CodeGraph is a local-first code intelligence tool that gives AI coding agents a pre-indexed understanding of a repository. Instead of forcing agents to repeatedly scan files with grep, glob, and read commands, it builds a searchable knowledge graph of symbols, relationships, call graphs, and code structure. The project uses deterministic parsing rather than LLM summaries, which makes its indexed data more predictable and grounded in the actual source tree. It is designed for tools such as Claude Code, Codex, Cursor, and OpenCode, with MCP support for agent access. CodeGraph stores project data locally, which helps reduce token usage and repeated file exploration. Its main purpose is to make agent-assisted coding faster, cheaper, and more context-aware.
Features
- Local-first code knowledge graph
- Symbol and call graph indexing
- MCP server support for coding agents
- SQLite and full-text search storage
- Deterministic AST-based extraction
- Designed to reduce agent tool calls