Graphify: The AI-Powered Knowledge Graph Builder for Code — 89,379 GitHub Stars

Quick Answer

Graphify is an AI-powered knowledge graph builder that transforms your entire project—code, documentation, PDFs, images, and videos—into a queryable graph structure. With 89,379 GitHub stars and 8,722 forks, it's the fastest-growing code intelligence tool on GitHub. Built on tree-sitter AST parsing for deterministic, local-first code analysis (no LLM required for code), Graphify lets you query relationships, trace dependencies, and understand complex codebases without grepping through files. Works with 20+ AI coding assistants including Claude Code, Cursor, Codex, Gemini CLI, and GitHub Copilot. Use Graphify if you need: A way to understand large codebases quickly, trace how components connect, find unexpected dependencies, or get AI-powered insights into your project architecture without sending code to external servers.

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The Problem: Why Graphify Exists

Modern codebases are complex. A typical SaaS application has:

• Hundreds of files across multiple languages (Python, TypeScript, SQL, Terraform)
• Cross-file dependencies that are hard to trace manually
• Documentation scattered across READMEs, PDFs, design docs, and inline comments
• Hidden relationships between seemingly unrelated components
• Rationale buried in commit messages, PRs, and ADR documents

Traditional tools fall short:

• grep/find: No semantic understanding, just text matching
• IDE navigation: Limited to immediate file relationships
• Vector search (RAG): Embeddings lose structural context, can't trace paths
• Documentation tools: Separate from code, no cross-referencing

Graphify solves these problems by building a real knowledge graph—not a vector index—where every node is a concept (class, function, module, document) and every edge is an explained relationship (calls, imports, inherits, references). The result: you can ask questions like "How does authentication connect to the database?" and get a traversable path, not just matching text. The project launched in early 2025 and exploded to 89,379 stars in under 6 months—a testament to developers' hunger for better code intelligence tools.

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Key Features

1. Local-First Code Parsing (No LLM Required)

Graphify uses tree-sitter to parse your code into Abstract Syntax Trees (AST). This means:

• Zero API costs for code analysis
• Fully local processing—your code never leaves your machine
• Deterministic results—same code always produces the same graph
• 36+ languages supported: Python, TypeScript, JavaScript, Go, Rust, Java, C++, Ruby, C#, Kotlin, Swift, PHP, and more

# Code extraction is completely local
graphify extract ./src

# No API keys needed, no tokens consumed
# Tree-sitter parses Python, TypeScript, Go, Rust, etc.

Only documentation, PDFs, images, and videos use LLM APIs (and only if you configure one). For pure code analysis, Graphify is 100% local and free.

2. Confidence Tags: EXTRACTED vs INFERRED

Every relationship in the graph carries a confidence tag:

• EXTRACTED: Explicitly found in the source code (e.g., import, class Foo extends Bar)
• INFERRED: Resolved by Graphify's analysis (e.g., cross-file function calls, implicit dependencies)

$ graphify explain "UserService"
Node: UserService
  Source:    services/user.py L45
  Community: 3
  Degree:    82

Connections (82):
  --> DatabasePool [uses] [EXTRACTED]
  --> AuthMiddleware [calls] [EXTRACTED]
  --> RateLimiter [uses] [INFERRED]
  --> EmailService [calls] [INFERRED]
  ...

This transparency means you always know what was read directly from the code versus what was inferred, preventing false confidence in the graph's accuracy.

3. Query, Path, Explain Commands

Once the graph is built, you query it instead of reading files: Query: Ask natural language questions

$ graphify query "what connects auth to the database?"
# Returns a scoped subgraph showing the authentication flow

Path: Trace how two concepts connect

$ graphify path "FastAPI" "ModelField"
Shortest path (3 hops):
  FastAPI --uses--> DefaultPlaceholder <--references-- get_request_handler() --references--> ModelField

Explain: Understand a single concept in context

$ graphify explain "APIRouter"
Node: APIRouter
  Source:    routing.py L2210
  Community: 2
  Degree:    47

Connections (47):
  --> RequestValidationError [uses] [INFERRED]
  --> Dependant [uses] [INFERRED]
  --> .get() [method] [EXTRACTED]
  ...

4. God Nodes & Community Detection

Graphify automatically identifies:

• God nodes: The most-connected concepts in your project. Everything flows through these. If you're new to a codebase, start here.
• Communities: The graph is split into subsystems using the Leiden algorithm, with LLM-free labels. Each community represents a functional area (auth, database, API, frontend).

