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.

Works in Claude Code, Codex, OpenCode, Kilo Code, Cursor, Gemini CLI, GitHub Copilot CLI, VS Code Copilot Chat, Aider, Amp, OpenClaw, Factory Droid, Trae, Hermes, Kimi Code, Kiro, Pi, Devin CLI, and Google Antigravity.

/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

For a readable architecture page with Mermaid call-flow diagrams, run:

graphify export callflow-html

Prerequisites

macOS quick install (Homebrew):

brew install [email protected] 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

Install

Official package: The PyPI package is graphifyy (double-y). Other graphify* packages on PyPI are not affiliated. The CLI command is still graphify.

Step 1 — install the package:

# Recommended (uv puts graphify on PATH automatically):
uv tool install graphifyy

# Alternatives:
pipx install graphifyy
pip install graphifyy  # may need PATH setup — see note below

Step 2 — register the skill with your AI assistant:

graphify install

That's it. 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.

PowerShell note: Use graphify . not /graphify . — the leading slash is a path separator in PowerShell.

graphify: command not found? Use uv tool install graphifyy or pipx install graphifyy — both put the CLI on PATH automatically. With plain pip, add ~/.local/bin (Linux) or ~/Library/Python/3.x/bin (Mac) to your PATH, or run python -m graphify.

Avoid pip install on Mac/Windows if possible. The skill resolves Python at runtime from graphify-out/.graphify_python; if that points to a different environment than where pip installed the package, you'll get ModuleNotFoundError: No module named 'graphify'. uv tool install and pipx install isolate the package in their own env and avoid this entirely.

Git hooks and uv tool / pipx: graphify hook install embeds the current interpreter path directly into the hook scripts at install time, so the post-commit hook fires correctly even in GUI git clients and CI runners where ~/.local/bin is not on PATH. If you reinstall or upgrade graphify, re-run graphify hook install to refresh the embedded path.

Pick your platform

Codex users also need multi_agent = true under [features] in ~/.codex/config.toml for parallel extraction. CodeBuddy uses the same Agent tool and PreToolUse hook mechanism as Claude Code. Factory Droid uses the Task tool for parallel subagent dispatch. OpenClaw and Aider use sequential extraction (parallel agent support is still early on those platforms). Trae uses the Agent tool for parallel subagent dispatch and does not support PreToolUse hooks — AGENTS.md is the always-on mechanism.

--platform agents (alias --platform skills) targets the generic cross-framework Agent-Skills locations: the spec's user-global ~/.agents/skills/ (read by npx skills and spec-compliant frameworks) for a global install, and ./.agents/skills/ for a project (--project) install. The bare graphify install stays single-platform (Claude Code) by design — use the named agents platform when you want the skill discoverable by any framework that reads .agents/skills.

Codex uses $graphify instead of /graphify.

Optional extras

Install only what you need:

Make your assistant always use the graph

Run this once in your project after building a graph:

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. On platforms that support payload-bearing hooks (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. On the others (Codex, OpenCode, Cursor, etc.), the persistent instruction files (AGENTS.md, .cursor/rules/, etc.) provide the same query-first guidance. GRAPH_REPORT.md is still available for broad architecture review.

CodeBuddy does the same two things as Claude Code: writes a CODEBUDDY.md section telling CodeBuddy to read graphify-out/GRAPH_REPORT.md before answering architecture questions, and installs PreToolUse hooks (.codebuddy/settings.json) that fire before Bash search commands and file reads, nudging toward graphify query instead.

Codex writes to AGENTS.md and also installs a PreToolUse hook in .codex/hooks.json that fires before every Bash tool call — same always-on mechanism as Claude Code.

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).

Kilo Code installs the Graphify skill to ~/.config/kilo/skills/graphify/SKILL.md and a native /graphify command to ~/.config/kilo/command/graphify.md. graphify kilo install also writes AGENTS.md plus a native tool.execute.before plugin (.kilo/plugins/graphify.js + .kilo/kilo.json or .kilo/kilo.jsonc registration) so Kilo gets the same always-on graph reminder behavior through native .kilo config.

Cursor writes .cursor/rules/graphify.mdc with alwaysApply: true — Cursor includes it in every conversation automatically, no hook needed.

