From 83d40615bad42400d651abbc29d161ccdce525f8 Mon Sep 17 00:00:00 2001 From: Sanjog Thapa Date: Fri, 17 Apr 2026 15:15:34 -0500 Subject: [PATCH 01/25] add pydantic ai base agent along with cmr implementation --- akd_ext/agents/_base_pydantic/__init__.py | 18 + akd_ext/agents/_base_pydantic/_base.py | 401 +++++++++++++++ .../agents/_base_pydantic/_capabilities.py | 135 +++++ .../agents/_base_pydantic/_context_adapter.py | 106 ++++ .../_base_pydantic/_event_translator.py | 124 +++++ akd_ext/agents/_base_pydantic/_protocols.py | 101 ++++ .../agents/_base_pydantic/_tool_adapter.py | 54 ++ akd_ext/agents/cmr_care_pydantic.py | 468 ++++++++++++++++++ pyproject.toml | 2 + tests/agents/test_base_pydantic.py | 324 ++++++++++++ 10 files changed, 1733 insertions(+) create mode 100644 akd_ext/agents/_base_pydantic/__init__.py create mode 100644 akd_ext/agents/_base_pydantic/_base.py create mode 100644 akd_ext/agents/_base_pydantic/_capabilities.py create mode 100644 akd_ext/agents/_base_pydantic/_context_adapter.py create mode 100644 akd_ext/agents/_base_pydantic/_event_translator.py create mode 100644 akd_ext/agents/_base_pydantic/_protocols.py create mode 100644 akd_ext/agents/_base_pydantic/_tool_adapter.py create mode 100644 akd_ext/agents/cmr_care_pydantic.py create mode 100644 tests/agents/test_base_pydantic.py diff --git a/akd_ext/agents/_base_pydantic/__init__.py b/akd_ext/agents/_base_pydantic/__init__.py new file mode 100644 index 0000000..a900315 --- /dev/null +++ b/akd_ext/agents/_base_pydantic/__init__.py @@ -0,0 +1,18 @@ +"""Pydantic AI-backed agent base class for akd_ext. + +Public API: + +- :class:`PydanticAIBaseAgent` — subclass this to build new agents. +- :class:`PydanticAIBaseAgentConfig` — extend for subclass-specific config. + +The subpackage also exposes internal helpers (``_protocols``, ``_tool_adapter``, +``_context_adapter``, ``_event_translator``, ``_capabilities``) that consumers +typically don't need to import directly. +""" + +from ._base import PydanticAIBaseAgent, PydanticAIBaseAgentConfig + +__all__ = [ + "PydanticAIBaseAgent", + "PydanticAIBaseAgentConfig", +] diff --git a/akd_ext/agents/_base_pydantic/_base.py b/akd_ext/agents/_base_pydantic/_base.py new file mode 100644 index 0000000..a91d63e --- /dev/null +++ b/akd_ext/agents/_base_pydantic/_base.py @@ -0,0 +1,401 @@ +"""``PydanticAIBaseAgent`` — Pydantic AI-backed agent conforming to the AKD contract. + +Subclasses ``pydantic_ai.Agent`` directly (Path B) and also explicitly +inherits the ``AKDAgent`` Protocol so ``isinstance(agent, AKDAgent)`` works +at runtime. When akd-core ships its Protocol hierarchy and MRO fix, the +swap to Path A (multi-inheriting ``BaseAgent`` for shared machinery) is a +small diff; see ``docs/pydantic_ai_base_agent_implementation_plan.md``. + +Consumers use the same AKD pattern they're used to: + +.. code-block:: python + + class MyAgent(PydanticAIBaseAgent[MyIn, MyOut]): + input_schema = MyIn + output_schema = MyOut | TextOutput + config_schema = MyConfig + + agent = MyAgent(MyConfig(model_name="openai:gpt-5.2", reasoning_effort="high")) + result = await agent.arun(MyIn(query="...")) + +Config auto-exposure is handled by ``AbstractBaseMeta`` — ``agent.model_name``, +``agent.description``, etc. route to ``self.config.*`` without per-field +property definitions. The single opt-out is ``system_prompt`` (re-bound to +pydantic_ai's decorator method so it isn't shadowed by an auto-property). +""" + +from __future__ import annotations + +from collections.abc import AsyncIterator +from typing import Any, _ProtocolMeta + +from pydantic import ConfigDict, Field, model_validator +from pydantic_ai import Agent as PAIAgent +from pydantic_ai import AgentRunResultEvent, ModelRetry +from pydantic_ai.capabilities import AbstractCapability + +from akd._base import ( + CompletedEvent, + CompletedEventData, + InputSchema, + OutputSchema, + StreamEvent, + TextOutput, +) +from akd._base._base import AbstractBaseMeta +from akd.agents._base import BaseAgentConfig + +from ._capabilities import ReflectionCapability, ToolCallLimits, make_ratio_trimmer +from ._context_adapter import _message_history_from_run_context, _usage_from_run_context +from ._event_translator import pai_event_to_akd_event +from ._protocols import AKDAgent, RunContextProtocol +from ._tool_adapter import akd_to_pai_tool + +# --------------------------------------------------------------------------- +# Joint metaclass +# --------------------------------------------------------------------------- +# Explicit Protocol inheritance (AKDAgent) drags in ``_ProtocolMeta``; +# AKD's config auto-exposure is driven by ``AbstractBaseMeta``. Both subclass +# ``ABCMeta`` but neither subclasses the other, so combining them on a single +# class raises ``TypeError: metaclass conflict``. The small joint subclass +# below resolves the MRO: a PydanticAIBaseAgent subclass can satisfy both. + + +class PydanticAIAgentMeta(_ProtocolMeta, AbstractBaseMeta): + """Joint metaclass: Protocol introspection + AKD config auto-exposure. + + AKD's ``AbstractBaseMeta`` auto-creates a property per config field on + every subclass, which would shadow attributes like pydantic_ai.Agent's + ``system_prompt`` decorator method on every inheritor. The ``SKIP_AUTO_EXPOSE`` + set names fields that must never get an auto-property — after the base + metaclass runs, we restore the inherited attribute from the MRO. + """ + + SKIP_AUTO_EXPOSE: frozenset[str] = frozenset({"system_prompt"}) + + def __new__(mcs, name, bases, dct): + cls = super().__new__(mcs, name, bases, dct) + # Undo auto-exposure for skipped fields: replace any property the + # base metaclass installed with the inherited attribute from an MRO base. + for field_name in mcs.SKIP_AUTO_EXPOSE: + current = cls.__dict__.get(field_name) + if isinstance(current, property): + for base in cls.__mro__[1:]: + if field_name in vars(base): + setattr(cls, field_name, vars(base)[field_name]) + break + return cls + + +# --------------------------------------------------------------------------- +# Config +# --------------------------------------------------------------------------- + + +class PydanticAIBaseAgentConfig(BaseAgentConfig): + """AKD-style config that is also a superset of ``pydantic_ai.Agent`` kwargs. + + Inherits the full ``BaseAgentConfig`` surface (``model_name``, ``system_prompt``, + ``tools``, ``reasoning_effort``, etc.) and adds pydantic_ai-specific fields. + ``extra="allow"`` forwards any additional future pydantic_ai kwargs via + ``model_extra`` without requiring this class to be updated. + """ + + model_config = ConfigDict(extra="allow", arbitrary_types_allowed=True) + + capabilities: list[Any] = Field( + default_factory=list, + description=( + "Pydantic AI capabilities (Thinking, WebSearch, Hooks, custom). " + "Merged with any capabilities auto-derived from AKD scalar fields." + ), + ) + history_processors: list[Any] = Field( + default_factory=list, + description="Pydantic AI history processor callables; merged with config-derived processors.", + ) + + # -- Silence AKD-core's litellm-based config validators -------------- + # BaseAgentConfig defines ``validate_max_tokens_against_model`` and + # ``validate_reasoning_params`` as @model_validator(mode="after") hooks + # that call ``litellm.get_model_info(self.model_name)`` and + # ``litellm.supports_reasoning(model=self.model_name)``. Those lookups + # expect litellm's bare model names (e.g. ``gpt-5.2``), not the + # ``provider:model`` format pydantic_ai requires (e.g. ``openai:gpt-5.2``), + # so they emit misleading ERROR / WARNING logs for every construction. + # pydantic_ai handles model resolution itself, so we override both to no-ops. + + @model_validator(mode="after") + def validate_max_tokens_against_model(self): + return self + + @model_validator(mode="after") + def validate_reasoning_params(self): + return self + + +# --------------------------------------------------------------------------- +# Agent +# --------------------------------------------------------------------------- + + +class PydanticAIBaseAgent[InSchema: InputSchema, OutSchema: OutputSchema]( + PAIAgent, + AKDAgent, + metaclass=PydanticAIAgentMeta, +): + """Pydantic AI-backed agent conforming to the AKD ``AKDAgent`` protocol. + + Subclass this class to build new agents. Subclasses override: + + - ``input_schema`` / ``output_schema`` / ``config_schema`` — class attrs + - ``check_output`` — semantic output validation (optional) + - ``_build_capabilities_from_scalars`` — additional scalar→capability mappings + - ``_build_history_processors_from_scalars`` — additional history processors + - ``_to_prompt`` — custom prompt rendering from ``InputSchema`` + - ``_adapt_tools`` — custom tool adapter logic (rarely needed) + """ + + # Subclasses override these three class attributes. + input_schema: type[InSchema] = InputSchema # type: ignore[assignment] + output_schema: type[OutSchema] = OutputSchema # type: ignore[assignment] + config_schema: type[PydanticAIBaseAgentConfig] = PydanticAIBaseAgentConfig + + # Opt out of metaclass auto-exposure for ``system_prompt``: pydantic_ai's + # ``system_prompt`` is a decorator method used to register dynamic system + # prompts, and we must not shadow it with a config-routing property. + # Re-binding here puts ``system_prompt`` in the class dict, which + # ``AbstractBaseMeta`` treats as "already defined, skip auto-exposure". + system_prompt = PAIAgent.system_prompt + + # ── Construction ────────────────────────────────────────────────────── + + def __init__(self, config: PydanticAIBaseAgentConfig | None = None) -> None: + self.config = config or self.config_schema() + + # Forward-compat: any unknown fields the caller put on the config + # (via ``extra="allow"``) pass straight through to pydantic_ai. + extra_kwargs = dict(self.config.model_extra or {}) + + super().__init__( + model=self.config.model_name, + system_prompt=self.config.system_prompt, + name=self.config.name, + description=self.config.description, + retries=self.config.num_retries, + output_type=self.output_schema, + tools=self._adapt_tools(self.config.tools), + capabilities=[ + *self._build_capabilities_from_scalars(), + *self.config.capabilities, + ], + history_processors=[ + *self._build_history_processors_from_scalars(), + *self.config.history_processors, + ], + **extra_kwargs, + ) + + self._register_akd_output_validator() + + # ── AKD contract: arun / astream ────────────────────────────────────── + + async def arun( + self, + params: InSchema, + run_context: RunContextProtocol | None = None, + **kwargs: Any, + ) -> OutSchema: + """AKD entry point. Bridges ``InputSchema`` → ``pydantic_ai.Agent.run`` → ``OutputSchema``.""" + prompt = self._to_prompt(params) + result = await self.run( + prompt, + deps=self._deps_from_run_context(run_context), + message_history=_message_history_from_run_context(run_context), + usage=_usage_from_run_context(run_context), + **kwargs, + ) + return result.output + + async def astream( + self, + params: InSchema, + run_context: RunContextProtocol | None = None, + **kwargs: Any, + ) -> AsyncIterator[StreamEvent]: + """AKD stream entry point. Translates pydantic_ai events → AKD ``StreamEvent``s. + + Uses ``pydantic_ai.Agent.run_stream_events`` rather than ``self.iter`` + because it is a plain async iterator with no ``async with`` at the + call site. Pydantic_ai handles the anyio-backed task decoupling + internally, so wrapping it in this async generator is safe even when + the consumer cancels mid-stream (e.g. marimo cancelling an in-flight + stream when the user sends a follow-up). Using ``iter()`` here is + unsafe in that setting because its context manager must exit in the + same task that entered it. + + ``run_stream_events`` emits an :class:`AgentRunResultEvent` as its + final event, carrying the run's ``output`` — we unwrap that into + AKD's :class:`CompletedEvent` so callers that rely on the AKD + contract (like ``agent_chat.py``) see the final answer. + """ + # Drop legacy AKD kwargs that pydantic_ai's run_stream_events + # doesn't accept. ``token_batch_size`` was a no-op on the OpenAI SDK + # runner; nothing in pydantic_ai batches events by count. Call sites + # should stop passing it, but we strip defensively for backward compat. + kwargs.pop("token_batch_size", None) + + prompt = self._to_prompt(params) + async for pai_event in self.run_stream_events( + prompt, + deps=self._deps_from_run_context(run_context), + message_history=_message_history_from_run_context(run_context), + usage=_usage_from_run_context(run_context), + **kwargs, + ): + # Terminal result event → emit AKD CompletedEvent with the output. + if isinstance(pai_event, AgentRunResultEvent): + if pai_event.result.output is not None: + yield CompletedEvent( + data=CompletedEventData(output=pai_event.result.output), + ) + continue + akd_event = pai_event_to_akd_event(pai_event) + if akd_event is not None: + yield akd_event + + # ── Prompt rendering ────────────────────────────────────────────────── + + def _to_prompt(self, params: InSchema) -> str: + """Convert an ``InputSchema`` instance to the prompt string pydantic_ai expects. + + Default: pretty-printed JSON dump. Subclasses override for custom + templates or more readable renderings. + """ + return params.model_dump_json(indent=2) + + # ── Run-context helpers ─────────────────────────────────────────────── + + def _deps_from_run_context(self, run_context: RunContextProtocol | None) -> Any: + """Extract pydantic_ai ``deps`` from the run context, if any. + + AKD run contexts don't carry deps. If a caller passes a + ``pydantic_ai.RunContext`` directly, forward its ``deps`` field so + tool authors can use pydantic_ai-native dependency injection. + Subclasses override to inject their own deps construction. + """ + from pydantic_ai import RunContext as PAIRunContext + + if isinstance(run_context, PAIRunContext): + return run_context.deps + return None + + # ── Zone 1: scalar-driven capability / history-processor construction ─ + + def _build_capabilities_from_scalars(self) -> list[AbstractCapability]: + """Derive capabilities from AKD scalar config fields shared by all agents. + + Subclasses override to append their own scalar→capability mappings; call + ``super()._build_capabilities_from_scalars()`` first to inherit the defaults. + """ + caps: list[AbstractCapability] = [] + + if self.config.reasoning_effort: + from pydantic_ai.capabilities import Thinking + + # pydantic_ai's Thinking currently only accepts `effort`. + # AKD's `reasoning_summary` is dormant for now; if pydantic_ai + # adds a summary knob later, expose it here. + caps.append(Thinking(effort=self.config.reasoning_effort)) + + if self.config.max_tool_iterations or self.config.max_tool_calls: + caps.append( + ToolCallLimits( + max_iterations=self.config.max_tool_iterations, + max_calls=self.config.max_tool_calls, + ), + ) + + if self.config.reflection_prompt: + caps.append(ReflectionCapability(prompt=self.config.reflection_prompt)) + + return caps + + def _build_history_processors_from_scalars(self) -> list: + """Derive history processors from AKD scalar config fields. + + Subclasses override to append their own; call ``super()`` first. + """ + procs: list = [] + if self.config.enable_trimming: + procs.append(make_ratio_trimmer(1 - self.config.trim_ratio)) + # ``trim_ratio`` in AKD is the *target retention* ratio (0.75 = keep 75%). + # The trimmer we pass expects the *drop* fraction, hence 1 - ratio. + return procs + + # ── Tool adaptation ────────────────────────────────────────────────── + + def _adapt_tools(self, tools: list) -> list: + """Convert AKD ``BaseTool`` instances to pydantic_ai ``Tool`` objects. + + Native pydantic_ai tools (``pydantic_ai.Tool`` instances, decorated + functions, toolsets, etc.) pass through unchanged. Anything structurally + conforming to ``AKDTool`` gets wrapped via ``akd_to_pai_tool``. + """ + from akd.tools._base import BaseTool + + adapted = [] + for tool in tools: + if isinstance(tool, BaseTool): + adapted.append(akd_to_pai_tool(tool)) + else: + adapted.append(tool) + return adapted + + # ── check_output bridge ────────────────────────────────────────────── + + def _register_akd_output_validator(self) -> None: + """Wire ``self.check_output`` up as a pydantic_ai output validator. + + Runs once at ``__init__`` time. The closure captures ``self`` and + late-binds ``self.check_output``, so subclass overrides are picked + up polymorphically. + """ + + @self.output_validator + def _akd_check(output): + msg = self.check_output(output) + if msg is not None: + raise ModelRetry(msg) + return output + + def check_output(self, output) -> str | None: + """Semantic output validation beyond pydantic_ai's schema enforcement. + + Default: + + - ``TextOutput`` instances pass through untouched (agent is asking a + clarifying question; the outer caller handles the multi-turn loop). + - Structured outputs are rejected if they define ``is_empty()`` and + it returns ``True`` (an empty answer is never a valid terminal + output). The ``hasattr`` guard keeps this backward-compatible with + schemas that don't declare ``is_empty``. + + Return ``None`` to accept; return a string to ask the model for a + retry with that message as the prompt. + """ + if isinstance(output, TextOutput): + return None + is_empty = getattr(output, "is_empty", None) + if callable(is_empty) and is_empty(): + return ( + "Output is empty. Provide a complete structured answer with meaningful content in all required fields." + ) + return None + + +__all__ = [ + "PydanticAIAgentMeta", + "PydanticAIBaseAgent", + "PydanticAIBaseAgentConfig", +] diff --git a/akd_ext/agents/_base_pydantic/_capabilities.py b/akd_ext/agents/_base_pydantic/_capabilities.py new file mode 100644 index 0000000..ab77dda --- /dev/null +++ b/akd_ext/agents/_base_pydantic/_capabilities.py @@ -0,0 +1,135 @@ +"""Custom pydantic_ai Capabilities built from AKD scalar config fields. + +These are the concrete implementations behind ``PydanticAIBaseAgent``'s +``_build_capabilities_from_scalars`` / ``_build_history_processors_from_scalars`` +hooks. + +Factories in this module return ready-to-use pydantic_ai ``Capability`` or +``history_processor`` objects. The base agent constructs them at ``__init__`` +time from the relevant ``BaseAgentConfig`` fields. + +Contents: + +- ``ToolCallLimits(max_iterations=..., max_calls=...)`` — Hooks-based cap on + tool-loop iterations and total tool calls; raises AKD's ``UsageLimitExceeded`` + (which pydantic_ai surfaces via ``UnexpectedModelBehavior`` to the caller). +- ``ReflectionCapability(prompt=...)`` — Hooks-based injector that prepends + ``prompt`` as a system-level nudge on every model request after the first. +- ``make_ratio_trimmer(trim_ratio=...)`` — stateless history processor that + drops the oldest fraction of the conversation when invoked. +""" + +from __future__ import annotations + +from collections.abc import Callable +from typing import Any + +from pydantic_ai.capabilities.hooks import Hooks + +from akd._base.errors import MaxToolCallsExceeded, MaxToolIterationsExceeded + + +def ToolCallLimits( + *, + max_iterations: int | None = None, + max_calls: int | None = None, +) -> Hooks: + """Build a Hooks capability that caps tool-loop iterations and tool calls. + + Counters live in closure state; each agent instance gets its own ``Hooks`` + via ``_build_capabilities_from_scalars``. Counters reset on agent + construction, not per-run — which mirrors AKD's historical ReAct cap + semantics. Callers who need per-run reset should reconstruct the agent. + + Raises: + MaxToolIterationsExceeded: when ``max_iterations`` is set and the + model-request loop would exceed the cap on the next round. + MaxToolCallsExceeded: when ``max_calls`` is set and the total number + of tool executions would exceed the cap. + """ + hooks = Hooks() + state = {"iterations": 0, "calls": 0} + + if max_iterations is not None: + + @hooks.on.before_model_request + async def _cap_iterations(ctx, request_context): # noqa: ARG001 + state["iterations"] += 1 + if state["iterations"] > max_iterations: + raise MaxToolIterationsExceeded( + f"Exceeded max tool iterations ({max_iterations}).", + ) + return request_context + + if max_calls is not None: + + @hooks.on.before_tool_execute + async def _cap_calls(ctx, *, call, tool_def, args): # noqa: ARG001 + state["calls"] += 1 + if state["calls"] > max_calls: + raise MaxToolCallsExceeded( + f"Exceeded max tool calls ({max_calls}).", + ) + return args + + return hooks + + +def ReflectionCapability(*, prompt: str) -> Hooks: + """Build a Hooks capability that injects ``prompt`` before each model request + after the first. + + Implementation: a counter tracks how many model requests have fired; + from the second onwards, we prepend the reflection prompt as a system + instruction to the pending request. The first request is skipped so the + reflection prompt never leaks into the initial turn. + """ + from pydantic_ai.messages import ModelRequest, SystemPromptPart + + hooks = Hooks() + state = {"turn": 0} + + @hooks.on.before_model_request + async def _inject_reflection(ctx, request_context): # noqa: ARG001 + state["turn"] += 1 + if state["turn"] == 1: + return request_context + # Prepend the reflection as a system message on this turn + request_context.messages = [ + ModelRequest(parts=[SystemPromptPart(content=prompt)]), + *request_context.messages, + ] + return request_context + + return hooks + + +def make_ratio_trimmer(trim_ratio: float) -> Callable[[list[Any]], list[Any]]: + """Return a stateless history processor that drops the oldest + ``trim_ratio`` fraction of messages. + + A ``trim_ratio`` of ``0.3`` drops the oldest 30% of the message list on + each call. ``0.0`` is a no-op; ``1.0`` drops everything (don't do that). + The first message (usually the system prompt) is preserved so the agent's + instructions survive trimming. + """ + if not 0 <= trim_ratio < 1: + raise ValueError(f"trim_ratio must be in [0, 1), got {trim_ratio!r}") + + def processor(messages: list[Any]) -> list[Any]: + if not messages or trim_ratio == 0: + return messages + # Keep the first message (usually system), trim the oldest portion + # of the rest, keep the tail. + head, rest = messages[:1], messages[1:] + drop_count = int(len(rest) * trim_ratio) + return [*head, *rest[drop_count:]] + + return processor + + +__all__ = [ + "ReflectionCapability", + "ToolCallLimits", + "make_ratio_trimmer", +] diff --git a/akd_ext/agents/_base_pydantic/_context_adapter.py b/akd_ext/agents/_base_pydantic/_context_adapter.py new file mode 100644 index 0000000..5d707ac --- /dev/null +++ b/akd_ext/agents/_base_pydantic/_context_adapter.py @@ -0,0 +1,106 @@ +"""Helpers that reshape AKD run-context values to what pydantic_ai expects. + +Both ``akd.RunContext`` and ``pydantic_ai.RunContext`` satisfy the +``RunContextProtocol`` structurally for the shared fields, but the types of +``usage`` and ``messages`` differ between the two sides: + +- AKD stores messages as OpenAI-style dicts; pydantic_ai stores typed + ``ModelMessage`` objects. +- AKD has its own ``RunUsage`` Pydantic BaseModel; pydantic_ai has its own + ``RunUsage`` dataclass. + +These helpers detect the incoming shape and convert when necessary, so a +caller can pass *either* an AKD ``RunContext`` or a ``pydantic_ai.RunContext`` +to ``PydanticAIBaseAgent.arun`` / ``.astream`` and get the right thing wired +through to ``super().run(...)``. +""" + +from __future__ import annotations + +from typing import Any + +from pydantic_ai.messages import ( + ModelMessage, + ModelRequest, + ModelResponse, + SystemPromptPart, + TextPart, + UserPromptPart, +) +from pydantic_ai.result import RunUsage as PAIRunUsage + +from ._protocols import SupportsUsage + + +def _akd_dicts_to_pai_messages(messages: list[dict[str, Any]]) -> list[ModelMessage]: + """Convert AKD OpenAI-style message dicts to pydantic_ai ``ModelMessage`` objects. + + Each dict is expected to have ``{"role": "...", "content": "..."}``. + + - ``system`` / ``user`` / ``tool`` → ``ModelRequest`` with the matching part + - ``assistant`` → ``ModelResponse`` with a ``TextPart`` + """ + out: list[ModelMessage] = [] + for msg in messages: + role = msg.get("role") + content = msg.get("content", "") + if role == "system": + out.append(ModelRequest(parts=[SystemPromptPart(content=content)])) + elif role == "user": + out.append(ModelRequest(parts=[UserPromptPart(content=content)])) + elif role == "assistant": + out.append(ModelResponse(parts=[TextPart(content=content)])) + # Roles we don't know how to translate (e.g. "tool" returns) are + # skipped silently; the caller is expected to pass history we can + # faithfully represent. Extending this map is a local change. + return out + + +def _usage_to_pai(usage: SupportsUsage) -> PAIRunUsage: + """Build a ``pydantic_ai.RunUsage`` from anything matching ``SupportsUsage``. + + Pass-through if the value is already a ``pydantic_ai.RunUsage``. Otherwise + copy the three structural fields; all other ``RunUsage`` fields default to 0. + """ + if isinstance(usage, PAIRunUsage): + return usage + return PAIRunUsage( + input_tokens=usage.input_tokens, + output_tokens=usage.output_tokens, + requests=usage.requests, + ) + + +def _message_history_from_run_context(run_context: Any | None) -> list[ModelMessage] | None: + """Extract a pydantic_ai-shaped message history from any ``RunContextProtocol``. + + Returns ``None`` if the context has no messages. + + Detects whether the incoming ``messages`` are dicts (AKD) or already + ``ModelMessage`` instances (pydantic_ai) and adapts accordingly. + """ + if run_context is None or not getattr(run_context, "messages", None): + return None + msgs = run_context.messages + first = msgs[0] + if isinstance(first, dict): + return _akd_dicts_to_pai_messages(msgs) + return msgs # already pydantic_ai ModelMessage objects + + +def _usage_from_run_context(run_context: Any | None) -> PAIRunUsage | None: + """Extract a pydantic_ai ``RunUsage`` from any ``RunContextProtocol``.""" + if run_context is None: + return None + usage = getattr(run_context, "usage", None) + if usage is None: + return None + return _usage_to_pai(usage) + + +__all__ = [ + "_akd_dicts_to_pai_messages", + "_message_history_from_run_context", + "_usage_from_run_context", + "_usage_to_pai", +] diff --git a/akd_ext/agents/_base_pydantic/_event_translator.py b/akd_ext/agents/_base_pydantic/_event_translator.py new file mode 100644 index 0000000..ccc047c --- /dev/null +++ b/akd_ext/agents/_base_pydantic/_event_translator.py @@ -0,0 +1,124 @@ +"""Translate pydantic_ai stream events into AKD ``StreamEvent`` objects. + +``astream`` on ``PydanticAIBaseAgent`` wraps ``pydantic_ai.Agent.run_stream_events()`` +and yields whatever we can translate. Events we can't faithfully map are +dropped (return ``None``); the caller iterator still terminates naturally when +the run completes, which is the AKD signal for end-of-stream anyway. + +Translation table: + +| pydantic_ai event | AKD event | +|--------------------------------------------------|-----------------------| +| ``PartDeltaEvent(delta=TextPartDelta)`` | ``StreamingTokenEvent`` | +| ``PartStartEvent(part=ThinkingPart)`` | ``ThinkingEvent`` | +| ``PartDeltaEvent(delta=ThinkingPartDelta)`` | ``ThinkingEvent`` | +| ``FunctionToolCallEvent`` / ``BuiltinToolCallEvent`` | ``ToolCallingEvent`` | +| ``FunctionToolResultEvent`` / ``BuiltinToolResultEvent`` | ``ToolResultEvent`` | +| ``FinalResultEvent`` | (dropped; iterator end signals completion) | +| ``PartStartEvent(part=TextPart)``, ``PartEndEvent`` | (dropped; no AKD analogue) | + +A future ``CompletedEvent`` emission with the actual output value is the +responsibility of ``astream`` itself (after the iterator exhausts), not this +function — this translator is per-event. +""" + +from __future__ import annotations + +from pydantic_ai.messages import ( + BuiltinToolCallEvent, + BuiltinToolResultEvent, + FunctionToolCallEvent, + FunctionToolResultEvent, + PartDeltaEvent, + PartStartEvent, + TextPartDelta, + ThinkingPart, + ThinkingPartDelta, + ToolCallPart, + ToolCallPartDelta, + ToolReturnPart, +) + +from akd._base import ( + StreamEvent, + StreamingEventData, + StreamingTokenEvent, + ThinkingEvent, + ThinkingEventData, + ToolCall, + ToolCallingEvent, + ToolCallingEventData, + ToolResult, + ToolResultEvent, + ToolResultEventData, +) + + +def pai_event_to_akd_event(pai_event) -> StreamEvent | None: + """Translate a single pydantic_ai stream event to an AKD ``StreamEvent``. + + Returns ``None`` for events we don't map; callers should skip those. + """ + # Text delta → streaming token + if isinstance(pai_event, PartDeltaEvent): + delta = pai_event.delta + if isinstance(delta, TextPartDelta): + token = getattr(delta, "content_delta", "") or "" + return StreamingTokenEvent(data=StreamingEventData(token=token)) + if isinstance(delta, ThinkingPartDelta): + thinking = getattr(delta, "content_delta", "") or "" + return ThinkingEvent(data=ThinkingEventData(thinking_content=thinking, streaming=True)) + if isinstance(delta, ToolCallPartDelta): + # Structured-output runs stream the args JSON of the final-output + # tool call. Surface each chunk as a streaming token so UIs see + # the model assembling its answer rather than nothing. + args_delta = getattr(delta, "args_delta", None) or "" + if isinstance(args_delta, dict): + args_delta = str(args_delta) + return StreamingTokenEvent(data=StreamingEventData(token=args_delta)) + return None + + # Thinking part started → emit a (non-streaming) thinking marker + if isinstance(pai_event, PartStartEvent): + part = pai_event.part + if isinstance(part, ThinkingPart): + content = getattr(part, "content", "") or "" + return ThinkingEvent(data=ThinkingEventData(thinking_content=content)) + # TextPart start and other part kinds don't have a direct AKD analogue + return None + + # Function tool call + if isinstance(pai_event, (FunctionToolCallEvent, BuiltinToolCallEvent)): + part = pai_event.part + if isinstance(part, ToolCallPart): + return ToolCallingEvent( + data=ToolCallingEventData( + tool_call=ToolCall( + tool_call_id=part.tool_call_id or "", + tool_name=part.tool_name, + arguments=part.args_as_dict() if hasattr(part, "args_as_dict") else (part.args or {}), + ), + ), + ) + return None + + # Function tool result + if isinstance(pai_event, (FunctionToolResultEvent, BuiltinToolResultEvent)): + result = pai_event.result + if isinstance(result, ToolReturnPart): + return ToolResultEvent( + data=ToolResultEventData( + result=ToolResult( + tool_call_id=result.tool_call_id or "", + tool_name=result.tool_name, + content=result.content, + ), + ), + ) + # RetryPromptPart results (tool retries) don't map to a success event + return None + + return None + + +__all__ = ["pai_event_to_akd_event"] diff --git a/akd_ext/agents/_base_pydantic/_protocols.py b/akd_ext/agents/_base_pydantic/_protocols.py new file mode 100644 index 0000000..e7014f0 --- /dev/null +++ b/akd_ext/agents/_base_pydantic/_protocols.py @@ -0,0 +1,101 @@ +"""Local structural Protocol stand-ins. + +Mirrors the akd-core protocol hierarchy that the Accelerated Discovery team +is preparing to ship in ``akd._base.structures``. Keeping a local copy now +decouples this work from that timeline; the subpackage swaps to the akd-core +imports (or deletes this module and re-exports from akd-core) once it lands. + +The structural hierarchy: + +- ``SupportsUsage`` — token-counter shape (AKD ``RunUsage`` and pydantic_ai ``RunUsage`` both satisfy) +- ``RunContextProtocol`` — run-context shape (AKD ``RunContext`` and pydantic_ai ``RunContext`` both satisfy) +- ``AKDExecutable`` — base contract: schemas + ``arun`` (shared by agents and tools) +- ``AKDAgent`` — ``AKDExecutable`` + ``astream`` +- ``AKDTool`` — ``AKDExecutable`` + ``name``/``description`` + conversion helpers +""" + +from __future__ import annotations + +from collections.abc import AsyncIterator, Awaitable, Callable +from typing import Any, Protocol, runtime_checkable + + +@runtime_checkable +class SupportsUsage(Protocol): + """Structural shape for usage / token-counter objects. + + Both ``akd._base.structures.RunUsage`` (Pydantic BaseModel) and + ``pydantic_ai.RunUsage`` (dataclass) satisfy this structurally. + """ + + input_tokens: int + output_tokens: int + requests: int + + +@runtime_checkable +class RunContextProtocol(Protocol): + """Structural supertype for run-context-like objects. + + Both ``akd._base.structures.RunContext`` and ``pydantic_ai.RunContext`` + satisfy this structurally for the shared fields below. For AKD-specific + fields (``human_response``, ``extra``) or pydantic_ai-specific fields + (``deps``, ``tool_call_id``, etc.), narrow to the concrete type at the + call site via ``isinstance`` check. + """ + + messages: list[Any] | None + usage: SupportsUsage + run_id: str | None + + +@runtime_checkable +class AKDExecutable(Protocol): + """Base contract: schemas + ``arun``. Shared by agents and tools.""" + + input_schema: type[Any] + output_schema: type[Any] + config_schema: type[Any] | None + + async def arun( + self, + params: Any, + run_context: RunContextProtocol | None = ..., + **kwargs: Any, + ) -> Any: ... + + +@runtime_checkable +class AKDAgent(AKDExecutable, Protocol): + """Agent contract: ``AKDExecutable`` + ``astream``.""" + + def astream( + self, + params: Any, + run_context: RunContextProtocol | None = ..., + **kwargs: Any, + ) -> AsyncIterator[Any]: ... + + +@runtime_checkable +class AKDTool(AKDExecutable, Protocol): + """Tool contract: ``AKDExecutable`` + naming and conversion helpers.""" + + name: str + description: str + + def as_function( + self, + mode: str | None = ..., + ) -> Callable[..., Awaitable[Any]]: ... + + def as_tool_definition(self) -> dict[str, Any]: ... + + +__all__ = [ + "AKDAgent", + "AKDExecutable", + "AKDTool", + "RunContextProtocol", + "SupportsUsage", +] diff --git a/akd_ext/agents/_base_pydantic/_tool_adapter.py b/akd_ext/agents/_base_pydantic/_tool_adapter.py new file mode 100644 index 0000000..0a88ef5 --- /dev/null +++ b/akd_ext/agents/_base_pydantic/_tool_adapter.py @@ -0,0 +1,54 @@ +"""Adapter from AKD ``BaseTool``-protocol instances to ``pydantic_ai.Tool``. + +``BaseTool.as_function()`` already returns a typed async callable with the +right signature metadata for framework introspection. We only need to: + +1. Wrap it so ``pydantic.ValidationError`` / ``akd.SchemaValidationError`` + become ``pydantic_ai.ModelRetry`` (so the LLM can self-correct on bad + tool-call args); all other exceptions propagate and halt the run per + pydantic_ai conventions. +2. Preserve the signature metadata that pydantic_ai introspects to build + the tool's JSON schema. +3. Hand the callable to ``pydantic_ai.Tool`` with name/description pulled + from the AKD tool. + +``sequential``, ``timeout``, and per-tool ``max_retries`` stay at their +pydantic_ai defaults. AKD tools are stateless by design so parallel +execution is safe; per-tool knobs can arrive later via a dedicated +``PydanticAIBaseTool(BaseTool)`` that extends ``BaseToolConfig``. +""" + +from __future__ import annotations + +from pydantic import ValidationError +from pydantic_ai import ModelRetry +from pydantic_ai import Tool as PAITool + +from akd._base.errors import SchemaValidationError + +from ._protocols import AKDTool + + +def akd_to_pai_tool(akd_tool: AKDTool) -> PAITool: + """Adapt an AKD-protocol tool into a ``pydantic_ai.Tool``.""" + raw_fn = akd_tool.as_function() + + async def wrapped(*args, **kwargs): + try: + return await raw_fn(*args, **kwargs) + except (ValidationError, SchemaValidationError) as e: + raise ModelRetry(str(e)) from e + + wrapped.__name__ = raw_fn.__name__ + wrapped.__doc__ = raw_fn.__doc__ + wrapped.__signature__ = raw_fn.__signature__ + wrapped.__annotations__ = raw_fn.__annotations__ + + return PAITool( + wrapped, + name=akd_tool.name, + description=akd_tool.description or akd_tool.__class__.__doc__, + ) + + +__all__ = ["akd_to_pai_tool"] diff --git a/akd_ext/agents/cmr_care_pydantic.py b/akd_ext/agents/cmr_care_pydantic.py new file mode 100644 index 0000000..3ff9835 --- /dev/null +++ b/akd_ext/agents/cmr_care_pydantic.py @@ -0,0 +1,468 @@ +"""CMR CARE Pydantic Agent — NASA CMR dataset discovery on ``PydanticAIBaseAgent``. + +Parallel to :mod:`akd_ext.agents.cmr_care` (which is built on ``OpenAIBaseAgent``), +this module rebuilds the same agent on top of ``PydanticAIBaseAgent`` so the +new base class can be validated against a real-shaped AKD agent. Input/output +schemas are reused from :mod:`akd_ext.agents.cmr_care` to keep parity. + +The ``system_prompt`` is a **placeholder** — replace +``CMR_CARE_PYDANTIC_SYSTEM_PROMPT`` below with the real CARE prompt before +running against a live model. +""" + +from __future__ import annotations + +from typing import Literal + +from pydantic import Field + +from akd._base import TextOutput + +from akd_ext.agents._base_pydantic import ( + PydanticAIBaseAgent, + PydanticAIBaseAgentConfig, +) +from akd_ext.agents.cmr_care import ( + CMRCareAgentInputSchema, + CMRCareAgentOutputSchema, +) + +from loguru import logger + +# ----------------------------------------------------------------------------- +# System Prompt +# ----------------------------------------------------------------------------- +# PLACEHOLDER — paste the real CMR CARE system prompt here before running +# this agent against a live model. The parity tests use ``TestModel`` so they +# don't depend on the prompt content. + +CMR_CARE_PYDANTIC_SYSTEM_PROMPT = """ROLE + You are the NASA Earthdata / CMR Scientific Data Discovery Agent. + You are a non-decision-making, human-in-the-loop scientific data discovery assistant whose sole function is to help users discover, organize, and understand NASA Earthdata CMR datasets relevant to Earth science questions. + You are not a scientific authority, analyst, or recommender. + + OBJECTIVE + Enable transparent, reproducible, and user-controlled discovery and ranking of NASA Earthdata (CMR) datasets that may answer an Earth science question, including indirect (multi-hop) discovery when direct datasets are insufficient. + Your success criteria are: + Scientific relevance is reflected only through metadata + All assumptions are surfaced and confirmed by the user + Users clearly understand why datasets appear + No dataset is selected, endorsed, or judged for suitability + + CONTEXT & INPUTS + You operate only within Earth science domains: + Atmosphere + Ocean + Land + Cryosphere + Biosphere + Solid Earth + You accept: + A free-text science question + An explicitly selected user expertise level (Intermediate / Advanced) + You may use only the following tools and data sources along with the attached context documents:  + NASA CMR Search API (REST) — collection discovery only + GCMD Keyword Management System (KMS) — vocabulary mapping only + Semantic Scholar API — optional, user-approved indirect discovery only  + Google Scholar as a last resort. + Earthdata Search Web App — link handoff only (no API calls) + + CONSTRAINTS & STYLE RULES + Non-Negotiable Guardrails You must never: + Recommend, select, or endorse datasets + Claim quality, accuracy, uncertainty, or suitability + Draw conclusions, trends, causality, or implications + Infer or fabricate missing metadata + Automate spatial, temporal, or variable assumptions + Execute searches without explicit user confirmation + Perform downloads or request credentials + Operate outside Earth science + All missing or ambiguous metadata must be treated as unknown. + All indirect (multi-hop) inference requires explicit user approval. + + PROCESS + You must follow this canonical reasoning loop exactly: + Primary Loop (Direct Discovery First) + Interpret the user query into: + Phenomenon + Explicit variables + Expand scientific synonyms (candidate terms only) + Clarify (blocking): + Variables + Spatial bounds + Temporal bounds + Indirect inference permission (if needed) + Map terms → GCMD keywords + Translate GCMD concepts → CMR API parameters + Search CMR Collections (retrieve multiple candidates) + Rank datasets: + Primary: metadata relevance + Secondary: usage (tie-breaker only) + Explain relevance and gaps (no recommendations) + Conditional Multi-Hop Loop (Only If Needed) + Detect gaps in direct results + Identify indirect variables + Search Semantic Scholar (rate-limited) + Exclude variables that cannot map to GCMD + Obtain explicit user approval + Re-run the entire loop + If scope is non-Earth science → respond "I'm sorry, this query falls outside my area of expertise in Earth science data discovery. I'm unable to assist with this request." and stop. + + + OUTPUT FORMAT + All responses must follow this structure exactly. No free-form text is allowed outside these sections. + 1. Clarifying Questions + You must ask clarifying questions to the user to reduce ambiquity in search. + 2. Interpreted Scope + Restate user intent without inference + Separate confirmed inputs vs unresolved ambiguities + List phenomenon, variables, spatial & temporal bounds + 3. Curated / Ranked CMR Dataset List + For each dataset (CMR only), include: + Short Name + CMR Concept ID + Variables (verbatim) + Temporal Coverage + Spatial Coverage + ProcessingLevelId + Explicitly listed missing or ambiguous metadata + Ranking reflects metadata relevance only. + 4. Search Reproducibility Log + CMR endpoints used + Query parameters + GCMD mappings + Paging behavior + Ranking logic + UTC timestamps + 5. Fact-Check / User Verification List + Items the user must confirm manually + Variable definitions, QA flags, caveats + Documentation links only + No interpretation + + + CONDITIONAL SECTIONS + Tabular Summary → only if ≥2 datasets + JSON Audit Block → only if datasets returned (pure JSON, null for missing fields, no inference) + + + STOP / DEGRADED OUTPUT + If blocked due to missing inputs, ambiguity, or tool failure, output only: + “Here’s what I cannot determine and what I need from you.” + Then list: + What cannot be determined + Why + Exact user action required + Stop immediately. + + ADDITIONAL CONTEXT : + + # CMR Search API Documentation + + ## Overview + + The Common Metadata Repository (CMR) Search API provides access to NASA Earth science metadata, enabling programmatic discovery and retrieval of collections, granules, and related concepts. This REST-based API supports multiple search parameters, result formats, and authentication methods. + + **Base URL**: `https://cmr.earthdata.nasa.gov/search/` + + ## Key Concepts & Terminology + + ### Core CMR Concepts + - **Collection**: A grouping of related data files or granules, representing a dataset + - **Granule**: Individual data files within a collection (e.g., a single HDF file) + - **Concept ID**: Unique identifier for CMR concepts in format `-` + - Collection concept IDs start with "C" (e.g., `C123456-LPDAAC_ECS`) + - Granule concept IDs start with "G" (e.g., `G123456-LPDAAC_ECS`) + - **Provider**: Data center or organization that hosts the data (e.g., LPDAAC_ECS, NSIDC_ECS) + - **Instrument**: Sensor that collected the data (e.g., MODIS, VIIRS, ASTER) + - **Platform**: Satellite or aircraft carrying the instrument (e.g., Terra, Aqua, Landsat-8) + - **Dataset**: Another term for collection, representing a coherent set of data + + ### Metadata Standards + - **UMM** (Unified Metadata Model): NASA's standard for Earth science metadata + - **ECHO**: Legacy metadata format (Earth Observing System Clearinghouse) + - **DIF** (Directory Interchange Format): GCMD metadata format + - **STAC** (Spatio-Temporal Asset Catalog): Modern geospatial metadata standard + + ## API Endpoints + + ### Primary Search Endpoints + - **Collections**: `/search/collections` - Search for datasets/collections + - **Granules**: `/search/granules` - Search for individual data files + - **Variables**: `/search/variables` - Search for science variables + - **Services**: `/search/services` - Search for data services + - **Tools**: `/search/tools` - Search for analysis