Graphify-Labs/graphify

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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.

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). Needs Python 3.10+ and uv (or pipx):

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.

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 "<question>" returns a scoped subgraph for a plain-language question, and graphify path A B traces how any two things connect.

What you get out of the box:

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.

Full guides and reference live at docs.graphify.com:

• Quickstart: build your first graph
• CLI reference: every command and flag
• Ask better graph questions: querying patterns
• Configuration: environment variables and tuning
• Supported inputs: languages and file types
• Team workflows and PR review
• Troubleshooting and How it works

macOS quick install (Homebrew):

brew install python@3.12 uv

Windows quick install:

winget install astral-sh.uv

Ubuntu/Debian:

sudo apt install python3.12 python3-pip pipx
# or install uv:
curl -LsSf https://astral.sh/uv/install.sh | sh

The official source repository is Graphify-Labs/graphify.

The two-command quickstart is at the top of this README. This section covers the install methods, project-scoped installs, and every supported platform.

Install methods (pick one):

uv tool install graphifyy   # recommended, isolated env (if 'graphify' isn't found after, run: uv tool update-shell)
pipx install graphifyy      # alternative
pip install graphifyy       # may need PATH setup, see Troubleshooting below

Then run graphify install once. It detects which assistant you run and registers the skill, so there is no per-assistant setup. Open your AI assistant and type /graphify .

To install the assistant skill into the current repository instead of your user profile, add --project:

graphify install --project
graphify install --project --platform codex

Project-scoped installs write under the current directory, for example .claude/skills/graphify/SKILL.md or .agents/skills/graphify/SKILL.md (plus a references/ sidecar the skill loads on demand), and print a git add hint for files that can be committed. Per-platform commands that support project-scoped installs accept the same flag, for example graphify claude install --project or graphify codex install --project.

Run this once in your project after building a graph:

Run graphify <platform> install once in your project, for example graphify claude install or graphify codex install (or graphify install --platform <name>).

This writes a small config file that tells your assistant to consult the knowledge graph for codebase questions, preferring scoped queries like graphify query "<question>" over reading the full report or grepping raw files.

• Hook platforms (Claude Code, Gemini CLI): a hook fires automatically before search-style tool calls (and, on Claude Code, before reading source files one by one via the Read/Glob tools) and nudges your assistant toward the graph path.
• Instruction-file platforms (Codex, OpenCode, Cursor, etc.): persistent instruction files (AGENTS.md, .cursor/rules/, etc.) provide the same query-first guidance.

GRAPH_REPORT.md is still available for broad architecture review.

Exactly which files each platform writes (CodeBuddy's CODEBUDDY.md + PreToolUse hooks, Codex's AGENTS.md, Kilo's native .kilo plugin, Cursor's alwaysApply rule, and so on) is covered in Always-on setup.

To remove graphify from all platforms at once: graphify uninstall (add --purge to also delete graphify-out/). Or use the per-platform command (e.g. graphify claude uninstall).

The instructions installed by graphify install and those supplied with a Graphify MCP connection describe different ways to query a graph:

Both encourage scoped graph queries before broad source searches. Choose the instructions that match the tools your assistant can actually access; neither block establishes a universal precedence rule for your assistant's instruction files. If you use both integrations, identify which graph each queries and choose a primary query path instead of copying two unconditional instruction blocks into AGENTS.md.

graphify install installs the local assistant integration; it does not establish an MCP connection. An MCP client does not need to run local CLI queries to use its connected server. A locally hosted server still needs its own Graphify installation and graph, while a hosted connection may serve a graph that is not present in the client's working directory. If the expected tools are missing, check the relevant CLI installation or MCP connection rather than treating the other setup's instructions as interchangeable.

GRAPH_REPORT.md summarizes the god nodes, communities, and key paths for broad architecture review. How the graph is built and what it contains: How graphify works.

graphify parses ~40 programming languages locally with tree-sitter AST, and maps docs, PDFs, images, and audio/video through an optional semantic pass. Full list: Supported inputs.

/graphify .                        # build graph for current folder
/graphify ./docs --update          # re-extract only changed files
/graphify . --cluster-only         # rerun clustering without re-extracting
/graphify . --cluster-only --resolution 1.5      # more granular communities
/graphify . --cluster-only --exclude-hubs 99     # suppress utility super-hubs from god-node rankings
/graphify . --no-viz               # skip the HTML, just the report + JSON
/graphify . --wiki                 # build a markdown wiki from the graph
graphify export callflow-html      # Mermaid architecture/call-flow HTML (auto-regenerates on every git commit if hook is installed)

/graphify query "what connects auth to the database?"
/graphify path "UserService" "DatabasePool"
/graphify explain "RateLimiter"

/graphify add https://arxiv.org/abs/1706.03762   # fetch a paper and add it
/graphify add <youtube-url>                       # transcribe and add a video

graphify hook install              # auto-rebuild on commit + branch checkout (run `graphify update .` after `git pull`: see "Recommended workflow" below)
graphify merge-graphs a.json b.json              # combine two graphs

graphify prs                       # PR dashboard: CI state, review status, worktree mapping
graphify prs 42                    # deep dive on PR #42 with graph impact
graphify prs --triage              # AI ranks your review queue (uses whatever backend is configured)
graphify prs --conflicts           # PRs sharing graph communities: merge-order risk

See the full command reference below.

graphify honors .gitignore and supports --exclude patterns and a project .graphifyignore. Details: Configuration.