What's in the report

• God nodes — the most-connected concepts in your project. Everything flows through these.
• Surprising connections — links between things that live in different files or modules. Ranked by how unexpected they are.
• The "why" — inline comments (# NOTE:, # WHY:, # HACK:), docstrings, and design rationale from docs are extracted as separate nodes linked to the code they explain.
• Suggested questions — 4–5 questions the graph is uniquely positioned to answer.
• Confidence tags — every inferred relationship is marked EXTRACTED, INFERRED, or AMBIGUOUS. You always know what was found vs guessed.

What files it handles

Code is extracted locally with no API calls (AST via tree-sitter). Everything else goes through your AI assistant's model API.

Google Drive for desktop .gdoc, .gsheet, and .gslides files are shortcut pointers, not document content. To include native Google Docs, Sheets, and Slides in a headless extraction, install and authenticate the gws CLI, then run:

uv tool install "graphifyy[google]"  # needed for Google Sheets table rendering
gws auth login -s drive
graphify extract ./docs --google-workspace

You can also set GRAPHIFY_GOOGLE_WORKSPACE=1. Graphify exports shortcuts into graphify-out/converted/ as Markdown sidecars, then extracts those files.

Common commands

/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 git commit
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.

Ignoring files

Create a .graphifyignore in your project root — same syntax as .gitignore, including ! negation.

.gitignore is respected automatically. graphify reads the .gitignore in each directory. If a .graphifyignore is also present, the two are merged — .graphifyignore patterns are evaluated last, so they win on conflicts (including ! negations). Adding a .graphifyignore only ever excludes more; it never re-includes a file your .gitignore already excluded. Subdirectory scoping works the same way as git — an ignore file only affects its own subtree.

# .graphifyignore
node_modules/
dist/
*.generated.py

# only index src/, ignore everything else
*
!src/
!src/**

Team setup

graphify-out/ is meant to be committed to git so everyone on the team starts with a map.

Recommended .gitignore additions:

graphify-out/cost.json        # local only
# graphify-out/cache/         # optional: commit for speed, skip to keep repo small

manifest.json is now portable — keys are stored as relative paths and re-anchored on load, so committing it is safe and avoids a full rebuild on first checkout.

Workflow:

1. One person runs /graphify . and commits graphify-out/.
2. Everyone pulls — their assistant reads the graph immediately.
3. Run graphify hook install to auto-rebuild after each commit (AST only, no API cost). This also sets up a git merge driver so graph.json is never left with conflict markers — two devs committing in parallel get their graphs union-merged automatically.
4. When docs or papers change, run /graphify --update to refresh those nodes.

Using the graph directly

# query the graph from the terminal
graphify query "show the auth flow"
graphify query "what connects DigestAuth to Response?" --graph graphify-out/graph.json

# expose the graph as an MCP server (for repeated tool-call access)
python -m graphify.serve graphify-out/graph.json
python -m graphify.serve --graph graphify-out/graph.json  # --graph flag also accepted

# register with Kimi Code:
kimi mcp add --transport stdio graphify -- python -m graphify.serve graphify-out/graph.json

# or serve over HTTP so a whole team points at one URL (no local graphify needed):
python -m graphify.serve graphify-out/graph.json --transport http --port 8080
python -m graphify.serve graphify-out/graph.json --transport http --host 0.0.0.0 --api-key "$SECRET"

The MCP server gives your assistant structured access: query_graph, get_node, get_neighbors, shortest_path, list_prs, get_pr_impact, triage_prs.

Shared HTTP server

--transport stdio (the default) spawns one local server per developer. --transport http serves the same tools over the MCP Streamable HTTP transport, so a single shared process can serve the graph for the whole team — clients point their IDE MCP config at http://<host>:8080/mcp instead of running graphify locally.

The default 127.0.0.1 bind is loopback-only. Set --host 0.0.0.0 and --api-key together when exposing on a shared host. Run it in a container:

docker build -t graphify .
docker run -p 8080:8080 -v "$(pwd)/graphify-out:/data" graphify \
  /data/graph.json --transport http --host 0.0.0.0 --api-key "$SECRET"

WSL / Linux note: Ubuntu ships python3, not python. Use a venv to avoid conflicts:

python3 -m venv .venv && .venv/bin/pip install "graphifyy[mcp]"

Environment variables

These are only needed for headless / CI extraction (graphify extract). When running via the /graphify skill inside your IDE, the model API is provided by your IDE session — no extra keys needed.

Privacy

• Code files — processed locally via tree-sitter. Nothing leaves your machine. A code-only corpus requires no API key — graphify extract runs fully offline.
• 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.