tools + + ### Utility Endpoints + - **Autocomplete**: `/search/autocomplete` - Get search suggestions + - **Facets**: Access faceted search capabilities + + ## Authentication + + ### Token Types + 1. **EDL Bearer Token**: Earth Data Login token + 2. **Launchpad Token**: Legacy authentication system + + ### Authentication Methods + - **Authorization Header**: `Authorization: Bearer ` + - **Token Parameter**: `?token=` in URL + + ### Example + ```bash + # Using Authorization header + curl -H "Authorization: Bearer YOUR_TOKEN" "https://cmr.earthdata.nasa.gov/search/collections" + + # Using token parameter + curl "https://cmr.earthdata.nasa.gov/search/collections?token=YOUR_TOKEN" + ``` + + ## Request Parameters + + ### Common Parameters + - `page_size`: Number of results per page (default: 10, max: 2000) + - `page_num`: Page number to return (1-based) + - `sort_key`: Field(s) to sort results by + - `concept_id`: Search by unique identifier + - `provider`: Filter by data provider + - `token`: Authentication token + + ### Collection Search Parameters + - `keyword`: Text search across collection metadata + - `short_name`: Collection short name + - `version`: Collection version + - `temporal`: Temporal range in format `YYYY-MM-DDTHH:mm:ssZ,YYYY-MM-DDTHH:mm:ssZ` + - `platform`: Platform/satellite name + - `instrument`: Instrument name + - `science_keywords`: Science keyword hierarchy + - `project`: Project or mission name + - `processing_level`: Data processing level (L0, L1A, L1B, L2, L3, L4) + - `data_center`: Data center name + - `archive_center`: Archive center name + - `spatial`: Spatial search parameters + + ### Granule Search Parameters + - `collection_concept_id`: Filter granules by collection + - `temporal`: Temporal range for granule search + - `bounding_box`: Spatial bounding box `[west,south,east,north]` + - `point`: Point search `[longitude,latitude]` + - `polygon`: Polygon search (WKT format) + - `producer_granule_id`: Producer-assigned granule ID + - `online_only`: Return only online-accessible granules + - `downloadable`: Return only downloadable granules + - `cloud_cover`: Cloud cover percentage range + + ### Advanced Parameters + - `options[case_sensitive]`: Case-sensitive search (true/false) + - `options[pattern]`: Enable pattern matching (true/false) + - `options[ignore_case]`: Ignore case in search (true/false) + - `options[and]`: AND logic for multiple values (true/false) + + ## Response Formats + + ### Supported Formats + - **JSON**: Default format, comprehensive metadata + - **XML**: Various XML schemas available + - **ATOM**: XML feed format + - **CSV**: Comma-separated values + - **KML**: Keyhole Markup Language for mapping + - **STAC**: Spatio-Temporal Asset Catalog + - **UMM JSON**: Unified Metadata Model JSON + + ### Format Selection + - **Accept Header**: `Accept: application/json` + - **Extension**: `.json`, `.xml`, `.atom`, `.csv`, `.kml`, `.stac` + - **Format Parameter**: `?format=json` + + ### Example Response Structure (JSON) + ```json + { + "hits": 1234, + "took": 45, + "items": [ + { + "concept_id": "C123456-LPDAAC_ECS", + "revision_id": 1, + "provider_id": "LPDAAC_ECS", + "short_name": "MOD09A1", + "version_id": "6.1", + "meta": { + "concept_type": "collection", + "native_id": "MOD09A1_V6.1", + "provider_id": "LPDAAC_ECS" + }, + "umm": { + "EntryTitle": "MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V061", + "ShortName": "MOD09A1", + "Version": "6.1", + "DataDates": [ + { + "Date": "2000-02-18T00:00:00.000Z", + "Type": "CREATE" + } + ], + "Platforms": [ + { + "ShortName": "Terra", + "LongName": "Earth Observing System, Terra" + } + ] + } + } + ] + } + ``` + + ## Rate Limiting & Performance + + ### Limits + - **Request Timeout**: 180 seconds maximum + - **Query Timeout**: 170 seconds internal timeout + - **Rate Limiting**: 429 status code with `retry-after` header + - **URL Length**: ~6,000 characters maximum + + ### Optimization Tips + - Use `page_size` for pagination instead of large single requests + - Implement exponential backoff for rate limit responses + - Use specific search parameters to reduce result sets + - Consider using scroll/search-after for large result sets + + ## Error Handling + + ### Common HTTP Status Codes + - **200**: Success + - **400**: Bad Request (invalid parameters) + - **401**: Unauthorized (authentication required) + - **403**: Forbidden (insufficient permissions) + - **404**: Not Found + - **429**: Too Many Requests (rate limited) + - **500**: Internal Server Error + + ### Error Response Format + ```json + { + "errors": [ + { + "code": "INVALID_PARAMETER", + "message": "Parameter 'temporal' is not valid" + } + ] + } + ``` + + ## Common Usage Patterns + + ### 1. Find Collections by Keyword + ```bash + GET /search/collections?keyword=temperature&provider=NSIDC_ECS + ``` + + ### 2. Get Granules for a Specific Collection + ```bash + GET /search/granules?collection_concept_id=C123456-LPDAAC_ECS&temporal=2023-01-01T00:00:00Z,2023-12-31T23:59:59Z + ``` + + ### 3. Spatial Search + ```bash + GET /search/collections?bounding_box=-180,-90,180,90&platform=Terra + ``` + + ### 4. Paginated Results + ```bash + GET /search/collections?page_size=50&page_num=2&sort_key=short_name + ``` + + ## Integration Notes for MCP Server + + ### Typical Workflow + 1. **Collection Discovery**: Search collections using keywords, platform, or instrument + 2. **Collection Selection**: Choose appropriate collection based on metadata + 3. **Granule Search**: Find granules within selected collection using temporal/spatial filters + 4. **Data Access**: Use granule metadata to access actual data files + + ### Key Fields for MCP Integration + - **Collection**: `concept_id`, `short_name`, `version_id`, `entry_title` + - **Granule**: `concept_id`, `producer_granule_id`, `online_access_urls` + - **Temporal**: `temporal_extent`, `temporal` + - **Spatial**: `bounding_box`, `polygons` + + ### Authentication Considerations + - EDL tokens are preferred for new integrations + - Tokens should be securely stored and refreshed as needed + - Consider implementing token validation before API calls + + ## Additional Resources + + - **CMR Documentation**: https://cmr.earthdata.nasa.gov/search/site/docs/search/api.html + - **UMM Specification**: https://earthdata.nasa.gov/eosdis/science-system-description/eosdis-components/common-metadata-repository +""" + + +# ----------------------------------------------------------------------------- +# Configuration +# ----------------------------------------------------------------------------- + + +class CMRCarePydanticConfig(PydanticAIBaseAgentConfig): + """Config for ``CMRCarePydanticAgent``. + + Mirrors ``CMRCareConfig`` from ``cmr_care.py`` but points at the Pydantic AI + base class. Tools / MCP wiring is left out of the default — add a + ``pydantic_ai.capabilities.MCP(...)`` entry to ``capabilities=`` when you + want the agent to hit the real CMR MCP endpoint. + """ + + description: str = Field( + default=( + "Earth science dataset discovery agent using NASA's Common Metadata Repository (CMR). " + "Helps users discover, organize, and understand NASA Earthdata datasets across atmosphere, " + "ocean, land, cryosphere, biosphere, and solid earth domains. " + "Outputs are delivered via a structured schema and interactive chat with the user " + "for clarification, guidance, approval gates, or status updates." + ), + ) + system_prompt: str = Field(default=CMR_CARE_PYDANTIC_SYSTEM_PROMPT) + model_name: str = Field(default="openai:gpt-5.2") + reasoning_effort: Literal["low", "medium", "high"] | None = Field(default="medium") + + +# ----------------------------------------------------------------------------- +# Agent +# ----------------------------------------------------------------------------- + + +class CMRCarePydanticAgent(PydanticAIBaseAgent[CMRCareAgentInputSchema, CMRCareAgentOutputSchema]): + """Earth Science Data Search Agent on ``PydanticAIBaseAgent``. + + Parity build of ``CMRCareAgent`` on the new Pydantic AI base class; uses + the same input/output schemas so downstream callers can swap between the + two implementations without changing their contract. + """ + + input_schema = CMRCareAgentInputSchema + output_schema = CMRCareAgentOutputSchema | TextOutput + config_schema = CMRCarePydanticConfig + + def check_output(self, output) -> str | None: + if isinstance(output, CMRCareAgentOutputSchema) and not output.result.strip(): + return "Result is empty. Provide search reasoning and details." + return super().check_output(output) + + +__all__ = [ + "CMR_CARE_PYDANTIC_SYSTEM_PROMPT", + "CMRCarePydanticAgent", + "CMRCarePydanticConfig", +] + +if __name__ == "__main__": + import asyncio + + async def main(): + agent = CMRCarePydanticAgent(CMRCarePydanticConfig(debug=True)) + logger.info(f"Agent description: {agent.description}") + question = "Can you find me datasets about sea ice?" + + async for event in agent.astream(CMRCareAgentInputSchema(query=question)): + logger.info(event) + + asyncio.run(main()) diff --git a/pyproject.toml b/pyproject.toml index f42950e..44a073a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,6 +20,7 @@ dependencies = [ "akd @ git+https://github.com/NASA-IMPACT/accelerated-discovery.git@develop", "fastmcp>=2.0.0", "openai-agents>=0.6.7", + "pydantic-ai>=0.2.0", "PyGithub>=2.1.1", ] @@ -85,6 +86,7 @@ quote-style = "double" [dependency-groups] dev = [ + "marimo>=0.23.1", "pytest>=9.0.2", "pytest-asyncio>=1.3.0", "pytest-cov>=7.0.0", diff --git a/tests/agents/test_base_pydantic.py b/tests/agents/test_base_pydantic.py new file mode 100644 index 0000000..f75dcbf --- /dev/null +++ b/tests/agents/test_base_pydantic.py @@ -0,0 +1,324 @@ +"""Tests for ``PydanticAIBaseAgent`` and its supporting subpackage. + +Uses pydantic_ai's ``TestModel`` for deterministic behavior instead of +touching real model providers. Existing agents (``CMRCareAgent`` et al.) +are deliberately untouched here — this suite exercises a dedicated test +agent built on top of ``PydanticAIBaseAgent`` instead. +""" + +from __future__ import annotations + +import pytest +from pydantic import Field +from pydantic_ai import ModelRetry +from pydantic_ai.models.test import TestModel + +from akd._base import InputSchema, OutputSchema, TextOutput +from akd._base.errors import SchemaValidationError +from akd.tools._base import BaseTool + +from akd_ext.agents._base_pydantic import PydanticAIBaseAgent, PydanticAIBaseAgentConfig +from akd_ext.agents._base_pydantic._protocols import ( + AKDAgent, + AKDTool, + RunContextProtocol, + SupportsUsage, +) +from akd_ext.agents._base_pydantic._tool_adapter import akd_to_pai_tool + + +# --------------------------------------------------------------------------- +# Fixtures: a small test agent exercising the base class's surface +# --------------------------------------------------------------------------- + + +class _EchoInput(InputSchema): + """Input schema for the echo test agent.""" + + query: str = Field(..., description="Free-form query text") + + +class _EchoOutput(OutputSchema): + """Output schema for the echo test agent.""" + + answer: str = Field(..., description="Echoed answer from the agent") + + def is_empty(self) -> bool: + return not self.answer.strip() + + +class _EchoConfig(PydanticAIBaseAgentConfig): + """Config for the echo test agent; adds one subclass-specific scalar.""" + + enable_extra_capability: bool = Field(default=False) + + +class _EchoAgent(PydanticAIBaseAgent[_EchoInput, _EchoOutput]): + """Minimal test agent exercising the base class's feature surface.""" + + input_schema = _EchoInput + output_schema = _EchoOutput | TextOutput + config_schema = _EchoConfig + + +# --------------------------------------------------------------------------- +# Structural tests (no model interaction) +# --------------------------------------------------------------------------- + + +def test_agent_instantiates_with_defaults(): + agent = _EchoAgent() + assert isinstance(agent.config, _EchoConfig) + assert agent.input_schema is _EchoInput + + +def test_agent_instantiates_with_custom_config(): + cfg = _EchoConfig(model_name="test", description="Echo agent for tests") + agent = _EchoAgent(cfg) + assert agent.config is cfg + + +def test_metaclass_auto_exposes_config_fields(): + """agent.model_name / agent.description route to self.config.* without hand-written properties.""" + cfg = _EchoConfig(model_name="test", description="hello") + agent = _EchoAgent(cfg) + assert agent.model_name == "test" + assert agent.description == "hello" + + +def test_system_prompt_not_shadowed_by_auto_exposure(): + """pydantic_ai's ``system_prompt`` decorator method must still work.""" + agent = _EchoAgent() + # Must be a callable, not a string + assert callable(agent.system_prompt) + # Must come from pydantic_ai.Agent, not a property descriptor + from pydantic_ai import Agent as PAIAgent + + assert _EchoAgent.system_prompt is PAIAgent.system_prompt + + +def test_agent_is_runtime_akd_agent(): + """Explicit Protocol inheritance means isinstance(agent, AKDAgent) works.""" + agent = _EchoAgent() + assert isinstance(agent, AKDAgent) + + +def test_config_is_runtime_run_context_protocol(): + """Sanity check: SupportsUsage and RunContextProtocol are importable and checkable.""" + # Concrete instances satisfy these structurally in practice; here we just + # confirm the symbols exist and are runtime-checkable. + assert hasattr(SupportsUsage, "_is_runtime_protocol") + assert hasattr(RunContextProtocol, "_is_runtime_protocol") + + +# --------------------------------------------------------------------------- +# check_output bridge +# --------------------------------------------------------------------------- + + +def test_check_output_passes_through_text_output(): + agent = _EchoAgent() + assert agent.check_output(TextOutput(content="Ask the user?")) is None + + +def test_check_output_rejects_empty_structured_output(): + agent = _EchoAgent() + msg = agent.check_output(_EchoOutput(answer=" ")) + assert isinstance(msg, str) + assert "empty" in msg.lower() + + +def test_check_output_accepts_non_empty_structured_output(): + agent = _EchoAgent() + assert agent.check_output(_EchoOutput(answer="real answer")) is None + + +# --------------------------------------------------------------------------- +# arun / astream with TestModel (no external calls) +# --------------------------------------------------------------------------- + + +@pytest.mark.asyncio +async def test_arun_returns_output_schema(): + agent = _EchoAgent() + with agent.override(model=TestModel()): + result = await agent.arun(_EchoInput(query="hello")) + # TestModel produces a best-effort structured output matching the schema + assert isinstance(result, (_EchoOutput, TextOutput)) + + +@pytest.mark.asyncio +async def test_astream_yields_akd_stream_events(): + agent = _EchoAgent() + from akd._base import StreamEvent + + with agent.override(model=TestModel()): + events = [ev async for ev in agent.astream(_EchoInput(query="hello"))] + # Every yielded event must be an AKD StreamEvent (or subclass thereof); may be empty + # with TestModel depending on its stream behavior, but nothing foreign should appear. + assert all(isinstance(ev, StreamEvent) for ev in events) + + +@pytest.mark.asyncio +async def test_astream_emits_completed_event_with_output(): + """astream must emit a terminal CompletedEvent carrying the final output + so callers matching the AKD contract (e.g. agent_chat.py) see the answer.""" + from akd._base import CompletedEvent + + agent = _EchoAgent() + with agent.override(model=TestModel()): + events = [ev async for ev in agent.astream(_EchoInput(query="hello"))] + + # At least one CompletedEvent, and the last event must carry the output. + completed = [ev for ev in events if isinstance(ev, CompletedEvent)] + assert completed, "astream should emit a CompletedEvent when the run finishes" + assert isinstance(events[-1], CompletedEvent) + assert completed[-1].data.output is not None + + +# --------------------------------------------------------------------------- +# Tool adapter +# --------------------------------------------------------------------------- + + +class _GreetInput(InputSchema): + """Input schema for the greet tool.""" + + name: str = Field(..., description="Name to greet") + + +class _GreetOutput(OutputSchema): + """Output schema for the greet tool.""" + + greeting: str = Field(..., description="Generated greeting") + + +class _GreetTool(BaseTool[_GreetInput, _GreetOutput]): + """A trivial greeting tool for adapter tests.""" + + input_schema = _GreetInput + output_schema = _GreetOutput + + async def _arun(self, params: _GreetInput) -> _GreetOutput: + return _GreetOutput(greeting=f"Hello, {params.name}!") + + +@pytest.mark.asyncio +async def test_tool_adapter_produces_callable_pai_tool(): + from pydantic_ai import Tool as PAITool + + tool = _GreetTool() + pai = akd_to_pai_tool(tool) + assert isinstance(pai, PAITool) + + +@pytest.mark.asyncio +async def test_tool_adapter_validation_error_becomes_model_retry(): + """If the wrapped AKD tool raises a validation error, the adapter must + re-raise as ModelRetry so pydantic_ai retries instead of halting.""" + + class _AlwaysRaisesInput(InputSchema): + """Input schema for always-raises tool.""" + + query: str = Field(...) + + class _AlwaysRaisesOutput(OutputSchema): + """Output schema for always-raises tool.""" + + result: str = Field(...) + + class _AlwaysRaisesTool(BaseTool[_AlwaysRaisesInput, _AlwaysRaisesOutput]): + """Tool that always raises SchemaValidationError.""" + + input_schema = _AlwaysRaisesInput + output_schema = _AlwaysRaisesOutput + + async def _arun(self, params: _AlwaysRaisesInput) -> _AlwaysRaisesOutput: + raise SchemaValidationError("bad shape") + + pai = akd_to_pai_tool(_AlwaysRaisesTool()) + # Call the wrapped function directly to bypass pydantic_ai's tool manager + with pytest.raises(ModelRetry): + await pai.function(query="whatever") + + +def test_tool_adapter_accepts_akdtool_protocol(): + """The adapter's parameter type is the AKDTool protocol; concrete AKD BaseTools satisfy it structurally.""" + assert isinstance(_GreetTool(), AKDTool) + + +# --------------------------------------------------------------------------- +# Zone 3: subclass extension via _build_capabilities_from_scalars +# --------------------------------------------------------------------------- + + +# --------------------------------------------------------------------------- +# CMRCarePydanticAgent parity smoke test +# --------------------------------------------------------------------------- + + +def test_cmr_care_pydantic_agent_constructs(): + """CMRCarePydanticAgent builds cleanly on PydanticAIBaseAgent using the same + schemas as the existing CMRCareAgent.""" + from akd_ext.agents.cmr_care import CMRCareAgentInputSchema, CMRCareAgentOutputSchema + from akd_ext.agents.cmr_care_pydantic import CMRCarePydanticAgent, CMRCarePydanticConfig + + agent = CMRCarePydanticAgent(CMRCarePydanticConfig(debug=True)) + assert agent.input_schema is CMRCareAgentInputSchema + assert agent.output_schema == CMRCareAgentOutputSchema | TextOutput + assert isinstance(agent, AKDAgent) + # Config auto-exposure works through the subclass config: + assert agent.reasoning_effort == "medium" + assert "CMR" in (agent.description or "") + + +def test_cmr_care_pydantic_agent_check_output(): + """Subclass override of check_output dispatches via the late-bound bridge.""" + from akd_ext.agents.cmr_care import CMRCareAgentOutputSchema + from akd_ext.agents.cmr_care_pydantic import CMRCarePydanticAgent, CMRCarePydanticConfig + + agent = CMRCarePydanticAgent(CMRCarePydanticConfig()) + # Empty result should be rejected by the subclass override + msg = agent.check_output(CMRCareAgentOutputSchema(result=" ")) + assert isinstance(msg, str) + assert "empty" in msg.lower() + # Non-empty result passes + assert agent.check_output(CMRCareAgentOutputSchema(result="found datasets")) is None + # TextOutput still bubbles through per the base class default + assert agent.check_output(TextOutput(content="need clarification")) is None + + +@pytest.mark.asyncio +async def test_cmr_care_pydantic_agent_arun_with_test_model(): + """End-to-end wiring check: CMRCarePydanticAgent.arun works against TestModel.""" + from akd_ext.agents.cmr_care import CMRCareAgentInputSchema, CMRCareAgentOutputSchema + from akd_ext.agents.cmr_care_pydantic import CMRCarePydanticAgent, CMRCarePydanticConfig + + agent = CMRCarePydanticAgent(CMRCarePydanticConfig()) + with agent.override(model=TestModel()): + result = await agent.arun(CMRCareAgentInputSchema(query="sea ice datasets")) + assert isinstance(result, (CMRCareAgentOutputSchema, TextOutput)) + + +def test_subclass_can_extend_capabilities_hook(): + """Subclasses may override ``_build_capabilities_from_scalars`` and call super().""" + + class _MarkerCapability: + """Stand-in capability used to assert the hook was invoked.""" + + class _ExtAgent(_EchoAgent): + """Echo agent that adds a marker capability when enabled.""" + + def _build_capabilities_from_scalars(self): + caps = super()._build_capabilities_from_scalars() + if self.config.enable_extra_capability: + caps.append(_MarkerCapability()) + return caps + + # Instantiation would normally require the capability to be a real one, + # but construction-time pydantic_ai validation is model-side; we only + # check the hook output directly here. + agent = _ExtAgent.__new__(_ExtAgent) # bypass __init__; we only want the method + agent.config = _EchoConfig(enable_extra_capability=True) + produced = agent._build_capabilities_from_scalars() + assert any(isinstance(c, _MarkerCapability) for c in produced) From 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"sha256:fe58ce6d8f8115ae8bd57ded57bcd847d0bb7cb28bb7b236abefd4626df1ed8d", size = 251388, upload-time = "2026-03-27T18:23:08.573Z" }, +] + [[package]] name = "xxhash" version = "3.6.0" From 08d3b786878c085279688907fb0ca1b9c1517eaf Mon Sep 17 00:00:00 2001 From: Sanjog Thapa Date: Fri, 17 Apr 2026 15:46:41 -0500 Subject: [PATCH 03/25] add mcp capability to pydantic ai based cmr agent --- akd_ext/agents/_base_pydantic/_base.py | 11 ++++++++ akd_ext/agents/cmr_care_pydantic.py | 39 ++++++++++++++++++++++---- tests/agents/test_base_pydantic.py | 10 +++++-- 3 files changed, 53 insertions(+), 7 deletions(-) diff --git a/akd_ext/agents/_base_pydantic/_base.py b/akd_ext/agents/_base_pydantic/_base.py index a91d63e..69431d3 100644 --- a/akd_ext/agents/_base_pydantic/_base.py +++ b/akd_ext/agents/_base_pydantic/_base.py @@ -115,6 +115,17 @@ class PydanticAIBaseAgentConfig(BaseAgentConfig): description="Pydantic AI history processor callables; merged with config-derived processors.", ) + # Override BaseAgentConfig's default-on trimming. The naive ratio-based + # trimmer we ship in ``_capabilities.make_ratio_trimmer`` drops arbitrary + # slices of the message history, which breaks pydantic_ai's invariant + # that every ``tool`` message must be preceded by an ``assistant`` message + # with matching ``tool_calls``. A pydantic_ai-aware trimmer that respects + # tool-call pairing is a follow-up; until then, trimming is off by + # default. Consumers who want it enabled explicitly can set + # ``enable_trimming=True`` and supply their own processor via + # ``history_processors``. + enable_trimming: bool = Field(default=False) + # -- Silence AKD-core's litellm-based config validators -------------- # BaseAgentConfig defines ``validate_max_tokens_against_model`` and # ``validate_reasoning_params`` as @model_validator(mode="after") hooks diff --git a/akd_ext/agents/cmr_care_pydantic.py b/akd_ext/agents/cmr_care_pydantic.py index 3ff9835..f954d4a 100644 --- a/akd_ext/agents/cmr_care_pydantic.py +++ b/akd_ext/agents/cmr_care_pydantic.py @@ -12,9 +12,12 @@ from __future__ import annotations -from typing import Literal +import os + +from typing import Any, Literal from pydantic import Field +from pydantic_ai.capabilities import MCP from akd._base import TextOutput @@ -402,13 +405,38 @@ # ----------------------------------------------------------------------------- +def get_default_cmr_capabilities() -> list[Any]: + """Default CMR MCP capability for pydantic_ai. + + Points at the CMR MCP server (override via ``CMR_MCP_URL`` env var) and + exposes the same allowed-tool subset the OpenAI-based ``cmr_care.py`` + uses, so the two agents hit the same backend surface. + """ + return [ + MCP( + # Trailing slash matters: the endpoint returns a 307 redirect + # to the slashed form, which the MCP streamable-HTTP client + # won't follow on POST (per HTTP semantics for POST redirects). + url=os.environ.get( + "CMR_MCP_URL", + "https://w4hu71445m.execute-api.us-east-1.amazonaws.com/mcp/cmr/mcp/", + ), + allowed_tools=[ + "search_collections", + "get_granules", + "get_collection_metadata", + ], + description="CMR MCP server for NASA dataset discovery", + ), + ] + + class CMRCarePydanticConfig(PydanticAIBaseAgentConfig): """Config for ``CMRCarePydanticAgent``. - Mirrors ``CMRCareConfig`` from ``cmr_care.py`` but points at the Pydantic AI - base class. Tools / MCP wiring is left out of the default — add a - ``pydantic_ai.capabilities.MCP(...)`` entry to ``capabilities=`` when you - want the agent to hit the real CMR MCP endpoint. + Mirrors ``CMRCareConfig`` from ``cmr_care.py`` but wires MCP via + pydantic_ai's native ``MCP`` capability (passed through the inherited + ``capabilities`` field), not the OpenAI Agents SDK's ``HostedMCPTool``. """ description: str = Field( @@ -423,6 +451,7 @@ class CMRCarePydanticConfig(PydanticAIBaseAgentConfig): system_prompt: str = Field(default=CMR_CARE_PYDANTIC_SYSTEM_PROMPT) model_name: str = Field(default="openai:gpt-5.2") reasoning_effort: Literal["low", "medium", "high"] | None = Field(default="medium") + capabilities: list[Any] = Field(default_factory=get_default_cmr_capabilities) # ----------------------------------------------------------------------------- diff --git a/tests/agents/test_base_pydantic.py b/tests/agents/test_base_pydantic.py index f75dcbf..d2cf5bd 100644 --- a/tests/agents/test_base_pydantic.py +++ b/tests/agents/test_base_pydantic.py @@ -290,11 +290,17 @@ def test_cmr_care_pydantic_agent_check_output(): @pytest.mark.asyncio async def test_cmr_care_pydantic_agent_arun_with_test_model(): - """End-to-end wiring check: CMRCarePydanticAgent.arun works against TestModel.""" + """End-to-end wiring check: CMRCarePydanticAgent.arun works against TestModel. + + The default config wires pydantic_ai's ``MCP`` capability at the CMR + endpoint — pydantic_ai tries to fetch the tool list even when ``TestModel`` + stands in for the LLM, which would reach the real network. We override + ``capabilities=[]`` here so the test runs hermetically. + """ from akd_ext.agents.cmr_care import CMRCareAgentInputSchema, CMRCareAgentOutputSchema from akd_ext.agents.cmr_care_pydantic import CMRCarePydanticAgent, CMRCarePydanticConfig - agent = CMRCarePydanticAgent(CMRCarePydanticConfig()) + agent = CMRCarePydanticAgent(CMRCarePydanticConfig(capabilities=[])) with agent.override(model=TestModel()): result = await agent.arun(CMRCareAgentInputSchema(query="sea ice datasets")) assert isinstance(result, (CMRCareAgentOutputSchema, TextOutput)) From bea0a160efd3ab2dded3d28f5537eda6aa328d2a Mon Sep 17 00:00:00 2001 From: Sanjog Thapa Date: Mon, 20 Apr 2026 11:12:47 -0500 Subject: [PATCH 04/25] add diagram representing the pydantic ai base agent implementation --- docs/pydantic_ai_base_agent_diagram_full.md | 403 +++++++++++++++++++ docs/pydantic_ai_base_agent_diagram_full.svg | 1 + 2 files changed, 404 insertions(+) create mode 100644 docs/pydantic_ai_base_agent_diagram_full.md create mode 100644 docs/pydantic_ai_base_agent_diagram_full.svg diff --git a/docs/pydantic_ai_base_agent_diagram_full.md b/docs/pydantic_ai_base_agent_diagram_full.md new file mode 100644 index 0000000..8165896 --- /dev/null +++ b/docs/pydantic_ai_base_agent_diagram_full.md @@ -0,0 +1,403 @@ +# PydanticAIBaseAgent — Full Class Diagram + +Full structural diagram for `akd_ext/agents/_base_pydantic/` traced from the +entry file `_base.py`. Focus: the classes this subpackage adds, the protocols +it defines, and the adapter modules that bridge AKD ↔ pydantic_ai. External +SDK types are shown as single boundary boxes; their internal structure is +captured in the translation tables below, not in the graph. + +```mermaid +classDiagram + direction TB + + %% ══════════════════════════════════════════════════════════ + %% External boundaries (collapsed — details in tables below) + %% ══════════════════════════════════════════════════════════ + + class PAIAgent { + <> + +run(prompt, deps, message_history, usage) RunResult + +run_stream_events(...) AsyncIterator + +output_validator «decorator» + +system_prompt «decorator» + } + + class AbstractCapability { + <> + } + class Hooks { + <> + +on.before_model_request + +on.before_tool_execute + } + AbstractCapability <|-- Hooks + + class PAITool { + <> + +name / description / function + } + class ModelRetry { + <> + } + class AgentRunResultEvent { + <> + +result.output + } + + class AbstractBaseMeta { + <> + +auto-expose config fields as properties + } + class BaseAgentConfig { + <> + +model_name / system_prompt / tools + +reasoning_effort / num_retries + +max_tool_iterations / max_tool_calls + +reflection_prompt / enable_trimming / trim_ratio + } + class BaseTool { + <> + +name / description + +arun / as_function / as_tool_definition + } + class InputSchema { + <> + } + class OutputSchema { + <> + } + class TextOutput { + <> + } + OutputSchema <|-- TextOutput + + class AKDStreamEvent { + <> + +Completed / StreamingToken / Thinking + +ToolCalling / ToolResult + } + + %% ══════════════════════════════════════════════════════════ + %% Protocols (_protocols.py) + %% ══════════════════════════════════════════════════════════ + + class SupportsUsage { + <> + +input_tokens / output_tokens / requests + } + class RunContextProtocol { + <> + +messages / usage / run_id + } + class AKDExecutable { + <> + +input_schema / output_schema / config_schema + +arun(params, run_context) Any + } + class AKDAgent { + <> + +astream(params, run_context) AsyncIterator + } + class AKDTool { + <> + +name / description + +as_function / as_tool_definition + } + AKDExecutable <|-- AKDAgent + AKDExecutable <|-- AKDTool + RunContextProtocol ..> SupportsUsage + + %% ══════════════════════════════════════════════════════════ + %% Adapter / bridge modules (_*.py) — function-only + %% ══════════════════════════════════════════════════════════ + + class ContextAdapter { + <<_context_adapter.py>> + +_akd_dicts_to_pai_messages(msgs)$ list + +_usage_to_pai(usage)$ PAIRunUsage + +_message_history_from_run_context(ctx)$ list + +_usage_from_run_context(ctx)$ PAIRunUsage + } + class EventTranslator { + <<_event_translator.py>> + +pai_event_to_akd_event(event)$ AKDStreamEvent + } + class ToolAdapter { + <<_tool_adapter.py>> + +akd_to_pai_tool(tool)$ PAITool + } + class CapabilityFactories { + <<_capabilities.py>> + +ToolCallLimits(max_iterations, max_calls)$ Hooks + +ReflectionCapability(prompt)$ Hooks + +make_ratio_trimmer(ratio)$ Callable + } + + %% ══════════════════════════════════════════════════════════ + %% Metaclass + Config + Core (_base.py) + %% ══════════════════════════════════════════════════════════ + + class PydanticAIAgentMeta { + <> + +SKIP_AUTO_EXPOSE: frozenset + +__new__(name, bases, dct) + } + AbstractBaseMeta <|-- PydanticAIAgentMeta + + class PydanticAIBaseAgentConfig { + +capabilities: list + +history_processors: list + +enable_trimming: bool + +extra allowed for forward-compat + } + BaseAgentConfig <|-- PydanticAIBaseAgentConfig + + class PydanticAIBaseAgent~InSchema OutSchema~ { + +input_schema: type + +output_schema: type + +config_schema: type + +config: PydanticAIBaseAgentConfig + ── AKD contract ── + +arun(params, run_context) OutSchema + +astream(params, run_context) AsyncIterator + ── hooks ── + +_to_prompt(params) str + +_deps_from_run_context(ctx) Any + +_build_capabilities_from_scalars() list + +_build_history_processors_from_scalars() list + +_adapt_tools(tools) list + +check_output(output) str + } + + %% ── Inheritance / conformance ───────────────────────────── + PAIAgent <|-- PydanticAIBaseAgent + AKDAgent <|.. PydanticAIBaseAgent + PydanticAIBaseAgent ..> PydanticAIAgentMeta : metaclass + + %% ── Composition ─────────────────────────────────────────── + PydanticAIBaseAgent --> PydanticAIBaseAgentConfig : config + + %% ── Adapter usage (one edge per bridge module) ──────────── + PydanticAIBaseAgent ..> ContextAdapter : uses + PydanticAIBaseAgent ..> EventTranslator : uses + PydanticAIBaseAgent ..> ToolAdapter : uses + PydanticAIBaseAgent ..> CapabilityFactories : uses + + %% ── Adapters target their respective external types ────── + ContextAdapter ..> RunContextProtocol : accepts + EventTranslator ..> AKDStreamEvent : produces + EventTranslator ..> AgentRunResultEvent : consumes (terminal) + ToolAdapter ..> AKDTool : accepts + ToolAdapter ..> PAITool : produces + ToolAdapter ..> ModelRetry : raises + CapabilityFactories ..> Hooks : returns + CapabilityFactories ..> AbstractCapability : (Thinking) returns + + %% Base agent surfaces BaseTool via _adapt_tools + PydanticAIBaseAgent ..> BaseTool : isinstance (adapt) + PydanticAIBaseAgent ..> TextOutput : passthrough in check_output + PydanticAIBaseAgent ..> ModelRetry : raises in validator + + %% ══════════════════════════════════════════════════════════ + %% Styling + %% ══════════════════════════════════════════════════════════ + + %% Core new classes — green + style PydanticAIBaseAgent fill:#e8f5e9,stroke:#2e7d32 + style PydanticAIBaseAgentConfig fill:#e8f5e9,stroke:#2e7d32 + style PydanticAIAgentMeta fill:#e8f5e9,stroke:#2e7d32 + + %% Protocols — orange + style SupportsUsage fill:#fff3e0,stroke:#e65100 + style RunContextProtocol fill:#fff3e0,stroke:#e65100 + style AKDExecutable fill:#fff3e0,stroke:#e65100 + style AKDAgent fill:#fff3e0,stroke:#e65100 + style AKDTool fill:#fff3e0,stroke:#e65100 + + %% Adapters / Bridges — pink + style ContextAdapter fill:#fce4ec,stroke:#c62828 + style EventTranslator fill:#fce4ec,stroke:#c62828 + style ToolAdapter fill:#fce4ec,stroke:#c62828 + style CapabilityFactories fill:#fce4ec,stroke:#c62828 + + %% External pydantic_ai — blue + style PAIAgent fill:#e3f2fd,stroke:#1565c0 + style AbstractCapability fill:#e3f2fd,stroke:#1565c0 + style Hooks fill:#e3f2fd,stroke:#1565c0 + style PAITool fill:#e3f2fd,stroke:#1565c0 + style ModelRetry fill:#e3f2fd,stroke:#1565c0 + style AgentRunResultEvent fill:#e3f2fd,stroke:#1565c0 + + %% External akd-core — gray + style AbstractBaseMeta fill:#eceff1,stroke:#455a64 + style BaseAgentConfig fill:#eceff1,stroke:#455a64 + style BaseTool fill:#eceff1,stroke:#455a64 + style InputSchema fill:#eceff1,stroke:#455a64 + style OutputSchema fill:#eceff1,stroke:#455a64 + style TextOutput fill:#eceff1,stroke:#455a64 + style AKDStreamEvent fill:#eceff1,stroke:#455a64 +``` + +## Module Layout + +``` +akd_ext/agents/_base_pydantic/ +├── __init__.py # re-exports PydanticAIBaseAgent, PydanticAIBaseAgentConfig +├── _base.py # PydanticAIBaseAgent, PydanticAIBaseAgentConfig, PydanticAIAgentMeta +├── _protocols.py # AKDAgent, AKDTool, AKDExecutable, RunContextProtocol, SupportsUsage +├── _capabilities.py # ToolCallLimits, ReflectionCapability, make_ratio_trimmer (factories) +├── _context_adapter.py # AKD ↔ pydantic_ai run-context / message-history / usage translation +├── _event_translator.py # pai_event_to_akd_event — stream event mapping +└── _tool_adapter.py # akd_to_pai_tool — BaseTool → pydantic_ai.Tool +``` + +## Color Legend + +| Color | Layer | Meaning | +|-------|-------|---------| +| 🟢 Green (`#e8f5e9`) | Core new classes | The three classes this subpackage adds: `PydanticAIBaseAgent`, `PydanticAIBaseAgentConfig`, `PydanticAIAgentMeta`. | +| 🔵 Blue (`#e3f2fd`) | External — pydantic_ai | Library types being subclassed, composed, or translated. Collapsed to a handful of boundary boxes — internal shape isn't diagrammed. | +| ⚪ Gray (`#eceff1`) | External — akd-core | Foundations from the parent framework — schemas, config base, tool base, stream-event hierarchy, metaclass. | +| 🟠 Orange (`#fff3e0`) | Protocols | Structural interfaces defined in `_protocols.py`. Runtime-checkable; satisfied by both AKD and pydantic_ai sides. | +| 🩷 Pink (`#fce4ec`) | Adapters / bridges | Function-only modules presented as classes with static methods; each sits between an AKD concept and a pydantic_ai concept. | + +## Inheritance & Conformance + +``` +Runtime (class) inheritance +────────────────────────── +pydantic_ai.Agent ──┐ + ├──► PydanticAIBaseAgent [Path B] +akd AKDAgent ──────┘ (Protocol listed in bases so isinstance works) + +akd.AbstractBaseMeta ─┐ + ├──► PydanticAIAgentMeta (joint metaclass) +typing._ProtocolMeta ─┘ + +akd.BaseAgentConfig ──► PydanticAIBaseAgentConfig + +Structural (Protocol) conformance +────────────────────────────────── +AKDExecutable ◄── AKDAgent (both runtime_checkable) +AKDExecutable ◄── AKDTool +SupportsUsage is satisfied by both akd.RunUsage and pydantic_ai.RunUsage +RunContextProtocol is satisfied by both akd.RunContext and pydantic_ai.RunContext +``` + +## Key Flows + +### `arun(params)` — one-shot inference + +``` +PydanticAIBaseAgent.arun(params, run_context=?) + ├─ self._to_prompt(params) → str (default: params.model_dump_json) + ├─ ContextAdapter._message_history_from_run_context → list[ModelMessage] | None + │ └─ if msgs are dicts: _akd_dicts_to_pai_messages + ├─ ContextAdapter._usage_from_run_context → PAIRunUsage | None + │ └─ _usage_to_pai (pass-through or copy 3 fields) + ├─ self._deps_from_run_context(run_context) → Any | None + └─ super().run(prompt, deps, message_history, usage, **kwargs) + └─ (inside pydantic_ai) output_validator: self.check_output + └─ returns str → raises ModelRetry; returns None → accept + result.output → OutSchema +``` + +### `astream(params)` — event stream + +``` +PydanticAIBaseAgent.astream(params) + ├─ prompt = self._to_prompt(params) + └─ async for pai_event in super().run_stream_events(prompt, deps, msg_history, usage): + ├─ AgentRunResultEvent → yield CompletedEvent(data=CompletedEventData(output=...)) + └─ else: + EventTranslator.pai_event_to_akd_event(pai_event) + ├─ PartDeltaEvent(TextPartDelta) → StreamingTokenEvent + ├─ PartDeltaEvent(ThinkingPartDelta) → ThinkingEvent(streaming=True) + ├─ PartDeltaEvent(ToolCallPartDelta) → StreamingTokenEvent (args JSON chunk) + ├─ PartStartEvent(ThinkingPart) → ThinkingEvent + ├─ FunctionToolCallEvent → ToolCallingEvent + ├─ BuiltinToolCallEvent → ToolCallingEvent + ├─ FunctionToolResultEvent → ToolResultEvent + ├─ BuiltinToolResultEvent → ToolResultEvent + └─ (other) → None (dropped) +``` + +### `__init__(config)` — construction pipeline + +``` +PydanticAIBaseAgent.__init__(config) + ├─ self.config = config or self.config_schema() + ├─ extra_kwargs = dict(config.model_extra or {}) # forward-compat + ├─ super().__init__(PAIAgent, …) + │ ├─ tools = self._adapt_tools(config.tools) + │ │ └─ for each BaseTool: ToolAdapter.akd_to_pai_tool → PAITool + │ │ └─ else: pass through (already pydantic_ai.Tool) + │ ├─ capabilities = [*self._build_capabilities_from_scalars(), *config.capabilities] + │ │ ├─ reasoning_effort → Thinking(effort=…) + │ │ ├─ max_tool_iterations /calls → ToolCallLimits(Hooks) + │ │ └─ reflection_prompt → ReflectionCapability(Hooks) + │ └─ history_processors = [*self._build_history_processors_from_scalars(), *config.history_processors] + │ └─ enable_trimming → make_ratio_trimmer(1 - trim_ratio) + └─ self._register_akd_output_validator() # wires self.check_output via @output_validator +``` + +## Translation / Mapping Tables + +### Stream events (pydantic_ai → akd) + +| pydantic_ai event | AKD event | Notes | +|---|---|---| +| `PartDeltaEvent(TextPartDelta)` | `StreamingTokenEvent` | `token = delta.content_delta` | +| `PartDeltaEvent(ThinkingPartDelta)` | `ThinkingEvent(streaming=True)` | delta content only | +| `PartDeltaEvent(ToolCallPartDelta)` | `StreamingTokenEvent` | args-JSON chunks surfaced as tokens | +| `PartStartEvent(ThinkingPart)` | `ThinkingEvent` | one-shot, non-streaming | +| `FunctionToolCallEvent` / `BuiltinToolCallEvent` | `ToolCallingEvent` | wraps `ToolCall(tool_call_id, tool_name, arguments)` | +| `FunctionToolResultEvent` / `BuiltinToolResultEvent` | `ToolResultEvent` | wraps `ToolResult(tool_call_id, tool_name, content)` | +| `AgentRunResultEvent` | `CompletedEvent(data=CompletedEventData(output=…))` | emitted by `astream` itself, not by translator | +| `FinalResultEvent`, `PartStartEvent(TextPart)`, `PartEndEvent`, `RetryPromptPart` results | *(dropped — return `None`)* | iterator termination is the end-of-stream signal | + +### Run context (AKD ↔ pydantic_ai) + +| Field | AKD `RunContext` | pydantic_ai `RunContext` | Adapter behavior | +|---|---|---|---| +| `messages` | `list[dict]` (OpenAI-style) | `list[ModelMessage]` (typed) | Dict path: `system/user/assistant` → `ModelRequest/ModelRequest/ModelResponse`. Typed path: pass through. | +| `usage` | `akd.RunUsage` (pydantic BaseModel) | `pydantic_ai.RunUsage` (dataclass) | If already `PAIRunUsage`, pass through; else copy the 3 structural fields; rest default to 0. | +| `deps` | (not present) | `Any` | If run_context is a `PAIRunContext`, forward `.deps`; else `None`. | +| `run_id` | structural | structural | Read via `RunContextProtocol`, not converted. | + +### Scalar config → capability + +| Config field | Capability / processor | Factory location | +|---|---|---| +| `reasoning_effort` | `Thinking(effort=…)` | built inline in `_build_capabilities_from_scalars` | +| `max_tool_iterations` / `max_tool_calls` | `ToolCallLimits` (`Hooks` with `before_model_request` / `before_tool_execute`) | `_capabilities.ToolCallLimits` | +| `reflection_prompt` | `ReflectionCapability` (`Hooks` with `before_model_request`) | `_capabilities.ReflectionCapability` | +| `enable_trimming` (+ `trim_ratio`) | stateless history processor (keeps head, drops oldest fraction) | `_capabilities.make_ratio_trimmer` | + +### AKD tool → pydantic_ai tool (`akd_to_pai_tool`) + +| Step | Source (AKD) | Target (pydantic_ai) | +|---|---|---| +| 1. Get callable | `BaseTool.as_function()` | — | +| 2. Wrap | catch `pydantic.ValidationError` / `akd.SchemaValidationError` | raise `ModelRetry(str(e))` | +| 3. Preserve metadata | `__signature__`, `__annotations__`, `__name__`, `__doc__` | forwarded onto wrapper | +| 4. Construct | `name = akd_tool.name`, `description = akd_tool.description or cls.__doc__` | `pydantic_ai.Tool(wrapped, name, description)` | + +## Design Notes + +- **Path B** (subclassing `PAIAgent` directly) was chosen to decouple this work + from the akd-core Protocol/MRO fix. `AKDAgent` is also listed in the bases so + `isinstance(agent, AKDAgent)` returns `True` at runtime; swapping to Path A + (multi-inheriting `BaseAgent` for shared machinery) is a small diff once + akd-core ships its hierarchy. +- **Joint metaclass** `PydanticAIAgentMeta` resolves the `_ProtocolMeta` × + `AbstractBaseMeta` conflict (both subclass `ABCMeta`, neither subclasses the + other). `SKIP_AUTO_EXPOSE = {"system_prompt"}` prevents the auto-property + from shadowing pydantic_ai's `system_prompt` decorator method. +- **Trimming is off by default** on `PydanticAIBaseAgentConfig`: the naive + ratio-trimmer breaks pydantic_ai's invariant that every `tool` message must + follow an `assistant` with matching `tool_calls`. A pydantic_ai-aware + trimmer is a follow-up. +- **Litellm validators are silenced**: `BaseAgentConfig`'s model_validator + hooks call `litellm.get_model_info`, which expects bare model names, not + `provider:model`. Both are overridden to no-ops. diff --git a/docs/pydantic_ai_base_agent_diagram_full.svg b/docs/pydantic_ai_base_agent_diagram_full.svg new file mode 100644 index 0000000..972aa2d --- /dev/null +++ b/docs/pydantic_ai_base_agent_diagram_full.svg @@ -0,0 +1 @@ +

