Reliable, trustworthy, trackable AI workflows for science.
Runs 100% locally. No API keys, no cloud services. Your data never leaves your machine.
Named after great physicist John Archibald Wheeler, Niels Bohr's longtime collaborator. Wheeler and Bohr worked by talking. Bohr would pace, thinking out loud. Wheeler would push back, sharpen the question, sketch the math. The best ideas emerged from the conversation, not from either person alone. That's the model here.
New to Wheeler? See the Getting Started Guide for a complete walkthrough with Neo4j Desktop setup.
Already have Neo4j running?
git clone https://github.com/maxwellsdm1867/wheeler.git
cd wheeler
bash bin/setup.sh # creates venv, installs deps, inits schemaThen open Claude Code and start working:
cd ~/my-research-project
claude
/wh:init # set up project, create graph schema
/wh:start # begin each session herePrerequisites: Python 3.11+, Node.js, Claude Code (Max subscription), Neo4j Desktop (free)
Science requires reproducibility. As AI gets embedded in research workflows, the gap between "AI helped me" and "here's the auditable chain of how this result was produced" becomes a credibility problem.
Wheeler is built on four pillars:
Traceable results. When Wheeler creates a finding, it automatically records what script ran, what data it consumed, what papers informed the approach, and when it happened. One tool call builds the full provenance chain. The agent focuses on science; infrastructure handles bookkeeping.
Change propagation. When a script changes or data is updated, Wheeler flags every downstream finding as stale and reduces its stability score. You always know what to trust and what needs re-verification.
Context management. All components read from and write to the same graph, so a finding from data analysis immediately informs subsequent literature searches, experimental design, and manuscript preparation. Information is progressively disclosed and retrieved only when relevant.
Executable research artifact. The knowledge graph moves beyond the static PDF. It is an executable map of discovery: any scientist can inherit the full experimental context of a project, explore how results connect, and build directly on top of prior work.
Wheeler gives you a fluid cycle, not a rigid pipeline. Enter at any point, skip stages, repeat them.
TOGETHER you + wheeler, thinking out loud
discuss plan chat pair write note ask
|
v remaining work is grinding
HANDOFF propose independent tasks
handoff you approve, modify, or keep talking
|
v
INDEPENDENT wheeler works alone
wh queue "..." logged, stops at decision points
|
v
RECONVENE results + flags + surprises
reconvene back to TOGETHER
| Command | What it does |
|---|---|
/wh:start |
Route to the right command (or type your task) |
/wh:discuss |
Sharpen the research question through structured dialogue |
/wh:plan |
Structure tasks with waves, assignees, checkpoints |
/wh:execute |
Run analyses, log findings to graph with provenance |
/wh:write |
Draft text with strict citation enforcement |
/wh:ingest |
Bootstrap graph from existing code, data, papers |
/wh:add |
General-purpose ingest: text, DOI, file, URL |
/wh:note |
Quick-capture an insight, observation, or idea |
/wh:compile |
Compile graph into synthesis documents with citations |
/wh:dream |
Consolidate: promote tiers, detect communities, link orphans |
/wh:pair |
Live co-work: scientist drives, Wheeler assists |
/wh:ask |
Query the graph, trace provenance chains |
/wh:status |
Show progress, suggest next action |
/wh:handoff |
Propose tasks for independent execution |
/wh:reconvene |
Review results from independent work |
More commands
| Command | What it does |
|---|---|
/wh:chat |
Quick discussion, no execution |
/wh:triage |
Triage GitHub issues against planned work |
/wh:report |
Generate work log from graph (time period) |
/wh:close |
End-of-session provenance sweep |
/wh:pause / /wh:resume |
Save and restore investigation state |
/wh:update |
Check for Wheeler updates |
/wh:dev-feedback |
File bugs from inside your session |
Wheeler can run tasks without you present:
wh queue "search for papers on SRM models" # sonnet, 10 turns, logged
wh quick "check graph status" # haiku, 3 turns, fast
wh dream # graph consolidationWheeler never does your thinking. Every task gets tagged: SCIENTIST (judgment calls), WHEELER (grinding), or PAIR (collaborative). Decision points are flagged as checkpoints, not guessed at.