Troubleshooting

graphify: command not found after pip install graphifyy pip installs scripts to a user bin directory that may not be on your PATH. Fix:

• macOS: add ~/Library/Python/3.x/bin to your PATH in ~/.zshrc
• Linux: add ~/.local/bin to your PATH in ~/.bashrc
• Or use uv tool install graphifyy / pipx install graphifyy — both manage PATH automatically.

python -m graphify works but graphify command doesn't Your shell's PATH doesn't include the Python scripts directory. Use uv or pipx instead of plain pip.

/graphify . causes "path not recognized" in PowerShell PowerShell treats a leading / as a path separator. Use graphify . (no slash) on Windows.

Graph has fewer nodes after --update or rebuild If a refactor deleted files, the old nodes linger. Pass --force (or set GRAPHIFY_FORCE=1) to overwrite even when the rebuild has fewer nodes.

Graph has duplicate nodes for the same entity (ghost duplicates) Ghost duplicates (same symbol appearing twice — once from AST extraction with a source location, once from semantic extraction without) are now automatically merged at build time. If you see this in a graph built before v0.8.33, run a full re-extract to clean up:

graphify extract . --force

Ollama runs out of VRAM / context window exceeded The KV-cache window is auto-sized but may be too large for your GPU. Reduce it:

GRAPHIFY_OLLAMA_NUM_CTX=8192 graphify extract ./docs --backend ollama --token-budget 4000

LLM returned invalid JSON / Unterminated string warnings The model's JSON response hit its output-token limit and was cut off mid-string. graphify auto-recovers (it splits the chunk and re-extracts the halves, and an oversized single document is first sliced at heading/paragraph boundaries so the whole file is still covered), so these warnings are noisy but not data loss. To reduce the churn, raise the output cap or shrink each chunk's output:

GRAPHIFY_MAX_OUTPUT_TOKENS=16384 graphify extract . --mode deep   # lift the cap
graphify extract . --mode deep --token-budget 4000                # smaller input chunks -> smaller output

With a cloud gateway like OpenRouter, prefer --backend openai (set OPENAI_BASE_URL) over the Ollama shim — it's a cleaner OpenAI-compatible path. If the model has its own max-output ceiling, lowering --token-budget is the reliable lever.

Graph HTML is too large to open in a browser (>5000 nodes) Skip HTML generation and use the JSON directly:

graphify cluster-only ./my-project --no-viz
graphify query "..."

graph.json has conflict markers after two devs commit at once Run graphify hook install — it sets up a git merge driver that union-merges graph.json automatically so conflicts never happen.

Extraction returns empty nodes/edges for docs or PDFs Docs, PDFs, and images require an LLM call — code-only corpora need no key. Check that your API key is set and the backend is correct:

ANTHROPIC_API_KEY=sk-... graphify extract ./docs --backend claude

Skill version mismatch warning in your IDE Your installed graphify version is different from the skill file. Update:

uv tool upgrade graphifyy
graphify install  # overwrites the skill file

Full command reference

/graphify                          # run on current directory
/graphify ./raw                    # run on a specific folder
/graphify ./raw --mode deep        # more aggressive relationship extraction
/graphify ./raw --update           # re-extract only changed files
/graphify ./raw --directed         # preserve edge direction
/graphify ./raw --cluster-only     # rerun clustering on existing graph
/graphify ./raw --no-viz           # skip HTML visualization
/graphify ./raw --obsidian         # generate Obsidian vault
/graphify ./raw --wiki             # build agent-crawlable markdown wiki
/graphify ./raw --svg              # export graph.svg
/graphify ./raw --graphml          # export for Gephi / yEd
/graphify ./raw --neo4j            # generate cypher.txt for Neo4j
/graphify ./raw --neo4j-push bolt://localhost:7687
/graphify ./raw --falkordb         # generate cypher.txt for FalkorDB
/graphify ./raw --falkordb-push falkordb://localhost:6379
/graphify ./raw --watch            # auto-sync as files change
/graphify ./raw --mcp              # start MCP stdio server

/graphify add https://arxiv.org/abs/1706.03762
/graphify add <video-url>
/graphify add https://... --author "Name" --contributor "Name"