metaclass

config

uses

uses

uses

uses

accepts

produces

consumes (terminal)

accepts

produces

raises

returns

(Thinking) returns

isinstance (adapt)

passthrough in check_output

raises in validator

«pydantic-ai»

PAIAgent

+output_validator «decorator»

+system_prompt «decorator»

+run(prompt, deps, message_history, usage) : RunResult

+run_stream_events(...) : AsyncIterator

«pydantic-ai»

AbstractCapability

«pydantic-ai»

Hooks

+on.before_model_request

+on.before_tool_execute

«pydantic-ai»

PAITool

+name / description / function

«pydantic-ai Exception»

ModelRetry

«pydantic-ai»

AgentRunResultEvent

+result.output

«akd ABCMeta»

AbstractBaseMeta

+auto-expose config fields as properties

«akd»

BaseAgentConfig

+model_name / system_prompt / tools

+reasoning_effort / num_retries

+max_tool_iterations / max_tool_calls

+reflection_prompt / enable_trimming / trim_ratio

«akd»

BaseTool

+name / description

+arun / as_function / as_tool_definition

«akd»

InputSchema

«akd»

OutputSchema

«akd»

TextOutput

«akd»

AKDStreamEvent

+Completed / StreamingToken / Thinking

+ToolCalling / ToolResult

«Protocol»

SupportsUsage

+input_tokens / output_tokens / requests

«Protocol»

RunContextProtocol

+messages / usage / run_id

«Protocol»

AKDExecutable

+input_schema / output_schema / config_schema

+arun(params, run_context) : Any

«Protocol»

AKDAgent

+astream(params, run_context) : AsyncIterator

«Protocol»

AKDTool

+name / description

+as_function / as_tool_definition

«_context_adapter.py»

ContextAdapter

+_akd_dicts_to_pai_messages(msgs) : list

+_usage_to_pai(usage) : PAIRunUsage

+_message_history_from_run_context(ctx) : list

+_usage_from_run_context(ctx) : PAIRunUsage

«_event_translator.py»

EventTranslator

+pai_event_to_akd_event(event) : AKDStreamEvent

«_tool_adapter.py»

ToolAdapter

+akd_to_pai_tool(tool) : PAITool

«_capabilities.py»

CapabilityFactories

+ToolCallLimits(max_iterations, max_calls) : Hooks

+ReflectionCapability(prompt) : Hooks

+make_ratio_trimmer(ratio) : Callable

«metaclass»

PydanticAIAgentMeta

+SKIP_AUTO_EXPOSE: frozenset

+new(name, bases, dct)

