graphify-code-graph

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name: graphify-code-graph description: Build a deterministic, queryable knowledge graph over any codebase with graphify (tree-sitter AST over 25+ languages, Leiden clustering, flat graph.json, zero LLM cost for code-only corpora) and query it via CLI or MCP. Use inside a client repo for code-level impact analysis (graphify affected), symbol path tracing, and architecture questions that grep answers poorly. Complements the brain's connectome/lineage graphs: graphify maps CODE SYMBOLS, the connectome maps CONCEPTS and governance. auto-promoted 2026-07-16 from repo-deep-learn (https://github.com/Graphify-Labs/graphify). metadata: type: tool-skill source: https://github.com/Graphify-Labs/graphify (MIT) deep-learn: knowledge/repo-deep-learn/graphify/2026-07-16.md

Graphify: deterministic code knowledge graph

When to use

• A code question spans many files ("what breaks if I change X?", "how does A reach B?") and grep gives partial, noisy answers.
• Onboarding into a large or unfamiliar codebase (client repo, OSS dependency).
• Repeated architecture questions in one repo: build the graph once, query it every session at near-zero token cost.

Not for concept/governance recall. Which skill knows this, where does a rule live, what derives from a doc: that is the brain's connectome (query_connectome.py) and lineage (impact-radius.py). Graphify maps code symbols; those map concepts. Two layers, keep both.

Install (one time per machine)

uv tool install graphifyy      # or: pipx install graphifyy
graphify install               # registers the /graphify skill with the AI assistant

The PyPI package is graphifyy (double y); the CLI is graphify.

Build the graph (inside the target repo)

graphify .          # writes graphify-out/graph.json + GRAPH_REPORT.md + graph.html
graphify update .   # incremental re-extract of changed paths only, AST-only, no API cost
graphify watch      # keep it fresh on file changes

The code pass is fully deterministic: tree-sitter AST, no LLM, no API key, $0. Only the optional semantic pass (docs, PDFs, images, video, community labels) calls an LLM; without a key it is skipped entirely. Add graphify-out/ to the repo's .gitignore unless the team wants the artifact versioned.

Query it

graphify query "how does auth reach the billing module"
graphify affected src/auth/session.py    # code-level impact radius
graphify path NodeA NodeB                # shortest path between symbols
graphify explain "PaymentProcessor"      # natural-language node explanation
graphify diagnose                        # graph health

Matching is case-folded substring + IDF over the flat graph.json (NetworkX node-link format). No server, no embeddings. For assistant integration without shell calls there is an MCP server: graphify-mcp (stdio/HTTP).

Arm usage pattern

Run it inside ONE arm's repo; the graph lives in that repo's graphify-out/ and never leaves the arm (arm isolation applies to the artifact like to any other client data). Start every code-impact question with graphify affected <file> and fall back to grep only for symbols the graph does not know, same seek-before-scan discipline as the brain's graphs.

Gotchas

• Edges carry confidence (EXTRACTED / INFERRED / AMBIGUOUS, 0.55-0.95 rubric): trust EXTRACTED, verify INFERRED before acting on it.
• The exporter refuses to silently shrink an existing graph (their issue #479). The brain adopted the same invariant in scripts/generate_neural_map.py (SHRINK-GUARD).
• Exports available when a human wants to browse: Obsidian vault, GraphML (Gephi/yEd), SVG, Cypher (Neo4j/FalkorDB).

See also

• [[repomix]]: flat single-file codebase packing, the non-graph alternative for one-shot context stuffing.
• [[4d-paradigm-protocol]]: graphify affected is the code-level twin of the concept-level Impact Radius scan.