/graphify query "what connects attention to the optimizer?"
/graphify query "..." --dfs --budget 1500
/graphify path "DigestAuth" "Response"
/graphify explain "SwinTransformer"

graphify save-result --question "Q" --answer "A" --nodes Foo Bar --outcome useful   # record how a Q&A turned out (work memory; outcome ∈ useful|dead_end|corrected)
graphify reflect                   # aggregate graphify-out/memory/ outcomes into reflections/LESSONS.md
graphify reflect --if-stale        # no-op when LESSONS.md is already newer than every input (cheap to run each session)
graphify reflect --out docs/LESSONS.md    # write the lessons doc somewhere else
graphify reflect --graph graphify-out/graph.json  # also group lessons by community

graphify uninstall                 # remove from all platforms in one shot
graphify uninstall --purge         # also delete graphify-out/
graphify uninstall --project --platform codex  # remove project-scoped install files only

graphify hook install              # post-commit + post-checkout hooks
graphify hook uninstall
graphify hook status

# always-on assistant instructions - platform-specific
graphify claude install            # CLAUDE.md + PreToolUse hook (Claude Code)
graphify claude uninstall
graphify codebuddy install         # CODEBUDDY.md + PreToolUse hook (CodeBuddy)
graphify codebuddy uninstall
graphify codex install             # AGENTS.md + PreToolUse hook in .codex/hooks.json (Codex)
graphify opencode install          # AGENTS.md + tool.execute.before plugin (OpenCode)
graphify kilo install              # native Kilo skill + /graphify command + AGENTS.md + .kilo plugin
graphify kilo uninstall
graphify cursor install            # .cursor/rules/graphify.mdc (Cursor)
graphify cursor uninstall
graphify gemini install            # GEMINI.md + BeforeTool hook (Gemini CLI)
graphify gemini uninstall
graphify copilot install           # skill file (GitHub Copilot CLI)
graphify copilot uninstall
graphify aider install             # AGENTS.md (Aider)
graphify aider uninstall
graphify claw install              # AGENTS.md (OpenClaw)
graphify claw uninstall
graphify droid install             # AGENTS.md (Factory Droid)
graphify droid uninstall
graphify trae install              # AGENTS.md (Trae)
graphify trae uninstall
graphify trae-cn install           # AGENTS.md (Trae CN)
graphify trae-cn uninstall
graphify hermes install             # AGENTS.md + ~/.hermes/skills/ (Hermes)
graphify hermes uninstall
graphify amp install               # skill file (Amp)
graphify amp uninstall
graphify agents install            # ~/.agents/skills/ + AGENTS.md (cross-framework; alias: graphify skills)
graphify agents uninstall
graphify kiro install               # .kiro/skills/ + .kiro/steering/graphify.md (Kiro IDE/CLI)
graphify kiro uninstall
graphify pi install                # skill file (Pi coding agent)
graphify pi uninstall
graphify devin install             # skill file + .windsurf/rules/graphify.md (Devin CLI)
graphify devin uninstall
graphify antigravity install       # .agents/rules + .agents/workflows (Google Antigravity)
graphify antigravity uninstall

graphify extract ./docs                        # headless LLM extraction for CI (no IDE needed)
graphify extract ./docs --backend gemini       # explicit backend: gemini, kimi, claude, openai, deepseek, ollama, bedrock, or claude-cli
graphify extract ./docs --backend gemini --model gemini-3.1-pro-preview
graphify extract ./docs --backend ollama       # local Ollama (set OLLAMA_BASE_URL / OLLAMA_MODEL) - no API key needed for loopback
OPENAI_BASE_URL=http://localhost:8080/v1 OPENAI_MODEL=my-model graphify extract ./docs --backend openai   # any OpenAI-compatible server (llama.cpp, vLLM, LM Studio)
ANTHROPIC_BASE_URL=http://localhost:4000 ANTHROPIC_MODEL=my-model graphify extract ./docs --backend claude   # any Anthropic-compatible endpoint (LiteLLM proxy, gateways)
GRAPHIFY_OLLAMA_NUM_CTX=32768 graphify extract ./docs --backend ollama   # override KV-cache window (auto-sized by default)
GRAPHIFY_OLLAMA_KEEP_ALIVE=0 graphify extract ./docs --backend ollama    # unload model after each chunk (saves VRAM on small GPUs)
graphify extract ./docs --backend bedrock      # AWS Bedrock via IAM - no API key, uses AWS credential chain
graphify extract ./docs --backend claude-cli   # route through Claude Code CLI - no API key, uses your Claude subscription
graphify extract ./docs --backend azure        # Azure OpenAI (set AZURE_OPENAI_API_KEY + AZURE_OPENAI_ENDPOINT)
graphify extract ./docs --max-workers 16       # AST parallelism (also GRAPHIFY_MAX_WORKERS)
graphify extract --postgres "postgresql://user:pass@host/db"   # introspect live PostgreSQL schema directly
graphify extract ./my-workspace --cargo        # introspect Rust Cargo workspace dependencies directly
graphify extract ./docs --token-budget 30000   # smaller semantic chunks for local/small models
graphify extract ./docs --max-concurrency 2    # fewer parallel LLM calls (useful for local inference)
graphify extract ./docs --api-timeout 900      # longer HTTP timeout for slow local models (default 600s)
graphify extract ./docs --google-workspace     # export .gdoc/.gsheet/.gslides via gws before extraction
graphify extract ./docs --mode deep            # richer semantic extraction via extended system prompt
graphify extract ./docs --no-cluster           # raw extraction only, skip clustering
graphify extract ./docs --force                # overwrite graph.json even if new graph has fewer nodes (use after refactors or to clear ghost duplicates)
graphify extract ./docs --dedup-llm            # LLM tiebreaker for ambiguous entity pairs (uses same API key)
graphify extract ./docs --global --as myrepo   # extract and register into the cross-project global graph
GRAPHIFY_MAX_OUTPUT_TOKENS=32768 graphify extract ./docs --backend claude  # raise output cap for dense corpora