Create a .graphifyrc in the scan root for settings that belong to the project rather than to one run. One key=value per line; lines beginning with # are comments.

Ambiguous extensions. Some extensions mean different languages in different projects: .inc is PHP on pfSense, Pascal in a Delphi tree, SQL or assembly elsewhere; .h is C or C++; .m is Objective-C or MATLAB. graphify has to pick one global default, and when it picks wrong the file does not fail: it parses as the wrong language and yields a handful of incidental nodes, so the graph looks populated while the real symbols are missing. Declare what the extension means in your repo:

# .graphifyrc
# A language name:
language.inc=php
# Or an extension graphify already knows:
language.tpl=.ts
# Baked into the git hooks (see Team setup):
viz_node_limit=0

The declaration flows through classification, extractor selection, grammar and resolution, imports, graphify watch, and the AST cache (a file re-parsed under a different language never reuses the old entry), and the same .graphifyrc is read by extract, update, the hooks, the MCP server, and the /graphify skill. A typo is reported once on stderr and the scan continues with the defaults. How declarations interact with caching, incremental rebuilds, and import resolution: Configuration.

Commit or share the graph so your whole team queries the same context, and review pull requests with graph context. See Share context with your team and Review pull requests.

Beyond your AI assistant, query graph.json straight from the CLI with graphify query, graphify path, and graphify explain. A name that matches several nodes is refused with the candidates listed rather than guessed. Scripts can read the outcome from the exit code: 0 an answer, 3 an ambiguous name, 4 no matching node. See Ask better graph questions and the query reference.

Backends, API keys, hook behavior, and tuning are controlled by environment variables. Full table: Configuration.

• Code files: processed locally via tree-sitter. Nothing leaves your machine. A code-only corpus requires no API key: graphify extract runs fully offline. On a mixed repo, add --code-only to index just the code and skip the docs/PDFs/images that would otherwise need an LLM.
• Video / audio: transcribed locally with faster-whisper. Nothing leaves your machine.
• Docs, PDFs, images: sent to your AI assistant for semantic extraction (via the /graphify skill, using whatever model your IDE session runs). Headless graphify extract requires GEMINI_API_KEY / GOOGLE_API_KEY (Gemini), MOONSHOT_API_KEY (Kimi), ANTHROPIC_API_KEY (Claude), OPENAI_API_KEY (OpenAI), DEEPSEEK_API_KEY (DeepSeek), a running Ollama instance (OLLAMA_BASE_URL), AWS credentials via the standard provider chain (Bedrock - no API key needed, uses IAM), or the claude CLI binary (Claude Code - no API key needed, uses your Claude subscription). The --dedup-llm flag uses the same key.
• Data residency: graphify extract auto-detects which provider to use based on which API key is set (priority: Gemini → Kimi → Claude → OpenAI → DeepSeek → Azure → Bedrock → Ollama). For code with data-residency requirements, use --backend ollama (fully local) or pass an explicit --backend flag. Kimi (MOONSHOT_API_KEY) routes to Moonshot AI servers in China.
• No telemetry, no usage tracking, no analytics.
• Query logging: every graphify query, graphify path, graphify explain, and MCP query_graph call is logged to ~/.cache/graphify-queries.log in JSON Lines format (timestamp, question, corpus, nodes returned, duration). Full subgraph responses are not stored by default. Set GRAPHIFY_QUERY_LOG_DISABLE=1 to opt out, or GRAPHIFY_QUERY_LOG=/dev/null to silence without disabling the code path.

Common install and extraction issues and their fixes: Troubleshooting.

Every command and flag, with examples: CLI reference.

• docs.graphify.com: full documentation: guides, command reference, and integrations
• How it works: the extraction pipeline, community detection, confidence scoring, benchmarks
• ARCHITECTURE.md: module breakdown, how to add a language
• Optional integrations: Docker MCP Toolkit + SQLite
• The Memory Layer: the book on the ideas behind graphify, the architecture end to end

Graphify Cloud is the always-on layer built on top of graphify. Instead of a graph you rebuild on demand per folder, it keeps one live graph across your entire software development lifecycle:

• 80% fewer tokens: always-on memory means your agents stop re-reading and re-explaining the codebase every session.
• Monorepo and cross-repo: one connected graph across every service and repository, not a graph per folder.
• Formal verification: check architectural and dependency invariants against the live graph.
• Code review with a bird's-eye view of your SDLC: see how a change ripples across the whole system before you merge.
• SDLC connectors: pull in Sentry, Jira, and more, so incidents, tickets, and code live in one graph.
• Always-on: updates continuously in the background across code, docs, and meetings.

Start a free 14-day trial at app.graphify.com →

Contributions are welcome. See CONTRIBUTING.md for the development setup, the test and CI-parity commands, the git workflow, and what makes a strong contribution (worked examples and extraction bug reports are the most useful). Architecture and how to add a language: ARCHITECTURE.md.

New here? Say hi on Discord or in GitHub Discussions.

Made with contrib.rocks.

The README is available in 32 languages. Use the language switcher at the top of this file to read it in yours, or browse docs/translations/. To improve a translation or add a new one, open a pull request against the matching file there.

Building something in the graphify ecosystem? That is encouraged. If your project uses "graphify" in its name (for example graphify-dashboard or graphify-action), please add a short note to your README clarifying that it is community-built and not affiliated with or endorsed by Graphify Labs. "graphify" and the graphify logo are marks of Graphify Labs; please do not use them in a way that implies official status.