# Graft — Code Intelligence for AI Coding Agents

> A code-graph engine that indexes your codebase into a knowledge graph, giving AI coding agents contextual understanding with fewer tokens and faster responses.

## Install

Save in your project root:

# Graft — Code Intelligence for AI Coding Agents

## Quick Use
```bash
npx graft init
npx graft index
# Use with Claude Code, Cursor, Codex, or Gemini via the MCP server
npx graft serve
```

## Introduction
Graft is a TypeScript-based code intelligence engine that builds a persistent knowledge graph of your codebase. It parses source files with tree-sitter, extracts symbols, relationships, and call chains, then serves this structured context to AI coding agents. The result: agents understand your code faster, use fewer tokens, and produce more accurate edits.

## What Graft Does
- Indexes codebases into a queryable knowledge graph of symbols and their relationships
- Provides an MCP server that AI coding agents can query for contextual code understanding
- Reduces token consumption by serving precise, relevant context instead of raw file dumps
- Supports incremental re-indexing so the graph stays current as code changes
- Works with Claude Code, Cursor, Codex, Gemini CLI, and any MCP-compatible agent

## Architecture Overview
Graft uses tree-sitter parsers to produce ASTs for each source file, then extracts a graph of definitions, references, imports, and call sites. This graph is stored in a local SQLite database for fast querying. The MCP server exposes graph queries as tools that agents can call — for example, asking for all callers of a function, or the dependency chain of a module. The indexer runs as a background process that watches for file changes and updates the graph incrementally.

## Self-Hosting & Configuration
- Initialize with npx graft init in your project root to generate a config file
- Run npx graft index to build the initial code graph
- Start the MCP server with npx graft serve and point your agent to it
- Configure language support and excluded directories in graft.config.json
- Set the graph database path and cache size for large monorepos

## Key Features
- Tree-sitter-based parsing with support for 50+ programming languages
- Incremental indexing keeps the graph fresh without full rebuilds
- MCP server integrates with all major AI coding agents out of the box
- Sub-millisecond query responses for symbol lookups and relationship traversals
- Open source with no external API dependencies — everything runs locally

## Comparison with Similar Tools
- **Graphify** — builds knowledge graphs from code; Graft focuses on MCP-native agent integration
- **Codebase Memory MCP** — persistent code index; Graft uses tree-sitter for precise AST-level graphs
- **Sourcegraph** — enterprise code search; Graft is local-first and agent-oriented
- **Codegraph** — similar graph approach; Graft emphasizes incremental updates and MCP serving

## FAQ
**Q: Does Graft work with monorepos?**
A: Yes. Configure workspace roots in the config file and Graft indexes them as a unified graph.

**Q: Which languages are supported?**
A: Any language with a tree-sitter grammar — TypeScript, Python, Go, Rust, Java, C/C++, and more.

**Q: How much disk space does the graph use?**
A: Typically 1-5% of the source code size, stored in a local SQLite database.

**Q: Can I use Graft without an AI agent?**
A: Yes. The CLI includes query commands for exploring the code graph directly.

## Sources
- https://github.com/trailhq/Graft

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Source: https://tokrepo.com/en/workflows/asset-904cea6a
Author: AI Open Source