graphify

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  <a href="https://graphify.com"><img src="https://raw.githubusercontent.com/Graphify-Labs/graphify/v8/docs/graphify-logo.png" width="360" height="189" alt="Graphify"/></a>
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<details><summary><b>Read this in other languages</b></summary>

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<p align="center">
  <b>Cut agent token spend by 80%.</b> Graphify Cloud is always-on memory that governs your agents, keeps code quality high, and proves correctness with formal verification. <a href="https://app.graphify.com/login"><b>Start a free 14-day trial &rarr;</b></a>
</p>

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) or `INFERRED` (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.

> [!NOTE]
> **Want this always-on?** [Graphify Cloud](https://app.graphify.com/login) keeps the graph live across your entire SDLC: monorepo and cross-repo support, formal verification, and code review with a bird's-eye view of your SDLC, plus connectors for Sentry, Jira, and more so incidents and tickets sit in the same graph as your code. **[Start a free 14-day trial at app.graphify.com](https://app.graphify.com/login)**.

<p align="center">
  <img src="https://raw.githubusercontent.com/Graphify-Labs/graphify/v8/docs/graph-hero.png" alt="graphify's interactive graph.html showing the FastAPI codebase as a force-directed knowledge graph with a legend of detected communities" width="900">
</p>
<p align="center">
  <em>The FastAPI codebase mapped by graphify. Every node is a concept, colors are detected communities, and the whole thing is clickable in graph.html.</em>
</p>

**Get started** (30 seconds). Needs Python 3.10+ and uv (or pipx):

```bash
uv tool install graphifyy      # install the CLI (or: pipx install graphifyy)
graphify install               # detects your assistant and registers the skill
```

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
```

The persisted graph includes `graph.schema_version` so integrations can detect
incompatible format changes, plus `graph.graphify_version` identifying the
Graphify release that produced it.

**One install, any assistant, any OS.** Claude Code, Cursor, Codex, Gemini CLI, GitHub Copilot, and 20+ more, on macOS, Linux, and Windows: [pick your platform](#install).

---

## See it in action

<p align="center">
  <img src="https://raw.githubusercontent.com/Graphify-Labs/graphify/v8/docs/demo-path.svg" alt="graphify path query: a terminal asks for the shortest path between FastAPI and ModelField, and the answer lights up hop by hop across the knowledge graph" width="900">
</p>

Once the graph is built you query it instead of reading files. Real output, graphify run on the FastAPI codebase shown above:

```text
$ 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 "<question>"` 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 |

> [!TIP]
> Running this across many repos or a monorepo? [Graphify Cloud](https://app.graphify.com/login) does it continuously and cuts agent token spend by 80%, with cross-repo links, SDLC-wide code review, formal verification, and Sentry/Jira connectors. [Start a free 14-day trial &rarr;](https://app.graphify.com/login)

---

## Benchmarks: 0.497 recall@10, 0 LLM credits

| 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 |
| ERPNext cross-tool (n=6) | key-fact coverage | **82.0%** | grep/read baseline 70.8% |
| Graph build | LLM credits | **0** | per-token for most s