$ graphify report
Top God Nodes:
1. DatabasePool (degree: 156) - Central connection pooling
2. AuthMiddleware (degree: 134) - Authentication flow
3. APIRouter (degree: 128) - Request routing
4. Config (degree: 119) - Configuration management

Communities (7 detected):

Community 0: Authentication & Authorization (23 nodes)
Community 1: Database & ORM (31 nodes)
Community 2: API & Routing (45 nodes)
Community 3: Business Logic (67 nodes)

...

5. Cross-File Link Resolution

Graphify resolves relationships across file boundaries:

• calls: Function A in auth.py calls function B in database.py
• imports: Module X imports from module Y
• inherits: Class Foo extends class Bar (even across packages)
• mixes_in: Mixin patterns resolved across modules
• references: Documentation links to code, code cites ADRs

This is powered by tree-sitter's AST analysis, which understands language-specific syntax and semantics, not just text patterns.

6. Beyond Code: Docs, PDFs, Images, Videos

Graphify doesn't stop at code. It maps your entire knowledge base: Supported file types:

• Code: 36+ languages via tree-sitter
• Docs: Markdown, HTML, RST, YAML, TXT
• PDFs: Extracts text, tables, diagrams (requires graphifyy[pdf])
• Office: DOCX, XLSX (requires graphifyy[office])
• Images: PNG, JPG, WebP, GIF (analyzed via LLM)
• Video/Audio: MP4, MOV, MP3, WAV (transcribed via faster-whisper)
• YouTube: Any video URL (transcribed and analyzed)

# Add a research paper to the graph
graphify add https://arxiv.org/abs/1706.03762

# Add a tutorial video
graphify add https://www.youtube.com/watch?v=example

# Add design docs
graphify extract ./docs/design/

All of these become nodes in the same graph, linked to the code they describe or implement.

7. Rationale & Design Decisions

Graphify extracts and links:

• Inline comments: # NOTE:, # WHY:, # HACK: become first-class nodes
• Docstrings: Function/class documentation linked to the code
• ADR/RFC citations: Design documents referenced in code comments
• Commit messages: Git history can be integrated

This means you can query not just "what does this do?" but "why was this designed this way?"

8. Works with 20+ AI Assistants

Graphify is a skill, not a standalone tool. It integrates with:

• Claude Code (native hook support)
• Cursor (via .cursor/rules/)
• Codex (AGENTS.md + hooks)
• Gemini CLI
• GitHub Copilot CLI
• VS Code Copilot Chat
• OpenCode
• Aider
• OpenClaw
• Kilo Code
• Trae
• And 10+ more...

Installation is one command:

graphify install  # Claude Code (default)
graphify install --platform cursor
graphify install --platform codex
graphify install --platform opencode

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Technical Architecture

Tree-Sitter AST Parsing

At the core, Graphify uses tree-sitter—a parser generator tool and incremental parsing library—to build Abstract Syntax Trees for your code. Why tree-sitter?

• Incremental parsing: Only re-parse changed files, not the entire codebase
• Error tolerance: Parses even broken code, extracting what it can
• Language-agnostic: Same API for 36+ languages
• Deterministic: Same code always produces the same AST
• No LLM needed: Pure algorithmic parsing, zero API costs

What tree-sitter extracts:

• Function/method definitions
• Class definitions and inheritance
• Import statements
• Function calls
• Variable assignments
• Type annotations
• Comments (including # NOTE:, # WHY:)

# Example: Python code
def authenticate_user(username: str, password: str) -> User:
    """Validate credentials against database."""
    # WHY: Using bcrypt for password hashing (security requirement)
    hashed = bcrypt.hashpw(password.encode('utf-8'), bcrypt.gensalt())
    user = db.query(User).filter(User.username == username).first()
    if user and bcrypt.checkpw(password.encode('utf-8'), user.password_hash):
        return user
    raise AuthenticationError("Invalid credentials")

Tree-sitter extracts:

• Node: authenticate_user (function)
• Edges:

- calls → bcrypt.hashpw (EXTRACTED) - calls → db.query (EXTRACTED) - calls → bcrypt.checkpw (EXTRACTED) - uses → User (EXTRACTED) - raises → AuthenticationError (EXTRACTED)

• Comment node: "WHY: Using bcrypt for password hashing (security requirement)" linked to the function

Graph Database: JSON-Based

Unlike many tools that require Neo4j or other graph databases, Graphify uses a simple JSON file (graph.json) as its storage format. Why JSON?