The core primitive: one tool call creates a finding AND its full W3C PROV-DM provenance chain. You never write this directly; slash commands handle it. But under the hood, this is what happens:
add_finding(
description="Midget and parasol cells have similar clusters of fitted SRM parameters",
confidence=0.85,
execution_kind="script", # auto-creates Execution activity
used_entities="D-abc123,S-def456", # auto-links inputs
)Wheeler internally creates the Finding, an Execution activity node, links inputs (Dataset, Script) via USED, links the output via WAS_GENERATED_BY, sets a stability score, and dual-writes to Neo4j and JSON. The provenance chain is always complete because the agent never had to remember to create it.
Every entity carries a stability score (0.0-1.0) encoding epistemic trust: primary data = 1.0, published papers = 0.9, validated scripts = 0.7, LLM-generated findings = 0.3. When an upstream entity changes, stability decays downstream: new = source * (0.8 ^ hops). Changed scripts propagate stale flags through the entire dependency chain.
The graph is an index over files, not a document store. Each node stores an ID, type, tier, title, path, and timestamps. Full content lives in knowledge/{id}.json. Human-browsable rendering lives in synthesis/{id}.md (Obsidian-compatible with YAML frontmatter and [[backlinks]]). When you need connections, ask the graph. When you need content, read the file.
11 entity types: Finding, Hypothesis, OpenQuestion, Dataset, Paper, Script, Execution, Document, ResearchNote, Plan, Ledger.
14 relationship types: 6 W3C PROV standard (USED, WAS_GENERATED_BY, WAS_DERIVED_FROM, WAS_INFORMED_BY, WAS_ATTRIBUTED_TO, WAS_ASSOCIATED_WITH) + 8 Wheeler semantic (SUPPORTS, CONTRADICTS, CITES, APPEARS_IN, RELEVANT_TO, AROSE_FROM, DEPENDS_ON, CONTAINS).
46 MCP tools across 5 servers (mutations, queries, search, ops, legacy monolith).
See ARCHITECTURE.md for the complete technical spec: module dependency map, PROV schema, MCP tool listing, hardening patterns, design decisions.
v0.7.0 (2026-04-20) — Graph-as-source-of-truth enforcement
- Graph-first plan lifecycle: Every
/wh:*act now queries the graph first for plan identity, status, and session state. Filesystem files (STATE.md, .continue-here.md) become rendered views, not authoritative sources. query_planstool: New MCP tool to search Plan nodes by keyword and status (draft/approved/in-progress/completed), available on all servers. 49 tools total.- Read-before-mutate hooks: Claude Code PreToolUse hook blocks file-bearing mutations unless the file was Read or Written first in the session, enforcing grounded provenance.
- Process provenance: Pause, handoff, and close events are recorded as Execution nodes in the graph, making the research process itself auditable.
- Session continuation notes:
/wh:pausewrites continuation context as graph-backed ResearchNote nodes linked to the active plan, not just a flat file.
v0.6.3 (2026-04-19) — Proactive context, cleaner search results
- Proactive graph context: Research acts (plan, execute, pair, chat) now call
search_contextautomatically when the input is about research topics, returning clean summarized results instead of raw model dumps. - Neo4j connection diagnostics: Helpful error messages when Neo4j Desktop isn't running, auth fails, or the database is unreachable.
- Execution tracking:
add_executionandquery_executionsMCP tools wired up across all five servers. 46 tools total. /wh:bumpskill: Version bump workflow that updates version strings, doc counts, and changelog in one command.
v0.6.2 (2026-04-18) — Auto-routing, /wh:start entry point
- Auto-routing: 20 command descriptions rewritten with narrow triggers requiring explicit Wheeler/knowledge-graph vocabulary. Commands auto-fire only for unambiguous Wheeler actions, never for general coding.
