedTokens: 0.20, outputTokens: 12.00 }, + { inputTokens: 4.00, cachedTokens: 0.40, outputTokens: 18.00 }, + ], + 'gemini-2.5-pro': [ + { maxInputTokens: 200000, inputTokens: 1.25, cachedTokens: 0.13, outputTokens: 10.00 }, + { inputTokens: 2.50, cachedTokens: 0.25, outputTokens: 15.00 }, + ], + 'gemini-2.5-flash': { + inputTokens: 0.30, + cachedTokens: 0.03, + outputTokens: 2.50, + }, + 'gemini-3-flash-preview': { + inputTokens: 0.50, + cachedTokens: 0.05, + outputTokens: 3.00, + }, + 'gemini-3.1-flash-lite-preview' : { + inputTokens: 0.25, + cachedTokens: 0.025, + outputTokens: 1.50, + }, + 'gemini-3.5-flash': { + inputTokens: 1.50, + cachedTokens: 0.15, + outputTokens: 9.00, + }, + default: { + inputTokens: 4.00, + cachedTokens: 0.40, + outputTokens: 18.00, + }, +}; + +// ─── OpenAI ────────────────────────────────────────────────────────────────── +// Source: https://developers.openai.com/api/docs/pricing +// Reasoning tokens are billed at the output token rate and are already included +// in completion_tokens, so they must not be double-counted. +// cachedTokens are a subset of inputTokens and billed at the cached rate instead. +// Aliases resolve before the pricing lookup. +export const openaiAliases = { + 'gpt-5': 'gpt-5.5', // same as newest gpt-5.X model + 'gpt-5-mini': 'gpt-5.4-mini', // same as newest gpt-5.X mini model +}; + +export const openai = { + 'gpt-5.5': [ + { maxInputTokens: 272000, inputTokens: 5.00, cachedTokens: 0.50, outputTokens: 30.00 }, + { inputTokens: 10.00, cachedTokens: 1.00, outputTokens: 45.00 }, + ], + 'gpt-5.4-mini': { + inputTokens: 0.75, + cachedTokens: 0.08, + outputTokens: 4.50, + }, + default: { + inputTokens: 10.00, + cachedTokens: 1.00, + outputTokens: 45.00, + }, +}; + +// ─── Lookup helper ─────────────────────────────────────────────────────────── + +/** + * Returns the pricing tier for a given provider/model/inputTokenCount. + * Unknown providers fall back to the OpenAI pricing table. + * Unknown models fall back to the provider's "default" entry, then to openai's default. + * @param {string} provider - use Provider enum from TokenUsageReporter.js (others fall back to openai) + * @param {string} model + * @param {number} inputTokens - used to select the correct tier for tiered models + * @returns {Object} pricing object with per-token-type rates + */ +export function getPricing(provider, model, inputTokens = 0) { + let table, aliases, resolvedProvider; + if (provider === Provider.ANTHROPIC) { + table = anthropic; aliases = {}; resolvedProvider = Provider.ANTHROPIC; + } else if (provider === Provider.OPENAI) { + table = openai; aliases = openaiAliases; resolvedProvider = Provider.OPENAI; + } else if (provider === Provider.GOOGLE) { + table = gemini; aliases = {}; resolvedProvider = Provider.GOOGLE; + } else { + logger.error(`!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!`); + logger.error(`[pricing] !!! UNKNOWN PROVIDER "${provider}" !!! falling back to openai pricing — UPDATE pricing.js`); + logger.error(`!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!`); + table = openai; aliases = openaiAliases; resolvedProvider = 'openai'; + } + + const resolvedModel = aliases[model] ?? model; + let entry = table[resolvedModel]; + if (!entry) { + logger.error(`!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!`); + logger.error(`[pricing] !!! UNKNOWN MODEL "${model}" for provider "${resolvedProvider}" !!! falling back to "${resolvedProvider}" default rates — UPDATE pricing.js`); + logger.error(`!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!`); + entry = table['default']; + if (!entry) { + logger.error(`!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!`); + logger.error(`[pricing] !!! NO DEFAULT for provider "${resolvedProvider}" !!! falling back to openai default rates — UPDATE pricing.js`); + logger.error(`!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!`); + entry = openai['default']; + } + } + + if (Array.isArray(entry)) { + for (const tier of entry) { + if (tier.maxInputTokens === undefined || inputTokens <= tier.maxInputTokens) { + return tier; + } + } + return entry[entry.length - 1]; + } + + return entry; +}