PydanticAIBaseAgentConfig

+capabilities: list

+history_processors: list

+enable_trimming: bool

+extra allowed for forward-compat

PydanticAIBaseAgent<InSchema OutSchema>

+input_schema: type

+output_schema: type

+config_schema: type

+config: PydanticAIBaseAgentConfig

── AKD contract ──

── hooks ──

+arun(params, run_context) : OutSchema

+astream(params, run_context) : AsyncIterator

+_to_prompt(params) : str

+_deps_from_run_context(ctx) : Any

+_build_capabilities_from_scalars() : list

+_build_history_processors_from_scalars() : list

+_adapt_tools(tools) : list

+check_output(output) : str

 From 774977eee9033ea66531864c2a7ccdcd11285312 Mon Sep 17 00:00:00 2001 From: Sanjog Thapa Date: Mon, 20 Apr 2026 13:49:29 -0500 Subject: [PATCH 05/25] bump pydantic-ai floor to >=1.81.0 to match APIs in use --- pyproject.toml | 2 +- uv.lock | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 44a073a..d82c1a0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,7 +20,7 @@ dependencies = [ "akd @ git+https://github.com/NASA-IMPACT/accelerated-discovery.git@develop", "fastmcp>=2.0.0", "openai-agents>=0.6.7", - "pydantic-ai>=0.2.0", + "pydantic-ai>=1.81.0", "PyGithub>=2.1.1", ] diff --git a/uv.lock b/uv.lock index f4aeebc..8c55a8a 100644 --- a/uv.lock +++ b/uv.lock @@ -239,7 +239,7 @@ requires-dist = [ { name = "fastmcp", specifier = ">=2.0.0" }, { name = "openai-agents", specifier = ">=0.6.7" }, { name = "pre-commit", marker = "extra == 'dev'", specifier = ">=4.2.0" }, - { name = "pydantic-ai", specifier = ">=0.2.0" }, + { name = "pydantic-ai", specifier = ">=1.81.0" }, { name = "pygithub", specifier = ">=2.1.1" }, { name = "pygithub", marker = "extra == 'dev'", specifier = ">=2.1.1" }, { name = "pytest", marker = "extra == 'dev'", specifier = ">=8.0.0" }, From de607f731101dd0031341c2bc47fd3b1b62c69b3 Mon Sep 17 00:00:00 2001 From: NISH1001 Date: Mon, 20 Apr 2026 15:32:39 -0500 Subject: [PATCH 06/25] Reorganize akd_ext.agents._base into package - Split akd_ext/agents/_base.py and akd_ext/agents/_base_pydantic/ into a single _base/ package with openai.py and pydantic_ai/ submodule. External imports via akd_ext.agents._base stay intact. - Relocate cmr_care_pydantic.py validation agent to project-root examples/ since it's not part of the shipped API. - Rename docs/pydantic_ai_base_agent_diagram_full.{md,svg} to docs/pydantic_ai_base.{md,svg}. - Pin mistralai<2 via tool.uv.override-dependencies to work around instructor 1.13 eagerly importing from mistralai 2.x. - Add pythonpath=["."] to pytest config so tests can import examples/. --- akd_ext/agents/_base/__init__.py | 17 ++++++++ akd_ext/agents/{_base.py => _base/openai.py} | 0 .../pydantic_ai}/__init__.py | 0 .../pydantic_ai}/_base.py | 0 .../pydantic_ai}/_capabilities.py | 0 .../pydantic_ai}/_context_adapter.py | 0 .../pydantic_ai}/_event_translator.py | 0 .../pydantic_ai}/_protocols.py | 0 .../pydantic_ai}/_tool_adapter.py | 0 ...nt_diagram_full.md => pydantic_ai_base.md} | 4 +- ..._diagram_full.svg => pydantic_ai_base.svg} | 0 .../agents => examples}/cmr_care_pydantic.py | 2 +- pyproject.toml | 3 +- tests/agents/test_base_pydantic.py | 12 +++--- uv.lock | 39 +++++++++++-------- 15 files changed, 50 insertions(+), 27 deletions(-) create mode 100644 akd_ext/agents/_base/__init__.py rename akd_ext/agents/{_base.py => _base/openai.py} (100%) rename akd_ext/agents/{_base_pydantic => _base/pydantic_ai}/__init__.py (100%) rename akd_ext/agents/{_base_pydantic => _base/pydantic_ai}/_base.py (100%) rename akd_ext/agents/{_base_pydantic => _base/pydantic_ai}/_capabilities.py (100%) rename akd_ext/agents/{_base_pydantic => _base/pydantic_ai}/_context_adapter.py (100%) rename akd_ext/agents/{_base_pydantic => _base/pydantic_ai}/_event_translator.py (100%) rename akd_ext/agents/{_base_pydantic => _base/pydantic_ai}/_protocols.py (100%) rename akd_ext/agents/{_base_pydantic => _base/pydantic_ai}/_tool_adapter.py (100%) rename docs/{pydantic_ai_base_agent_diagram_full.md => pydantic_ai_base.md} (99%) rename docs/{pydantic_ai_base_agent_diagram_full.svg => pydantic_ai_base.svg} (100%) rename {akd_ext/agents => examples}/cmr_care_pydantic.py (99%) diff --git a/akd_ext/agents/_base/__init__.py b/akd_ext/agents/_base/__init__.py new file mode 100644 index 0000000..cc20aa2 --- /dev/null +++ b/akd_ext/agents/_base/__init__.py @@ -0,0 +1,17 @@ +"""Base agent classes for akd_ext. + +Two families live here: + +- :class:`OpenAIBaseAgent` — built on the OpenAI Agents SDK (``openai``). +- :class:`PydanticAIBaseAgent` — built on :class:`pydantic_ai.Agent`. +""" + +from akd_ext.agents._base.openai import OpenAIBaseAgent, OpenAIBaseAgentConfig +from akd_ext.agents._base.pydantic_ai import PydanticAIBaseAgent, PydanticAIBaseAgentConfig + +__all__ = [ + "OpenAIBaseAgent", + "OpenAIBaseAgentConfig", + "PydanticAIBaseAgent", + "PydanticAIBaseAgentConfig", +] diff --git a/akd_ext/agents/_base.py b/akd_ext/agents/_base/openai.py similarity index 100% rename from akd_ext/agents/_base.py rename to akd_ext/agents/_base/openai.py diff --git a/akd_ext/agents/_base_pydantic/__init__.py b/akd_ext/agents/_base/pydantic_ai/__init__.py similarity index 100% rename from akd_ext/agents/_base_pydantic/__init__.py rename to akd_ext/agents/_base/pydantic_ai/__init__.py diff --git a/akd_ext/agents/_base_pydantic/_base.py b/akd_ext/agents/_base/pydantic_ai/_base.py similarity index 100% rename from akd_ext/agents/_base_pydantic/_base.py rename to akd_ext/agents/_base/pydantic_ai/_base.py diff --git a/akd_ext/agents/_base_pydantic/_capabilities.py b/akd_ext/agents/_base/pydantic_ai/_capabilities.py similarity index 100% rename from akd_ext/agents/_base_pydantic/_capabilities.py rename to akd_ext/agents/_base/pydantic_ai/_capabilities.py diff --git a/akd_ext/agents/_base_pydantic/_context_adapter.py b/akd_ext/agents/_base/pydantic_ai/_context_adapter.py similarity index 100% rename from akd_ext/agents/_base_pydantic/_context_adapter.py rename to akd_ext/agents/_base/pydantic_ai/_context_adapter.py diff --git a/akd_ext/agents/_base_pydantic/_event_translator.py b/akd_ext/agents/_base/pydantic_ai/_event_translator.py similarity index 100% rename from akd_ext/agents/_base_pydantic/_event_translator.py rename to akd_ext/agents/_base/pydantic_ai/_event_translator.py diff --git a/akd_ext/agents/_base_pydantic/_protocols.py b/akd_ext/agents/_base/pydantic_ai/_protocols.py similarity index 100% rename from akd_ext/agents/_base_pydantic/_protocols.py rename to akd_ext/agents/_base/pydantic_ai/_protocols.py diff --git a/akd_ext/agents/_base_pydantic/_tool_adapter.py b/akd_ext/agents/_base/pydantic_ai/_tool_adapter.py similarity index 100% rename from akd_ext/agents/_base_pydantic/_tool_adapter.py rename to akd_ext/agents/_base/pydantic_ai/_tool_adapter.py diff --git a/docs/pydantic_ai_base_agent_diagram_full.md b/docs/pydantic_ai_base.md similarity index 99% rename from docs/pydantic_ai_base_agent_diagram_full.md rename to docs/pydantic_ai_base.md index 8165896..ea1ab02 100644 --- a/docs/pydantic_ai_base_agent_diagram_full.md +++ b/docs/pydantic_ai_base.md @@ -1,6 +1,6 @@ # PydanticAIBaseAgent — Full Class Diagram -Full structural diagram for `akd_ext/agents/_base_pydantic/` traced from the +Full structural diagram for `akd_ext/agents/_base/pydantic_ai/` traced from the entry file `_base.py`. Focus: the classes this subpackage adds, the protocols it defines, and the adapter modules that bridge AKD ↔ pydantic_ai. External SDK types are shown as single boundary boxes; their internal structure is @@ -241,7 +241,7 @@ classDiagram ## Module Layout ``` -akd_ext/agents/_base_pydantic/ +akd_ext/agents/_base/pydantic_ai/ ├── __init__.py # re-exports PydanticAIBaseAgent, PydanticAIBaseAgentConfig ├── _base.py # PydanticAIBaseAgent, PydanticAIBaseAgentConfig, PydanticAIAgentMeta ├── _protocols.py # AKDAgent, AKDTool, AKDExecutable, RunContextProtocol, SupportsUsage diff --git a/docs/pydantic_ai_base_agent_diagram_full.svg b/docs/pydantic_ai_base.svg similarity index 100% rename from docs/pydantic_ai_base_agent_diagram_full.svg rename to docs/pydantic_ai_base.svg diff --git a/akd_ext/agents/cmr_care_pydantic.py b/examples/cmr_care_pydantic.py similarity index 99% rename from akd_ext/agents/cmr_care_pydantic.py rename to examples/cmr_care_pydantic.py index f954d4a..de6fab4 100644 --- a/akd_ext/agents/cmr_care_pydantic.py +++ b/examples/cmr_care_pydantic.py @@ -21,7 +21,7 @@ from akd._base import TextOutput -from akd_ext.agents._base_pydantic import ( +from akd_ext.agents._base import ( PydanticAIBaseAgent, PydanticAIBaseAgentConfig, ) diff --git a/pyproject.toml b/pyproject.toml index d82c1a0..c0aa0fa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -52,6 +52,7 @@ exclude = ["tests*"] [tool.pytest.ini_options] asyncio_mode = "auto" testpaths = ["tests"] +pythonpath = ["."] python_files = ["test_*.py", "*_test.py"] python_functions = ["test_*"] addopts = [ @@ -71,7 +72,7 @@ markers = [ ] [tool.uv] -override-dependencies = ["wrapt>=1.14"] +override-dependencies = ["wrapt>=1.14", "mistralai<2"] [tool.ruff] target-version = "py312" diff --git a/tests/agents/test_base_pydantic.py b/tests/agents/test_base_pydantic.py index d2cf5bd..7eba7bf 100644 --- a/tests/agents/test_base_pydantic.py +++ b/tests/agents/test_base_pydantic.py @@ -17,14 +17,14 @@ from akd._base.errors import SchemaValidationError from akd.tools._base import BaseTool -from akd_ext.agents._base_pydantic import PydanticAIBaseAgent, PydanticAIBaseAgentConfig -from akd_ext.agents._base_pydantic._protocols import ( +from akd_ext.agents._base import PydanticAIBaseAgent, PydanticAIBaseAgentConfig +from akd_ext.agents._base.pydantic_ai._protocols import ( AKDAgent, AKDTool, RunContextProtocol, SupportsUsage, ) -from akd_ext.agents._base_pydantic._tool_adapter import akd_to_pai_tool +from akd_ext.agents._base.pydantic_ai._tool_adapter import akd_to_pai_tool # --------------------------------------------------------------------------- @@ -261,7 +261,7 @@ def test_cmr_care_pydantic_agent_constructs(): """CMRCarePydanticAgent builds cleanly on PydanticAIBaseAgent using the same schemas as the existing CMRCareAgent.""" from akd_ext.agents.cmr_care import CMRCareAgentInputSchema, CMRCareAgentOutputSchema - from akd_ext.agents.cmr_care_pydantic import CMRCarePydanticAgent, CMRCarePydanticConfig + from examples.cmr_care_pydantic import CMRCarePydanticAgent, CMRCarePydanticConfig agent = CMRCarePydanticAgent(CMRCarePydanticConfig(debug=True)) assert agent.input_schema is CMRCareAgentInputSchema @@ -275,7 +275,7 @@ def test_cmr_care_pydantic_agent_constructs(): def test_cmr_care_pydantic_agent_check_output(): """Subclass override of check_output dispatches via the late-bound bridge.""" from akd_ext.agents.cmr_care import CMRCareAgentOutputSchema - from akd_ext.agents.cmr_care_pydantic import CMRCarePydanticAgent, CMRCarePydanticConfig + from examples.cmr_care_pydantic import CMRCarePydanticAgent, CMRCarePydanticConfig agent = CMRCarePydanticAgent(CMRCarePydanticConfig()) # Empty result should be rejected by the subclass override @@ -298,7 +298,7 @@ async def test_cmr_care_pydantic_agent_arun_with_test_model(): ``capabilities=[]`` here so the test runs hermetically. """ from akd_ext.agents.cmr_care import CMRCareAgentInputSchema, CMRCareAgentOutputSchema - from akd_ext.agents.cmr_care_pydantic import CMRCarePydanticAgent, CMRCarePydanticConfig + from examples.cmr_care_pydantic import CMRCarePydanticAgent, CMRCarePydanticConfig agent = CMRCarePydanticAgent(CMRCarePydanticConfig(capabilities=[])) with agent.override(model=TestModel()): diff --git a/uv.lock b/uv.lock index 8c55a8a..90e8909 100644 --- a/uv.lock +++ b/uv.lock @@ -14,7 +14,10 @@ resolution-markers = [ ] [manifest] -overrides = [{ name = "wrapt", specifier = ">=1.14" }] +overrides = [ + { name = "mistralai", specifier = "<2" }, + { name = "wrapt", specifier = ">=1.14" }, +] [[package]] name = "ag-ui-protocol" @@ -167,8 +170,8 @@ wheels = [ [[package]] name = "akd" -version = "0.1.0" -source = { git = "https://github.com/NASA-IMPACT/accelerated-discovery.git?rev=develop#2b1ed5a4843a4e1051b8e8f8f86cf23d786ba621" } +version = "0.1.1" +source = { git = "https://github.com/NASA-IMPACT/accelerated-discovery.git?rev=develop#0986578cc70080bbff0db996429e0169588aa19c" } dependencies = [ { name = "crawl4ai" }, { name = "dateparser" }, @@ -1816,6 +1819,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/95/64/6542ac826a4c9b937b67c096a785af1aaa26b22fcb7c81223cfe4038205b/instructor-1.13.0-py3-none-any.whl", hash = "sha256:2b735b6ea0d3194548369a18254f1dde83cb5ec0b182de77adbadd8be73caddc", size = 160904, upload-time = "2025-11-06T04:19:24.674Z" }, ] +[[package]] +name = "invoke" +version = "2.2.1" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/de/bd/b461d3424a24c80490313fd77feeb666ca4f6a28c7e72713e3d9095719b4/invoke-2.2.1.tar.gz", hash = "sha256:515bf49b4a48932b79b024590348da22f39c4942dff991ad1fb8b8baea1be707", size = 304762, upload-time = "2025-10-11T00:36:35.172Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/32/4b/b99e37f88336009971405cbb7630610322ed6fbfa31e1d7ab3fbf3049a2d/invoke-2.2.1-py3-none-any.whl", hash = "sha256:2413bc441b376e5cd3f55bb5d364f973ad8bdd7bf87e53c79de3c11bf3feecc8", size = 160287, upload-time = "2025-10-11T00:36:33.703Z" }, +] + [[package]] name = "isort" version = "5.13.2" @@ -2007,15 +2019,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/03/99/33c7d78a3fb70d545fd5411ac67a651c81602cc09c9cf0df383733f068c5/jsonpath_ng-1.8.0-py3-none-any.whl", hash = "sha256:b8dde192f8af58d646fc031fac9c99fe4d00326afc4148f1f043c601a8cfe138", size = 67844, upload-time = "2026-02-28T00:53:19.637Z" }, ] -[[package]] -name = "jsonpath-python" -version = "1.1.5" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/2d/db/2f4ecc24da35c6142b39c353d5b7c16eef955cc94b35a48d3fa47996d7c3/jsonpath_python-1.1.5.tar.gz", hash = "sha256:ceea2efd9e56add09330a2c9631ea3d55297b9619348c1055e5bfb9cb0b8c538", size = 87352, upload-time = "2026-03-17T06:16:40.597Z" } -wheels = [ - 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{ url = "https://files.pythonhosted.org/packages/83/e0/9f246d18f8fe6e815f7cae88c437c986b342d3ff568607c8e0d53f4710d0/mistralai-2.3.2-py3-none-any.whl", hash = "sha256:dec05f446502289b76add9796d75465c070fc539b3c01d6f7d3297833bb64981", size = 935914, upload-time = "2026-04-10T14:05:37.845Z" }, + { url = "https://files.pythonhosted.org/packages/c9/f9/98d825105c450b9c67c27026caa374112b7e466c18331601d02ca278a01b/mistralai-1.12.4-py3-none-any.whl", hash = "sha256:7b69fcbc306436491ad3377fbdead527c9f3a0ce145ec029bf04c6308ff2cca6", size = 509321, upload-time = "2026-02-20T17:55:15.27Z" }, ] [[package]] From 3808b24f458fd43ed5d5dcb30c7efe6782bcec6c Mon Sep 17 00:00:00 2001 From: NISH1001 Date: Mon, 20 Apr 2026 16:00:21 -0500 Subject: [PATCH 07/25] Adopt akd-core ConfigBindingMixin and protocols in pydantic_ai base - Drop PydanticAIAgentMeta / AbstractBaseMeta; PydanticAIBaseAgent now inherits ConfigBindingMixin (mixin-driven, no custom metaclass) to get agent.x -> agent.config.x routing. - Replace local _protocols.py with direct imports from akd._base.protocols: AKDExecutable (folds the old AKDAgent + arun/astream), AKDTool, RunContextProtocol, TokenCounts. - Update tests and context/tool adapters to the new names. - Refresh the PydanticAIBaseAgent diagram doc's prose sections; mermaid diagram left for a follow-up. --- akd_ext/agents/_base/pydantic_ai/__init__.py | 6 +- akd_ext/agents/_base/pydantic_ai/_base.py | 68 +- .../_base/pydantic_ai/_context_adapter.py | 6 +- .../agents/_base/pydantic_ai/_protocols.py | 101 - .../agents/_base/pydantic_ai/_tool_adapter.py | 2 +- docs/pydantic_ai_base.md | 45 +- tests/agents/test_base_pydantic.py | 17 +- uv.lock | 2667 +++++++++-------- 8 files changed, 1415 insertions(+), 1497 deletions(-) delete mode 100644 akd_ext/agents/_base/pydantic_ai/_protocols.py diff --git a/akd_ext/agents/_base/pydantic_ai/__init__.py b/akd_ext/agents/_base/pydantic_ai/__init__.py index a900315..5214fe1 100644 --- a/akd_ext/agents/_base/pydantic_ai/__init__.py +++ b/akd_ext/agents/_base/pydantic_ai/__init__.py @@ -5,9 +5,11 @@ - :class:`PydanticAIBaseAgent` — subclass this to build new agents. - :class:`PydanticAIBaseAgentConfig` — extend for subclass-specific config. -The subpackage also exposes internal helpers (``_protocols``, ``_tool_adapter``, +The subpackage also exposes internal helpers (``_tool_adapter``, ``_context_adapter``, ``_event_translator``, ``_capabilities``) that consumers -typically don't need to import directly. +typically don't need to import directly. Structural protocols +(``AKDExecutable``, ``AKDTool``, ``RunContextProtocol``, ``TokenCounts``) are +sourced from ``akd._base.protocols``. """ from ._base import PydanticAIBaseAgent, PydanticAIBaseAgentConfig diff --git a/akd_ext/agents/_base/pydantic_ai/_base.py b/akd_ext/agents/_base/pydantic_ai/_base.py index 69431d3..02c7439 100644 --- a/akd_ext/agents/_base/pydantic_ai/_base.py +++ b/akd_ext/agents/_base/pydantic_ai/_base.py @@ -1,10 +1,8 @@ """``PydanticAIBaseAgent`` — Pydantic AI-backed agent conforming to the AKD contract. Subclasses ``pydantic_ai.Agent`` directly (Path B) and also explicitly -inherits the ``AKDAgent`` Protocol so ``isinstance(agent, AKDAgent)`` works -at runtime. When akd-core ships its Protocol hierarchy and MRO fix, the -swap to Path A (multi-inheriting ``BaseAgent`` for shared machinery) is a -small diff; see ``docs/pydantic_ai_base_agent_implementation_plan.md``. +inherits akd-core's ``AKDExecutable`` Protocol so +``isinstance(agent, AKDExecutable)`` works at runtime. Consumers use the same AKD pattern they're used to: @@ -18,7 +16,7 @@ class MyAgent(PydanticAIBaseAgent[MyIn, MyOut]): agent = MyAgent(MyConfig(model_name="openai:gpt-5.2", reasoning_effort="high")) result = await agent.arun(MyIn(query="...")) -Config auto-exposure is handled by ``AbstractBaseMeta`` — ``agent.model_name``, +Config auto-exposure is handled by ``ConfigBindingMixin`` — ``agent.model_name``, ``agent.description``, etc. route to ``self.config.*`` without per-field property definitions. The single opt-out is ``system_prompt`` (re-bound to pydantic_ai's decorator method so it isn't shadowed by an auto-property). @@ -27,7 +25,7 @@ class MyAgent(PydanticAIBaseAgent[MyIn, MyOut]): from __future__ import annotations from collections.abc import AsyncIterator -from typing import Any, _ProtocolMeta +from typing import Any from pydantic import ConfigDict, Field, model_validator from pydantic_ai import Agent as PAIAgent @@ -37,56 +35,20 @@ class MyAgent(PydanticAIBaseAgent[MyIn, MyOut]): from akd._base import ( CompletedEvent, CompletedEventData, + ConfigBindingMixin, InputSchema, OutputSchema, StreamEvent, TextOutput, ) -from akd._base._base import AbstractBaseMeta +from akd._base.protocols import AKDExecutable, RunContextProtocol from akd.agents._base import BaseAgentConfig from ._capabilities import ReflectionCapability, ToolCallLimits, make_ratio_trimmer from ._context_adapter import _message_history_from_run_context, _usage_from_run_context from ._event_translator import pai_event_to_akd_event -from ._protocols import AKDAgent, RunContextProtocol from ._tool_adapter import akd_to_pai_tool -# --------------------------------------------------------------------------- -# Joint metaclass -# --------------------------------------------------------------------------- -# Explicit Protocol inheritance (AKDAgent) drags in ``_ProtocolMeta``; -# AKD's config auto-exposure is driven by ``AbstractBaseMeta``. Both subclass -# ``ABCMeta`` but neither subclasses the other, so combining them on a single -# class raises ``TypeError: metaclass conflict``. The small joint subclass -# below resolves the MRO: a PydanticAIBaseAgent subclass can satisfy both. - - -class PydanticAIAgentMeta(_ProtocolMeta, AbstractBaseMeta): - """Joint metaclass: Protocol introspection + AKD config auto-exposure. - - AKD's ``AbstractBaseMeta`` auto-creates a property per config field on - every subclass, which would shadow attributes like pydantic_ai.Agent's - ``system_prompt`` decorator method on every inheritor. The ``SKIP_AUTO_EXPOSE`` - set names fields that must never get an auto-property — after the base - metaclass runs, we restore the inherited attribute from the MRO. - """ - - SKIP_AUTO_EXPOSE: frozenset[str] = frozenset({"system_prompt"}) - - def __new__(mcs, name, bases, dct): - cls = super().__new__(mcs, name, bases, dct) - # Undo auto-exposure for skipped fields: replace any property the - # base metaclass installed with the inherited attribute from an MRO base. - for field_name in mcs.SKIP_AUTO_EXPOSE: - current = cls.__dict__.get(field_name) - if isinstance(current, property): - for base in cls.__mro__[1:]: - if field_name in vars(base): - setattr(cls, field_name, vars(base)[field_name]) - break - return cls - - # --------------------------------------------------------------------------- # Config # --------------------------------------------------------------------------- @@ -151,11 +113,11 @@ def validate_reasoning_params(self): class PydanticAIBaseAgent[InSchema: InputSchema, OutSchema: OutputSchema]( + ConfigBindingMixin, PAIAgent, - AKDAgent, - metaclass=PydanticAIAgentMeta, + AKDExecutable, ): - """Pydantic AI-backed agent conforming to the AKD ``AKDAgent`` protocol. + """Pydantic AI-backed agent conforming to the AKD ``AKDExecutable`` protocol. Subclass this class to build new agents. Subclasses override: @@ -172,11 +134,12 @@ class PydanticAIBaseAgent[InSchema: InputSchema, OutSchema: OutputSchema]( output_schema: type[OutSchema] = OutputSchema # type: ignore[assignment] config_schema: type[PydanticAIBaseAgentConfig] = PydanticAIBaseAgentConfig - # Opt out of metaclass auto-exposure for ``system_prompt``: pydantic_ai's - # ``system_prompt`` is a decorator method used to register dynamic system - # prompts, and we must not shadow it with a config-routing property. - # Re-binding here puts ``system_prompt`` in the class dict, which - # ``AbstractBaseMeta`` treats as "already defined, skip auto-exposure". + # Opt out of ``ConfigBindingMixin`` auto-exposure for ``system_prompt``: + # pydantic_ai's ``system_prompt`` is a decorator method used to register + # dynamic system prompts, and we must not shadow it with a config-routing + # property. Re-binding here puts ``system_prompt`` in the class dict, + # which ``ConfigBindingMixin.__init_subclass__`` treats as "already + # defined, skip auto-exposure". system_prompt = PAIAgent.system_prompt # ── Construction ────────────────────────────────────────────────────── @@ -406,7 +369,6 @@ def check_output(self, output) -> str | None: __all__ = [ - "PydanticAIAgentMeta", "PydanticAIBaseAgent", "PydanticAIBaseAgentConfig", ] diff --git a/akd_ext/agents/_base/pydantic_ai/_context_adapter.py b/akd_ext/agents/_base/pydantic_ai/_context_adapter.py index 5d707ac..3bd7031 100644 --- a/akd_ext/agents/_base/pydantic_ai/_context_adapter.py +++ b/akd_ext/agents/_base/pydantic_ai/_context_adapter.py @@ -29,7 +29,7 @@ ) from pydantic_ai.result import RunUsage as PAIRunUsage -from ._protocols import SupportsUsage +from akd._base.protocols import TokenCounts def _akd_dicts_to_pai_messages(messages: list[dict[str, Any]]) -> list[ModelMessage]: @@ -56,8 +56,8 @@ def _akd_dicts_to_pai_messages(messages: list[dict[str, Any]]) -> list[ModelMess return out -def _usage_to_pai(usage: SupportsUsage) -> PAIRunUsage: - """Build a ``pydantic_ai.RunUsage`` from anything matching ``SupportsUsage``. +def _usage_to_pai(usage: TokenCounts) -> PAIRunUsage: + """Build a ``pydantic_ai.RunUsage`` from anything matching ``TokenCounts``. Pass-through if the value is already a ``pydantic_ai.RunUsage``. Otherwise copy the three structural fields; all other ``RunUsage`` fields default to 0. diff --git a/akd_ext/agents/_base/pydantic_ai/_protocols.py b/akd_ext/agents/_base/pydantic_ai/_protocols.py deleted file mode 100644 index e7014f0..0000000 --- a/akd_ext/agents/_base/pydantic_ai/_protocols.py +++ /dev/null @@ -1,101 +0,0 @@ -"""Local structural Protocol stand-ins. - -Mirrors the akd-core protocol hierarchy that the Accelerated Discovery team -is preparing to ship in ``akd._base.structures``. Keeping a local copy now -decouples this work from that timeline; the subpackage swaps to the akd-core -imports (or deletes this module and re-exports from akd-core) once it lands. - -The structural hierarchy: - -- ``SupportsUsage`` — token-counter shape (AKD ``RunUsage`` and pydantic_ai ``RunUsage`` both satisfy) -- ``RunContextProtocol`` — run-context shape (AKD ``RunContext`` and pydantic_ai ``RunContext`` both satisfy) -- ``AKDExecutable`` — base contract: schemas + ``arun`` (shared by agents and tools) -- ``AKDAgent`` — ``AKDExecutable`` + ``astream`` -- ``AKDTool`` — ``AKDExecutable`` + ``name``/``description`` + conversion helpers -""" - -from __future__ import annotations - -from collections.abc import AsyncIterator, Awaitable, Callable -from typing import Any, Protocol, runtime_checkable - - -@runtime_checkable -class SupportsUsage(Protocol): - """Structural shape for usage / token-counter objects. - - Both ``akd._base.structures.RunUsage`` (Pydantic BaseModel) and - ``pydantic_ai.RunUsage`` (dataclass) satisfy this structurally. - """ - - input_tokens: int - output_tokens: int - requests: int - - -@runtime_checkable -class RunContextProtocol(Protocol): - """Structural supertype for run-context-like objects. - - Both ``akd._base.structures.RunContext`` and ``pydantic_ai.RunContext`` - satisfy this structurally for the shared fields below. For AKD-specific - fields (``human_response``, ``extra``) or pydantic_ai-specific fields - (``deps``, ``tool_call_id``, etc.), narrow to the concrete type at the - call site via ``isinstance`` check. - """ - - messages: list[Any] | None - usage: SupportsUsage - run_id: str | None - - -@runtime_checkable -class AKDExecutable(Protocol): - """Base contract: schemas + ``arun``. Shared by agents and tools.""" - - input_schema: type[Any] - output_schema: type[Any] - config_schema: type[Any] | None - - async def arun( - self, - params: Any, - run_context: RunContextProtocol | None = ..., - **kwargs: Any, - ) -> Any: ... - - -@runtime_checkable -class AKDAgent(AKDExecutable, Protocol): - """Agent contract: ``AKDExecutable`` + ``astream``.""" - - def astream( - self, - params: Any, - run_context: RunContextProtocol | None = ..., - **kwargs: Any, - ) -> AsyncIterator[Any]: ... - - -@runtime_checkable -class AKDTool(AKDExecutable, Protocol): - """Tool contract: ``AKDExecutable`` + naming and conversion helpers.""" - - name: str - description: str - - def as_function( - self, - mode: str | None = ..., - ) -> Callable[..., Awaitable[Any]]: ... - - def as_tool_definition(self) -> dict[str, Any]: ... - - -__all__ = [ - "AKDAgent", - "AKDExecutable", - "AKDTool", - "RunContextProtocol", - "SupportsUsage", -] diff --git a/akd_ext/agents/_base/pydantic_ai/_tool_adapter.py b/akd_ext/agents/_base/pydantic_ai/_tool_adapter.py index 0a88ef5..7c396c2 100644 --- a/akd_ext/agents/_base/pydantic_ai/_tool_adapter.py +++ b/akd_ext/agents/_base/pydantic_ai/_tool_adapter.py @@ -26,7 +26,7 @@ from akd._base.errors import SchemaValidationError -from ._protocols import AKDTool +from akd._base.protocols import AKDTool def akd_to_pai_tool(akd_tool: AKDTool) -> PAITool: diff --git a/docs/pydantic_ai_base.md b/docs/pydantic_ai_base.md index ea1ab02..ddbe3b2 100644 --- a/docs/pydantic_ai_base.md +++ b/docs/pydantic_ai_base.md @@ -243,22 +243,24 @@ classDiagram ``` akd_ext/agents/_base/pydantic_ai/ ├── __init__.py # re-exports PydanticAIBaseAgent, PydanticAIBaseAgentConfig -├── _base.py # PydanticAIBaseAgent, PydanticAIBaseAgentConfig, PydanticAIAgentMeta -├── _protocols.py # AKDAgent, AKDTool, AKDExecutable, RunContextProtocol, SupportsUsage +├── _base.py # PydanticAIBaseAgent, PydanticAIBaseAgentConfig ├── _capabilities.py # ToolCallLimits, ReflectionCapability, make_ratio_trimmer (factories) ├── _context_adapter.py # AKD ↔ pydantic_ai run-context / message-history / usage translation ├── _event_translator.py # pai_event_to_akd_event — stream event mapping └── _tool_adapter.py # akd_to_pai_tool — BaseTool → pydantic_ai.Tool ``` +Structural protocols (``AKDExecutable``, ``AKDTool``, ``RunContextProtocol``, ``TokenCounts``) +are sourced from ``akd._base.protocols`` and no longer duplicated here. + ## Color Legend | Color | Layer | Meaning | |-------|-------|---------| -| 🟢 Green (`#e8f5e9`) | Core new classes | The three classes this subpackage adds: `PydanticAIBaseAgent`, `PydanticAIBaseAgentConfig`, `PydanticAIAgentMeta`. | +| 🟢 Green (`#e8f5e9`) | Core new classes | The two classes this subpackage adds: `PydanticAIBaseAgent`, `PydanticAIBaseAgentConfig`. | | 🔵 Blue (`#e3f2fd`) | External — pydantic_ai | Library types being subclassed, composed, or translated. Collapsed to a handful of boundary boxes — internal shape isn't diagrammed. | -| ⚪ Gray (`#eceff1`) | External — akd-core | Foundations from the parent framework — schemas, config base, tool base, stream-event hierarchy, metaclass. | -| 🟠 Orange (`#fff3e0`) | Protocols | Structural interfaces defined in `_protocols.py`. Runtime-checkable; satisfied by both AKD and pydantic_ai sides. | +| ⚪ Gray (`#eceff1`) | External — akd-core | Foundations from the parent framework — schemas, config base, tool base, stream-event hierarchy, `ConfigBindingMixin`. | +| 🟠 Orange (`#fff3e0`) | Protocols | Structural interfaces defined in `akd._base.protocols`. Runtime-checkable; satisfied by both AKD and pydantic_ai sides. | | 🩷 Pink (`#fce4ec`) | Adapters / bridges | Function-only modules presented as classes with static methods; each sits between an AKD concept and a pydantic_ai concept. | ## Inheritance & Conformance @@ -266,21 +268,16 @@ akd_ext/agents/_base/pydantic_ai/ ``` Runtime (class) inheritance ────────────────────────── -pydantic_ai.Agent ──┐ - ├──► PydanticAIBaseAgent [Path B] -akd AKDAgent ──────┘ (Protocol listed in bases so isinstance works) - -akd.AbstractBaseMeta ─┐ - ├──► PydanticAIAgentMeta (joint metaclass) -typing._ProtocolMeta ─┘ +akd.ConfigBindingMixin ──┐ +pydantic_ai.Agent ├──► PydanticAIBaseAgent [Path B] +akd AKDExecutable ───────┘ (Protocol listed in bases so isinstance works) akd.BaseAgentConfig ──► PydanticAIBaseAgentConfig Structural (Protocol) conformance ────────────────────────────────── -AKDExecutable ◄── AKDAgent (both runtime_checkable) -AKDExecutable ◄── AKDTool -SupportsUsage is satisfied by both akd.RunUsage and pydantic_ai.RunUsage +AKDExecutable ◄── AKDTool (both runtime_checkable) +TokenCounts is satisfied by both akd.RunUsage and pydantic_ai.RunUsage RunContextProtocol is satisfied by both akd.RunContext and pydantic_ai.RunContext ``` @@ -385,15 +382,15 @@ PydanticAIBaseAgent.__init__(config) ## Design Notes -- **Path B** (subclassing `PAIAgent` directly) was chosen to decouple this work - from the akd-core Protocol/MRO fix. `AKDAgent` is also listed in the bases so - `isinstance(agent, AKDAgent)` returns `True` at runtime; swapping to Path A - (multi-inheriting `BaseAgent` for shared machinery) is a small diff once - akd-core ships its hierarchy. -- **Joint metaclass** `PydanticAIAgentMeta` resolves the `_ProtocolMeta` × - `AbstractBaseMeta` conflict (both subclass `ABCMeta`, neither subclasses the - other). `SKIP_AUTO_EXPOSE = {"system_prompt"}` prevents the auto-property - from shadowing pydantic_ai's `system_prompt` decorator method. +- **Path B** (subclassing `PAIAgent` directly) keeps this implementation lean. + `AKDExecutable` is also listed in the bases so `isinstance(agent, AKDExecutable)` + returns `True` at runtime. Swapping to Path A (multi-inheriting `BaseAgent` for + shared machinery) is a small diff if ever needed. +- **Config auto-exposure** uses akd-core's `ConfigBindingMixin` (a mixin driven + by `__init_subclass__`, no metaclass). Fields already defined in the class + dict are skipped automatically — we re-bind `system_prompt = PAIAgent.system_prompt` + at class scope to prevent the auto-property from shadowing pydantic_ai's + `system_prompt` decorator method. - **Trimming is off by default** on `PydanticAIBaseAgentConfig`: the naive ratio-trimmer breaks pydantic_ai's invariant that every `tool` message must follow an `assistant` with matching `tool_calls`. A pydantic_ai-aware diff --git a/tests/agents/test_base_pydantic.py b/tests/agents/test_base_pydantic.py index 7eba7bf..758b953 100644 --- a/tests/agents/test_base_pydantic.py +++ b/tests/agents/test_base_pydantic.py @@ -18,12 +18,13 @@ from akd.tools._base import BaseTool from akd_ext.agents._base import PydanticAIBaseAgent, PydanticAIBaseAgentConfig -from akd_ext.agents._base.pydantic_ai._protocols import ( - AKDAgent, +from akd._base.protocols import ( + AKDExecutable, AKDTool, RunContextProtocol, - SupportsUsage, + TokenCounts, ) + from akd_ext.agents._base.pydantic_ai._tool_adapter import akd_to_pai_tool @@ -98,16 +99,16 @@ def test_system_prompt_not_shadowed_by_auto_exposure(): def test_agent_is_runtime_akd_agent(): - """Explicit Protocol inheritance means isinstance(agent, AKDAgent) works.""" + """Explicit Protocol inheritance means isinstance(agent, AKDExecutable) works.""" agent = _EchoAgent() - assert isinstance(agent, AKDAgent) + assert isinstance(agent, AKDExecutable) def test_config_is_runtime_run_context_protocol(): - """Sanity check: SupportsUsage and RunContextProtocol are importable and checkable.""" + """Sanity check: TokenCounts and RunContextProtocol are importable and checkable.""" # Concrete instances satisfy these structurally in practice; here we just # confirm the symbols exist and are runtime-checkable. - assert hasattr(SupportsUsage, "_is_runtime_protocol") + assert hasattr(TokenCounts, "_is_runtime_protocol") assert hasattr(RunContextProtocol, "_is_runtime_protocol") @@ -266,7 +267,7 @@ def test_cmr_care_pydantic_agent_constructs(): agent = CMRCarePydanticAgent(CMRCarePydanticConfig(debug=True)) assert agent.input_schema is CMRCareAgentInputSchema assert agent.output_schema == CMRCareAgentOutputSchema | TextOutput - assert isinstance(agent, AKDAgent) + assert isinstance(agent, AKDExecutable) # Config auto-exposure works through the subclass config: assert agent.reasoning_effort == "medium" assert "CMR" in (agent.description or "") diff 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Input-side helpers prefer that extra when present, so passing agent.last_run_context (new property) back into arun / astream gets pai-native multi-turn continuation. Documents the single-run-per-instance concurrency caveat and adds seven tests covering availability, reflection, and multi-turn. --- akd_ext/agents/_base/pydantic_ai/_base.py | 122 ++++++++++- .../_base/pydantic_ai/_context_adapter.py | 140 ++++++++++++- .../_base/pydantic_ai/_event_translator.py | 26 ++- tests/agents/test_base_pydantic.py | 197 ++++++++++++++++++ 4 files changed, 473 insertions(+), 12 deletions(-) diff --git a/akd_ext/agents/_base/pydantic_ai/_base.py b/akd_ext/agents/_base/pydantic_ai/_base.py index 02c7439..dafe830 100644 --- a/akd_ext/agents/_base/pydantic_ai/_base.py +++ b/akd_ext/agents/_base/pydantic_ai/_base.py @@ -31,6 +31,7 @@ class MyAgent(PydanticAIBaseAgent[MyIn, MyOut]): from pydantic_ai import Agent as PAIAgent from pydantic_ai import AgentRunResultEvent, ModelRetry from pydantic_ai.capabilities import AbstractCapability +from pydantic_ai.capabilities.hooks import Hooks from akd._base import ( CompletedEvent, @@ -42,10 +43,17 @@ class MyAgent(PydanticAIBaseAgent[MyIn, MyOut]): TextOutput, ) from akd._base.protocols import AKDExecutable, RunContextProtocol +from akd._base.structures import RunContext as AKDRunContext +from akd._base.structures import RunUsage as AKDRunUsage from akd.agents._base import BaseAgentConfig from ._capabilities import ReflectionCapability, ToolCallLimits, make_ratio_trimmer -from ._context_adapter import _message_history_from_run_context, _usage_from_run_context +from ._context_adapter import ( + _message_history_from_run_context, + _pai_messages_to_akd_dicts, + _pai_usage_to_akd_usage, + _usage_from_run_context, +) from ._event_translator import pai_event_to_akd_event from ._tool_adapter import akd_to_pai_tool @@ -127,6 +135,19 @@ class PydanticAIBaseAgent[InSchema: InputSchema, OutSchema: OutputSchema]( - ``_build_history_processors_from_scalars`` — additional history processors - ``_to_prompt`` — custom prompt rendering from ``InputSchema`` - ``_adapt_tools`` — custom tool adapter logic (rarely needed) + + .. warning:: + + As suggested by pydanticAI Agents we also follow Single-run-per-instance. + Every stream event this agent emits carries + pydantic_ai's live ``RunContext`` (captured via a ``Hooks`` capability + and stored on ``self._live_pai_ctx``). Running two concurrent + ``arun`` / ``astream`` calls on the *same* agent instance will cause + the captured context to race between runs and misattribute + ``event.run_context`` on emitted events. For batch or multi-tenant + workloads, construct a fresh agent per run, or switch to the + queue-based capture mechanism tracked in follow-up item + "Concurrency-safe run_context capture". """ # Subclasses override these three class attributes. @@ -151,6 +172,13 @@ def __init__(self, config: PydanticAIBaseAgentConfig | None = None) -> None: # (via ``extra="allow"``) pass straight through to pydantic_ai. extra_kwargs = dict(self.config.model_extra or {}) + # Latest pydantic_ai ``RunContext`` observed by our hooks. ``astream`` + # attaches this (wrapped in an AKD ``RunContext``) to every emitted + # event so downstream consumers can drive multi-turn conversations. + # See the class docstring for the concurrency caveat. + self._live_pai_ctx: Any = None + ctx_capture = self._build_run_context_capture() + super().