graphify

Turn any folder of source code, documentation, papers, images, or videos into a queryable knowledge graph.

Point graphify at a directory and you get back a graphify-out/ folder. The three files most people interact with directly:

graphify-out/
├── graph.json                  full graph — query without re-reading your files
├── graph.html                  open in any browser — interactive viz
└── GRAPH_REPORT.md             key concepts, surprising connections, suggested questions

A handful of sidecars alongside them carry intermediate state for incremental runs, the report, and assistant integration:

graphify-out/
├── manifest.json               per-file fingerprint for incremental updates
├── .graphify_root              marker so child runs find the project root
├── .graphify_analysis.json     analysis sidecar feeding GRAPH_REPORT.md
├── .graphify_labels.json       community label cache (skip the LLM next time)
├── stage_02_extract.json       cached extraction output for incremental runs
└── .graphify_semantic_marker   set when semantic extraction has already run

Additional output is written under graphify-out/ only when you ask for it: wiki/ (per-community articles, graphify export wiki), GRAPH_TREE.html (graphify tree), cypher.txt (graphify export neo4j), <YYYY-MM-DD>/ backups (when graph.json is overwritten), and memory/ (saved Q&A from graphify save-result).

Then ask it questions instead of grepping:

graphify query "where is the rate limiter defined"
graphify path  "request_handler" "database_pool"
graphify explain "AuthMiddleware"

This is the Rust reimplementation of graphify. The CLI surface is 1:1 with the Python reference — every public command, output file, and observable side-effect of python -m graphify has a Rust equivalent, and outputs are byte-identical where the test suite asserts it.

Features

• 26+ languages, parsed with tree-sitter: Rust, Python, TypeScript, JavaScript, Go, Java, C, C++, C#, Ruby, PHP, Swift, Kotlin, Scala, Bash, Lua, Elixir, Haskell, OCaml, Zig, Solidity, R, Julia, HTML, CSS, SQL, … Also reads .NET project files (.sln, .csproj, .fsproj, .vbproj) and Razor components (.razor, .cshtml) for package, project-reference, target-framework, and @code extraction, Verilog/SystemVerilog (.v, .sv, .svh), BYOND DreamMaker (.dm, .dme source plus .dmi icon sheets, .dmm maps, and .dmf interface forms), and MCP config files (.mcp.json, claude_desktop_config.json, mcp.json, mcp_servers.json) — servers, commands, packages, and env-var names (values are never read).
• Package manifests & doc links — apm.yml, pyproject.toml, go.mod, and pom.xml become canonical package nodes with depends_on edges (a package referenced across manifests collapses to one hub node); PowerShell .psm1 modules and .psd1 manifests emit imports_from edges; and Markdown links (inline, reference-style, and [[wikilinks]]) become references edges so hub docs (index.md, tables of contents) connect to the documents they link instead of orphaning.
• Documents, papers, images, video — PDF, DOCX, audio transcription, OCR, Google Workspace exports. Untrusted office/PDF files are screened before parsing (50 MiB on-disk cap; .docx/.xlsx zip-bomb guard at 512 MiB decompressed / 200:1 ratio) and silently skipped if they exceed the limits, so a malicious corpus file cannot OOM a scan.
• Local-first — graph.json lives next to your code; no daemon, no cloud, no account.
• Optional LLM-driven semantic extraction through OpenAI, Claude (Anthropic), Gemini, DeepSeek, Kimi (Moonshot), Ollama, Bedrock, Azure OpenAI, or any OpenAI-compatible custom provider registered with graphify provider add. The --backend identifiers are openai, claude, gemini, deepseek, kimi, ollama, bedrock, and azure. The openai and claude backends also honour OPENAI_BASE_URL/OPENAI_MODEL and ANTHROPIC_BASE_URL/ANTHROPIC_MODEL to reach self-hosted OpenAI-compatible servers (llama.cpp, vLLM, LM Studio) or Anthropic-compatible proxies (LiteLLM) without a custom-provider entry. Vision-capable backends read raster images (PNG/JPG/GIF/WebP) as pixels, so a diagram or screenshot becomes a graph node; non-vision backends record it as a text-reference node instead.
• Structural introspection — graphify extract --cargo adds crate -> crate dependency edges from Cargo.toml manifests; --postgres <DSN> adds a live PostgreSQL schema (requires the postgres build feature).
• LLM community naming — graphify label (or cluster-only) auto-names graph communities with the configured backend; degrades to Community N placeholders when no backend is available.
• AI-assistant integration — drop-in installers for Claude Code, CodeBuddy, Codex, Amp, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Aider, Factory Droid, Trae, Hermes, Kiro, Kilo Code, Pi, Devin CLI, Google Antigravity, and more.
• MCP server for any MCP-capable assistant (graphify serve) — stdio by default, or Streamable HTTP (--transport http, requires the http build feature) so one shared process can host the graph for a team.
• Git hooks + merge driver so two branches editing the same graph.json produce a union-merged result.
• Cross-repo global graph — aggregate every project you care about into one ~/.graphify/global-graph.json.
• Deterministic outputs — same inputs on the same machine produce byte-identical JSON.

