graphify

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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) 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.

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.

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 / mixesin 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 |

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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.

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Prerequisites

| Requirement | Minimum | Check | Install | |---|---|---|---| | Python | 3.10+ | python --version | python.org | | uv (recommended) | any | uv --version | curl -LsSf https://astral.sh/uv/install.sh \| sh | | pipx (alternative) | any | pipx --version | pip install pipx |

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

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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 (isolated env; if 'graphify' isn't found after, run: uv tool update-shell):
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? uv tool install / pipx install put the graphify command in their tool bin dir (/.local/bin). If your shell can't find it right after install — common on a