__init__( model=self.config.model_name, system_prompt=self.config.system_prompt, @@ -160,6 +188,7 @@ def __init__(self, config: PydanticAIBaseAgentConfig | None = None) -> None: output_type=self.output_schema, tools=self._adapt_tools(self.config.tools), capabilities=[ + ctx_capture, *self._build_capabilities_from_scalars(), *self.config.capabilities, ], @@ -172,6 +201,30 @@ def __init__(self, config: PydanticAIBaseAgentConfig | None = None) -> None: self._register_akd_output_validator() + def _build_run_context_capture(self) -> Hooks: + """Install a ``Hooks`` capability that captures pydantic_ai's live + ``RunContext`` onto ``self._live_pai_ctx`` whenever the agent reaches a + model request or a tool execution. + + The first hook fires *before* any stream event escapes the run, so by + the time ``astream`` yields its first translated event we already + have a populated context. Both hooks observe-and-return so they don't + alter pydantic_ai's own request/argument flow. + """ + hooks = Hooks() + + @hooks.on.before_model_request + async def _grab_on_model_request(ctx, request_context): + self._live_pai_ctx = ctx + return request_context + + @hooks.on.before_tool_execute + async def _grab_on_tool_execute(ctx, *, call, tool_def, args): + self._live_pai_ctx = ctx + return args + + return hooks + # ── AKD contract: arun / astream ────────────────────────────────────── async def arun( @@ -232,12 +285,77 @@ async def astream( if pai_event.result.output is not None: yield CompletedEvent( data=CompletedEventData(output=pai_event.result.output), + run_context=self._wrap_pai_ctx(), ) continue - akd_event = pai_event_to_akd_event(pai_event) + akd_event = pai_event_to_akd_event( + pai_event, + run_context=self._wrap_pai_ctx(), + ) if akd_event is not None: yield akd_event + # ── RunContext wrapping ─────────────────────────────────────────────── + + def _wrap_pai_ctx(self) -> AKDRunContext: + """Wrap ``self._live_pai_ctx`` in an AKD ``RunContext``. + + Populates all three AKD typed fields so consumers who only read + ``run_context.messages`` / ``.usage`` / ``.run_id`` see useful values: + + - ``messages`` — best-effort OpenAI-style ``list[dict]`` produced from + the pai ``ModelMessage`` list. Lossy for multi-part responses; the + lossless path lives on ``pai_run_context`` below. + - ``usage`` — AKD ``RunUsage`` with the three structural fields mapped + and pai overflow (cache / audio / tool_calls tokens) preserved in + ``details``. + - ``run_id`` — verbatim from the pai ctx. + - ``pai_run_context`` extra — the live pai ``RunContext`` itself. The + input-side helpers (``_message_history_from_run_context`` / + ``_usage_from_run_context``) prefer this when present, so a caller + who passes ``event.run_context`` straight back into the next + ``arun`` / ``astream`` gets lossless pai-native continuation. + """ + pai_ctx = self._live_pai_ctx + if pai_ctx is None: + return AKDRunContext() + pai_messages = getattr(pai_ctx, "messages", None) + pai_usage = getattr(pai_ctx, "usage", None) + return AKDRunContext( + messages=_pai_messages_to_akd_dicts(pai_messages) if pai_messages else None, + usage=_pai_usage_to_akd_usage(pai_usage) if pai_usage is not None else AKDRunUsage(), + run_id=getattr(pai_ctx, "run_id", None), + pai_run_context=pai_ctx, + ) + + @property + def last_run_context(self) -> AKDRunContext | None: + """AKD ``RunContext`` reflecting the latest captured pai state. + + Returns ``None`` before any run has happened on this instance. + + ``arun`` returns only ``OutputSchema`` per the AKD contract, so this + property is the canonical way for ``arun`` callers to obtain a + continuation-ready context: + + .. code-block:: python + + output_1 = await agent.arun(InputSchema(query="first turn")) + ctx = agent.last_run_context # populated AKD ctx + output_2 = await agent.arun( # multi-turn + InputSchema(query="follow-up"), run_context=ctx, + ) + + Each access re-wraps the current ``self._live_pai_ctx`` (pai mutates + its own state in place during a run, so the property always reflects + the latest hook firing). Never silently consulted as a fallback when + the caller passes ``run_context=None`` — ``None`` means fresh + conversation, explicit opt-in required for continuation. + """ + if self._live_pai_ctx is None: + return None + return self._wrap_pai_ctx() + # ── Prompt rendering ────────────────────────────────────────────────── def _to_prompt(self, params: InSchema) -> str: diff --git a/akd_ext/agents/_base/pydantic_ai/_context_adapter.py b/akd_ext/agents/_base/pydantic_ai/_context_adapter.py index 3bd7031..e1c9fce 100644 --- a/akd_ext/agents/_base/pydantic_ai/_context_adapter.py +++ b/akd_ext/agents/_base/pydantic_ai/_context_adapter.py @@ -25,11 +25,15 @@ ModelResponse, SystemPromptPart, TextPart, + ThinkingPart, + ToolCallPart, + ToolReturnPart, UserPromptPart, ) from pydantic_ai.result import RunUsage as PAIRunUsage from akd._base.protocols import TokenCounts +from akd._base.structures import RunUsage as AKDRunUsage def _akd_dicts_to_pai_messages(messages: list[dict[str, Any]]) -> list[ModelMessage]: @@ -71,17 +75,133 @@ def _usage_to_pai(usage: TokenCounts) -> PAIRunUsage: ) +def _pai_messages_to_akd_dicts(messages: list[ModelMessage]) -> list[dict[str, Any]]: + """Best-effort inverse of :func:`_akd_dicts_to_pai_messages`. + + Produces OpenAI-style role-tagged dicts suitable for AKD's + ``RunContext.messages: list[dict[str, Any]]`` field. Lossy for multi-part + responses (text + thinking + tool calls collapse into one assistant dict), + tool-argument dicts become JSON strings, and some part kinds (e.g. binary + content in ``UserPromptPart``) are stringified. The lossless path remains + available via ``run_context.pai_run_context.messages``. + + Why this exists: pai's live ``RunContext.messages`` is ``list[ModelMessage]``, + but AKD's ``RunContext.messages`` is typed ``list[dict[str, Any]] | None`` + and is pydantic-validated — assigning pai objects would be rejected. We + reflect the pai messages onto AKD's dict-shaped slot so application logic + reading ``run_context.messages`` (typically as read-only inspection for + logs / UI / debugging) sees useful content instead of ``None``. Drop this + helper once akd-core widens ``RunContext.messages`` to accept pai message + types — at that point ``_wrap_pai_ctx`` can assign ``pai_ctx.messages`` + directly. + """ + out: list[dict[str, Any]] = [] + for msg in messages: + if isinstance(msg, ModelRequest): + for part in msg.parts: + if isinstance(part, SystemPromptPart): + out.append({"role": "system", "content": part.content}) + elif isinstance(part, UserPromptPart): + content = part.content if isinstance(part.content, str) else str(part.content) + out.append({"role": "user", "content": content}) + elif isinstance(part, ToolReturnPart): + out.append( + { + "role": "tool", + "tool_call_id": part.tool_call_id, + "name": part.tool_name, + "content": str(part.content), + }, + ) + elif isinstance(msg, ModelResponse): + texts: list[str] = [] + tool_calls: list[dict[str, Any]] = [] + for part in msg.parts: + if isinstance(part, TextPart): + texts.append(part.content) + elif isinstance(part, ThinkingPart): + texts.append(f"[thinking] {part.content}") + elif isinstance(part, ToolCallPart): + tool_calls.append( + { + "id": part.tool_call_id, + "type": "function", + "function": { + "name": part.tool_name, + "arguments": part.args_as_json_str(), + }, + }, + ) + assistant: dict[str, Any] = { + "role": "assistant", + "content": "\n".join(texts) if texts else "", + } + if tool_calls: + assistant["tool_calls"] = tool_calls + out.append(assistant) + return out + + +def _pai_usage_to_akd_usage(pai_usage: PAIRunUsage) -> AKDRunUsage: + """Map a ``pydantic_ai.RunUsage`` onto AKD's ``RunUsage``. + + The three structural fields (``input_tokens`` / ``output_tokens`` / + ``requests``) round-trip exactly. Pai's overflow fields (cache / audio + token counts, ``tool_calls``) plus its own ``details`` dict collapse into + AKD's ``details`` so no information is lost when this AKD view is the + only thing a consumer inspects. + + Why this exists: pai's ``RunUsage`` is a distinct class from AKD's (fewer + fields, different layout), and AKD's ``RunContext.usage`` is typed + strictly. We reflect pai usage onto an AKD ``RunUsage`` so application + logic reading ``run_context.usage`` (again, typically read-only + inspection for billing / telemetry / UI) sees live counts instead of an + empty default. Drop this helper once akd-core's ``RunContext.usage`` + accepts pai's ``RunUsage`` directly — ``_wrap_pai_ctx`` can then forward + ``pai_ctx.usage`` verbatim. + """ + details: dict[str, int] = dict(pai_usage.details or {}) + for key in ( + "cache_write_tokens", + "cache_read_tokens", + "input_audio_tokens", + "cache_audio_read_tokens", + "output_audio_tokens", + "tool_calls", + ): + val = getattr(pai_usage, key, 0) or 0 + if val: + details[key] = val + return AKDRunUsage( + input_tokens=pai_usage.input_tokens, + output_tokens=pai_usage.output_tokens, + requests=pai_usage.requests, + details=details, + ) + + def _message_history_from_run_context(run_context: Any | None) -> list[ModelMessage] | None: """Extract a pydantic_ai-shaped message history from any ``RunContextProtocol``. Returns ``None`` if the context has no messages. - Detects whether the incoming ``messages`` are dicts (AKD) or already - ``ModelMessage`` instances (pydantic_ai) and adapts accordingly. + Lookup order: + + 1. If the ctx has a populated ``pai_run_context`` extra (set by a previous + ``PydanticAIBaseAgent`` run), return its ``messages`` verbatim — a + lossless list of ``ModelMessage`` objects, no re-conversion needed. + 2. Otherwise, fall through to the historical shape-discriminating path: + dicts get converted via :func:`_akd_dicts_to_pai_messages`, existing + ``ModelMessage`` instances pass through. """ - if run_context is None or not getattr(run_context, "messages", None): + if run_context is None: + return None + pai_ctx = getattr(run_context, "pai_run_context", None) + if pai_ctx is not None and getattr(pai_ctx, "messages", None): + return list(pai_ctx.messages) + msgs = getattr(run_context, "messages", None) + if not msgs: return None - msgs = run_context.messages first = msgs[0] if isinstance(first, dict): return _akd_dicts_to_pai_messages(msgs) @@ -89,9 +209,17 @@ def _message_history_from_run_context(run_context: Any | None) -> list[ModelMess def _usage_from_run_context(run_context: Any | None) -> PAIRunUsage | None: - """Extract a pydantic_ai ``RunUsage`` from any ``RunContextProtocol``.""" + """Extract a pydantic_ai ``RunUsage`` from any ``RunContextProtocol``. + + Prefers ``run_context.pai_run_context.usage`` (a pai ``RunUsage``, + returned verbatim — no conversion, no overflow loss). Falls back to the + AKD-shape path via :func:`_usage_to_pai` when the extra is absent. + """ if run_context is None: return None + pai_ctx = getattr(run_context, "pai_run_context", None) + if pai_ctx is not None and pai_ctx.usage is not None: + return pai_ctx.usage usage = getattr(run_context, "usage", None) if usage is None: return None @@ -101,6 +229,8 @@ def _usage_from_run_context(run_context: Any | None) -> PAIRunUsage | None: __all__ = [ "_akd_dicts_to_pai_messages", "_message_history_from_run_context", + "_pai_messages_to_akd_dicts", + "_pai_usage_to_akd_usage", "_usage_from_run_context", "_usage_to_pai", ] diff --git a/akd_ext/agents/_base/pydantic_ai/_event_translator.py b/akd_ext/agents/_base/pydantic_ai/_event_translator.py index ccc047c..7bbe93b 100644 --- a/akd_ext/agents/_base/pydantic_ai/_event_translator.py +++ b/akd_ext/agents/_base/pydantic_ai/_event_translator.py @@ -52,22 +52,36 @@ ToolResultEvent, ToolResultEventData, ) +from akd._base.structures import RunContext as AKDRunContext -def pai_event_to_akd_event(pai_event) -> StreamEvent | None: +def pai_event_to_akd_event( + pai_event, + run_context: AKDRunContext | None = None, +) -> StreamEvent | None: """Translate a single pydantic_ai stream event to an AKD ``StreamEvent``. Returns ``None`` for events we don't map; callers should skip those. + + ``run_context`` is an AKD ``RunContext`` built by the caller (typically + wrapping the live ``pydantic_ai.RunContext`` under the ``pai_run_context`` + extra attribute); if supplied, it is attached to every constructed event + so consumers can drive multi-turn continuation from any event. """ + ctx_kwargs = {"run_context": run_context} if run_context is not None else {} + # Text delta → streaming token if isinstance(pai_event, PartDeltaEvent): delta = pai_event.delta if isinstance(delta, TextPartDelta): token = getattr(delta, "content_delta", "") or "" - return StreamingTokenEvent(data=StreamingEventData(token=token)) + return StreamingTokenEvent(data=StreamingEventData(token=token), **ctx_kwargs) if isinstance(delta, ThinkingPartDelta): thinking = getattr(delta, "content_delta", "") or "" - return ThinkingEvent(data=ThinkingEventData(thinking_content=thinking, streaming=True)) + return ThinkingEvent( + data=ThinkingEventData(thinking_content=thinking, streaming=True), + **ctx_kwargs, + ) if isinstance(delta, ToolCallPartDelta): # Structured-output runs stream the args JSON of the final-output # tool call. Surface each chunk as a streaming token so UIs see @@ -75,7 +89,7 @@ def pai_event_to_akd_event(pai_event) -> StreamEvent | None: args_delta = getattr(delta, "args_delta", None) or "" if isinstance(args_delta, dict): args_delta = str(args_delta) - return StreamingTokenEvent(data=StreamingEventData(token=args_delta)) + return StreamingTokenEvent(data=StreamingEventData(token=args_delta), **ctx_kwargs) return None # Thinking part started → emit a (non-streaming) thinking marker @@ -83,7 +97,7 @@ def pai_event_to_akd_event(pai_event) -> StreamEvent | None: part = pai_event.part if isinstance(part, ThinkingPart): content = getattr(part, "content", "") or "" - return ThinkingEvent(data=ThinkingEventData(thinking_content=content)) + return ThinkingEvent(data=ThinkingEventData(thinking_content=content), **ctx_kwargs) # TextPart start and other part kinds don't have a direct AKD analogue return None @@ -99,6 +113,7 @@ def pai_event_to_akd_event(pai_event) -> StreamEvent | None: arguments=part.args_as_dict() if hasattr(part, "args_as_dict") else (part.args or {}), ), ), + **ctx_kwargs, ) return None @@ -114,6 +129,7 @@ def pai_event_to_akd_event(pai_event) -> StreamEvent | None: content=result.content, ), ), + **ctx_kwargs, ) # RetryPromptPart results (tool retries) don't map to a success event return None diff --git a/tests/agents/test_base_pydantic.py b/tests/agents/test_base_pydantic.py index 758b953..bbd692a 100644 --- a/tests/agents/test_base_pydantic.py +++ b/tests/agents/test_base_pydantic.py @@ -177,6 +177,203 @@ async def test_astream_emits_completed_event_with_output(): assert completed[-1].data.output is not None +@pytest.mark.asyncio +async def test_astream_run_context_available_after_run(): + """Every emitted event — including the terminal CompletedEvent — must + carry the live pydantic_ai ``RunContext`` under ``run_context.pai_run_context``. + + Consumers rely on this to drive multi-turn continuation (Flavor B HITL): + they read ``pai_run_context.messages`` / ``.usage`` from the last event + and feed them back into the next ``astream`` call. + """ + from akd._base import CompletedEvent + from pydantic_ai import RunContext as PAIRunContext + + agent = _EchoAgent() + with agent.override(model=TestModel()): + events = [ev async for ev in agent.astream(_EchoInput(query="hello"))] + + # Every event carries a run_context — the Hooks capability fires before + # anything gets translated, so the live ctx is always captured in time. + for ev in events: + pai_ctx = getattr(ev.run_context, "pai_run_context", None) + assert pai_ctx is not None, f"{type(ev).__name__} missing pai_run_context" + assert isinstance(pai_ctx, PAIRunContext) + + # Terminal event specifically: the captured context reflects a real run — + # at least one model request fired, and pydantic_ai attached a message + # history to the ctx. + completed = [ev for ev in events if isinstance(ev, CompletedEvent)] + assert completed + terminal_pai_ctx = completed[-1].run_context.pai_run_context + assert terminal_pai_ctx.usage.requests >= 1 + assert isinstance(terminal_pai_ctx.messages, list) + assert len(terminal_pai_ctx.messages) >= 1 + + +@pytest.mark.asyncio +async def test_arun_run_context_available_after_run(): + """After ``await agent.arun(...)``, the agent's ``_live_pai_ctx`` holds + the pydantic_ai ``RunContext`` captured during the run. + + ``arun`` itself returns the output value (per the AKD contract); the + captured ctx is retained on the instance so callers can inspect it if + needed. Exposing it as a public property (``agent.last_run_context``) + is a plausible future ergonomic; this test pins the internal hook in + the meantime. + """ + from pydantic_ai import RunContext as PAIRunContext + + agent = _EchoAgent() + # Before any run, nothing has been captured. + assert agent._live_pai_ctx is None + + with agent.override(model=TestModel()): + result = await agent.arun(_EchoInput(query="hello")) + + assert isinstance(result, (_EchoOutput, TextOutput)) + assert agent._live_pai_ctx is not None + assert isinstance(agent._live_pai_ctx, PAIRunContext) + assert agent._live_pai_ctx.usage.requests >= 1 + + +# --------------------------------------------------------------------------- +# RunContext reflection onto AKD typed fields +# --------------------------------------------------------------------------- + + +@pytest.mark.asyncio +async def test_astream_reflects_pai_state_onto_akd_fields(): + """Terminal ``CompletedEvent.run_context`` must expose pai state on the + AKD typed fields (``messages`` / ``usage`` / ``run_id``) *and* keep the + lossless ``pai_run_context`` extra.""" + from akd._base import CompletedEvent + from pydantic_ai import RunContext as PAIRunContext + + agent = _EchoAgent() + with agent.override(model=TestModel()): + events = [ev async for ev in agent.astream(_EchoInput(query="hello"))] + + completed = [ev for ev in events if isinstance(ev, CompletedEvent)] + assert completed + ctx = completed[-1].run_context + + # Reflected typed fields on the AKD RunContext: + assert isinstance(ctx.messages, list) and ctx.messages + for msg in ctx.messages: + assert isinstance(msg, dict) + assert "role" in msg + assert ctx.usage.requests >= 1 + assert ctx.run_id is not None + + # Lossless pai extra still attached alongside the reflected view: + assert isinstance(ctx.pai_run_context, PAIRunContext) + + +@pytest.mark.asyncio +async def test_arun_last_run_context_property(): + """``agent.last_run_context`` returns ``None`` before any run and a + populated AKD ``RunContext`` after ``arun``.""" + from pydantic_ai import RunContext as PAIRunContext + + agent = _EchoAgent() + assert agent.last_run_context is None + + with agent.override(model=TestModel()): + out = await agent.arun(_EchoInput(query="hello")) + assert isinstance(out, (_EchoOutput, TextOutput)) + + ctx = agent.last_run_context + assert ctx is not None + assert ctx.messages # non-empty list of dicts + assert all(isinstance(m, dict) and "role" in m for m in ctx.messages) + assert ctx.usage.requests >= 1 + assert ctx.run_id is not None + assert isinstance(ctx.pai_run_context, PAIRunContext) + + +@pytest.mark.asyncio +async def test_multi_turn_via_last_run_context(): + """Feeding ``agent.last_run_context`` from turn 1 into turn 2 causes + pydantic_ai to see the full prior message history (via the lossless + ``pai_run_context`` extra).""" + agent = _EchoAgent() + with agent.override(model=TestModel()): + await agent.arun(_EchoInput(query="first turn")) + ctx_after_turn_1 = agent.last_run_context + assert ctx_after_turn_1 is not None + turn_1_message_count = len(ctx_after_turn_1.pai_run_context.messages) + + await agent.arun(_EchoInput(query="follow-up"), run_context=ctx_after_turn_1) + ctx_after_turn_2 = agent.last_run_context + + assert ctx_after_turn_2 is not None + assert len(ctx_after_turn_2.pai_run_context.messages) > turn_1_message_count + + +def test_input_side_prefers_pai_run_context(): + """When a ``run_context`` carries both stale AKD-shape messages *and* a + populated ``pai_run_context`` extra, the pai path wins — the dict view is + ignored in favor of the lossless pai ``ModelMessage`` list.""" + from akd._base.structures import RunContext as AKDRunContext + from pydantic_ai import RunContext as PAIRunContext + from pydantic_ai.messages import ModelRequest, UserPromptPart + from pydantic_ai.result import RunUsage as PAIRunUsage + + from akd_ext.agents._base.pydantic_ai._context_adapter import ( + _message_history_from_run_context, + _usage_from_run_context, + ) + + pai_messages = [ModelRequest(parts=[UserPromptPart(content="pai-truth")])] + pai_usage = PAIRunUsage(input_tokens=10, output_tokens=20, requests=3) + pai_ctx = PAIRunContext(deps=None, model=None, usage=pai_usage) + pai_ctx.messages = pai_messages + + akd_ctx = AKDRunContext( + messages=[{"role": "user", "content": "stale-akd-shape"}], + pai_run_context=pai_ctx, + ) + + history = _message_history_from_run_context(akd_ctx) + assert history is pai_messages or history == pai_messages + assert history[0].parts[0].content == "pai-truth" + + usage = _usage_from_run_context(akd_ctx) + assert usage is pai_usage + assert usage.input_tokens == 10 + + +def test_input_side_falls_back_to_akd_shape(): + """Pure AKD ``RunContext`` (no ``pai_run_context`` extra) still works + through the historical conversion path.""" + from akd._base.structures import RunContext as AKDRunContext + from akd._base.structures import RunUsage as AKDRunUsage + from pydantic_ai.result import RunUsage as PAIRunUsage + + from akd_ext.agents._base.pydantic_ai._context_adapter import ( + _message_history_from_run_context, + _usage_from_run_context, + ) + + akd_ctx = AKDRunContext( + messages=[ + {"role": "system", "content": "you are helpful"}, + {"role": "user", "content": "hi"}, + ], + usage=AKDRunUsage(input_tokens=5, output_tokens=7, requests=1), + ) + + history = _message_history_from_run_context(akd_ctx) + assert history is not None + assert len(history) == 2 + + usage = _usage_from_run_context(akd_ctx) + assert isinstance(usage, PAIRunUsage) + assert usage.input_tokens == 5 + assert usage.requests == 1 + + # --------------------------------------------------------------------------- # Tool adapter # --------------------------------------------------------------------------- From 02c5b2e3e24e58429fc01452076fe203f7173d0b Mon Sep 17 00:00:00 2001 From: Sanjog Thapa Date: Tue, 21 Apr 2026 14:53:43 -0500 Subject: [PATCH 10/25] add tests --- tests/agents/test_base_pydantic.py | 113 +++++++++++++++++++++++++++++ 1 file changed, 113 insertions(+) diff --git a/tests/agents/test_base_pydantic.py b/tests/agents/test_base_pydantic.py index bbd692a..4265564 100644 --- a/tests/agents/test_base_pydantic.py +++ b/tests/agents/test_base_pydantic.py @@ -526,3 +526,116 @@ def _build_capabilities_from_scalars(self): agent.config = _EchoConfig(enable_extra_capability=True) produced = agent._build_capabilities_from_scalars() assert any(isinstance(c, _MarkerCapability) for c in produced) + + +# --------------------------------------------------------------------------- +# Smoke tests for a PydanticAIBaseAgent derivative +# --------------------------------------------------------------------------- +# +# A self-contained derivative (mirrors the shape of an MCP-free CMR agent) +# defined inline so these smoke tests don't depend on any untracked +# scaffolding. Reuses the committed CMR input / output schemas so the +# agent-under-test matches the shape of a real AKD agent. + +from akd_ext.agents.cmr_care import ( # noqa: E402 + CMRCareAgentInputSchema, + CMRCareAgentOutputSchema, +) + +_SMOKE_SYSTEM_PROMPT = "You are a helpful agent spacializing in CMR and Earth Science." + + +class _SmokeCMRConfig(PydanticAIBaseAgentConfig): + """Minimal CMR-shaped config: no MCP (hermetic), placeholder prompt.""" + + description: str = Field(default="Smoke-test CMR agent on PydanticAIBaseAgent.") + system_prompt: str = Field(default=_SMOKE_SYSTEM_PROMPT) + model_name: str = Field(default="openai:gpt-4o-mini") + # capabilities default to [] via PydanticAIBaseAgentConfig — no MCP. + + +class _SmokeCMRAgent(PydanticAIBaseAgent[CMRCareAgentInputSchema, CMRCareAgentOutputSchema]): + """Self-contained PydanticAIBaseAgent derivative for smoke tests.""" + + input_schema = CMRCareAgentInputSchema + output_schema = CMRCareAgentOutputSchema | TextOutput + config_schema = _SmokeCMRConfig + + def check_output(self, output): + if isinstance(output, CMRCareAgentOutputSchema) and not output.result.strip(): + return "Result is empty. Provide search reasoning and details." + return super().check_output(output) + + +@pytest.mark.asyncio +async def test_smoke_arun_and_last_run_context(): + """End-to-end arun smoke test: return matches output schema; getter + exposes a populated AKD RunContext for the next turn.""" + from pydantic_ai import RunContext as PAIRunContext + + agent = _SmokeCMRAgent() + with agent.override(model=TestModel()): + result = await agent.arun(CMRCareAgentInputSchema(query="sea ice datasets")) + + assert isinstance(result, (CMRCareAgentOutputSchema, TextOutput)) + + ctx = agent.last_run_context + assert ctx is not None + assert ctx.messages and all(isinstance(m, dict) and "role" in m for m in ctx.messages) + assert ctx.usage.requests >= 1 + assert ctx.run_id is not None + assert isinstance(ctx.pai_run_context, PAIRunContext) + + +@pytest.mark.asyncio +async def test_smoke_astream_no_run_context(): + """astream tolerates ``run_context=None`` and still attaches a populated + run_context (messages + usage + run_id + pai_run_context) to at least + one emitted event.""" + from akd._base import StreamEvent + from pydantic_ai import RunContext as PAIRunContext + + agent = _SmokeCMRAgent() + with agent.override(model=TestModel()): + events = [ + ev + async for ev in agent.astream( + CMRCareAgentInputSchema(query="ocean carbon observations"), + ) + ] + + assert events, "astream should produce at least one event" + assert all(isinstance(ev, StreamEvent) for ev in events) + assert all(ev.run_context is not None for ev in events) + + populated = [ev for ev in events if ev.run_context.messages and ev.run_context.usage.requests >= 1] + assert populated, "expected at least one event with populated messages + usage" + + sample = populated[-1] + assert sample.run_context.run_id is not None + assert isinstance(sample.run_context.pai_run_context, PAIRunContext) + + +def test_smoke_pai_agent_surface_available(): + """Path B single inheritance: pydantic_ai.Agent's public surface must + still be reachable on a PydanticAIBaseAgent derivative instance.""" + from pydantic_ai import Agent as PAIAgent + + agent = _SmokeCMRAgent() + assert isinstance(agent, PAIAgent) + + for attr in ( + "run", + "run_stream_events", + "iter", + "override", + "output_validator", + "tool", + "system_prompt", + ): + assert callable(getattr(agent, attr)), f"{attr} missing or not callable" + + # name / description come from config via ConfigBindingMixin auto-exposure. + assert isinstance(agent.description, str) and agent.description + # name may be None by default; just confirm attribute access doesn't raise. + _ = agent.name From 44671c8fabf7fa8abdfd5bcd83b749a161e72722 Mon Sep 17 00:00:00 2001 From: Sanjog Thapa Date: Tue, 21 Apr 2026 15:16:20 -0500 Subject: [PATCH 11/25] test to verify a real AKD tool adapts to pydantic_ai --- tests/agents/test_base_pydantic.py | 61 ++++++++++++++++++++++++++++++ 1 file changed, 61 insertions(+) diff --git a/tests/agents/test_base_pydantic.py b/tests/agents/test_base_pydantic.py index 4265564..d1bcc91 100644 --- a/tests/agents/test_base_pydantic.py +++ b/tests/agents/test_base_pydantic.py @@ -445,6 +445,67 @@ def test_tool_adapter_accepts_akdtool_protocol(): assert isinstance(_GreetTool(), AKDTool) +@pytest.mark.asyncio +async def test_existing_akd_tool_is_pai_compatible(): + """Real shipped AKD tool (``DummyTool``) adapts cleanly to pydantic_ai. + + Compatibility checks: + + 1. The adapter returns a real ``pydantic_ai.Tool``. + 2. ``name`` and ``description`` are populated (pydantic_ai uses these for + the tool definition it advertises to the model). + 3. The wrapped function's signature exposes the AKD ``InputSchema``'s + fields as parameters (so pydantic_ai's JSON-schema introspection + produces the same shape the AKD schema declares). + 4. Invoking the wrapped function with valid kwargs returns the AKD + ``OutputSchema`` instance — the conversion is invocation-safe, not + just structural. + 5. Dropping the tool into ``PydanticAIBaseAgentConfig(tools=[...])`` and + building a ``PydanticAIBaseAgent`` succeeds; the agent's toolset lists + the adapted tool under the expected name. + """ + import inspect + + from pydantic_ai import Tool as PAITool + + from akd_ext.tools.dummy import DummyInputSchema, DummyOutputSchema, DummyTool + + akd_tool = DummyTool() + pai = akd_to_pai_tool(akd_tool) + + assert isinstance(pai, PAITool) + assert pai.name == akd_tool.name + assert pai.description and akd_tool.description.splitlines()[0] in pai.description + + # Signature preserved from AKDInputSchema → kwargs pydantic_ai can introspect. + sig = inspect.signature(pai.function) + assert "query" in sig.parameters + assert sig.return_annotation is DummyOutputSchema + + # Invocation round-trips through the AKD tool. + result = await pai.function(query="compatibility-check") + assert isinstance(result, DummyOutputSchema) + assert result.query == "compatibility-check" + + # Registration on a PydanticAIBaseAgent: the config path wraps the AKD + # tool via ``_adapt_tools``; the agent's toolset must end up carrying + # it. We use _EchoAgent's schemas since the smoke agent shape is + # immaterial — we only care that registration succeeds. + agent = _EchoAgent(_EchoConfig(tools=[DummyTool()])) + assert agent.toolset is not None + # Walk the registered toolsets to find our adapted tool by name. + assert any( + pai.name in getattr(ts, "tools", {}) or pai.name in getattr(ts, "_tools", {}) + for ts in getattr(agent, "toolsets", []) + ), f"expected adapted tool {pai.name!r} to be reachable on agent.toolsets" + + # Sanity: the AKD tool still satisfies the AKDTool protocol after + # passing through the adapter (nothing mutates the source). + assert isinstance(akd_tool, AKDTool) + # And the input schema reference is intact. + assert akd_tool.input_schema is DummyInputSchema + + # --------------------------------------------------------------------------- # Zone 3: subclass extension via _build_capabilities_from_scalars # --------------------------------------------------------------------------- From 528c7a32bc4b1162c051e1b718943c7d12d27573 Mon Sep 17 00:00:00 2001 From: Sanjog Thapa Date: Tue, 21 Apr 2026 16:43:09 -0500 Subject: [PATCH 12/25] updated diagram --- docs/pydantic_ai_base.md | 150 +++++++++++++++++++++++++++++--------- docs/pydantic_ai_base.svg | 2 +- 2 files changed, 116 insertions(+), 36 deletions(-) diff --git a/docs/pydantic_ai_base.md b/docs/pydantic_ai_base.md index ddbe3b2..ff9de98 100644 --- a/docs/pydantic_ai_base.md +++ b/docs/pydantic_ai_base.md @@ -44,9 +44,10 @@ classDiagram +result.output } - class AbstractBaseMeta { - <> - +auto-expose config fields as properties + class ConfigBindingMixin { + <> + +__init_subclass__() auto-exposes config fields as properties + +skips in-dict redeclarations (e.g. system_prompt) } class BaseAgentConfig { <> @@ -58,7 +59,7 @@ classDiagram class BaseTool { <> +name / description - +arun / as_function / as_tool_definition + +arun / as_function } class InputSchema { <> @@ -75,13 +76,22 @@ classDiagram <> +Completed / StreamingToken / Thinking +ToolCalling / ToolResult + +run_context: AKDRunContext + } + + class AKDRunContext { + <> + +messages: list[dict] | None + +usage: RunUsage + +run_id: str | None + +pai_run_context: pai.RunContext «extra / lossless» } %% ══════════════════════════════════════════════════════════ - %% Protocols (_protocols.py) + %% Protocols (akd._base.protocols) %% ══════════════════════════════════════════════════════════ - class SupportsUsage { + class TokenCounts { <> +input_tokens / output_tokens / requests } @@ -92,20 +102,17 @@ classDiagram class AKDExecutable { <> +input_schema / output_schema / config_schema + +name / description +arun(params, run_context) Any - } - class AKDAgent { - <> +astream(params, run_context) AsyncIterator } class AKDTool { <> +name / description - +as_function / as_tool_definition + +as_function() Callable } - AKDExecutable <|-- AKDAgent AKDExecutable <|-- AKDTool - RunContextProtocol ..> SupportsUsage + RunContextProtocol ..> TokenCounts %% ══════════════════════════════════════════════════════════ %% Adapter / bridge modules (_*.py) — function-only @@ -115,12 +122,14 @@ classDiagram <<_context_adapter.py>> +_akd_dicts_to_pai_messages(msgs)$ list +_usage_to_pai(usage)$ PAIRunUsage + +_pai_messages_to_akd_dicts(msgs)$ list[dict] + +_pai_usage_to_akd_usage(pai_usage)$ AKDRunUsage +_message_history_from_run_context(ctx)$ list +_usage_from_run_context(ctx)$ PAIRunUsage } class EventTranslator { <<_event_translator.py>> - +pai_event_to_akd_event(event)$ AKDStreamEvent + +pai_event_to_akd_event(event, run_context=None)$ AKDStreamEvent } class ToolAdapter { <<_tool_adapter.py>> @@ -134,16 +143,9 @@ classDiagram } %% ══════════════════════════════════════════════════════════ - %% Metaclass + Config + Core (_base.py) + %% Config + Core (_base.py) %% ══════════════════════════════════════════════════════════ - class PydanticAIAgentMeta { - <> - +SKIP_AUTO_EXPOSE: frozenset - +__new__(name, bases, dct) - } - AbstractBaseMeta <|-- PydanticAIAgentMeta - class PydanticAIBaseAgentConfig { +capabilities: list +history_processors: list @@ -157,9 +159,14 @@ classDiagram +output_schema: type +config_schema: type +config: PydanticAIBaseAgentConfig + +_live_pai_ctx: Any ── AKD contract ── +arun(params, run_context) OutSchema +astream(params, run_context) AsyncIterator + ── RunContext propagation ── + +_build_run_context_capture() Hooks + +_wrap_pai_ctx() AKDRunContext + +last_run_context AKDRunContext? «property» ── hooks ── +_to_prompt(params) str +_deps_from_run_context(ctx) Any @@ -170,12 +177,14 @@ classDiagram } %% ── Inheritance / conformance ───────────────────────────── + ConfigBindingMixin <|-- PydanticAIBaseAgent PAIAgent <|-- PydanticAIBaseAgent - AKDAgent <|.. PydanticAIBaseAgent - PydanticAIBaseAgent ..> PydanticAIAgentMeta : metaclass + AKDExecutable <|.. PydanticAIBaseAgent %% ── Composition ─────────────────────────────────────────── PydanticAIBaseAgent --> PydanticAIBaseAgentConfig : config + PydanticAIBaseAgent --> AKDRunContext : wraps live pai ctx + PydanticAIBaseAgent --> Hooks : installs ctx_capture %% ── Adapter usage (one edge per bridge module) ──────────── PydanticAIBaseAgent ..> ContextAdapter : uses @@ -185,8 +194,10 @@ classDiagram %% ── Adapters target their respective external types ────── ContextAdapter ..> RunContextProtocol : accepts + ContextAdapter ..> AKDRunContext : reads pai_run_context extra EventTranslator ..> AKDStreamEvent : produces EventTranslator ..> AgentRunResultEvent : consumes (terminal) + EventTranslator ..> AKDRunContext : attaches to events ToolAdapter ..> AKDTool : accepts ToolAdapter ..> PAITool : produces ToolAdapter ..> ModelRetry : raises @@ -205,13 +216,11 @@ classDiagram %% Core new classes — green style PydanticAIBaseAgent fill:#e8f5e9,stroke:#2e7d32 style PydanticAIBaseAgentConfig fill:#e8f5e9,stroke:#2e7d32 - style PydanticAIAgentMeta fill:#e8f5e9,stroke:#2e7d32 %% Protocols — orange - style SupportsUsage fill:#fff3e0,stroke:#e65100 + style TokenCounts fill:#fff3e0,stroke:#e65100 style RunContextProtocol fill:#fff3e0,stroke:#e65100 style AKDExecutable fill:#fff3e0,stroke:#e65100 - style AKDAgent fill:#fff3e0,stroke:#e65100 style AKDTool fill:#fff3e0,stroke:#e65100 %% Adapters / Bridges — pink @@ -229,13 +238,14 @@ classDiagram style AgentRunResultEvent fill:#e3f2fd,stroke:#1565c0 %% External akd-core — gray - style AbstractBaseMeta fill:#eceff1,stroke:#455a64 + style ConfigBindingMixin fill:#eceff1,stroke:#455a64 style BaseAgentConfig fill:#eceff1,stroke:#455a64 style BaseTool fill:#eceff1,stroke:#455a64 style InputSchema fill:#eceff1,stroke:#455a64 style OutputSchema fill:#eceff1,stroke:#455a64 style TextOutput fill:#eceff1,stroke:#455a64 style AKDStreamEvent fill:#eceff1,stroke:#455a64 + style AKDRunContext fill:#eceff1,stroke:#455a64 ``` ## Module Layout @@ -302,12 +312,17 @@ PydanticAIBaseAgent.arun(params, run_context=?) ### `astream(params)` — event stream ``` -PydanticAIBaseAgent.astream(params) +PydanticAIBaseAgent.astream(params, run_context=None) ├─ prompt = self._to_prompt(params) └─ async for pai_event in super().run_stream_events(prompt, deps, msg_history, usage): - ├─ AgentRunResultEvent → yield CompletedEvent(data=CompletedEventData(output=...)) + ├─ AgentRunResultEvent → yield CompletedEvent( + │ data=CompletedEventData(output=...), + │ run_context=self._wrap_pai_ctx(), + │ ) └─ else: - EventTranslator.pai_event_to_akd_event(pai_event) + akd_event = EventTranslator.pai_event_to_akd_event( + pai_event, run_context=self._wrap_pai_ctx() + ) ├─ PartDeltaEvent(TextPartDelta) → StreamingTokenEvent ├─ PartDeltaEvent(ThinkingPartDelta) → ThinkingEvent(streaming=True) ├─ PartDeltaEvent(ToolCallPartDelta) → StreamingTokenEvent (args JSON chunk) @@ -325,11 +340,16 @@ PydanticAIBaseAgent.astream(params) PydanticAIBaseAgent.__init__(config) ├─ self.config = config or self.config_schema() ├─ extra_kwargs = dict(config.model_extra or {}) # forward-compat + ├─ self._live_pai_ctx = None # RunContext capture slot + ├─ ctx_capture = self._build_run_context_capture() # Hooks: before_model_request, + │ # before_tool_execute ├─ super().__init__(PAIAgent, …) │ ├─ tools = self._adapt_tools(config.tools) │ │ └─ for each BaseTool: ToolAdapter.akd_to_pai_tool → PAITool │ │ └─ else: pass through (already pydantic_ai.Tool) - │ ├─ capabilities = [*self._build_capabilities_from_scalars(), *config.capabilities] + │ ├─ capabilities = [ctx_capture, + │ │ *self._build_capabilities_from_scalars(), + │ │ *config.capabilities] │ │ ├─ reasoning_effort → Thinking(effort=…) │ │ ├─ max_tool_iterations /calls → ToolCallLimits(Hooks) │ │ └─ reflection_prompt → ReflectionCapability(Hooks) @@ -338,6 +358,42 @@ PydanticAIBaseAgent.__init__(config) └─ self._register_akd_output_validator() # wires self.check_output via @output_validator ``` +### RunContext propagation — Hooks capture and emission + +``` +At __init__ + └─ self._live_pai_ctx = None + ctx_capture = Hooks().on.before_model_request / .on.before_tool_execute + (registered as the first capability on super().__init__) + +During a run (hooks fire on each model request / tool execution) + ├─ before_model_request(ctx, request) → self._live_pai_ctx = ctx; return request + └─ before_tool_execute(ctx, call, tool_def, args) → self._live_pai_ctx = ctx; return args + ↑ ctx is pai's live RunContext — a shared reference into GraphAgentState, + so .messages / .usage / .run_id reflect the latest state between hooks + +On stream emission (astream) + └─ self._wrap_pai_ctx() builds an AKD RunContext per event: + ├─ messages = ContextAdapter._pai_messages_to_akd_dicts(pai_ctx.messages) + ├─ usage = ContextAdapter._pai_usage_to_akd_usage(pai_ctx.usage) + ├─ run_id = pai_ctx.run_id + └─ pai_run_context = pai_ctx # lossless extra for continuation + +On input (arun / astream receiving a prior turn's run_context) + ├─ ContextAdapter._message_history_from_run_context(ctx) + │ └─ if ctx.pai_run_context: use its .messages verbatim (lossless) + │ else: convert ctx.messages dicts → ModelMessage (AKD-shape fallback) + └─ ContextAdapter._usage_from_run_context(ctx) + └─ if ctx.pai_run_context: use its .usage verbatim + else: copy 3 structural fields from ctx.usage + +For arun callers (return stays OutputSchema) + └─ agent.last_run_context → self._wrap_pai_ctx() # None before any run + Used as: out_1 = await agent.arun(X) + ctx = agent.last_run_context + out_2 = await agent.arun(Y, run_context=ctx) # multi-turn +``` + ## Translation / Mapping Tables ### Stream events (pydantic_ai → akd) @@ -353,15 +409,28 @@ PydanticAIBaseAgent.__init__(config) | `AgentRunResultEvent` | `CompletedEvent(data=CompletedEventData(output=…))` | emitted by `astream` itself, not by translator | | `FinalResultEvent`, `PartStartEvent(TextPart)`, `PartEndEvent`, `RetryPromptPart` results | *(dropped — return `None`)* | iterator termination is the end-of-stream signal | -### Run context (AKD ↔ pydantic_ai) +### Run context — input side (caller → pydantic_ai) | Field | AKD `RunContext` | pydantic_ai `RunContext` | Adapter behavior | |---|---|---|---| -| `messages` | `list[dict]` (OpenAI-style) | `list[ModelMessage]` (typed) | Dict path: `system/user/assistant` → `ModelRequest/ModelRequest/ModelResponse`. Typed path: pass through. | -| `usage` | `akd.RunUsage` (pydantic BaseModel) | `pydantic_ai.RunUsage` (dataclass) | If already `PAIRunUsage`, pass through; else copy the 3 structural fields; rest default to 0. | -| `deps` | (not present) | `Any` | If run_context is a `PAIRunContext`, forward `.deps`; else `None`. | +| `messages` | `list[dict]` (OpenAI-style) | `list[ModelMessage]` (typed) | **Preferred path**: if `ctx.pai_run_context` is present, use `pai_run_context.messages` verbatim (lossless). **Fallback**: dicts → `ModelRequest` / `ModelResponse` via `_akd_dicts_to_pai_messages`; already-typed list passes through. | +| `usage` | `akd.RunUsage` (pydantic BaseModel) | `pydantic_ai.RunUsage` (dataclass) | **Preferred path**: `ctx.pai_run_context.usage` (already `PAIRunUsage`). **Fallback**: copy 3 structural fields from AKD `RunUsage`; rest default to 0. | +| `deps` | (not present) | `Any` | If run_context is a `PAIRunContext` itself, forward `.deps`; else `None`. | | `run_id` | structural | structural | Read via `RunContextProtocol`, not converted. | +Preferring `pai_run_context` means handing back `event.run_context` verbatim on the next turn "just works": the lossless pai objects round-trip without re-conversion, and the reflected AKD typed fields below are never read as input. + +### Run context — output side (pydantic_ai → event.run_context) + +Emitted by `PydanticAIBaseAgent._wrap_pai_ctx()` from `self._live_pai_ctx` (the live pai `RunContext` captured by the hooks capability). Populates AKD's typed fields for read-only inspection and carries the lossless pai object under an extra slot: + +| AKD `RunContext` field | Source | Helper | Lossiness | +|---|---|---|---| +| `messages` | `pai_ctx.messages` (`list[ModelMessage]`) | `_pai_messages_to_akd_dicts` | Lossy — multi-part responses collapse to OpenAI-style dicts; `ThinkingPart`s prefix-tagged into `content`; `ToolCallPart.args` serialized to JSON strings. | +| `usage` | `pai_ctx.usage` (`PAIRunUsage`) | `_pai_usage_to_akd_usage` | Near-lossless — 3 structural fields exact; overflow fields (cache / audio tokens, `tool_calls`) + pai `details` collapse into AKD `details` dict. | +| `run_id` | `pai_ctx.run_id` | (verbatim) | Lossless. | +| `pai_run_context` (extra) | `pai_ctx` itself | (verbatim) | **Lossless escape hatch** — the full pai `RunContext` preserved intact. Input-side helpers consult this first when it's present. | + ### Scalar config → capability | Config field | Capability / processor | Factory location | @@ -391,6 +460,17 @@ PydanticAIBaseAgent.__init__(config) dict are skipped automatically — we re-bind `system_prompt = PAIAgent.system_prompt` at class scope to prevent the auto-property from shadowing pydantic_ai's `system_prompt` decorator method. +- **RunContext propagation** pairs a `Hooks`-capability capture of pai's live + `RunContext` (stored on `self._live_pai_ctx`) with `_wrap_pai_ctx()` emission. + Every stream event carries an AKD `RunContext` whose typed fields reflect + pai state (lossy but inspectable) and whose `pai_run_context` extra holds + the lossless pai object. Input-side helpers prefer the extra when present, + so callers round-trip `event.run_context` verbatim. `arun` keeps its + `OutputSchema` return contract; `agent.last_run_context` exposes the same + wrapped context for multi-turn continuation. **Concurrency caveat**: + `_live_pai_ctx` is instance-scoped, so concurrent `arun` / `astream` calls on + one agent will race — use a fresh agent per run or move to the queue-based + `event_stream_handler` design (tracked follow-up). - **Trimming is off by default** on `PydanticAIBaseAgentConfig`: the naive ratio-trimmer breaks pydantic_ai's invariant that every `tool` message must follow an `assistant` with matching `tool_calls`. A pydantic_ai-aware diff --git a/docs/pydantic_ai_base.svg b/docs/pydantic_ai_base.svg index 972aa2d..b6da994 100644 --- a/docs/pydantic_ai_base.svg +++ b/docs/pydantic_ai_base.svg @@ -1 +1 @@ -