• Portable: Works everywhere, no database setup
• Queryable: Can be loaded into Python, JavaScript, or any language
• Version-controllable: Commit the graph to git, share with team
• Fast: In-memory queries, no network latency

Schema:

{
  "nodes": [
    {
      "id": "services/user.py:authenticate_user",
      "type": "function",
      "label": "authenticate_user",
      "source": "services/user.py",
      "line": 15,
      "community": 3,
      "degree": 12,
      "metadata": {
        "language": "python",
        "docstring": "Validate credentials against database."
      }
    }
  ],
  "edges": [
    {
      "source": "services/user.py:authenticate_user",
      "target": "models/user.py:User",
      "type": "uses",
      "confidence": "EXTRACTED",
      "metadata": {
        "line": 18
      }
    }
  ]
}

For large-scale deployments, Graphify supports pushing to Neo4j or FalkorDB:

uv tool install "graphifyy[neo4j]"
graphify push neo4j --uri bolt://localhost:7687

Leiden Community Detection

Graphify uses the Leiden algorithm to detect communities (clusters) in the graph. This is the same algorithm used by network scientists to study social networks, biological systems, and citation graphs. What communities reveal:

• Functional modules (auth, database, API, frontend)
• Hidden subsystems (e.g., all payment-related code scattered across files)
• Code organization issues (e.g., a "god module" that should be split)

Configuration:

# More granular communities
graphify . --cluster-only --resolution 1.5

# Suppress utility super-hubs (e.g., logging, config)
graphify . --cluster-only --exclude-hubs 99

MCP Server (Optional)

Graphify can run as an MCP (Model Context Protocol) server, allowing AI agents to query the graph programmatically:

uv tool install "graphifyy[mcp]"
graphify mcp-server

This enables AI-powered workflows:

• AI agent queries the graph before making code changes
• Automated architecture reviews
• Dependency impact analysis for PRs

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Benchmarks: How Graphify Compares

Graphify has been rigorously benchmarked against other memory and retrieval systems: | Benchmark | Metric | Graphify | Field Average | |-----------|--------|----------|---------------| | 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 | Key takeaways:

• 10x better recall than mem0 and supermemory on the LOCOMO benchmark
• Zero LLM costs for code analysis (tree-sitter parsing)
• Competitive QA accuracy with dense RAG systems, but without vector stores

Methodology: 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 details: BENCHMARKS.md

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Graphify vs Other Solutions

Graphify vs grep/find

| Feature | Graphify | grep/find | |---------|----------|-----------| | Semantic understanding | Yes (AST-based) | No (text matching) | | Cross-file relationships | Yes (resolved) | No (single file) | | Query natural language | Yes | No | | Trace paths | Yes (graph traversal) | No | | Speed | Fast (pre-built graph) | Fast (direct search) | | Learning curve | Medium (install + build graph) | Low (built-in) | When to use grep: Quick text searches, finding exact strings, one-off lookups. When to use Graphify: Understanding architecture, tracing dependencies, onboarding to new codebases, architectural reviews.

Graphify vs Vector Search (RAG)

| Feature | Graphify | RAG (Vector Search) | |---------|----------|---------------------| | Structure preservation | Yes (explicit edges) | No (embeddings lose structure) | | Trace paths | Yes (graph traversal) | No (similarity search only) | | Confidence tags | Yes (EXTRACTED/INFERRED) | No (black-box similarity) | | LLM costs | Zero for code | Per-token for everything | | Setup complexity | Low (one command) | Medium (vector DB, embeddings) | | Best for | Code, architecture | Documents, unstructured text | When to use RAG: Searching documentation, Q&A over unstructured text, finding similar passages. When to use Graphify: Understanding code structure, tracing dependencies, architectural analysis, finding "why" something was done.

Graphify vs IDE Navigation

| Feature | Graphify | IDE (VS Code, IntelliJ) | |---------|----------|-------------------------| | Cross-language | Yes (36+ languages) | Limited (per-project) | | Beyond code | Yes (docs, PDFs, videos) | No (code only) | | AI assistant integration | Yes (20+ platforms) | Limited (IDE-specific) | | Rationale extraction | Yes (comments, ADRs) | No | | Community detection | Yes (Leiden algorithm) | No | | Offline | Yes (fully local) | Yes | When to use IDE: Day-to-day navigation, refactoring, debugging. When to use Graphify: High-level architecture understanding, onboarding, cross-language projects, AI-assisted coding.

