Graphify: The AI Knowledge Graph Tool That Turns Your Entire Codebase Into a Queryable Map — 85,000+ GitHub Stars

Why Your AI Coding Assistant Is Missing Half the Picture

If you've ever watched your AI coding agent grep through dozens of files to answer a simple architecture question — "How does authentication connect to the database?" — you've seen the problem Graphify solves.

Current AI coding tools work file-by-file. They read one source file at a time, lose context between conversations, and can't see the relationships between your code. Graphify flips this model entirely: instead of reading files, you build a graph — and query it like a database.

The result? Your AI assistant stops guessing and starts traversing. Every function call, import, inheritance chain, and cross-module dependency becomes a traversable edge in a graph that your AI can query in milliseconds.

How Graphify Works: From Source Code to Knowledge Graph

Graphify's architecture is built on three principles: local-first extraction, explicit confidence tagging, and graph-based querying instead of vector similarity search.

Step 1: Local AST Extraction (Zero LLM Calls)

Code is parsed entirely locally using tree-sitter — the same parser used by GitHub's syntax highlighting. This means:

• No API costs for code extraction — zero tokens consumed
• Nothing leaves your machine — your proprietary code stays private
• 40+ languages supported — Python, TypeScript, Go, Rust, Java, C++, Swift, Kotlin, PHP, Ruby, and more

# Install Graphify CLI
uv tool install graphifyy

# Register the skill with your AI assistant
graphify install

# Build the knowledge graph for your project
/graphify .

The output is three files:

graphify-out/
├── graph.html          # Interactive browser visualization
├── GRAPH_REPORT.md     # Key concepts, surprising connections, suggested questions
└── graph.json          # The full queryable graph

Step 2: Confidence-Tagged Edges

Every relationship in Graphify carries a confidence tag:

• EXTRACTED — read directly from source code (explicit import, function call, class definition)
• INFERRED — resolved by Graphify's analysis (indirect dependencies, type inference, cross-file resolution)

This transparency means you always know what was found vs. what was guessed — a critical distinction when making architecture decisions.

Step 3: Query Instead of Grep

Once the graph is built, you stop reading files and start asking questions:

# Ask a natural language question
graphify query "what connects auth to the database?"

# Trace the path between two concepts
graphify path "UserService" "DatabasePool"

# Explain a single concept with all its connections
graphify explain "RateLimiter"

Each query returns a scoped subgraph — not a wall of text, but a structured set of nodes and edges your AI can reason over.

Real-World Example: Debugging a Production Incident

Imagine you're investigating a production error: RequestValidationError is being thrown unexpectedly. Normally, you'd ask your AI assistant to "find where RequestValidationError is used" — and it would grep through files one by one, missing indirect connections.

With Graphify, the query reveals the full chain instantly:

$ graphify explain "RequestValidationError"
Node: RequestValidationError
  Source: exceptions.py L42
  Community: 3 (Validation Layer)
  Degree: 18

Connections (18):
  --> Dependant [uses] [INFERRED]
  --> APIRouter.get() [method] [EXTRACTED]
  --> UserModel.validate() [calls] [EXTRACTED]
  <-- FastAPI.__init__ [imports] [EXTRACTED]
  <-- middleware/error_handler.py [references] [EXTRACTED]

Your AI assistant can now trace exactly how the error propagates — from the validation layer through the routing system to the error handler — and suggest a targeted fix. No file-by-file search. No missed connections.

Beyond Code: Docs, PDFs, Videos in One Graph

Graphify doesn't stop at source code. It maps your entire project ecosystem:

All of these feed into the same graph — so a design document node can link directly to the code it describes, and a Terraform resource can connect to the service it provisions.

Key Benefits

• Zero LLM cost for code — tree-sitter parsing is free and local; no tokens consumed for code extraction
• 40+ language support — from Python and TypeScript to Rust, Go, and Terraform
• 20+ AI assistant integrations — Claude Code, Cursor, Codex, Gemini CLI, OpenCode, GitHub Copilot, and more
• Queryable knowledge graph — not embeddings or vector search, but a real graph with traversable edges
• Confidence tags — EXTRACTED vs. INFERRED vs. AMBIGUOUS so you always know what's certain
• God node detection — instantly identify the most-connected concepts everything flows through
• Community detection — Leiden algorithm splits your project into logical subsystems automatically
• Auto-rebuild on git commit — graphify hook install keeps your graph fresh with every push
• Fully local — code never leaves your machine; only docs/media use an optional API
• 85,000+ GitHub stars — battle-tested by a massive developer community

Getting Started in Under a Minute

# 1. Install the CLI
uv tool install graphifyy

# 2. Register with your AI assistant
graphify install  # Claude Code (default)
graphify cursor install  # Cursor
graphify install --platform codex  # Codex
graphify install --platform opencode  # OpenCode
graphify install --platform gemini  # Gemini CLI

# 3. Build your graph
/graphify .

# 4. Query it
/graphify query "how does payment processing work?"

The graph.html output opens in any browser — click nodes, filter by community, search across your entire codebase visually.

Frequently Asked Questions

Is Graphify free and open-source?

Yes. Graphify is fully open-source under the MIT license. The core CLI and all AI assistant integrations are free. Optional extras for PDF, video, and specific database connectors are also free — they're just separate install targets to keep the base package lightweight.

Does Graphify send my code to an external API?

No. Code extraction uses tree-sitter AST parsing, which runs entirely on your machine. Zero API calls, zero tokens consumed, zero data leaves your computer. Only the optional semantic pass for docs, PDFs, and media uses an API — and only if you configure one.

How is Graphify different from vector-based RAG?

Vector RAG converts your code into embeddings and searches by similarity. Graphify builds a real knowledge graph with explicit, traversable edges. You can trace paths between concepts, see exactly how two modules connect, and distinguish extracted facts from inferences — capabilities that vector search fundamentally cannot provide.

Which AI coding assistants does Graphify support?

Graphify supports 20+ assistants including Claude Code, Cursor, Codex, Gemini CLI, OpenCode, GitHub Copilot, Aider, Kilo Code, Amp, Devin, Trae, Kimi Code, and the cross-framework Agent Skills spec. Each gets a native integration — hooks for Claude Code, rules files for Cursor, AGENTS.md for Codex, and so on.

How often do I need to rebuild the graph?

Run graphify hook install once and the graph auto-rebuilds on every git commit. You can also manually update with /graphify . --update to re-extract only changed files, or use --cluster-only to re-run community detection without re-parsing code.

What programming languages are supported?

Graphify supports 40+ languages via tree-sitter grammars: Python, TypeScript, JavaScript, Go, Rust, Java, C, C++, C#, Kotlin, Swift, Ruby, PHP, Lua, Zig, Scala, Elixir, Julia, Dart, Groovy, Objective-C, Fortran, Pascal, Verilog, and more. SQL, Terraform/HCL, and Salesforce Apex are also supported.

Can Graphify analyze non-code content?

Yes. Beyond source code, Graphify extracts and links Markdown docs, PDFs, Word/Excel files, images, videos (via transcription), YouTube URLs, SQL schemas, Terraform configs, and Google Workspace files — all into the same unified knowledge graph.