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Early access to the graphify platform is open before the public v1 launch: app.graphify.com
Type /graphify in your AI coding assistant and it maps your entire project (code, docs, PDFs, images, videos) into a knowledge graph you can query instead of grepping through files.
- Code maps for free, fully local. Code is parsed with tree-sitter AST: deterministic, no LLM, nothing leaves your machine. (Docs, PDFs, images and video use your assistant's model, or a configured API key, for a semantic pass.)
- Every edge is explained. Each connection is tagged
EXTRACTED(explicit in the source) orINFERRED(resolved by graphify), so you can tell what was read directly from what was inferred. - Not a vector index. No embeddings, no vector store: a real graph you traverse. Ask a question, trace the path between two things, or explain one concept.
Want this always-on, updating in the background across your code, docs, and meetings rather than only on demand? That is what we are building at graphify.com, and early access is open now at app.graphify.com.
The FastAPI codebase mapped by graphify. Every node is a concept, colors are detected communities, and the whole thing is clickable in graph.html.
Get started (30 seconds):
uv tool install graphifyy # install the CLI (or: pipx install graphifyy)
graphify install # register the skill with your AI assistant
Then, in your AI assistant:
/graphify .
That's it. You get three files:
graphify-out/
├── graph.html open in any browser — click nodes, filter, search
├── GRAPH_REPORT.md the highlights: key concepts, surprising connections, suggested questions
└── graph.json the full graph — query it anytime without re-reading your files
Works in Claude Code, Cursor, Codex, Gemini CLI, GitHub Copilot, and 15+ more — pick your platform.
See it in action
Once the graph is built you query it instead of reading files. Real output, graphify run on the FastAPI codebase shown above:
$ graphify explain "APIRouter"
Node: APIRouter
Source: routing.py L2210
Community: 2
Degree: 47
Connections (47):
--> RequestValidationError [uses] [INFERRED]
--> Dependant [uses] [INFERRED]
--> .get() [method] [EXTRACTED]
<-- __init__.py [imports] [EXTRACTED]
...
$ graphify path "FastAPI" "ModelField"
Shortest path (3 hops):
FastAPI --uses--> DefaultPlaceholder <--references-- get_request_handler() --references--> ModelField
Every edge carries a confidence tag (EXTRACTED = explicit in the source, INFERRED = derived by resolution), so you can tell what was read directly from what was inferred. graphify query "" returns a scoped subgraph for a plain-language question, and graphify path A B traces how any two things connect.
What it does
What you get out of the box:
| Capability | What you get |
|---|---|
| God nodes | The most-connected concepts, so you see what everything flows through |
| Communities | The graph split into subsystems (Leiden), with LLM-free labels |
| Cross-file links | calls / imports / inherits / mixes_in resolved across ~40 languages via tree-sitter AST |
| Query, path, explain | Ask a question, trace the path between two things, or explain one concept, all against graph.json |
| Rationale + doc refs | # NOTE: / # WHY: comments and ADR/RFC citations become first-class nodes linked to the code |
| Beyond code | Docs, PDFs, images, and video/audio all map into the same graph |
| Local-first | Code is parsed locally with tree-sitter (no LLM, nothing leaves your machine); only the semantic pass over docs/media calls a backend, and only if you configure one |
Benchmarks
| Benchmark | Metric | graphify | Field |
|---|---|---|---|
| LOCOMO (n=300) | recall@10 | 0.497 | mem0 0.048, supermemory 0.149 |
| LOCOMO (n=300) | QA accuracy | 45.3% | supermemory 49.7%, mem0 27.3% |
| LongMemEval-S (n=50) | QA accuracy | 76% | tied with dense RAG |
| Graph build | LLM credits | 0 | per-token for most systems |
Every system ran on the same harness with the same model and budgets, scored by a judge blind-validated against a second judge (90.6% agreement, Cohen's kappa 0.81). Full per-system tables, the code-intelligence result, and reproduction commands: BENCHMARKS.md.
Prerequisites
| Requirement | Minimum | Check | Install |
|---|---|---|---|
| Python | 3.10+ | python --version |
python.org |
| uv (recommended) |