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Installation & Setup Guide

Quick Start (30 seconds)

# Step 1: Install the CLI
uv tool install graphifyy

# Step 2: Register with your AI assistant
graphify install  # Claude Code (default)

# Step 3: Build the graph
# In your AI assistant, type:
/graphify .

That's it. You get three files:

graphify-out/
├── graph.html       # Interactive graph visualization
├── GRAPH_REPORT.md  # Summary: god nodes, communities, suggested questions
└── graph.json       # Full graph data for querying

Platform-Specific Installation

Claude Code:

graphify install
# Or for project-scoped install:
graphify install --project

Cursor:

graphify cursor install
# Writes to .cursor/rules/graphify.mdc

Codex:

graphify install --platform codex
# Also enable multi_agent in ~/.codex/config.toml:
# [features]
# multi_agent = true

OpenCode:

graphify install --platform opencode

Gemini CLI:

graphify install --platform gemini

GitHub Copilot CLI:

graphify install --platform copilot

VS Code Copilot Chat:

graphify vscode install

Optional Extras

Install only what you need:

# PDF extraction
uv tool install "graphifyy[pdf]"

# Office documents (DOCX, XLSX)
uv tool install "graphifyy[office]"

# Video/audio transcription
uv tool install "graphifyy[video]"

# MCP server
uv tool install "graphifyy[mcp]"

# Neo4j support
uv tool install "graphifyy[neo4j]"

# SQL schema extraction
uv tool install "graphifyy[sql]"

# PostgreSQL live introspection
uv tool install "graphifyy[postgres]"

# Terraform/HCL extraction
uv tool install "graphifyy[terraform]"

# Everything
uv tool install "graphifyy[all]"

Configure Auto-Consulting

After building a graph, tell your AI assistant to consult it automatically:

# Claude Code
graphify claude install

# Cursor
graphify cursor install

# Codex
graphify codex install

# OpenCode
graphify opencode install

This installs hooks or instruction files that make your AI assistant prefer graphify query over grepping files.

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Real-World Use Cases

1. Onboarding to a New Codebase

Scenario: You join a team with a 500k LOC codebase. Traditional approach: read READMEs, ask colleagues, grep around. With Graphify:

/graphify .
/graphify query "what are the main entry points?"
/graphify explain "AuthenticationFlow"
/graphify path "API Gateway" "Database"

Result: You understand the architecture in 30 minutes instead of 2 weeks.

2. Architectural Review

Scenario: Preparing for an architecture review meeting. Need to identify technical debt, god objects, and tightly coupled modules. With Graphify:

/graphify .
# Check GRAPH_REPORT.md for:
# - God nodes (overly connected components)
# - Communities (are they well-separated?)
# - Cross-community dependencies (coupling)

Result: Data-driven architectural insights, not just gut feelings.

3. Impact Analysis for PRs

Scenario: Reviewing a PR that changes a core utility function. Need to know what else might break. With Graphify:

graphify path "utils/helpers.py:format_date" "frontend/components/DateDisplay"
# Shows all paths from the changed function to dependent code

Result: Comprehensive impact analysis, catch edge cases before merge.

4. Documentation Generation

Scenario: Writing documentation for a complex system. Need to understand all the pieces and how they connect. With Graphify:

/graphify . --wiki
# Generates a markdown wiki from the graph
# Each node becomes a page with links to related concepts

Result: Auto-generated documentation that stays in sync with code.

5. Security Audit

Scenario: Auditing authentication and authorization code. Need to find all paths from user input to database. With Graphify:

graphify query "all paths from HTTP request to database write"
graphify path "login endpoint" "users table"

Result: Complete attack surface mapping, identify potential vulnerabilities.

6. Refactoring Planning

Scenario: Planning to split a monolithic module into microservices. Need to understand boundaries. With Graphify:

/graphify . --cluster-only --resolution 1.5
# Check communities - these are natural service boundaries
# Look at cross-community edges - these are API contracts

Result: Data-driven microservice decomposition, minimize coupling.

7. Cross-Language Projects

Scenario: Your project has Python backend, TypeScript frontend, Go microservices, and Terraform infrastructure. With Graphify:

/graphify .
# Builds a unified graph across all languages
# See how frontend API calls connect to backend functions
# Trace infrastructure dependencies (Terraform → services → databases)

Result: Holistic view of your entire system, not just one language.

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Advanced Features

Git Hooks for Auto-Rebuild

Automatically rebuild the graph on every commit:

graphify hook install

Now every git commit triggers a graph update, keeping it in sync with your code.

