Semantic code graph builder with 45 MCP tools, VS Code extension, and persistent memory for AI agents.
CodeGraph is easy to set up with strong trust signals. Check agent compatibility and use-case fit before adding it to your workflow.
gh repo view codegraph-ai/CodeGraph --webOpen the official README and confirm the supported install method.
Add the server entry to your MCP client config.
Restart your agent and verify that the server tools appear.
Repository setup guidance
Strong trust signals; still review the README and permissions before production use.
Last commit was about 96 days ago.
17 GitHub stars indicate community interest.
0 open issues signal maintenance load.
Codebase intelligence layer for AI coding agents: code health, git analytics, dead code detection, and architectural decisions via MCP.
Deep code indexing MCP server for AI agents with hybrid FTS5 + embedding search, call graphs, and multi-repo workspaces.
A project template that turns your repository into stable infrastructure for AI-assisted development.
CodeGraph creates a map of your codebase showing how functions, classes, and files relate to each other. It gives AI coding assistants structured understanding instead of just searching text. You can use it via MCP tools, a VS Code extension, or in CI pipelines.
CodeGraph is a cross-language code intelligence tool that builds a semantic graph of your codebase. It parses 37 languages using tree-sitter and exposes the graph through 45 MCP tools, a VS Code extension, and a persistent memory layer. AI agents can query the graph for structured code understanding—like finding callers, callees, dependencies, and impact analysis—instead of grepping through files. It supports multiple embedding models for semantic search, profiles to narrow the tool surface, and a graph-only mode for fast CI integration. The tool is designed for developers and AI agents working with large codebases, enabling better refactoring, code review, and knowledge management.
Semantic code graph builder with 45 MCP tools, VS Code extension, and persistent memory for AI agents.
AI agent developers building coding assistants, Software engineers working on large, multi-language codebases, DevOps engineers integrating code analysis into CI pipelines
The setup section provides repository-level starting guidance, not a guarantee of an independently verified installation. Check the official README and release notes.
Indexing may expose sensitive code structure if the workspace contains private repositories; ensure proper access controls. Embedding models run locally but may use significant CPU/GPU resources; graph-only mode avoids this.
Similar or complementary options to evaluate include repowise, srclight, attractor-guided-engineering-template.
Aug 20, 2026
Apache-2.0 license detected.
2 security/trust notes recorded.
Setup difficulty is 2/5.
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