/wh:startrouter: User-invoked entry point that analyzes your task and routes to the best/wh:*command. Accepts optional argument for immediate routing or asks interactively.- Routing test suite: 137 new tests covering tree sync, trigger patterns, domain anchoring, router structure, and false-positive resistance. Total: 1018 tests.
v0.6.1 (2026-04-16) — Bug fixes, update_node, parameter discoverability
- Stale driver fix (#9): MCP servers no longer return zeros on first
graph_statuscall. update_nodetool (#16): update fields on existing nodes after creation. Full triple-write, field validation, change_log, embedding updates. 44 tools total.graph_contexttopic filter (#10): optionaltopicparameter for filtered context injection.- Parameter discoverability (#11-14):
link_nodesrelationshipparameter now usesLiteraltype (JSON schema enum).add_notedocstring updated. Priority scale clarified. - Ingest data source discovery (#15): workspace scanner detects
.db/.sqlite/.sqlite3. Phase 0 in/wh:ingestasks about primary data sources.
v0.6.0 (2026-04-08) — Infrastructure hardening, GraphRAG, split MCP servers
- Infrastructure hardening: circuit breaker, consistency checker, trace IDs, write receipts, node change log, task contracts.
- GraphRAG enhancements: graph-expanded local search (
search_context), Neo4j fulltext index, community detection, entity resolution (propose_merge+execute_merge), retrieval quality metrics. - Split MCP servers: monolith available as 4 focused servers (
wheeler_core,wheeler_query,wheeler_mutations,wheeler_ops).
Claude Code (interactive)
├── /wh:* slash commands (.claude/commands/wh/*.md)
│ ├── /wh:start: intent router (invokes other commands)
│ ├── YAML frontmatter: tool restrictions per mode
│ └── System prompt: workflow + provenance protocol
│
├── MCP Servers (49 tools)
│ ├── wheeler_core (12): health, status, context, search, cypher
│ ├── wheeler_query (10): read-only query_* tools
│ ├── wheeler_mutations (17): add_*, link, delete, update, merge
│ ├── wheeler_ops (10): staleness, citations, consistency
│ └── wheeler (legacy monolith): same 49 tools, one server
│
bin/wh (headless)
└── claude -p with structured logging → .logs/*.json
Code structure
wheeler/
├── models.py # Pydantic v2: 11 node types, prefix mappings
├── config.py # YAML loader, Pydantic config models
├── provenance.py # Stability scoring, invalidation propagation
├── consistency.py # Cross-layer drift detection and repair
├── mcp_server.py # Legacy monolith: all 49 tools
├── mcp_core.py # Split server: health, context, search (12)
├── mcp_query.py # Split server: query_* read-only (10)
├── mcp_mutations.py # Split server: add_*, link, delete, update (15)
├── mcp_ops.py # Split server: staleness, citations (10)
├── mcp_shared.py # Shared: trace IDs, decorators, config
├── knowledge/ # File I/O: read, write, list, render, migrate
├── graph/ # Neo4j backend, circuit breaker, schema, context
├── search/ # Embeddings, RRF fusion, graph-expanded search
├── validation/ # Citation validation, ledger quality metrics
├── tools/graph_tools/ # Provenance-completing mutations + queries
└── workspace.py # Project file scanner
tests/ # 1023 tests
docs/ # Getting started, architecture, project spec
Bug reports: Use /wh:dev-feedback from inside a session to file structured issues, or report at GitHub Issues.
Tests: python -m pytest tests/ -v (1023 tests). E2E tests require a running Neo4j: python -m pytest tests/e2e/ -v.
Architecture: See ARCHITECTURE.md for the full technical spec (module dependency map, PROV schema, MCP tool listing, hardening patterns).
Project docs:
- Mission — four pillars, target audience, design north star
- Tech stack — components, infrastructure patterns, current gaps
- Roadmap — shipped versions, v0.8.0 phases, v1.0 criteria
- Getting started — install walkthrough with Neo4j Desktop
- Project spec — original design specification