PR Dashboard

Analyze pull requests with graph impact:

graphify prs              # 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
graphify prs --conflicts  # PRs sharing graph communities (merge-order risk)

Graph Merging

Combine multiple graphs (e.g., monorepo with separate services):

graphify merge-graphs service-a/graph.json service-b/graph.json

Interactive Visualization

Open graphify-out/graph.html in any browser:

• Click nodes to see details
• Filter by community, type, or degree
• Search for specific concepts
• Zoom and pan the force-directed layout

Export Options

# Mermaid architecture diagram
graphify export callflow-html

# SVG graph
graphify export svg

# Push to Neo4j
graphify push neo4j --uri bolt://localhost:7687

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Future Roadmap

The Graphify team (Y Combinator S26) is actively developing:

1. Enhanced AI Integration

• Real-time graph updates as you code
• AI-powered refactoring suggestions based on graph analysis
• Automated architecture violation detection

2. Collaboration Features

• Shared graphs for team onboarding
• Graph diffing between branches
• Collaborative annotation of nodes/edges

3. Performance Optimizations

• Incremental graph updates (only re-parse changed files)
• Distributed graph building for massive codebases
• Caching layer for frequently queried paths

4. More Languages & Frameworks

• Expanded tree-sitter grammar coverage
• Framework-specific patterns (React components, Django models, etc.)
• Infrastructure-as-code support (Kubernetes, CloudFormation)

5. Security Analysis

• Automated vulnerability detection via graph patterns
• Dependency chain analysis for supply chain security
• Secret detection in code and documentation

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Community & Contributors

Graphify is backed by Graphify Labs, a Y Combinator S26 company. The project has:

• 89,379 GitHub stars
• 8,722 forks
• 531 open issues (active development)
• Active Discord community for support and feature requests

Key contributors:

• The Graphify Labs core team
• Community contributors from the AI coding assistant ecosystem
• Partnerships with Claude Code, Cursor, Codex, and other platforms

Getting help:

• GitHub Issues
• Discord Community
• Documentation

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FAQ

1. Is Graphify free to use?

Yes, Graphify is open-source and free for personal and commercial use. Code analysis (tree-sitter parsing) requires zero API keys or costs. Only optional features like PDF extraction, video transcription, or semantic analysis of documents may use LLM APIs (and only if you configure one).

2. Does Graphify send my code to external servers?

No. Code is parsed locally using tree-sitter AST. Nothing leaves your machine for code analysis. Only documentation, PDFs, images, and videos may use external APIs if you configure them (optional).

3. How large of a codebase can Graphify handle?

Graphify has been tested on codebases with 500k+ lines of code. The graph build time scales linearly with codebase size. For very large monorepos, use --no-viz to skip HTML generation and focus on the JSON graph.

4. Can I use Graphify without an AI assistant?

Yes. Graphify works as a standalone CLI tool:

graphify extract ./src
graphify query "what connects auth to database?"
graphify path "FastAPI" "ModelField"

The AI assistant integration is optional but recommended for seamless workflows.

5. What's the difference between graphify and graphifyy?

The PyPI package is named graphifyy (double-y) due to a naming conflict. The CLI command is still graphify. So you install with uv tool install graphifyy but run with graphify.

6. Can I use Graphify for non-code files only?

Yes. You can build a graph of just documentation, PDFs, or any supported file type:

graphify extract ./docs/
graphify extract ./research-papers/

7. How does Graphify compare to GitHub's code search?

GitHub code search is text-based and limited to individual repositories. Graphify builds a semantic knowledge graph that understands relationships, traces paths, and works across multiple repositories (via graph merging).

8. Can I contribute to Graphify?

Yes! Graphify is open-source and welcomes contributions. Check the contributing guide and join the Discord community.

9. Does Graphify work with private repositories?

Yes. Graphify runs locally on your machine, so it works with any code you have access to—public or private. No authentication with external services required.

10. How do I update the graph when code changes?

# Option 1: Auto-rebuild on git commit
graphify hook install

# Option 2: Manual rebuild
graphify . --update  # Only re-extract changed files

# Option 3: Full rebuild
graphify .  # Re-extract everything

---

JSON-LD Schemas

BlogPosting Schema

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---

Unsplash Image Suggestion

Hero image: Knowledge network visualization Alternative options:

• Code on screen
• Network graph
• AI and code
• Abstract network

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Learn More

Interested in building tools like Graphify? Check out these courses on CoddyKit:

• AI with Python: Master AI development with Python—the language powering Graphify's core. Learn machine learning, neural networks, and AI agent development.

• Python: Deep dive into Python programming, from basics to advanced topics like AST parsing, decorators, and metaclasses.

• AI Agents: Learn how to build AI agents that can understand code, reason about architecture, and assist developers—the same patterns Graphify uses.

• SQL & Database: Master database design and querying—essential for understanding how code connects to data layers.

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Resources

• GitHub: Graphify-Labs/graphify
• PyPI: graphifyy package
• Website: graphify.com
• Discord: Join the community
• LinkedIn: Graphify Labs
• Benchmarks: BENCHMARKS.md
• Y Combinator: S26 batch

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Star count verified on July 17, 2026. Graphify is the fastest-growing code intelligence tool on GitHub.