Install

Requires Rust 1.95 or newer (rustup toolchain install stable).

cargo install --git https://github.com/bunkerlab-net/graphify.git

Or build from a local checkout:

git clone https://github.com/bunkerlab-net/graphify.git
cd graphify
cargo install --path .

Verify:

graphify --version
graphify --help

Quick start

From the root of any project:

graphify extract .

That runs the full pipeline (detect → extract → build → cluster → analyze → report → export) and writes graphify-out/ next to your code. Open graphify-out/graph.html to explore visually, or query the graph from the command line:

graphify query "how do we authenticate users"
graphify explain "AuthMiddleware"

Wire it into your AI assistant in one command:

graphify claude install      # or gemini / cursor / codex / copilot / vscode / ...

Your assistant will now call graphify query before reaching for grep / rg / find.

Documentation

See USAGE.md for the full command reference — every subcommand, every flag, environment variables, LLM backends, workflows, and editor integrations.

For development conventions (lint policy, porting rules, test layout, definition-of-done per crate), see AGENTS.md.

Workspace layout

graphify/
├── src/                       # graphify CLI binary
├── crates/                    # 29 focused workspace crates
│   ├── graphify-detect/       # filesystem walking + file-type detection
│   ├── graphify-extract/      # tree-sitter / document / media extractors
│   ├── graphify-build/        # graph construction
│   ├── graphify-cluster/      # community detection (Leiden, Louvain fallback)
│   ├── graphify-analyze/      # god-nodes, cohesion, communities
│   ├── graphify-report/       # GRAPH_REPORT.md generator
│   ├── graphify-export/       # HTML / SVG / GraphML / Obsidian / Cypher / Neo4j / FalkorDB
│   ├── graphify-html/         # interactive D3 viz + Mermaid call-flow HTML
│   ├── graphify-wiki/         # per-cluster wiki articles
│   ├── graphify-serve/        # MCP stdio server
│   ├── graphify-hooks/        # git hooks + per-platform assistant installers
│   ├── graphify-global/       # ~/.graphify/global-graph.json
│   ├── graphify-prs/          # GitHub PR triage
│   ├── graphify-llm/          # LLM backend abstraction
│   ├── graphify-security/     # SSRF guard, URL allowlist, graph-load size cap
│   ├── graphify-affected/     # reverse-traversal impact analysis (`graphify affected`)
│   ├── graphify-diagnostics/  # multigraph edge-collapse diagnostic
│   ├── graphify-multigraph-compat/  # runtime keyed-edge capability probe
│   ├── graphify-scip/         # SCIP-style JSON ingest
│   ├── graphify-semantic/     # LLM extraction fragment validator
│   └── ...                    # benchmark, cache, dedup, ingest, manifest, transcribe, validate, watch, google
└── graphify-py/               # read-only git submodule — Python reference

License

Apache-2.0.