codegraph

Product Introduction

1. Definition: codegraph is a local-first, open-source code intelligence tool and MCP (Model Context Protocol) server. Technically, it is a static analysis engine that transforms a source code repository into a queryable knowledge graph.
2. Core Value Proposition: It exists to solve the inefficiency of AI coding assistants (like Claude Code, Cursor, and Copilot) performing slow, token-heavy, and inaccurate codebase queries. By providing a pre-indexed, structured graph of code relationships, it enables faster, more precise code navigation and impact analysis directly within the developer's workflow.

Main Features

1. Tree-sitter AST Parsing: codegraph uses Tree-sitter, a robust incremental parsing library, to generate an accurate Abstract Syntax Tree (AST) for source files. This is how it works: instead of using regex or heuristic guesses, it parses the true grammatical structure of code across 20+ programming languages. This technical approach yields precise symbol extraction (functions, classes, variables) and draws authentic edges (calls, references, imports) between nodes in the graph, forming a reliable foundation for queries.
2. MCP Server Integration: The tool exposes the generated code knowledge graph via the Model Context Protocol (MCP). This feature allows AI coding agents from Claude Code, Cursor, Windsurf/opencode, and other MCP-compatible editors to query the local graph directly. How it works: The MCP server acts as a standardized bridge, enabling these AI agents to ask complex questions about code structure and dependencies in a handful of efficient calls, drastically reducing latency and token usage compared to traditional context window ingestion.
3. Graph-Based Impact Analysis: A core functionality is tracing the dependency graph of any code symbol. Developers can query for all callers and callees of a specific function or method. This enables comprehensive impact radius analysis before making changes, helping to understand the potential side-effects of modifications across the entire codebase, which is essential for safe refactoring and debugging.

Problems Solved

1. Pain Point: AI-powered coding tools suffer from slow performance and high operational costs when reasoning about large codebases, as they often need to re-parse or re-index vast amounts of context on every query. This leads to inaccurate assumptions about code relationships and high latency.
2. Target Audience: The primary user personas are software developers and engineers who regularly use AI coding assistants (Claude Code, Cursor, Copilot) for navigation, refactoring, and understanding complex legacy or unfamiliar codebases. It is particularly valuable for full-stack developers and tech leads managing large projects.
3. Use Cases: Essential scenarios include: onboarding to a new or large monorepo, performing safe refactoring of a core library function, understanding the propagation of a change during a debugging session, and providing AI pair programmers with deep, instantaneous code context without wasting tokens on irrelevant files.

Unique Advantages

1. Differentiation: Unlike cloud-based or editor-specific code search tools, codegraph is local-first and editor-agnostic. It does not send your code to a remote server, ensuring privacy. Compared to traditional "find references" IDE features, its graph is persistent, queryable, and shareable with multiple AI agents via the standardized MCP.
2. Key Innovation: The specific innovation is the combination of Tree-sitter for AST-accurate graph construction with the Model Context Protocol for standardized AI agent access. This creates a dedicated, high-fidelity "code context layer" that is separate from the LLM's general knowledge, optimizing the human-AI collaboration loop for software development tasks.

Frequently Asked Questions (FAQ)

1. How does codegraph improve AI coding assistant performance? codegraph improves AI assistant performance by pre-building a local knowledge graph of your codebase, allowing the AI to query precise structural relationships (like function calls and imports) in milliseconds. This eliminates the need for the AI to slowly re-read thousands of lines of code, resulting in faster, more accurate answers with significantly lower token consumption.
2. Is codegraph secure for proprietary code? Yes, codegraph is designed as a local-first tool. All parsing, graph building, and querying happen on your local machine. Your source code is never sent to an external server, making it a secure code intelligence solution for private and proprietary repositories.
3. What programming languages does codegraph support? codegraph supports over 20 programming languages through its integration with the Tree-sitter parsing ecosystem. This includes popular languages like JavaScript, TypeScript, Python, Go, Java, Rust, and C++, ensuring accurate AST parsing and graph generation for most modern tech stacks.
4. Can I use codegraph with my current editor? You can use codegraph with any editor or AI agent that supports the Model Context Protocol (MCP). This includes Claude Code, Cursor, Windsurf/opencode, and others. It functions as a backend MCP server, providing graph data to the frontend AI interface you already use.
5. How does codegraph handle changes to the codebase? codegraph leverages Tree-sitter's incremental parsing capabilities. This means it can efficiently update the knowledge graph when files change, re-parsing only the modified sections rather than the entire codebase, which keeps the index fast and up-to-date during active development.