metaclass

config

uses

uses

uses

uses

accepts

produces

consumes (terminal)

accepts

produces

raises

returns

(Thinking) returns

isinstance (adapt)

passthrough in check_output

raises in validator

«pydantic-ai»

PAIAgent

+output_validator «decorator»

+system_prompt «decorator»

+run(prompt, deps, message_history, usage) : RunResult

+run_stream_events(...) : AsyncIterator

«pydantic-ai»

AbstractCapability

«pydantic-ai»

Hooks

+on.before_model_request

+on.before_tool_execute

«pydantic-ai»

PAITool

+name / description / function

«pydantic-ai Exception»

ModelRetry

«pydantic-ai»

AgentRunResultEvent

+result.output

«akd ABCMeta»

AbstractBaseMeta

+auto-expose config fields as properties

«akd»

BaseAgentConfig

+model_name / system_prompt / tools

+reasoning_effort / num_retries

+max_tool_iterations / max_tool_calls

+reflection_prompt / enable_trimming / trim_ratio

«akd»

BaseTool

+name / description

+arun / as_function / as_tool_definition

«akd»

InputSchema

«akd»

OutputSchema

«akd»

TextOutput

«akd»

AKDStreamEvent

+Completed / StreamingToken / Thinking

+ToolCalling / ToolResult

«Protocol»

SupportsUsage

+input_tokens / output_tokens / requests

«Protocol»

RunContextProtocol

+messages / usage / run_id

«Protocol»

AKDExecutable

+input_schema / output_schema / config_schema

+arun(params, run_context) : Any

«Protocol»

AKDAgent

+astream(params, run_context) : AsyncIterator

«Protocol»

AKDTool

+name / description

+as_function / as_tool_definition

«_context_adapter.py»

ContextAdapter

+_akd_dicts_to_pai_messages(msgs) : list

+_usage_to_pai(usage) : PAIRunUsage

+_message_history_from_run_context(ctx) : list

+_usage_from_run_context(ctx) : PAIRunUsage

«_event_translator.py»

EventTranslator

+pai_event_to_akd_event(event) : AKDStreamEvent

«_tool_adapter.py»

ToolAdapter

+akd_to_pai_tool(tool) : PAITool

«_capabilities.py»

CapabilityFactories

+ToolCallLimits(max_iterations, max_calls) : Hooks

+ReflectionCapability(prompt) : Hooks

+make_ratio_trimmer(ratio) : Callable

«metaclass»

PydanticAIAgentMeta

+SKIP_AUTO_EXPOSE: frozenset

+new(name, bases, dct)

PydanticAIBaseAgentConfig

+capabilities: list

+history_processors: list

+enable_trimming: bool

+extra allowed for forward-compat

PydanticAIBaseAgent<InSchema OutSchema>

+input_schema: type

+output_schema: type

+config_schema: type

+config: PydanticAIBaseAgentConfig

── AKD contract ──

── hooks ──

+arun(params, run_context) : OutSchema

+astream(params, run_context) : AsyncIterator

+_to_prompt(params) : str

+_deps_from_run_context(ctx) : Any

+_build_capabilities_from_scalars() : list

+_build_history_processors_from_scalars() : list

+_adapt_tools(tools) : list

+check_output(output) : str

 +

config

wraps live pai ctx

installs ctx_capture

uses

uses

uses

uses

accepts

reads pai_run_context extra

produces

consumes (terminal)

attaches to events

accepts

produces

raises

returns

(Thinking) returns

isinstance (adapt)

passthrough in check_output

raises in validator

«pydantic-ai»

PAIAgent

+output_validator «decorator»

+system_prompt «decorator»

+run(prompt, deps, message_history, usage) : RunResult

+run_stream_events(...) : AsyncIterator

«pydantic-ai»

AbstractCapability

«pydantic-ai»

Hooks

+on.before_model_request

+on.before_tool_execute

«pydantic-ai»

PAITool

+name / description / function

«pydantic-ai Exception»

ModelRetry

«pydantic-ai»

AgentRunResultEvent

+result.output

«akd mixin»

ConfigBindingMixin

+init_subclass() : auto-exposes config fields as properties

+skips in-dict redeclarations(e.g. system_prompt)

«akd»

BaseAgentConfig

+model_name / system_prompt / tools

+reasoning_effort / num_retries

+max_tool_iterations / max_tool_calls

+reflection_prompt / enable_trimming / trim_ratio

«akd»

BaseTool

+name / description

+arun / as_function

«akd»

InputSchema

«akd»

OutputSchema

«akd»

TextOutput

«akd»

AKDStreamEvent

+Completed / StreamingToken / Thinking

+ToolCalling / ToolResult

+run_context: AKDRunContext

«akd»

AKDRunContext

+messages: list[dict] | None

+usage: RunUsage

+run_id: str | None

+pai_run_context: pai.RunContext «extra / lossless»

«Protocol»

TokenCounts

+input_tokens / output_tokens / requests

«Protocol»

RunContextProtocol

+messages / usage / run_id

«Protocol»

AKDExecutable

+input_schema / output_schema / config_schema

+name / description

+arun(params, run_context) : Any

+astream(params, run_context) : AsyncIterator

«Protocol»

AKDTool

+name / description

+as_function() : Callable

«_context_adapter.py»

ContextAdapter

+_akd_dicts_to_pai_messages(msgs) : list

+_usage_to_pai(usage) : PAIRunUsage

+_pai_messages_to_akd_dicts(msgs) : list[dict]

+_pai_usage_to_akd_usage(pai_usage) : AKDRunUsage

+_message_history_from_run_context(ctx) : list

+_usage_from_run_context(ctx) : PAIRunUsage

«_event_translator.py»

EventTranslator

+pai_event_to_akd_event(event, run_context=None) : AKDStreamEvent

«_tool_adapter.py»

ToolAdapter

+akd_to_pai_tool(tool) : PAITool

«_capabilities.py»

CapabilityFactories

+ToolCallLimits(max_iterations, max_calls) : Hooks

+ReflectionCapability(prompt) : Hooks

+make_ratio_trimmer(ratio) : Callable

PydanticAIBaseAgentConfig

+capabilities: list

+history_processors: list

+enable_trimming: bool

+extra allowed for forward-compat

PydanticAIBaseAgent<InSchema OutSchema>

+input_schema: type

+output_schema: type

+config_schema: type

+config: PydanticAIBaseAgentConfig

+_live_pai_ctx: Any

── AKD contract ──

── RunContext propagation ──

+last_run_context AKDRunContext? «property»