graphify export callflow-html                       # graphify-out/<project>-callflow.html
graphify export callflow-html --max-sections 8      # cap generated architecture sections
graphify export callflow-html --output docs/arch.html
graphify export callflow-html ./some-repo/graphify-out

graphify global add graphify-out/graph.json myrepo   # register a project graph into ~/.graphify/global.json
graphify global remove myrepo                         # remove a project from the global graph
graphify global list                                  # show all registered repos + node/edge counts
graphify global path                                  # print path to the global graph file

graphify prs                              # PR dashboard: CI, review, worktree, graph impact
graphify prs 42                           # deep dive on PR #42
graphify prs --triage                     # AI triage ranking (auto-detects backend from env)
graphify prs --worktrees                  # worktree → branch → PR mapping
graphify prs --conflicts                  # PRs sharing graph communities (merge-order risk)
graphify prs --base main                  # filter to PRs targeting a specific base branch
graphify prs --repo owner/repo            # run against a different GitHub repo
GRAPHIFY_TRIAGE_BACKEND=kimi graphify prs --triage   # use a specific backend for triage

graphify clone https://github.com/karpathy/nanoGPT
graphify merge-graphs a.json b.json --out merged.json
graphify --version                                    # print installed version
graphify watch ./src
graphify check-update ./src
graphify update ./src
graphify update ./src --no-cluster  # skip reclustering, write raw AST graph only
graphify update ./src --force       # overwrite even if new graph has fewer nodes
graphify cluster-only ./my-project
graphify cluster-only ./my-project --graph path/to/graph.json  # custom graph location
graphify cluster-only ./my-project --max-concurrency 16 --batch-size 200  # parallel community labeling (large graphs)
graphify cluster-only ./my-project --resolution 1.5            # more, smaller communities
graphify cluster-only ./my-project --exclude-hubs 99           # exclude p99 degree nodes from partitioning
graphify cluster-only ./my-project --no-label                  # keep "Community N" placeholders
graphify cluster-only ./my-project --backend=gemini            # backend for community naming
graphify cluster-only ./my-project --backend=gemini --model gemini-2.5-pro  # specific model
graphify label ./my-project                                    # (re)name communities with the configured backend
graphify label ./my-project --backend=openai --model gpt-4o   # force a specific backend and model

Community names: inside an agent (Claude Code, Gemini CLI) the agent names communities itself. When you run the bare CLI, cluster-only auto-names them with the configured backend (built-in or custom OpenAI-compatible provider) — pass --no-label to keep Community N, or run graphify label to (re)generate names on demand.

Learn more

• 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

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git clone https://github.com/safishamsi/graphify.git
cd graphify
git checkout v8                        # active development branch

# Create the project venv and install graphify + all extras + the dev group
# (pytest). uv installs the dev dependency group by default; pass --no-dev to
# skip it.
uv sync --all-extras

uv run graphify --version
uv run python -c "import graphify; print(graphify.__file__)"

uv run pytest tests/ -q                # run the full suite
uv run pytest tests/test_extract.py -q # one module
uv run pytest tests/ -q -k "python"    # filter by name