图形静态分析

Grafema

Grafema turns your codebase, infrastructure, knowledge, and workflows around it — into one queryable graph. For humans and AI.

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We treat code as text. But text is just a form.

What actually matters when you write code is the system you have in your head — its structure. Entities, invariants, limitations. Goals and purpose. And how all these things relate to each other.

Software is naturally an executable graph — and so is everything around it: your services, your decisions, your team's knowledge. Grafema uses compiler-grade AST parsers — containing years of community-shared knowledge for each language — to excavate the deepest possible model of your system, and turn it into a transparent, queryable, enrichable map that grounds your understanding of it.

We refuse to accept "that's impossible to analyze statically." You can read code and understand it — you have a mental model in your head. So it's a matter of good enough heuristics. Human brains are literally built on this.

It's not magic and won't cover 100% of your system on day one. There will be gaps and "Here be dragons" signs. You will slay these dragons one by one — extend analysis with your own rules, fill up the knowledge base. And if you contribute, you slay one for everyone.

Thinking in graphs is not easy. But once it clicks - you stop reading code and just navigate the system. And your AI minions too.

Welcome to the party!

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Licensed under FSL-1.1-Apache-2.0 — free to use, source available, converts to Apache 2.0 after 2 years. Details

v0.3.22 — Early access. Changelog | Known limitations

What does this file do? (compact DSL overview, 10-20x smaller than source)

grafema tldr src/server.ts

Capabilities

Analyze - ✅ Call graph — who calls what, across all files - ✅ Data flow — trace values source to sink, forward and backward - ✅ Control flow — CFG, reachability, branching paths - ✅ Data shapes — object structure through assignment chains - ✅ Effect propagation — transitive side-effect analysis through call graph - ✅ Symbolic execution - ✅ Cross-language & inter-process — service boundaries, message passing, remote calls - ⏳ Side effect chain analysis - ⏳ Inter-service contracts — message queue schemas, API schemas (OpenAPI, JSON Schema, gRPC) - ⏳ Infrastructure as Code — Terraform, Kubernetes, Docker

Query - ✅ CLI: tldr, who, wtf, why, check, overview - ✅ 40+ MCP tools for AI agents (graph queries, navigation, dataflow, knowledge, git history) - ✅ Datalog for custom structural queries - ✅ Cypher query language - ✅ Programmatic API (@grafema/util) - ✅ HexAtlas — visual code map (2D/3D) - ✅ VS Code extension

Document - ✅ grafema export --as docs-md — generate human-readable docs from the live graph - ✅ grafema export --as openapi-3.1 — auto-generate OpenAPI for HTTP routes - ✅ grafema export --as mcp-schema — JSON-RPC tool registry, directly servable by any MCP runtime - ✅ grafema export --as json-schema — Draft 2020-12 schemas per FEATURE - ✅ Intent sidecars (_ai/intents/...) — handwritten "when to use" + captured examples that augment autogen output - ✅ grafema features --duplicates — cross-modality dedup ("which CLI commands are wrappers around the same library function as which MCP tools")

Connect knowledge to code entities and flows - ✅ Knowledge base — decisions, ADRs linked to code nodes - ✅ Effects-DB & Registry — curated database of side effects and contract mappings for popular third-party packages across ecosystems (npm, PyPI, and more) - ⏳ Git integration — blame, churn, authorship

Enforce your rules - ✅ Architectural invariants as Datalog rules - ✅ grafema check — CI gate - ⏳ Code Quality Metrics — complexity, coupling, hotspots

Enrich with your own meaning - ✅ Custom node types and edges via plugins - ✅ Library callback enricher — auto-detect MCP tools, CLI commands - ✅ Manifest generation — API surface with effect annotations