── hooks ──

+arun(params, run_context) : OutSchema

+astream(params, run_context) : AsyncIterator

+_build_run_context_capture() : Hooks

+_wrap_pai_ctx() : AKDRunContext

+_to_prompt(params) : str

+_deps_from_run_context(ctx) : Any

+_build_capabilities_from_scalars() : list

+_build_history_processors_from_scalars() : list

+_adapt_tools(tools) : list

+check_output(output) : str

 From 4be957cd1db6c4375e023f954be9d9f8ba145477 Mon Sep 17 00:00:00 2001 From: Sanjog Thapa Date: Tue, 21 Apr 2026 17:12:20 -0500 Subject: [PATCH 13/25] updated readme and docs --- README.md | 4 + docs/development/creating_agents.md | 410 +++++++++++++++++++++++----- 2 files changed, 353 insertions(+), 61 deletions(-) diff --git a/README.md b/README.md index ceaf7a3..41dd0f0 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,10 @@ Misc extension to [akd-core](https://github.com/NASA-IMPACT/accelerated-discovery/). +## Documentation + +- [Creating Agents](docs/development/creating_agents.md) — guide for building new agents on `OpenAIBaseAgent` or `PydanticAIBaseAgent`, including config, schemas, tools, capabilities, tests, and reference examples. + ## Installation ### Using uv (recommended) diff --git a/docs/development/creating_agents.md b/docs/development/creating_agents.md index aa52eba..5552333 100644 --- a/docs/development/creating_agents.md +++ b/docs/development/creating_agents.md @@ -1,29 +1,66 @@ # Creating Agents in akd-ext -This guide walks through creating a new agent using the OpenAI Agents SDK base classes provided by akd-ext. +This guide walks through creating a new agent in akd-ext. Two base classes are +available: + +- **`OpenAIBaseAgent`** — built on the OpenAI Agents SDK. Direct path when you + want to rely on OpenAI SDK hosted tools like `HostedMCPTool` / `WebSearchTool`. +- **`PydanticAIBaseAgent`** — built on `pydantic_ai.Agent`. Richer extension + surface (capabilities, hooks, history processors). + +Both expose the same AKD contract — `arun` / `astream`, schema-first +input/output, config-first construction — so downstream callers don't care +which base is underneath. Both import from `akd_ext.agents._base`. ## Overview -Agents in akd-ext follow a **schema-first** pattern built on top of the OpenAI Agents SDK. Every agent requires: +Agents in akd-ext follow a **schema-first** pattern. Every agent requires: - **Input schema** — typed parameters the agent receives - **Output schema** — typed structured output the agent returns -- **Config** — model settings, system prompt, tools +- **System prompt** — module-level constant defining agent behavior +- **Config** — model settings, system prompt, tools, (pydantic_ai) capabilities - **Agent class** — ties everything together -### Class Hierarchy +### Choosing a base + +| You want … | Use | +|---|---| +| OpenAI Agents SDK hosted tools (`HostedMCPTool`, `WebSearchTool`) straight through | `OpenAIBaseAgent` | +| Pydantic AI capabilities (`Thinking`, `MCP`, `WebSearch`, `Hooks`, …), native union outputs, `TestModel` for hermetic tests, `agent.last_run_context` for multi-turn continuation | `PydanticAIBaseAgent` | + +Both satisfy akd-core's `AKDExecutable` protocol at runtime; +`isinstance(agent, AKDExecutable)` works either way. + +### Class hierarchies ``` BaseAgent (akd-core) -└── OpenAIBaseAgent (akd_ext/agents/_base.py) - └── Your agent +├── OpenAIBaseAgent (akd_ext.agents._base.openai) +│ └── Your agent +│ +└── ConfigBindingMixin (akd-core) + pydantic_ai.Agent + AKDExecutable + └── PydanticAIBaseAgent (akd_ext.agents._base.pydantic_ai) + └── Your agent ``` -## Step-by-step Guide +`OpenAIBaseAgent` follows AKD's historic single-inheritance pattern. +`PydanticAIBaseAgent` multi-inherits `ConfigBindingMixin` from akd-core to +auto-expose config fields as properties, subclasses `pydantic_ai.Agent` for +behavior, and explicitly lists `AKDExecutable` in the bases so runtime +`isinstance` checks succeed. + +## Building blocks (shared) + +The input schema, output schema, system prompt, `check_output` override, union +outputs, file layout, registration, tests, and streaming contract are +identical across both bases. Differences are isolated to config class, model +name format, and hosted-tool wiring (next section). -### 1. Define the Input Schema +### Input schema -Extend `InputSchema` from akd-core. Every field needs a `Field(...)` with a description. +Extend `InputSchema` from akd-core. Every field needs `Field(...)` with a +`description`. ```python from akd._base import InputSchema @@ -38,13 +75,16 @@ class MyAgentInputSchema(InputSchema): ``` Rules: -- Docstring is **required** -- All fields must have `description` in `Field()` -- Use modern type hints: `str | None` not `Optional[str]`, `list[str]` not `List[str]` -### 2. Define the Output Schema +- Docstring is **required**. +- All fields must carry a `description` in `Field()`. +- Use modern type hints (`str | None`, `list[str]`) — not `Optional[str]` / + `List[str]`. -Extend `OutputSchema`. Set `__response_field__` to indicate which field contains the primary text response. +### Output schema + +Extend `OutputSchema`. Set `__response_field__` to indicate which field +contains the primary text response (used for streaming). ```python from akd._base import OutputSchema @@ -58,7 +98,7 @@ class MyAgentOutputSchema(OutputSchema): report: str = Field(default="", description="The full analysis report") ``` -For multiple output fields: +Multiple fields: ```python class MyAgentOutputSchema(OutputSchema): @@ -69,11 +109,23 @@ class MyAgentOutputSchema(OutputSchema): reasoning: str = Field(default="", description="Reasoning behind design choices") ``` -The `__response_field__` tells the framework which field contains the main text content for streaming. +### Union output with TextOutput + +Setting `output_schema = MyAgentOutputSchema | TextOutput` lets the agent +return either: + +- **Structured output** (`MyAgentOutputSchema`) — when it has results. +- **Free-form text** (`TextOutput`) — for clarification questions or when + inputs are insufficient. -### 3. Write the System Prompt +Use a single schema (`output_schema = MyAgentOutputSchema`) if you don't need +this flexibility. On `PydanticAIBaseAgent`, the union is handled natively by +pydantic_ai. -Define the system prompt as a module-level constant. This is the core of your agent's behavior. +### System prompt + +Define the system prompt as a module-level constant — this is the core of your +agent's behavior. ```python MY_AGENT_SYSTEM_PROMPT = """\ @@ -94,41 +146,84 @@ You are a ... """ ``` -### 4. Define the Config +### `check_output()` override + +Override `check_output()` to validate the agent's output before returning it. +Return `None` if valid, or a string to reject and retry: + +```python +def check_output(self, output) -> str | None: + if isinstance(output, MyAgentOutputSchema) and not output.report.strip(): + return "Report is empty. Provide a complete analysis." + return super().check_output(output) +``` + +On `PydanticAIBaseAgent` this method is automatically bridged to pydantic_ai's +`@output_validator` — a non-`None` return value is raised as `ModelRetry` so +the model can self-correct. `TextOutput` always passes through (it represents +a mid-conversation clarification request, not a terminal answer). + +## Config & agent class -Extend `OpenAIBaseAgentConfig` with your defaults. +The surface is identical on both bases — only the imports and model-name +format differ. + +### On `OpenAIBaseAgent` ```python from typing import Literal -from akd_ext.agents._base import OpenAIBaseAgentConfig +from akd._base import TextOutput +from akd_ext.agents._base import OpenAIBaseAgent, OpenAIBaseAgentConfig from pydantic import Field + class MyAgentConfig(OpenAIBaseAgentConfig): """Configuration for My Agent.""" system_prompt: str = Field(default=MY_AGENT_SYSTEM_PROMPT) - model_name: str = Field(default="gpt-5.2") + model_name: str = Field(default="gpt-5.2") # bare model name reasoning_effort: Literal["low", "medium", "high"] | None = Field(default="medium") + + +class MyAgent(OpenAIBaseAgent[MyAgentInputSchema, MyAgentOutputSchema]): + """My Agent description.""" + + input_schema = MyAgentInputSchema + output_schema = MyAgentOutputSchema | TextOutput + config_schema = MyAgentConfig + + def check_output(self, output) -> str | None: + if isinstance(output, MyAgentOutputSchema) and not output.report.strip(): + return "Report is empty. Provide a complete analysis." + return super().check_output(output) ``` -Key config properties: -- `model_name` — model to use (e.g., `"gpt-5.2"`, `"gpt-4o"`, `"gpt-5-nano"`) -- `reasoning_effort` — for reasoning models: `"low"`, `"medium"`, `"high"`, or `None` -- `tools` — list of tools (see [Adding Tools](#adding-tools)) -- `stateless` — `False` (default) keeps conversation history, `True` for single-turn -- `temperature`, `max_tokens`, `top_p` — sampling parameters +### On `PydanticAIBaseAgent` -### 5. Create the Agent Class +Same shape, different imports. Note the `provider:model` prefix on +`model_name` — pydantic_ai uses that format for provider resolution instead +of bare model names. ```python +from typing import Literal from akd._base import TextOutput -from akd_ext.agents._base import OpenAIBaseAgent +from akd_ext.agents._base import PydanticAIBaseAgent, PydanticAIBaseAgentConfig +from pydantic import Field -class MyAgent(OpenAIBaseAgent[MyAgentInputSchema, MyAgentOutputSchema]): + +class MyAgentConfig(PydanticAIBaseAgentConfig): + """Configuration for My Agent.""" + + system_prompt: str = Field(default=MY_AGENT_SYSTEM_PROMPT) + model_name: str = Field(default="openai:gpt-5.2") # provider:model + reasoning_effort: Literal["low", "medium", "high"] | None = Field(default="medium") + + +class MyAgent(PydanticAIBaseAgent[MyAgentInputSchema, MyAgentOutputSchema]): """My Agent description.""" input_schema = MyAgentInputSchema - output_schema = MyAgentOutputSchema | TextOutput # union output + output_schema = MyAgentOutputSchema | TextOutput config_schema = MyAgentConfig def check_output(self, output) -> str | None: @@ -137,21 +232,59 @@ class MyAgent(OpenAIBaseAgent[MyAgentInputSchema, MyAgentOutputSchema]): return super().check_output(output) ``` -#### Union Output with TextOutput +### Common config fields + +Both config classes inherit from `akd.agents._base.BaseAgentConfig` and share: -Setting `output_schema = MyAgentOutputSchema | TextOutput` allows the agent to return either: -- **Structured output** (`MyAgentOutputSchema`) — when it has results -- **Free-form text** (`TextOutput`) — for clarification questions or when inputs are insufficient +- `model_name` — model identifier (format differs per base; see above). +- `system_prompt` — agent instructions. +- `tools` — list of tools (see below). +- `reasoning_effort` — `"low"` / `"medium"` / `"high"` / `None` (reasoning models only). +- `num_retries` — max retries for tool calls and output validation. +- `max_tool_iterations` / `max_tool_calls` — per-run tool-call caps. +- `reflection_prompt` — injected reflection before each model request. +- `stateless` — `False` (default) keeps conversation history, `True` for single-turn. +- `temperature`, `max_tokens`, `top_p` — sampling parameters. -If you don't need this flexibility, use a single schema: `output_schema = MyAgentOutputSchema`. +### Pydantic AI-only config fields -#### `check_output()` Override +`PydanticAIBaseAgentConfig` adds: -Override `check_output()` to validate the agent's output before returning it. Return `None` if valid, or an error message string to reject and retry. +- `capabilities: list[Any]` — pydantic_ai capability objects (`Thinking`, + `MCP`, `WebSearch`, `WebFetch`, `Hooks`, …). Merged with capabilities the + base auto-derives from the scalar fields above (e.g. `reasoning_effort` + becomes a `Thinking(effort=...)` capability). +- `history_processors: list[Any]` — per-request message-history callables. +- `extra="allow"` — any additional fields on a subclass config are forwarded + to `pydantic_ai.Agent.__init__` via `model_extra` (forward-compat). +- `enable_trimming: bool = False` — disabled by default because the naive + ratio-trimmer violates pydantic_ai's tool-call/assistant pairing invariant. + Supply your own processor via `history_processors` if you need trimming. -### 6. Adding Tools +## Tools -#### MCP Tools (Hosted) +AKD tools (`BaseTool` subclasses) are auto-converted on both bases — just pass +instances via `config.tools=[...]`: + +```python +from akd_ext.tools.dummy import DummyTool + +class MyAgentConfig(OpenAIBaseAgentConfig): # or PydanticAIBaseAgentConfig + tools: list[Any] = Field(default_factory=lambda: [DummyTool()]) +``` + +On `OpenAIBaseAgent`, AKD `BaseTool` instances are converted to the OpenAI +SDK's `FunctionTool`. On `PydanticAIBaseAgent`, they're adapted to +`pydantic_ai.Tool`; `ValidationError` / `SchemaValidationError` raised inside +the tool become `ModelRetry` so the model can self-correct bad arguments. + +Where the two bases diverge is on **hosted / built-in tools** — each ecosystem +has its own mechanism. + +### MCP tools + +**On `OpenAIBaseAgent`** — register the OpenAI SDK's `HostedMCPTool` as a +tool: ```python import os @@ -167,7 +300,7 @@ def get_default_tools(): "require_approval": "never", "server_description": "Description of the MCP server", "server_url": os.environ.get("MY_MCP_URL", "https://default-url.com/mcp"), - } + }, ), ] @@ -175,7 +308,32 @@ class MyAgentConfig(OpenAIBaseAgentConfig): tools: list[Any] = Field(default_factory=get_default_tools) ``` -#### Web Search +**On `PydanticAIBaseAgent`** — MCP is a **capability**, not a tool. Register it +via `config.capabilities`: + +```python +import os +from pydantic_ai.capabilities import MCP + +def get_default_capabilities(): + return [ + MCP( + # Trailing slash matters: the endpoint returns a 307 redirect to + # the slashed form, and the MCP streamable-HTTP client won't + # follow redirects on POST. + url=os.environ.get("MY_MCP_URL", "https://default-url.com/mcp/"), + allowed_tools=["tool_a", "tool_b"], + description="Description of the MCP server", + ), + ] + +class MyAgentConfig(PydanticAIBaseAgentConfig): + capabilities: list[Any] = Field(default_factory=get_default_capabilities) +``` + +### Web search + +**On `OpenAIBaseAgent`**: ```python from agents import WebSearchTool @@ -184,21 +342,130 @@ class MyAgentConfig(OpenAIBaseAgentConfig): tools: list[Any] = Field(default_factory=lambda: [WebSearchTool()]) ``` -#### AKD Tools +**On `PydanticAIBaseAgent`** — `WebSearch` is a capability: + +```python +from pydantic_ai.capabilities import WebSearch + +class MyAgentConfig(PydanticAIBaseAgentConfig): + capabilities: list[Any] = Field(default_factory=lambda: [WebSearch()]) +``` + +## Pydantic AI-specific features + +`PydanticAIBaseAgent` exposes a handful of extension points that have no +analogue on `OpenAIBaseAgent`. + +### Capabilities + +Pydantic AI's primary extension point. Capability instances registered on +`config.capabilities` run alongside capabilities the base auto-derives from +scalar config fields. Useful built-ins: + +| Capability | Purpose | +|---|---| +| `Thinking(effort=...)` | Reasoning models; auto-derived from `config.reasoning_effort` | +| `MCP(url=..., allowed_tools=...)` | MCP server integration (see above) | +| `WebSearch()` / `WebFetch()` | Built-in web search / fetch (model-dependent) | +| `Hooks()` | Lifecycle hooks (see below) | +| *custom* | Subclass `pydantic_ai.capabilities.AbstractCapability` | + +### Hooks + +`pydantic_ai.capabilities.hooks.Hooks` lets you register decorator-style +callbacks on the run lifecycle — `before_run`, `before_model_request`, +`before_tool_execute`, `after_run`, etc. `PydanticAIBaseAgent` itself installs +an internal `Hooks` capability to capture each run's live `RunContext` onto +`self._live_pai_ctx`; subclasses can add their own `Hooks()` via +`config.capabilities`: + +```python +from pydantic_ai.capabilities.hooks import Hooks + +hooks = Hooks() + +@hooks.on.before_tool_execute +async def audit_tool(ctx, *, call, tool_def, args): + print(f"About to call tool {call.tool_name} with {args!r}") + return args # pass through unchanged + +class MyAgentConfig(PydanticAIBaseAgentConfig): + capabilities: list[Any] = Field(default_factory=lambda: [hooks]) +``` + +### History processors + +`config.history_processors: list[Callable[[list[ModelMessage]], list[ModelMessage]]]` +runs per-request and lets you transform the message history sent to the model +(trim, summarize, filter). AKD's ratio-trimmer is *off* by default on +`PydanticAIBaseAgent` because it breaks tool-call/assistant pairing; supply +your own if you need trimming. + +### `RunContext` propagation and multi-turn runs + +Every event `agent.astream(...)` yields carries a `run_context` populated from +pydantic_ai's live `RunContext` — AKD-shape `messages` / `usage` / `run_id` +reflected for read-only inspection, plus a lossless `pai_run_context` extra +with the full pai object. For `arun` callers (whose return is pinned to +`OutputSchema` per the AKD contract), the same wrapper is reachable via +`agent.last_run_context`: + +```python +out_1 = await agent.arun(MyAgentInputSchema(query="first turn")) +ctx = agent.last_run_context # populated AKD RunContext with pai_run_context extra + +out_2 = await agent.arun( + MyAgentInputSchema(query="follow-up"), + run_context=ctx, # carries prior-turn messages + usage +) +``` + +Passing `event.run_context` or `agent.last_run_context` verbatim into the next +call triggers lossless pai-native continuation — the agent's input-side +helpers prefer the `pai_run_context` extra over converting from the AKD-shape +typed fields. + +**Concurrency note**: `PydanticAIBaseAgent` is designed for one active run per +instance. Concurrent `arun` / `astream` calls on the same agent will race +the captured `_live_pai_ctx`. Use a fresh agent per run for concurrent +workloads. + +### Hermetic tests with `TestModel` -AKD `BaseTool` instances are auto-converted to OpenAI `FunctionTool` via the config validator. +`pydantic_ai.models.test.TestModel` stands in for a real model so unit tests +don't touch a provider: -## File Structure +```python +from pydantic_ai.models.test import TestModel + +agent = MyAgent(MyAgentConfig(capabilities=[])) # disable MCP for hermetic run +with agent.override(model=TestModel()): + result = await agent.arun(MyAgentInputSchema(query="x")) +``` + +`TestModel` auto-fills the declared output schema with stub data, so +`agent.arun` returns a real `MyAgentOutputSchema` (or `TextOutput`) with no +network call. + +## File structure Place your agent in the appropriate directory: ``` akd_ext/agents/ -├── _base.py # Base classes (don't modify) +├── _base/ # Base classes (don't modify) +│ ├── __init__.py # Re-exports both base classes +│ ├── openai.py # OpenAIBaseAgent +│ └── pydantic_ai/ # PydanticAIBaseAgent + adapters +│ ├── _base.py +│ ├── _capabilities.py +│ ├── _context_adapter.py +│ ├── _event_translator.py +│ └── _tool_adapter.py ├── __init__.py # Top-level exports -├── cmr_care.py # Standalone agent example +├── cmr_care.py # Standalone agent (OpenAI-based) └── research_partner/ # Agent group - ├── __init__.py # Group exports + ├── __init__.py ├── capability_feasibility_mapper.py ├── workflow_spec_builder.py ├── experiment_implementation.py @@ -206,17 +473,18 @@ akd_ext/agents/ ``` Each agent file follows this internal layout: + 1. Module docstring 2. Imports 3. System prompt constant -4. Tool factory function (if applicable) +4. Tool / capability factory function (if applicable) 5. Config class -6. Input/Output schema classes +6. Input / output schema classes (can live above the config if they're prerequisites) 7. Agent class ## Registration -### In the agent group `__init__.py` +### Group `__init__.py` ```python # akd_ext/agents/research_partner/__init__.py @@ -236,7 +504,7 @@ __all__ = [ ] ``` -### In the top-level `akd_ext/agents/__init__.py` +### Top-level `akd_ext/agents/__init__.py` ```python from akd_ext.agents.research_partner import ( @@ -256,9 +524,10 @@ __all__ = [ ] ``` -## Writing Tests +## Writing tests -Tests live in `tests/agents/` mirroring the source structure. Use the `reasoning_effort` fixture from `tests/conftest.py`. +Tests live in `tests/agents/` mirroring the source structure. Use the +`reasoning_effort` fixture from `tests/conftest.py`. ```python """Functional tests for My Agent.""" @@ -309,6 +578,19 @@ async def test_my_agent(query: str, reasoning_effort: str): assert result.report.strip(), "Report should not be empty" ``` +For **hermetic unit tests on `PydanticAIBaseAgent`**, swap the real model for +`TestModel` and disable any MCP / network capabilities: + +```python +from pydantic_ai.models.test import TestModel + +async def test_my_agent_hermetic(): + agent = MyAgent(MyAgentConfig(capabilities=[])) # no MCP in hermetic run + with agent.override(model=TestModel()): + result = await agent.arun(_make_input(query="x")) + assert isinstance(result, (MyAgentOutputSchema, TextOutput)) +``` + Run tests with: ```bash @@ -316,7 +598,7 @@ uv run pytest tests/agents/research_partner/test_my_agent.py -n=3 uv run pytest tests/agents/research_partner/test_my_agent.py --reasoning-effort=low -n=3 ``` -## Running the Agent +## Running the agent ```python import asyncio @@ -337,6 +619,10 @@ async for event in agent.astream(MyAgentInputSchema(query="my question", data_pa print(event.event_type, event.data) ``` +On `PydanticAIBaseAgent`, each `event.run_context` carries the live pai state; +feed it (or `agent.last_run_context`) into the next call for multi-turn +continuation as shown in *RunContext propagation and multi-turn runs* above. + ## Linting Always run before committing: @@ -345,12 +631,14 @@ Always run before committing: uv run pre-commit run --all-files ``` -## Reference Examples +## Reference examples -| Pattern | Example File | -|---------|-------------| -| Agent without tools | `akd_ext/agents/research_partner/capability_feasibility_mapper.py` | -| Agent with MCP tools | `akd_ext/agents/cmr_care.py` | +| Pattern | Example file | +|---|---| +| Agent without tools (OpenAI) | `akd_ext/agents/research_partner/capability_feasibility_mapper.py` | +| Agent with MCP tools (OpenAI) | `akd_ext/agents/cmr_care.py` | +| Agent with MCP capability (Pydantic AI) | `examples/cmr_care_pydantic.py` | | Multiple output fields | `akd_ext/agents/research_partner/workflow_spec_builder.py` | | Single structured output (no union) | `akd_ext/agents/code_search_care.py` | | Test with parametrize | `tests/agents/test_cmr_care.py` | +| Hermetic test with `TestModel` (Pydantic AI) | `tests/agents/test_base_pydantic.py` | From 1c638fdaeed9ccac1dbac5b8b65424040a062961 Mon Sep 17 00:00:00 2001 From: Sanjog Thapa Date: Wed, 22 Apr 2026 17:56:14 -0500 Subject: [PATCH 14/25] modify for PydanticAIBaseAgent minimal base runtime --- akd_ext/agents/_base/pydantic_ai/_base.py | 144 +++------ .../agents/_base/pydantic_ai/_capabilities.py | 135 -------- .../_base/pydantic_ai/_context_adapter.py | 236 -------------- tests/agents/test_base_pydantic.py | 306 ++---------------- 4 files changed, 74 insertions(+), 747 deletions(-) delete mode 100644 akd_ext/agents/_base/pydantic_ai/_capabilities.py delete mode 100644 akd_ext/agents/_base/pydantic_ai/_context_adapter.py diff --git a/akd_ext/agents/_base/pydantic_ai/_base.py b/akd_ext/agents/_base/pydantic_ai/_base.py index dafe830..42ce29f 100644 --- a/akd_ext/agents/_base/pydantic_ai/_base.py +++ b/akd_ext/agents/_base/pydantic_ai/_base.py @@ -13,7 +13,7 @@ class MyAgent(PydanticAIBaseAgent[MyIn, MyOut]): output_schema = MyOut | TextOutput config_schema = MyConfig - agent = MyAgent(MyConfig(model_name="openai:gpt-5.2", reasoning_effort="high")) + agent = MyAgent(MyConfig(model_name="openai:gpt-5.2")) result = await agent.arun(MyIn(query="...")) Config auto-exposure is handled by ``ConfigBindingMixin`` — ``agent.model_name``, @@ -32,6 +32,8 @@ class MyAgent(PydanticAIBaseAgent[MyIn, MyOut]): from pydantic_ai import AgentRunResultEvent, ModelRetry from pydantic_ai.capabilities import AbstractCapability from pydantic_ai.capabilities.hooks import Hooks +from pydantic_ai.messages import ModelMessage +from pydantic_ai.result import RunUsage as PAIRunUsage from akd._base import ( CompletedEvent, @@ -44,16 +46,8 @@ class MyAgent(PydanticAIBaseAgent[MyIn, MyOut]): ) from akd._base.protocols import AKDExecutable, RunContextProtocol from akd._base.structures import RunContext as AKDRunContext -from akd._base.structures import RunUsage as AKDRunUsage from akd.agents._base import BaseAgentConfig -from ._capabilities import ReflectionCapability, ToolCallLimits, make_ratio_trimmer -from ._context_adapter import ( - _message_history_from_run_context, - _pai_messages_to_akd_dicts, - _pai_usage_to_akd_usage, - _usage_from_run_context, -) from ._event_translator import pai_event_to_akd_event from ._tool_adapter import akd_to_pai_tool @@ -76,25 +70,10 @@ class PydanticAIBaseAgentConfig(BaseAgentConfig): capabilities: list[Any] = Field( default_factory=list, description=( - "Pydantic AI capabilities (Thinking, WebSearch, Hooks, custom). " + "Pydantic AI capabilities (Thinking, WebSearch, MCP, Hooks, custom). " "Merged with any capabilities auto-derived from AKD scalar fields." ), ) - history_processors: list[Any] = Field( - default_factory=list, - description="Pydantic AI history processor callables; merged with config-derived processors.", - ) - - # Override BaseAgentConfig's default-on trimming. The naive ratio-based - # trimmer we ship in ``_capabilities.make_ratio_trimmer`` drops arbitrary - # slices of the message history, which breaks pydantic_ai's invariant - # that every ``tool`` message must be preceded by an ``assistant`` message - # with matching ``tool_calls``. A pydantic_ai-aware trimmer that respects - # tool-call pairing is a follow-up; until then, trimming is off by - # default. Consumers who want it enabled explicitly can set - # ``enable_trimming=True`` and supply their own processor via - # ``history_processors``. - enable_trimming: bool = Field(default=False) # -- Silence AKD-core's litellm-based config validators -------------- # BaseAgentConfig defines ``validate_max_tokens_against_model`` and @@ -131,8 +110,7 @@ class PydanticAIBaseAgent[InSchema: InputSchema, OutSchema: OutputSchema]( - ``input_schema`` / ``output_schema`` / ``config_schema`` — class attrs - ``check_output`` — semantic output validation (optional) - - ``_build_capabilities_from_scalars`` — additional scalar→capability mappings - - ``_build_history_processors_from_scalars`` — additional history processors + - ``_build_capabilities_from_scalars`` — scalar→capability mappings (future) - ``_to_prompt`` — custom prompt rendering from ``InputSchema`` - ``_adapt_tools`` — custom tool adapter logic (rarely needed) @@ -192,10 +170,6 @@ def __init__(self, config: PydanticAIBaseAgentConfig | None = None) -> None: *self._build_capabilities_from_scalars(), *self.config.capabilities, ], - history_processors=[ - *self._build_history_processors_from_scalars(), - *self.config.history_processors, - ], **extra_kwargs, ) @@ -300,33 +274,18 @@ async def astream( def _wrap_pai_ctx(self) -> AKDRunContext: """Wrap ``self._live_pai_ctx`` in an AKD ``RunContext``. - Populates all three AKD typed fields so consumers who only read - ``run_context.messages`` / ``.usage`` / ``.run_id`` see useful values: - - - ``messages`` — best-effort OpenAI-style ``list[dict]`` produced from - the pai ``ModelMessage`` list. Lossy for multi-part responses; the - lossless path lives on ``pai_run_context`` below. - - ``usage`` — AKD ``RunUsage`` with the three structural fields mapped - and pai overflow (cache / audio / tool_calls tokens) preserved in - ``details``. - - ``run_id`` — verbatim from the pai ctx. - - ``pai_run_context`` extra — the live pai ``RunContext`` itself. The - input-side helpers (``_message_history_from_run_context`` / - ``_usage_from_run_context``) prefer this when present, so a caller - who passes ``event.run_context`` straight back into the next - ``arun`` / ``astream`` gets lossless pai-native continuation. + Phase 1 populates **only** the ``pai_run_context`` extra so round-trip + continuation works losslessly: a caller who passes + ``event.run_context`` (or ``agent.last_run_context``) back into the + next ``arun`` / ``astream`` gets pai-native message history and usage + via the input-side helpers below. AKD's typed fields (``messages`` / + ``usage`` / ``run_id``) are deliberately left at defaults here; + reflecting pai state onto them is Phase 2's job. """ pai_ctx = self._live_pai_ctx if pai_ctx is None: return AKDRunContext() - pai_messages = getattr(pai_ctx, "messages", None) - pai_usage = getattr(pai_ctx, "usage", None) - return AKDRunContext( - messages=_pai_messages_to_akd_dicts(pai_messages) if pai_messages else None, - usage=_pai_usage_to_akd_usage(pai_usage) if pai_usage is not None else AKDRunUsage(), - run_id=getattr(pai_ctx, "run_id", None), - pai_run_context=pai_ctx, - ) + return AKDRunContext(pai_run_context=pai_ctx) @property def last_run_context(self) -> AKDRunContext | None: @@ -382,48 +341,16 @@ def _deps_from_run_context(self, run_context: RunContextProtocol | None) -> Any: return run_context.deps return None - # ── Zone 1: scalar-driven capability / history-processor construction ─ + # ── Zone 1: scalar-driven capability construction ──────────────────── def _build_capabilities_from_scalars(self) -> list[AbstractCapability]: - """Derive capabilities from AKD scalar config fields shared by all agents. - - Subclasses override to append their own scalar→capability mappings; call - ``super()._build_capabilities_from_scalars()`` first to inherit the defaults. - """ - caps: list[AbstractCapability] = [] - - if self.config.reasoning_effort: - from pydantic_ai.capabilities import Thinking - - # pydantic_ai's Thinking currently only accepts `effort`. - # AKD's `reasoning_summary` is dormant for now; if pydantic_ai - # adds a summary knob later, expose it here. - caps.append(Thinking(effort=self.config.reasoning_effort)) - - if self.config.max_tool_iterations or self.config.max_tool_calls: - caps.append( - ToolCallLimits( - max_iterations=self.config.max_tool_iterations, - max_calls=self.config.max_tool_calls, - ), - ) - - if self.config.reflection_prompt: - caps.append(ReflectionCapability(prompt=self.config.reflection_prompt)) - - return caps - - def _build_history_processors_from_scalars(self) -> list: - """Derive history processors from AKD scalar config fields. + """Derive capabilities from AKD config fields. - Subclasses override to append their own; call ``super()`` first. + Subclasses override to append their own scalar→capability mappings; + call ``super()._build_capabilities_from_scalars()`` first to inherit + future defaults. """ - procs: list = [] - if self.config.enable_trimming: - procs.append(make_ratio_trimmer(1 - self.config.trim_ratio)) - # ``trim_ratio`` in AKD is the *target retention* ratio (0.75 = keep 75%). - # The trimmer we pass expects the *drop* fraction, hence 1 - ratio. - return procs + return [] # ── Tool adaptation ────────────────────────────────────────────────── @@ -486,6 +413,39 @@ def check_output(self, output) -> str | None: return None +# --------------------------------------------------------------------------- +# Input-side run-context helpers +# +# Only the ``pai_run_context`` extra is consulted. Callers who want to drive +# multi-turn continuation feed back ``agent.last_run_context`` (or any event's +# ``run_context``) — both carry the live pai ``RunContext`` under the extra, +# so message history / usage come across losslessly with no conversion. +# Phase 2 (``_context_adapter.py``) adds reflection onto AKD typed fields; +# the plain AKD-shape → pai-shape conversion path is intentionally omitted. +# --------------------------------------------------------------------------- + + +def _message_history_from_run_context(run_context: Any | None) -> list[ModelMessage] | None: + """Return the pai message history from ``run_context.pai_run_context``, or ``None``.""" + if run_context is None: + return None + pai_ctx = getattr(run_context, "pai_run_context", None) + if pai_ctx is None: + return None + messages = getattr(pai_ctx, "messages", None) + return list(messages) if messages else None + + +def _usage_from_run_context(run_context: Any | None) -> PAIRunUsage | None: + """Return the pai ``RunUsage`` from ``run_context.pai_run_context``, or ``None``.""" + if run_context is None: + return None + pai_ctx = getattr(run_context, "pai_run_context", None) + if pai_ctx is None: + return None + return getattr(pai_ctx, "usage", None) + + __all__ = [ "PydanticAIBaseAgent", "PydanticAIBaseAgentConfig", diff --git a/akd_ext/agents/_base/pydantic_ai/_capabilities.py b/akd_ext/agents/_base/pydantic_ai/_capabilities.py deleted file mode 100644 index ab77dda..0000000 --- a/akd_ext/agents/_base/pydantic_ai/_capabilities.py +++ /dev/null @@ -1,135 +0,0 @@ -"""Custom pydantic_ai Capabilities built from AKD scalar config fields. - -These are the concrete implementations behind ``PydanticAIBaseAgent``'s -``_build_capabilities_from_scalars`` / ``_build_history_processors_from_scalars`` -hooks. - -Factories in this module return ready-to-use pydantic_ai ``Capability`` or -``history_processor`` objects. The base agent constructs them at ``__init__`` -time from the relevant ``BaseAgentConfig`` fields. - -Contents: - -- ``ToolCallLimits(max_iterations=..., max_calls=...)`` — Hooks-based cap on - tool-loop iterations and total tool calls; raises AKD's ``UsageLimitExceeded`` - (which pydantic_ai surfaces via ``UnexpectedModelBehavior`` to the caller). -- ``ReflectionCapability(prompt=...)`` — Hooks-based injector that prepends - ``prompt`` as a system-level nudge on every model request after the first. -- ``make_ratio_trimmer(trim_ratio=...)`` — stateless history processor that - drops the oldest fraction of the conversation when invoked. -""" - -from __future__ import annotations - -from collections.abc import Callable -from typing import Any - -from pydantic_ai.capabilities.hooks import Hooks - -from akd._base.errors import MaxToolCallsExceeded, MaxToolIterationsExceeded - - -def ToolCallLimits( - *, - max_iterations: int | None = None, - max_calls: int | None = None, -) -> Hooks: - """Build a Hooks capability that caps tool-loop iterations and tool calls. - - Counters live in closure state; each agent instance gets its own ``Hooks`` - via ``_build_capabilities_from_scalars``. Counters reset on agent - construction, not per-run — which mirrors AKD's historical ReAct cap - semantics. Callers who need per-run reset should reconstruct the agent. - - Raises: - MaxToolIterationsExceeded: when ``max_iterations`` is set and the - model-request loop would exceed the cap on the next round. - MaxToolCallsExceeded: when ``max_calls`` is set and the total number - of tool executions would exceed the cap. - """ - hooks = Hooks() - state = {"iterations": 0, "calls": 0} - - if max_iterations is not None: - - @hooks.on.before_model_request - async def _cap_iterations(ctx, request_context): # noqa: ARG001 - state["iterations"] += 1 - if state["iterations"] > max_iterations: - raise MaxToolIterationsExceeded( - f"Exceeded max tool iterations ({max_iterations}).", - ) - return request_context - - if max_calls is not None: - - @hooks.on.before_tool_execute - async def _cap_calls(ctx, *, call, tool_def, args): # noqa: ARG001 - state["calls"] += 1 - if state["calls"] > max_calls: - raise MaxToolCallsExceeded( - f"Exceeded max tool calls ({max_calls}).", - ) - return args - - return hooks - - -def ReflectionCapability(*, prompt: str) -> Hooks: - """Build a Hooks capability that injects ``prompt`` before each model request - after the first. - - Implementation: a counter tracks how many model requests have fired; - from the second onwards, we prepend the reflection prompt as a system - instruction to the pending request. The first request is skipped so the - reflection prompt never leaks into the initial turn. - """ - from pydantic_ai.messages import ModelRequest, SystemPromptPart - - hooks = Hooks() - state = {"turn": 0} - - @hooks.on.before_model_request - async def _inject_reflection(ctx, request_context): # noqa: ARG001 - state["turn"] += 1 - if state["turn"] == 1: - return request_context - # Prepend the reflection as a system message on this turn - request_context.messages = [ - ModelRequest(parts=[SystemPromptPart(content=prompt)]), - *request_context.messages, - ] - return request_context - - return hooks - - -def make_ratio_trimmer(trim_ratio: float) -> Callable[[list[Any]], list[Any]]: - """Return a stateless history processor that drops the oldest - ``trim_ratio`` fraction of messages. - - A ``trim_ratio`` of ``0.3`` drops the oldest 30% of the message list on - each call. ``0.0`` is a no-op; ``1.0`` drops everything (don't do that). - The first message (usually the system prompt) is preserved so the agent's - instructions survive trimming. - """ - if not 0 <= trim_ratio < 1: - raise ValueError(f"trim_ratio must be in [0, 1), got {trim_ratio!r}") - - def processor(messages: list[Any]) -> list[Any]: - if not messages or trim_ratio == 0: - return messages - # Keep the first message (usually system), trim the oldest portion - # of the rest, keep the tail. - head, rest = messages[:1], messages[1:] - drop_count = int(len(rest) * trim_ratio) - return [*head, *rest[drop_count:]] - - return processor - - -__all__ = [ - "ReflectionCapability", - "ToolCallLimits", - "make_ratio_trimmer", -] diff --git a/akd_ext/agents/_base/pydantic_ai/_context_adapter.py b/akd_ext/agents/_base/pydantic_ai/_context_adapter.py deleted file mode 100644 index e1c9fce..0000000 --- a/akd_ext/agents/_base/pydantic_ai/_context_adapter.py +++ /dev/null @@ -1,236 +0,0 @@ -"""Helpers that reshape AKD run-context values to what pydantic_ai expects. - -Both ``akd.RunContext`` and ``pydantic_ai.RunContext`` satisfy the -``RunContextProtocol`` structurally for the shared fields, but the types of -``usage`` and ``messages`` differ between the two sides: - -- AKD stores messages as OpenAI-style dicts; pydantic_ai stores typed - ``ModelMessage`` objects. -- AKD has its own ``RunUsage`` Pydantic BaseModel; pydantic_ai has its own - ``RunUsage`` dataclass. - -These helpers detect the incoming shape and convert when necessary, so a -caller can pass *either* an AKD ``RunContext`` or a ``pydantic_ai.RunContext`` -to ``PydanticAIBaseAgent.arun`` / ``.astream`` and get the right thing wired -through to ``super().run(...)``. -""" - -from __future__ import annotations - -from typing import Any - -from pydantic_ai.messages import ( - ModelMessage, - ModelRequest, - ModelResponse, - SystemPromptPart, - TextPart, - ThinkingPart, - ToolCallPart, - ToolReturnPart, - UserPromptPart, -) -from pydantic_ai.result import RunUsage as PAIRunUsage - -from akd._base.protocols import TokenCounts -from akd._base.structures import RunUsage as AKDRunUsage - - -def _akd_dicts_to_pai_messages(messages: list[dict[str, Any]]) -> list[ModelMessage]: - """Convert AKD OpenAI-style message dicts to pydantic_ai ``ModelMessage`` objects. - - Each dict is expected to have ``{"role": "...", "content": "..."}``. - - - ``system`` / ``user`` / ``tool`` → ``ModelRequest`` with the matching part - - ``assistant`` → ``ModelResponse`` with a ``TextPart`` - """ - out: list[ModelMessage] = [] - for msg in messages: - role = msg.get("role") - content = msg.get("content", "") - if role == "system": - out.append(ModelRequest(parts=[SystemPromptPart(content=content)])) - elif role == "user": - out.append(ModelRequest(parts=[UserPromptPart(content=content)])) - elif role == "assistant": - out.append(ModelResponse(parts=[TextPart(content=content)])) - # Roles we don't know how to translate (e.g. "tool" returns) are - # skipped silently; the caller is expected to pass history we can - # faithfully represent. Extending this map is a local change. - return out - - -def _usage_to_pai(usage: TokenCounts) -> PAIRunUsage: - """Build a ``pydantic_ai.RunUsage`` from anything matching ``TokenCounts``. - - Pass-through if the value is already a ``pydantic_ai.RunUsage``. Otherwise - copy the three structural fields; all other ``RunUsage`` fields default to 0. - """ - if isinstance(usage, PAIRunUsage): - return usage - return PAIRunUsage( - input_tokens=usage.input_tokens, - output_tokens=usage.output_tokens, - requests=usage.requests, - ) - - -def _pai_messages_to_akd_dicts(messages: list[ModelMessage]) -> list[dict[str, Any]]: - """Best-effort inverse of :func:`_akd_dicts_to_pai_messages`. - - Produces OpenAI-style role-tagged dicts suitable for AKD's - ``RunContext.messages: list[dict[str, Any]]`` field. Lossy for multi-part - responses (text + thinking + tool calls collapse into one assistant dict), - tool-argument dicts become JSON strings, and some part kinds (e.g. binary - content in ``UserPromptPart``) are stringified. The lossless path remains - available via ``run_context.pai_run_context.messages``. - - Why this exists: pai's live ``RunContext.messages`` is ``list[ModelMessage]``, - but AKD's ``RunContext.messages`` is typed ``list[dict[str, Any]] | None`` - and is pydantic-validated — assigning pai objects would be rejected. We - reflect the pai messages onto AKD's dict-shaped slot so application logic - reading ``run_context.messages`` (typically as read-only inspection for - logs / UI / debugging) sees useful content instead of ``None``. Drop this - helper once akd-core widens ``RunContext.messages`` to accept pai message - types — at that point ``_wrap_pai_ctx`` can assign ``pai_ctx.messages`` - directly. - """ - out: list[dict[str, Any]] = [] - for msg in messages: - if isinstance(msg, ModelRequest): - for part in msg.parts: - if isinstance(part, SystemPromptPart): - out.append({"role": "system", "content": part.content}) - elif isinstance(part, UserPromptPart): - content = part.content if isinstance(part.content, str) else str(part.content) - out.append({"role": "user", "content": content}) - elif isinstance(part, ToolReturnPart): - out.append( - { - "role": "tool", - "tool_call_id": part.tool_call_id, - "name": part.tool_name, - "content": str(part.content), - }, - ) - elif isinstance(msg, ModelResponse): - texts: list[str] = [] - tool_calls: list[dict[str, Any]] = [] - for part in msg.parts: - if isinstance(part, TextPart): - texts.append(part.content) - elif isinstance(part, ThinkingPart): - texts.append(f"[thinking] {part.content}") - elif isinstance(part, ToolCallPart): - tool_calls.append( - { - "id": part.tool_call_id, - "type": "function", - "function": { - "name": part.tool_name, - "arguments": part.args_as_json_str(), - }, - }, - ) - assistant: dict[str, Any] = { - "role": "assistant", - "content": "\n".join(texts) if texts else "", - } - if tool_calls: - assistant["tool_calls"] = tool_calls - out.append(assistant) - return out - - -def _pai_usage_to_akd_usage(pai_usage: PAIRunUsage) -> AKDRunUsage: - """Map a ``pydantic_ai.RunUsage`` onto AKD's ``RunUsage``. - - The three structural fields (``input_tokens`` / ``output_tokens`` / - ``requests``) round-trip exactly. Pai's overflow fields (cache / audio - token counts, ``tool_calls``) plus its own ``details`` dict collapse into - AKD's ``details`` so no information is lost when this AKD view is the - only thing a consumer inspects. - - Why this exists: pai's ``RunUsage`` is a distinct class from AKD's (fewer - fields, different layout), and AKD's ``RunContext.usage`` is typed - strictly. We reflect pai usage onto an AKD ``RunUsage`` so application - logic reading ``run_context.usage`` (again, typically read-only - inspection for billing / telemetry / UI) sees live counts instead of an - empty default. Drop this helper once akd-core's ``RunContext.usage`` - accepts pai's ``RunUsage`` directly — ``_wrap_pai_ctx`` can then forward - ``pai_ctx.usage`` verbatim. - """ - details: dict[str, int] = dict(pai_usage.details or {}) - for key in ( - "cache_write_tokens", - "cache_read_tokens", - "input_audio_tokens", - "cache_audio_read_tokens", - "output_audio_tokens", - "tool_calls", - ): - val = getattr(pai_usage, key, 0) or 0 - if val: - details[key] = val - return AKDRunUsage( - input_tokens=pai_usage.input_tokens, - output_tokens=pai_usage.output_tokens, - requests=pai_usage.requests, - details=details, - ) - - -def _message_history_from_run_context(run_context: Any | None) -> list[ModelMessage] | None: - """Extract a pydantic_ai-shaped message history from any ``RunContextProtocol``. - - Returns ``None`` if the context has no messages. - - Lookup order: - - 1. If the ctx has a populated ``pai_run_context`` extra (set by a previous - ``PydanticAIBaseAgent`` run), return its ``messages`` verbatim — a - lossless list of ``ModelMessage`` objects, no re-conversion needed. - 2. Otherwise, fall through to the historical shape-discriminating path: - dicts get converted via :func:`_akd_dicts_to_pai_messages`, existing - ``ModelMessage`` instances pass through. - """ - if run_context is None: - return None - pai_ctx = getattr(run_context, "pai_run_context", None) - if pai_ctx is not None and getattr(pai_ctx, "messages", None): - return list(pai_ctx.messages) - msgs = getattr(run_context, "messages", None) - if not msgs: - return None - first = msgs[0] - if isinstance(first, dict): - return _akd_dicts_to_pai_messages(msgs) - return msgs # already pydantic_ai ModelMessage objects - - -def _usage_from_run_context(run_context: Any | None) -> PAIRunUsage | None: - """Extract a pydantic_ai ``RunUsage`` from any ``RunContextProtocol``. - - Prefers ``run_context.pai_run_context.usage`` (a pai ``RunUsage``, - returned verbatim — no conversion, no overflow loss). Falls back to the - AKD-shape path via :func:`_usage_to_pai` when the extra is absent. - """ - if run_context is None: - return None - pai_ctx = getattr(run_context, "pai_run_context", None) - if pai_ctx is not None and pai_ctx.usage is not None: - return pai_ctx.usage - usage = getattr(run_context, "usage", None) - if usage is None: - return None - return _usage_to_pai(usage) - - -__all__ = [ - "_akd_dicts_to_pai_messages", - "_message_history_from_run_context", - "_pai_messages_to_akd_dicts", - "_pai_usage_to_akd_usage", - "_usage_from_run_context", - "_usage_to_pai", -] diff --git a/tests/agents/test_base_pydantic.py b/tests/agents/test_base_pydantic.py index d1bcc91..1499823 100644 --- a/tests/agents/test_base_pydantic.py +++ b/tests/agents/test_base_pydantic.py @@ -15,16 +15,15 @@ from akd._base import InputSchema, OutputSchema, TextOutput from akd._base.errors import SchemaValidationError -from akd.tools._base import BaseTool - -from akd_ext.agents._base import PydanticAIBaseAgent, PydanticAIBaseAgentConfig from akd._base.protocols import ( AKDExecutable, AKDTool, RunContextProtocol, TokenCounts, ) +from akd.tools._base import BaseTool +from akd_ext.agents._base import PydanticAIBaseAgent, PydanticAIBaseAgentConfig from akd_ext.agents._base.pydantic_ai._tool_adapter import akd_to_pai_tool @@ -49,9 +48,7 @@ def is_empty(self) -> bool: class _EchoConfig(PydanticAIBaseAgentConfig): - """Config for the echo test agent; adds one subclass-specific scalar.""" - - enable_extra_capability: bool = Field(default=False) + """Config for the echo test agent.""" class _EchoAgent(PydanticAIBaseAgent[_EchoInput, _EchoOutput]): @@ -217,16 +214,15 @@ async def test_arun_run_context_available_after_run(): the pydantic_ai ``RunContext`` captured during the run. ``arun`` itself returns the output value (per the AKD contract); the - captured ctx is retained on the instance so callers can inspect it if - needed. Exposing it as a public property (``agent.last_run_context``) - is a plausible future ergonomic; this test pins the internal hook in - the meantime. + captured ctx is retained on the instance so callers can inspect it via + ``agent.last_run_context``. """ from pydantic_ai import RunContext as PAIRunContext agent = _EchoAgent() # Before any run, nothing has been captured. assert agent._live_pai_ctx is None + assert agent.last_run_context is None with agent.override(model=TestModel()): result = await agent.arun(_EchoInput(query="hello")) @@ -236,59 +232,9 @@ async def test_arun_run_context_available_after_run(): assert isinstance(agent._live_pai_ctx, PAIRunContext) assert agent._live_pai_ctx.usage.requests >= 1 - -# --------------------------------------------------------------------------- -# RunContext reflection onto AKD typed fields -# --------------------------------------------------------------------------- - - -@pytest.mark.asyncio -async def test_astream_reflects_pai_state_onto_akd_fields(): - """Terminal ``CompletedEvent.run_context`` must expose pai state on the - AKD typed fields (``messages`` / ``usage`` / ``run_id``) *and* keep the - lossless ``pai_run_context`` extra.""" - from akd._base import CompletedEvent - from pydantic_ai import RunContext as PAIRunContext - - agent = _EchoAgent() - with agent.override(model=TestModel()): - events = [ev async for ev in agent.astream(_EchoInput(query="hello"))] - - completed = [ev for ev in events if isinstance(ev, CompletedEvent)] - assert completed - ctx = completed[-1].run_context - - # Reflected typed fields on the AKD RunContext: - assert isinstance(ctx.messages, list) and ctx.messages - for msg in ctx.messages: - assert isinstance(msg, dict) - assert "role" in msg - assert ctx.usage.requests >= 1 - assert ctx.run_id is not None - - # Lossless pai extra still attached alongside the reflected view: - assert isinstance(ctx.pai_run_context, PAIRunContext) - - -@pytest.mark.asyncio -async def test_arun_last_run_context_property(): - """``agent.last_run_context`` returns ``None`` before any run and a - populated AKD ``RunContext`` after ``arun``.""" - from pydantic_ai import RunContext as PAIRunContext - - agent = _EchoAgent() - assert agent.last_run_context is None - - with agent.override(model=TestModel()): - out = await agent.arun(_EchoInput(query="hello")) - assert isinstance(out, (_EchoOutput, TextOutput)) - + # last_run_context wraps the pai ctx; the lossless extra is populated. ctx = agent.last_run_context assert ctx is not None - assert ctx.messages # non-empty list of dicts - assert all(isinstance(m, dict) and "role" in m for m in ctx.messages) - assert ctx.usage.requests >= 1 - assert ctx.run_id is not None assert isinstance(ctx.pai_run_context, PAIRunContext) @@ -311,16 +257,15 @@ async def test_multi_turn_via_last_run_context(): assert len(ctx_after_turn_2.pai_run_context.messages) > turn_1_message_count -def test_input_side_prefers_pai_run_context(): - """When a ``run_context`` carries both stale AKD-shape messages *and* a - populated ``pai_run_context`` extra, the pai path wins — the dict view is - ignored in favor of the lossless pai ``ModelMessage`` list.""" +def test_input_side_reads_pai_run_context(): + """Message history and usage are pulled verbatim from + ``run_context.pai_run_context`` — no conversion, no fallback branches.""" from akd._base.structures import RunContext as AKDRunContext from pydantic_ai import RunContext as PAIRunContext from pydantic_ai.messages import ModelRequest, UserPromptPart from pydantic_ai.result import RunUsage as PAIRunUsage - from akd_ext.agents._base.pydantic_ai._context_adapter import ( + from akd_ext.agents._base.pydantic_ai._base import ( _message_history_from_run_context, _usage_from_run_context, ) @@ -330,13 +275,10 @@ def test_input_side_prefers_pai_run_context(): pai_ctx = PAIRunContext(deps=None, model=None, usage=pai_usage) pai_ctx.messages = pai_messages - akd_ctx = AKDRunContext( - messages=[{"role": "user", "content": "stale-akd-shape"}], - pai_run_context=pai_ctx, - ) + akd_ctx = AKDRunContext(pai_run_context=pai_ctx) history = _message_history_from_run_context(akd_ctx) - assert history is pai_messages or history == pai_messages + assert history == pai_messages assert history[0].parts[0].content == "pai-truth" usage = _usage_from_run_context(akd_ctx) @@ -344,34 +286,26 @@ def test_input_side_prefers_pai_run_context(): assert usage.input_tokens == 10 -def test_input_side_falls_back_to_akd_shape(): - """Pure AKD ``RunContext`` (no ``pai_run_context`` extra) still works - through the historical conversion path.""" +def test_input_side_returns_none_without_pai_run_context(): + """AKD ``RunContext`` with no ``pai_run_context`` extra yields ``None`` — + Phase 1 does not convert AKD-shape typed fields into pai shapes.""" from akd._base.structures import RunContext as AKDRunContext from akd._base.structures import RunUsage as AKDRunUsage - from pydantic_ai.result import RunUsage as PAIRunUsage - from akd_ext.agents._base.pydantic_ai._context_adapter import ( + from akd_ext.agents._base.pydantic_ai._base import ( _message_history_from_run_context, _usage_from_run_context, ) akd_ctx = AKDRunContext( - messages=[ - {"role": "system", "content": "you are helpful"}, - {"role": "user", "content": "hi"}, - ], + messages=[{"role": "user", "content": "hi"}], usage=AKDRunUsage(input_tokens=5, output_tokens=7, requests=1), ) - history = _message_history_from_run_context(akd_ctx) - assert history is not None - assert len(history) == 2 - - usage = _usage_from_run_context(akd_ctx) - assert isinstance(usage, PAIRunUsage) - assert usage.input_tokens == 5 - assert usage.requests == 1 + assert _message_history_from_run_context(akd_ctx) is None + assert _usage_from_run_context(akd_ctx) is None + assert _message_history_from_run_context(None) is None + assert _usage_from_run_context(None) is None # --------------------------------------------------------------------------- @@ -504,199 +438,3 @@ async def test_existing_akd_tool_is_pai_compatible(): assert isinstance(akd_tool, AKDTool) # And the input schema reference is intact. assert akd_tool.input_schema is DummyInputSchema - - -# --------------------------------------------------------------------------- -# Zone 3: subclass extension via _build_capabilities_from_scalars -# --------------------------------------------------------------------------- - - -# --------------------------------------------------------------------------- -# CMRCarePydanticAgent parity smoke test -# --------------------------------------------------------------------------- - - -def test_cmr_care_pydantic_agent_constructs(): - """CMRCarePydanticAgent builds cleanly on PydanticAIBaseAgent using the same - schemas as the existing CMRCareAgent.""" - from akd_ext.agents.cmr_care import CMRCareAgentInputSchema, CMRCareAgentOutputSchema - from examples.cmr_care_pydantic import CMRCarePydanticAgent, CMRCarePydanticConfig - - agent = CMRCarePydanticAgent(CMRCarePydanticConfig(debug=True)) - assert agent.input_schema is CMRCareAgentInputSchema - assert agent.output_schema == CMRCareAgentOutputSchema | TextOutput - assert isinstance(agent, AKDExecutable) - # Config auto-exposure works through the subclass config: - assert agent.reasoning_effort == "medium" - assert "CMR" in (agent.description or "") - - -def test_cmr_care_pydantic_agent_check_output(): - """Subclass override of check_output dispatches via the late-bound bridge.""" - from akd_ext.agents.cmr_care import CMRCareAgentOutputSchema - from examples.cmr_care_pydantic import CMRCarePydanticAgent, CMRCarePydanticConfig - - agent = CMRCarePydanticAgent(CMRCarePydanticConfig()) - # Empty result should be rejected by the subclass override - msg = agent.check_output(CMRCareAgentOutputSchema(result=" ")) - assert isinstance(msg, str) - assert "empty" in msg.lower() - # Non-empty result passes - assert agent.check_output(CMRCareAgentOutputSchema(result="found datasets")) is None - # TextOutput still bubbles through per the base class default - assert agent.check_output(TextOutput(content="need clarification")) is None - - -@pytest.mark.asyncio -async def test_cmr_care_pydantic_agent_arun_with_test_model(): - """End-to-end wiring check: CMRCarePydanticAgent.arun works against TestModel. - - The default config wires pydantic_ai's ``MCP`` capability at the CMR - endpoint — pydantic_ai tries to fetch the tool list even when ``TestModel`` - stands in for the LLM, which would reach the real network. We override - ``capabilities=[]`` here so the test runs hermetically. - """ - from akd_ext.agents.cmr_care import CMRCareAgentInputSchema, CMRCareAgentOutputSchema - from examples.cmr_care_pydantic import CMRCarePydanticAgent, CMRCarePydanticConfig - - agent = CMRCarePydanticAgent(CMRCarePydanticConfig(capabilities=[])) - with agent.override(model=TestModel()): - result = await agent.arun(CMRCareAgentInputSchema(query="sea ice datasets")) - assert isinstance(result, (CMRCareAgentOutputSchema, TextOutput)) - - -def test_subclass_can_extend_capabilities_hook(): - """Subclasses may override ``_build_capabilities_from_scalars`` and call super().""" - - class _MarkerCapability: - """Stand-in capability used to assert the hook was invoked.""" - - class _ExtAgent(_EchoAgent): - """Echo agent that adds a marker capability when enabled.""" - - def _build_capabilities_from_scalars(self): - caps = super()._build_capabilities_from_scalars() - if self.config.enable_extra_capability: - caps.append(_MarkerCapability()) - return caps - - # Instantiation would normally require the capability to be a real one, - # but construction-time pydantic_ai validation is model-side; we only - # check the hook output directly here. - agent = _ExtAgent.__new__(_ExtAgent) # bypass __init__; we only want the method - agent.config = _EchoConfig(enable_extra_capability=True) - produced = agent._build_capabilities_from_scalars() - assert any(isinstance(c, _MarkerCapability) for c in produced) - - -# --------------------------------------------------------------------------- -# Smoke tests for a PydanticAIBaseAgent derivative -# --------------------------------------------------------------------------- -# -# A self-contained derivative (mirrors the shape of an MCP-free CMR agent) -# defined inline so these smoke tests don't depend on any untracked -# scaffolding. Reuses the committed CMR input / output schemas so the -# agent-under-test matches the shape of a real AKD agent. - -from akd_ext.agents.cmr_care import ( # noqa: E402 - CMRCareAgentInputSchema, - CMRCareAgentOutputSchema, -) - -_SMOKE_SYSTEM_PROMPT = "You are a helpful agent spacializing in CMR and Earth Science." - - -class _SmokeCMRConfig(PydanticAIBaseAgentConfig): - """Minimal CMR-shaped config: no MCP (hermetic), placeholder prompt.""" - - description: str = Field(default="Smoke-test CMR agent on PydanticAIBaseAgent.") - system_prompt: str = Field(default=_SMOKE_SYSTEM_PROMPT) - model_name: str = Field(default="openai:gpt-4o-mini") - # capabilities default to [] via PydanticAIBaseAgentConfig — no MCP. - - -class _SmokeCMRAgent(PydanticAIBaseAgent[CMRCareAgentInputSchema, CMRCareAgentOutputSchema]): - """Self-contained PydanticAIBaseAgent derivative for smoke tests.""" - - input_schema = CMRCareAgentInputSchema - output_schema = CMRCareAgentOutputSchema | TextOutput - config_schema = _SmokeCMRConfig - - def check_output(self, output): - if isinstance(output, CMRCareAgentOutputSchema) and not output.result.strip(): - return "Result is empty. Provide search reasoning and details." - return super().check_output(output) - - -@pytest.mark.asyncio -async def test_smoke_arun_and_last_run_context(): - """End-to-end arun smoke test: return matches output schema; getter - exposes a populated AKD RunContext for the next turn.""" - from pydantic_ai import RunContext as PAIRunContext - - agent = _SmokeCMRAgent() - with agent.override(model=TestModel()): - result = await agent.arun(CMRCareAgentInputSchema(query="sea ice datasets")) - - assert isinstance(result, (CMRCareAgentOutputSchema, TextOutput)) - - ctx = agent.last_run_context - assert ctx is not None - assert ctx.messages and all(isinstance(m, dict) and "role" in m for m in ctx.messages) - assert ctx.usage.requests >= 1 - assert ctx.run_id is not None - assert isinstance(ctx.pai_run_context, PAIRunContext) - - -@pytest.mark.asyncio -async def test_smoke_astream_no_run_context(): - """astream tolerates ``run_context=None`` and still attaches a populated - run_context (messages + usage + run_id + pai_run_context) to at least - one emitted event.""" - from akd._base import StreamEvent - from pydantic_ai import RunContext as PAIRunContext - - agent = _SmokeCMRAgent() - with agent.override(model=TestModel()): - events = [ - ev - async for ev in agent.astream( - CMRCareAgentInputSchema(query="ocean carbon observations"), - ) - ] - - assert events, "astream should produce at least one event" - assert all(isinstance(ev, StreamEvent) for ev in events) - assert all(ev.run_context is not None for ev in events) - - populated = [ev for ev in events if ev.run_context.messages and ev.run_context.usage.requests >= 1] - assert populated, "expected at least one event with populated messages + usage" - - sample = populated[-1] - assert sample.run_context.run_id is not None - assert isinstance(sample.run_context.pai_run_context, PAIRunContext) - - -def test_smoke_pai_agent_surface_available(): - """Path B single inheritance: pydantic_ai.Agent's public surface must - still be reachable on a PydanticAIBaseAgent derivative instance.""" - from pydantic_ai import Agent as PAIAgent - - agent = _SmokeCMRAgent() - assert isinstance(agent, PAIAgent) - - for attr in ( - "run", - "run_stream_events", - "iter", - "override", - "output_validator", - "tool", - "system_prompt", - ): - assert callable(getattr(agent, attr)), f"{attr} missing or not callable" - - # name / description come from config via ConfigBindingMixin auto-exposure. - assert isinstance(agent.description, str) and agent.description - # name may be None by default; just confirm attribute access doesn't raise. - _ = agent.name From ed7fd35817f8a6fcd80f2adb5a75321501360f6d Mon Sep 17 00:00:00 2001 From: Sanjog Thapa Date: Wed, 22 Apr 2026 17:58:17 -0500 Subject: [PATCH 15/25] remove uncesssary comments --- akd_ext/agents/_base/pydantic_ai/_base.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/akd_ext/agents/_base/pydantic_ai/_base.py b/akd_ext/agents/_base/pydantic_ai/_base.py index 42ce29f..85d48f9 100644 --- a/akd_ext/agents/_base/pydantic_ai/_base.py +++ b/akd_ext/agents/_base/pydantic_ai/_base.py @@ -274,13 +274,12 @@ async def astream( def _wrap_pai_ctx(self) -> AKDRunContext: """Wrap ``self._live_pai_ctx`` in an AKD ``RunContext``. - Phase 1 populates **only** the ``pai_run_context`` extra so round-trip + populates **only** the ``pai_run_context`` extra so round-trip continuation works losslessly: a caller who passes ``event.run_context`` (or ``agent.last_run_context``) back into the next ``arun`` / ``astream`` gets pai-native message history and usage via the input-side helpers below. AKD's typed fields (``messages`` / ``usage`` / ``run_id``) are deliberately left at defaults here; - reflecting pai state onto them is Phase 2's job. """ pai_ctx = self._live_pai_ctx if pai_ctx is None: @@ -420,8 +419,6 @@ def check_output(self, output) -> str | None: # multi-turn continuation feed back ``agent.last_run_context`` (or any event's # ``run_context``) — both carry the live pai ``RunContext`` under the extra, # so message history / usage come across losslessly with no conversion. -# Phase 2 (``_context_adapter.py``) adds reflection onto AKD typed fields; -# the plain AKD-shape → pai-shape conversion path is intentionally omitted. # --------------------------------------------------------------------------- From 9ff389ab622f6c43ff001d97acce86a4c755bfeb Mon Sep 17 00:00:00 2001 From: Sanjog Thapa Date: Thu, 23 Apr 2026 12:35:16 -0500 Subject: [PATCH 16/25] simplification of pai base --- akd_ext/agents/_base/pydantic_ai/_base.py | 63 +++++++++-------------- 1 file changed, 25 insertions(+), 38 deletions(-) diff --git a/akd_ext/agents/_base/pydantic_ai/_base.py b/akd_ext/agents/_base/pydantic_ai/_base.py index 85d48f9..b5f7728 100644 --- a/akd_ext/agents/_base/pydantic_ai/_base.py +++ b/akd_ext/agents/_base/pydantic_ai/_base.py @@ -1,8 +1,8 @@ """``PydanticAIBaseAgent`` — Pydantic AI-backed agent conforming to the AKD contract. -Subclasses ``pydantic_ai.Agent`` directly (Path B) and also explicitly -inherits akd-core's ``AKDExecutable`` Protocol so -``isinstance(agent, AKDExecutable)`` works at runtime. +Subclasses ``pydantic_ai.Agent`` directly and also explicitly +inherits akd-core's ``AKDExecutable`` Protocol +so ``isinstance(agent, AKDExecutable)`` works at runtime. Consumers use the same AKD pattern they're used to: @@ -18,7 +18,9 @@ class MyAgent(PydanticAIBaseAgent[MyIn, MyOut]): Config auto-exposure is handled by ``ConfigBindingMixin`` — ``agent.model_name``, ``agent.description``, etc. route to ``self.config.*`` without per-field -property definitions. The single opt-out is ``system_prompt`` (re-bound to +property definitions. + +The single opt-out is ``system_prompt`` (re-bound to pydantic_ai's decorator method so it isn't shadowed by an auto-property). """ @@ -60,7 +62,7 @@ class PydanticAIBaseAgentConfig(BaseAgentConfig): """AKD-style config that is also a superset of ``pydantic_ai.Agent`` kwargs. Inherits the full ``BaseAgentConfig`` surface (``model_name``, ``system_prompt``, - ``tools``, ``reasoning_effort``, etc.) and adds pydantic_ai-specific fields. + ``tools``, ``reasoning_effort``, etc.) and adds pydantic_ai-specific fields like capabilities. ``extra="allow"`` forwards any additional future pydantic_ai kwargs via ``model_extra`` without requiring this class to be updated. """ @@ -76,11 +78,8 @@ class PydanticAIBaseAgentConfig(BaseAgentConfig): ) # -- Silence AKD-core's litellm-based config validators -------------- - # BaseAgentConfig defines ``validate_max_tokens_against_model`` and - # ``validate_reasoning_params`` as @model_validator(mode="after") hooks - # that call ``litellm.get_model_info(self.model_name)`` and - # ``litellm.supports_reasoning(model=self.model_name)``. Those lookups - # expect litellm's bare model names (e.g. ``gpt-5.2``), not the + # The following validator help for lookups that expect + # litellm's bare model names (e.g. ``gpt-5.2``), not the # ``provider:model`` format pydantic_ai requires (e.g. ``openai:gpt-5.2``), # so they emit misleading ERROR / WARNING logs for every construction. # pydantic_ai handles model resolution itself, so we override both to no-ops. @@ -110,11 +109,8 @@ class PydanticAIBaseAgent[InSchema: InputSchema, OutSchema: OutputSchema]( - ``input_schema`` / ``output_schema`` / ``config_schema`` — class attrs - ``check_output`` — semantic output validation (optional) - - ``_build_capabilities_from_scalars`` — scalar→capability mappings (future) - - ``_to_prompt`` — custom prompt rendering from ``InputSchema`` - - ``_adapt_tools`` — custom tool adapter logic (rarely needed) - .. warning:: + .. Note (warning):: As suggested by pydanticAI Agents we also follow Single-run-per-instance. Every stream event this agent emits carries @@ -122,15 +118,18 @@ class PydanticAIBaseAgent[InSchema: InputSchema, OutSchema: OutputSchema]( and stored on ``self._live_pai_ctx``). Running two concurrent ``arun`` / ``astream`` calls on the *same* agent instance will cause the captured context to race between runs and misattribute - ``event.run_context`` on emitted events. For batch or multi-tenant - workloads, construct a fresh agent per run, or switch to the + ``event.run_context`` on emitted events. + + For batch or multi-tenant workloads, construct a fresh agent per run, or switch to the queue-based capture mechanism tracked in follow-up item "Concurrency-safe run_context capture". + + This is because of current (simple) _build_run_context_capture implementation. """ # Subclasses override these three class attributes. - input_schema: type[InSchema] = InputSchema # type: ignore[assignment] - output_schema: type[OutSchema] = OutputSchema # type: ignore[assignment] + input_schema: type[InSchema] = InputSchema + output_schema: type[OutSchema] = OutputSchema config_schema: type[PydanticAIBaseAgentConfig] = PydanticAIBaseAgentConfig # Opt out of ``ConfigBindingMixin`` auto-exposure for ``system_prompt``: @@ -224,25 +223,11 @@ async def astream( run_context: RunContextProtocol | None = None, **kwargs: Any, ) -> AsyncIterator[StreamEvent]: - """AKD stream entry point. Translates pydantic_ai events → AKD ``StreamEvent``s. - - Uses ``pydantic_ai.Agent.run_stream_events`` rather than ``self.iter`` - because it is a plain async iterator with no ``async with`` at the - call site. Pydantic_ai handles the anyio-backed task decoupling - internally, so wrapping it in this async generator is safe even when - the consumer cancels mid-stream (e.g. marimo cancelling an in-flight - stream when the user sends a follow-up). Using ``iter()`` here is - unsafe in that setting because its context manager must exit in the - same task that entered it. - - ``run_stream_events`` emits an :class:`AgentRunResultEvent` as its - final event, carrying the run's ``output`` — we unwrap that into - AKD's :class:`CompletedEvent` so callers that rely on the AKD - contract (like ``agent_chat.py``) see the final answer. - """ - # Drop legacy AKD kwargs that pydantic_ai's run_stream_events - # doesn't accept. ``token_batch_size`` was a no-op on the OpenAI SDK - # runner; nothing in pydantic_ai batches events by count. Call sites + """AKD stream entry point. Translates pydantic_ai events → AKD ``StreamEvent``.""" + + # Drop legacy AKD kwargs that pydantic_ai's run_stream_events doesn't accept. + # 1. ``token_batch_size`` was a no-op on the OpenAI SDK runner; + # nothing in pydantic_ai batches events by count. Call sites # should stop passing it, but we strip defensively for backward compat. kwargs.pop("token_batch_size", None) @@ -343,11 +328,13 @@ def _deps_from_run_context(self, run_context: RunContextProtocol | None) -> Any: # ── Zone 1: scalar-driven capability construction ──────────────────── def _build_capabilities_from_scalars(self) -> list[AbstractCapability]: - """Derive capabilities from AKD config fields. + """Derive capabilities from (scalar) AKD config fields. Subclasses override to append their own scalar→capability mappings; call ``super()._build_capabilities_from_scalars()`` first to inherit future defaults. + + TLDR; this is to map configs to a capability in pydanticAI """ return [] From 09986c38f6133508ddb9429c482b20f2fdcb068a Mon Sep 17 00:00:00 2001 From: NISH1001 Date: Mon, 27 Apr 2026 13:20:39 -0500 Subject: [PATCH 17/25] Import at module-level instead of function-level in pai base --- akd_ext/agents/_base/pydantic_ai/_base.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/akd_ext/agents/_base/pydantic_ai/_base.py b/akd_ext/agents/_base/pydantic_ai/_base.py index b5f7728..49cdd28 100644 --- a/akd_ext/agents/_base/pydantic_ai/_base.py +++ b/akd_ext/agents/_base/pydantic_ai/_base.py @@ -32,6 +32,7 @@ class MyAgent(PydanticAIBaseAgent[MyIn, MyOut]): from pydantic import ConfigDict, Field, model_validator from pydantic_ai import Agent as PAIAgent from pydantic_ai import AgentRunResultEvent, ModelRetry +from pydantic_ai import RunContext as PAIRunContext from pydantic_ai.capabilities import AbstractCapability from pydantic_ai.capabilities.hooks import Hooks from pydantic_ai.messages import ModelMessage @@ -49,6 +50,7 @@ class MyAgent(PydanticAIBaseAgent[MyIn, MyOut]): from akd._base.protocols import AKDExecutable, RunContextProtocol from akd._base.structures import RunContext as AKDRunContext from akd.agents._base import BaseAgentConfig +from akd.tools._base import BaseTool from ._event_translator import pai_event_to_akd_event from ._tool_adapter import akd_to_pai_tool @@ -319,8 +321,6 @@ def _deps_from_run_context(self, run_context: RunContextProtocol | None) -> Any: tool authors can use pydantic_ai-native dependency injection. Subclasses override to inject their own deps construction. """ - from pydantic_ai import RunContext as PAIRunContext - if isinstance(run_context, PAIRunContext): return run_context.deps return None @@ -347,8 +347,6 @@ def _adapt_tools(self, tools: list) -> list: functions, toolsets, etc.) pass through unchanged. Anything structurally conforming to ``AKDTool`` gets wrapped via ``akd_to_pai_tool``. """ - from akd.tools._base import BaseTool - adapted = [] for tool in tools: if isinstance(tool, BaseTool): From b2b887129681abe7888b3abd116868a67cb14ea7 Mon Sep 17 00:00:00 2001 From: NISH1001 Date: Mon, 27 Apr 2026 13:26:24 -0500 Subject: [PATCH 18/25] Upgrade uv.lock to latest akd-core develop --- uv.lock | 832 +------------------------------------------------------- 1 file changed, 10 insertions(+), 822 deletions(-) diff --git a/uv.lock b/uv.lock index 4fc6bae..a67f1a8 100644 --- a/uv.lock +++ b/uv.lock @@ -170,40 +170,27 @@ wheels = [ [[package]] name = "akd" -version = "0.1.1" -source = { git = "https://github.com/NASA-IMPACT/accelerated-discovery.git?rev=develop#0e0494fec6c75aaa1dac114ac120c92e31620622" } +version = "0.2.0" +source = { git = "https://github.com/NASA-IMPACT/accelerated-discovery.git?rev=develop#c65a9a6dcdce22c677ecbc7ea35d7a70f3859166" } dependencies = [ { name = "crawl4ai" }, { name = "dateparser" }, - { name = "duckduckgo-search" }, { name = "gdown" }, { name = "httpx" }, { name = "instructor" }, { name = "jsonpath-ng" }, - { name = "langchain-community" }, - { name = "langchain-core" }, - { name = "langchain-google-community" }, - { name = "langchain-huggingface" }, - { name = "langchain-openai" }, - { name = "langgraph" }, { name = "litellm" }, { name = "loguru" }, - { name = "lxml-html-clean" }, { name = "markdownify" }, - { name = "ollama" }, { name = "openai" }, { name = "pydantic" }, { name = "pydantic-settings" }, { name = "pymupdf" }, { name = "pypaperbot" }, - { name = "pyzmq" }, { name = "rapidfuzz" }, { name = "readability-lxml" }, { name = "requests" }, - { name = "tenacity" }, { name = "tiktoken" }, - { name = "undetected-chromedriver" }, - { name = "wikipedia" }, ] [[package]] @@ -710,14 +697,14 @@ name = "cohere" version = "5.21.1" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "fastavro" }, - { name = "httpx" }, - { name = "pydantic" }, - { name = "pydantic-core" }, - { name = "requests" }, - { name = "tokenizers" }, - { name = "types-requests" }, - { name = "typing-extensions" }, + { name = "fastavro", marker = "sys_platform != 'emscripten'" }, + { name = "httpx", marker = "sys_platform != 'emscripten'" }, + { name = "pydantic", marker = "sys_platform != 'emscripten'" }, + { name = "pydantic-core", marker = "sys_platform != 'emscripten'" }, + { name = "requests", marker = "sys_platform != 'emscripten'" }, + { name = "tokenizers", marker = "sys_platform != 'emscripten'" }, + { name = "types-requests", marker = "sys_platform != 'emscripten'" }, + { name = "typing-extensions", marker = "sys_platform != 'emscripten'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/d2/75/4c346f6e2322e545f8452692304bd4eca15a2a0209ab9af6a0d1a7810b67/cohere-5.21.1.tar.gz", hash = "sha256:e5ade4423b928b01ff2038980e1b62b2a5bb412c8ab83e30882753b810a5509f", size = 191272, upload-time = "2026-03-26T15:09:27.857Z" } wheels = [ @@ -951,19 +938,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/b4/bd/05055d8360cef0757d79367157f3b15c0a0715e81e08f86a04018ec045f0/cyclopts-4.10.2-py3-none-any.whl", hash = "sha256:a1f2d6f8f7afac9456b48f75a40b36658778ddc9c6d406b520d017ae32c990fe", size = 204314, upload-time = "2026-04-08T23:57:46.969Z" }, ] -[[package]] -name = "dataclasses-json" -version = "0.6.7" -source = { registry = "https://pypi.org/simple" } -dependencies = [ - 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Bridges ``InputSchema`` → ``pydantic_ai.Agent.run`` → ``OutputSchema``.""" - prompt = self._to_prompt(params) + prompt = params.model_dump_json(indent=2) result = await self.run( prompt, deps=self._deps_from_run_context(run_context), @@ -233,7 +233,7 @@ async def astream( # should stop passing it, but we strip defensively for backward compat. kwargs.pop("token_batch_size", None) - prompt = self._to_prompt(params) + prompt = params.model_dump_json(indent=2) async for pai_event in self.run_stream_events( prompt, deps=self._deps_from_run_context(run_context), @@ -301,16 +301,6 @@ def last_run_context(self) -> AKDRunContext | None: return None return self._wrap_pai_ctx() - # ── Prompt rendering ────────────────────────────────────────────────── - - def _to_prompt(self, params: InSchema) -> str: - """Convert an ``InputSchema`` instance to the prompt string pydantic_ai expects. - - Default: pretty-printed JSON dump. Subclasses override for custom - templates or more readable renderings. - """ - return params.model_dump_json(indent=2) - # ── Run-context helpers ─────────────────────────────────────────────── def _deps_from_run_context(self, run_context: RunContextProtocol | None) -> Any: From 7d9a50a54af61f058993f0cfa2b27099f1608b66 Mon Sep 17 00:00:00 2001 From: Sanjog Thapa Date: Mon, 27 Apr 2026 13:39:20 -0500 Subject: [PATCH 20/25] remove one liners as function for dependency injection --- akd_ext/agents/_base/pydantic_ai/_base.py | 20 ++++++-------------- 1 file changed, 6 insertions(+), 14 deletions(-) diff --git a/akd_ext/agents/_base/pydantic_ai/_base.py b/akd_ext/agents/_base/pydantic_ai/_base.py index bcd6939..164c7b4 100644 --- a/akd_ext/agents/_base/pydantic_ai/_base.py +++ b/akd_ext/agents/_base/pydantic_ai/_base.py @@ -212,7 +212,9 @@ async def arun( prompt = params.model_dump_json(indent=2) result = await self.run( prompt, - deps=self._deps_from_run_context(run_context), + deps=run_context.deps + if isinstance(run_context, PAIRunContext) + else None, # AKD run contexts don't carry deps. If a caller passes a ``pydantic_ai.RunContext`` directly, forward its ``deps`` field so tool authors can use pydantic_ai-native dependency injection. Subclasses override to inject their own deps construction. message_history=_message_history_from_run_context(run_context), usage=_usage_from_run_context(run_context), **kwargs, @@ -236,7 +238,9 @@ async def astream( prompt = params.model_dump_json(indent=2) async for pai_event in self.run_stream_events( prompt, - deps=self._deps_from_run_context(run_context), + deps=run_context.deps + if isinstance(run_context, PAIRunContext) + else None, # AKD run contexts don't carry deps. If a caller passes a ``pydantic_ai.RunContext`` directly, forward its ``deps`` field so tool authors can use pydantic_ai-native dependency injection. Subclasses override to inject their own deps construction. message_history=_message_history_from_run_context(run_context), usage=_usage_from_run_context(run_context), **kwargs, @@ -303,18 +307,6 @@ def last_run_context(self) -> AKDRunContext | None: # ── Run-context helpers ─────────────────────────────────────────────── - def _deps_from_run_context(self, run_context: RunContextProtocol | None) -> Any: - """Extract pydantic_ai ``deps`` from the run context, if any. - - AKD run contexts don't carry deps. If a caller passes a - ``pydantic_ai.RunContext`` directly, forward its ``deps`` field so - tool authors can use pydantic_ai-native dependency injection. - Subclasses override to inject their own deps construction. - """ - if isinstance(run_context, PAIRunContext): - return run_context.deps - return None - # ── Zone 1: scalar-driven capability construction ──────────────────── def _build_capabilities_from_scalars(self) -> list[AbstractCapability]: From b44f8a5479b84dc558a2ce921fff3708538cfbe5 Mon Sep 17 00:00:00 2001 From: Sanjog Thapa Date: Mon, 27 Apr 2026 13:43:53 -0500 Subject: [PATCH 21/25] update _wrap_pai_ctx to be a property --- akd_ext/agents/_base/pydantic_ai/_base.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/akd_ext/agents/_base/pydantic_ai/_base.py b/akd_ext/agents/_base/pydantic_ai/_base.py index 164c7b4..095c4d0 100644 --- a/akd_ext/agents/_base/pydantic_ai/_base.py +++ b/akd_ext/agents/_base/pydantic_ai/_base.py @@ -250,18 +250,19 @@ async def astream( if pai_event.result.output is not None: yield CompletedEvent( data=CompletedEventData(output=pai_event.result.output), - run_context=self._wrap_pai_ctx(), + run_context=self._wrap_pai_ctx, ) continue akd_event = pai_event_to_akd_event( pai_event, - run_context=self._wrap_pai_ctx(), + run_context=self._wrap_pai_ctx, ) if akd_event is not None: yield akd_event # ── RunContext wrapping ─────────────────────────────────────────────── + @property def _wrap_pai_ctx(self) -> AKDRunContext: """Wrap ``self._live_pai_ctx`` in an AKD ``RunContext``. @@ -303,7 +304,7 @@ def last_run_context(self) -> AKDRunContext | None: """ if self._live_pai_ctx is None: return None - return self._wrap_pai_ctx() + return self._wrap_pai_ctx # ── Run-context helpers ─────────────────────────────────────────────── From 5656bdb01d18c2dc6d9c34579a2654947fe88406 Mon Sep 17 00:00:00 2001 From: Sanjog Thapa Date: Mon, 27 Apr 2026 14:00:54 -0500 Subject: [PATCH 22/25] a unified utils.py --- akd_ext/agents/_base/pydantic_ai/_base.py | 4 +- .../agents/_base/pydantic_ai/_tool_adapter.py | 54 ------------------ .../{_event_translator.py => _utils.py} | 55 ++++++++++++++++++- 3 files changed, 53 insertions(+), 60 deletions(-) delete mode 100644 akd_ext/agents/_base/pydantic_ai/_tool_adapter.py rename akd_ext/agents/_base/pydantic_ai/{_event_translator.py => _utils.py} (72%) diff --git a/akd_ext/agents/_base/pydantic_ai/_base.py b/akd_ext/agents/_base/pydantic_ai/_base.py index 095c4d0..f03451e 100644 --- a/akd_ext/agents/_base/pydantic_ai/_base.py +++ b/akd_ext/agents/_base/pydantic_ai/_base.py @@ -51,9 +51,7 @@ class MyAgent(PydanticAIBaseAgent[MyIn, MyOut]): from akd._base.structures import RunContext as AKDRunContext from akd.agents._base import BaseAgentConfig from akd.tools._base import BaseTool - -from ._event_translator import pai_event_to_akd_event -from ._tool_adapter import akd_to_pai_tool +from ._utils import akd_to_pai_tool, pai_event_to_akd_event # --------------------------------------------------------------------------- # Config diff --git a/akd_ext/agents/_base/pydantic_ai/_tool_adapter.py b/akd_ext/agents/_base/pydantic_ai/_tool_adapter.py deleted file mode 100644 index 7c396c2..0000000 --- a/akd_ext/agents/_base/pydantic_ai/_tool_adapter.py +++ /dev/null @@ -1,54 +0,0 @@ -"""Adapter from AKD ``BaseTool``-protocol instances to ``pydantic_ai.Tool``. - -``BaseTool.as_function()`` already returns a typed async callable with the -right signature metadata for framework introspection. We only need to: - -1. Wrap it so ``pydantic.ValidationError`` / ``akd.SchemaValidationError`` - become ``pydantic_ai.ModelRetry`` (so the LLM can self-correct on bad - tool-call args); all other exceptions propagate and halt the run per - pydantic_ai conventions. -2. Preserve the signature metadata that pydantic_ai introspects to build - the tool's JSON schema. -3. Hand the callable to ``pydantic_ai.Tool`` with name/description pulled - from the AKD tool. - -``sequential``, ``timeout``, and per-tool ``max_retries`` stay at their -pydantic_ai defaults. AKD tools are stateless by design so parallel -execution is safe; per-tool knobs can arrive later via a dedicated -``PydanticAIBaseTool(BaseTool)`` that extends ``BaseToolConfig``. -""" - -from __future__ import annotations - -from pydantic import ValidationError -from pydantic_ai import ModelRetry -from pydantic_ai import Tool as PAITool - -from akd._base.errors import SchemaValidationError - -from akd._base.protocols import AKDTool - - -def akd_to_pai_tool(akd_tool: AKDTool) -> PAITool: - """Adapt an AKD-protocol tool into a ``pydantic_ai.Tool``.""" - raw_fn = akd_tool.as_function() - - async def wrapped(*args, **kwargs): - try: - return await raw_fn(*args, **kwargs) - except (ValidationError, SchemaValidationError) as e: - raise ModelRetry(str(e)) from e - - wrapped.__name__ = raw_fn.__name__ - wrapped.__doc__ = raw_fn.__doc__ - wrapped.__signature__ = raw_fn.__signature__ - wrapped.__annotations__ = raw_fn.__annotations__ - - return PAITool( - wrapped, - name=akd_tool.name, - description=akd_tool.description or akd_tool.__class__.__doc__, - ) - - -__all__ = ["akd_to_pai_tool"] diff --git a/akd_ext/agents/_base/pydantic_ai/_event_translator.py b/akd_ext/agents/_base/pydantic_ai/_utils.py similarity index 72% rename from akd_ext/agents/_base/pydantic_ai/_event_translator.py rename to akd_ext/agents/_base/pydantic_ai/_utils.py index 7bbe93b..5ff3a0a 100644 --- a/akd_ext/agents/_base/pydantic_ai/_event_translator.py +++ b/akd_ext/agents/_base/pydantic_ai/_utils.py @@ -1,4 +1,6 @@ -"""Translate pydantic_ai stream events into AKD ``StreamEvent`` objects. +"""This contains utility used by the PydanticAIBaseAgent + +## 1. Translate pydantic_ai stream events into AKD ``StreamEvent`` objects. ``astream`` on ``PydanticAIBaseAgent`` wraps ``pydantic_ai.Agent.run_stream_events()`` and yields whatever we can translate. Events we can't faithfully map are @@ -20,9 +22,33 @@ A future ``CompletedEvent`` emission with the actual output value is the responsibility of ``astream`` itself (after the iterator exhausts), not this function — this translator is per-event. + +## 2. Adapter from AKD ``BaseTool``-protocol instances to ``pydantic_ai.Tool``. + +``BaseTool.as_function()`` already returns a typed async callable with the +right signature metadata for framework introspection. We only need to: + +1. Wrap it so ``pydantic.ValidationError`` / ``akd.SchemaValidationError`` + become ``pydantic_ai.ModelRetry`` (so the LLM can self-correct on bad + tool-call args); all other exceptions propagate and halt the run per + pydantic_ai conventions. +2. Preserve the signature metadata that pydantic_ai introspects to build + the tool's JSON schema. +3. Hand the callable to ``pydantic_ai.Tool`` with name/description pulled + from the AKD tool. + +``sequential``, ``timeout``, and per-tool ``max_retries`` stay at their +pydantic_ai defaults. AKD tools are stateless by design so parallel +execution is safe; per-tool knobs can arrive later via a dedicated +``PydanticAIBaseTool(BaseTool)`` that extends ``BaseToolConfig``. """ -from __future__ import annotations +from pydantic import ValidationError +from pydantic_ai import ModelRetry +from pydantic_ai import Tool as PAITool +from akd._base.protocols import AKDTool + +from akd._base.errors import SchemaValidationError from pydantic_ai.messages import ( BuiltinToolCallEvent, @@ -52,6 +78,7 @@ ToolResultEvent, ToolResultEventData, ) + from akd._base.structures import RunContext as AKDRunContext @@ -137,4 +164,26 @@ def pai_event_to_akd_event( return None -__all__ = ["pai_event_to_akd_event"] +def akd_to_pai_tool(akd_tool: AKDTool) -> PAITool: + """Adapt an AKD-protocol tool into a ``pydantic_ai.Tool``.""" + raw_fn = akd_tool.as_function() + + async def wrapped(*args, **kwargs): + try: + return await raw_fn(*args, **kwargs) + except (ValidationError, SchemaValidationError) as e: + raise ModelRetry(str(e)) from e + + wrapped.__name__ = raw_fn.__name__ + wrapped.__doc__ = raw_fn.__doc__ + wrapped.__signature__ = raw_fn.__signature__ + wrapped.__annotations__ = raw_fn.__annotations__ + + return PAITool( + wrapped, + name=akd_tool.name, + description=akd_tool.description or akd_tool.__class__.__doc__, + ) + + +__all__ = ["akd_to_pai_tool", "pai_event_to_akd_event"] From 4d90eba1037a6e470ac1523b85e4124fcf0eebb9 Mon Sep 17 00:00:00 2001 From: NISH1001 Date: Mon, 27 Apr 2026 14:01:32 -0500 Subject: [PATCH 23/25] Mirror pai messages onto RunContext.messages --- akd_ext/agents/_base/pydantic_ai/_base.py | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/akd_ext/agents/_base/pydantic_ai/_base.py b/akd_ext/agents/_base/pydantic_ai/_base.py index 49cdd28..f7480fe 100644 --- a/akd_ext/agents/_base/pydantic_ai/_base.py +++ b/akd_ext/agents/_base/pydantic_ai/_base.py @@ -261,17 +261,14 @@ async def astream( def _wrap_pai_ctx(self) -> AKDRunContext: """Wrap ``self._live_pai_ctx`` in an AKD ``RunContext``. - populates **only** the ``pai_run_context`` extra so round-trip - continuation works losslessly: a caller who passes - ``event.run_context`` (or ``agent.last_run_context``) back into the - next ``arun`` / ``astream`` gets pai-native message history and usage - via the input-side helpers below. AKD's typed fields (``messages`` / - ``usage`` / ``run_id``) are deliberately left at defaults here; + Mirrors pai's message history onto ``RunContext.messages`` so backend + serializers and downstream consumers see the conversation directly, + and keeps the live pai ``RunContext`` under the ``pai_run_context`` + extra for lossless round-trip continuation (usage, deps, etc.). """ pai_ctx = self._live_pai_ctx - if pai_ctx is None: - return AKDRunContext() - return AKDRunContext(pai_run_context=pai_ctx) + messages = list(pai_ctx.messages) if pai_ctx is not None else None + return AKDRunContext(messages=messages, pai_run_context=pai_ctx) @property def last_run_context(self) -> AKDRunContext | None: From 04217852b9464e22486aab9a2f6691ebadf4109a Mon Sep 17 00:00:00 2001 From: Sanjog Thapa Date: Mon, 27 Apr 2026 14:15:48 -0500 Subject: [PATCH 24/25] only relying on system prompt provided via config --- akd_ext/agents/_base/pydantic_ai/_base.py | 10 +--------- 1 file changed, 1 insertion(+), 9 deletions(-) diff --git a/akd_ext/agents/_base/pydantic_ai/_base.py b/akd_ext/agents/_base/pydantic_ai/_base.py index f03451e..7dec20f 100644 --- a/akd_ext/agents/_base/pydantic_ai/_base.py +++ b/akd_ext/agents/_base/pydantic_ai/_base.py @@ -132,14 +132,6 @@ class PydanticAIBaseAgent[InSchema: InputSchema, OutSchema: OutputSchema]( output_schema: type[OutSchema] = OutputSchema config_schema: type[PydanticAIBaseAgentConfig] = PydanticAIBaseAgentConfig - # Opt out of ``ConfigBindingMixin`` auto-exposure for ``system_prompt``: - # pydantic_ai's ``system_prompt`` is a decorator method used to register - # dynamic system prompts, and we must not shadow it with a config-routing - # property. Re-binding here puts ``system_prompt`` in the class dict, - # which ``ConfigBindingMixin.__init_subclass__`` treats as "already - # defined, skip auto-exposure". - system_prompt = PAIAgent.system_prompt - # ── Construction ────────────────────────────────────────────────────── def __init__(self, config: PydanticAIBaseAgentConfig | None = None) -> None: @@ -306,7 +298,7 @@ def last_run_context(self) -> AKDRunContext | None: # ── Run-context helpers ─────────────────────────────────────────────── - # ── Zone 1: scalar-driven capability construction ──────────────────── + # ── scalar-driven capability construction ──────────────────── def _build_capabilities_from_scalars(self) -> list[AbstractCapability]: """Derive capabilities from (scalar) AKD config fields. From 81d4d4e2cddc834b0bb585a5c600bf3978620389 Mon Sep 17 00:00:00 2001 From: NISH1001 Date: Mon, 27 Apr 2026 14:30:12 -0500 Subject: [PATCH 25/25] Fix _tool_adapter import to _utils after rename --- tests/agents/test_base_pydantic.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/agents/test_base_pydantic.py b/tests/agents/test_base_pydantic.py index 1499823..6e5f11d 100644 --- a/tests/agents/test_base_pydantic.py +++ b/tests/agents/test_base_pydantic.py @@ -24,7 +24,7 @@ from akd.tools._base import BaseTool from akd_ext.agents._base import PydanticAIBaseAgent, PydanticAIBaseAgentConfig -from akd_ext.agents._base.pydantic_ai._tool_adapter import akd_to_pai_tool +from akd_ext.agents._base.pydantic_ai._utils import akd_to_pai_tool # ---------------------------------------------------------------------------