Martin Vogel
0338685c06
Surface previously undocumented capabilities on the site, README, and package metadata: semantic vector search (bundled nomic-embed-code embeddings, fully local), SEMANTICALLY_RELATED / SIMILAR_TO clone detection, cross-repo intelligence, data-flow tracing, change-impact analysis, and ADR management. Broaden the cross-service entry to cover gRPC/GraphQL/tRPC and pub/sub channels. Add a "star on GitHub" nav CTA. GEO/SEO: add a question-style semantic-search section, extend the JSON-LD featureList and FAQPage, add semantic keywords, and refresh the sitemap lastmod. Correct the language count to 159 (was 155/158) everywhere while keeping the 157 vendored-grammar figure, update the Linux-kernel index stats to the measured 4.81M nodes / 7.72M edges, and make trace_path the canonical tool name (trace_call_path noted as an alias).
4.0 KiB
codebase-memory-mcp
The fastest and most efficient code intelligence engine for AI coding agents. Full-indexes an average repository in milliseconds, the Linux kernel (28M LOC, 75K files) in 3 minutes. Answers structural queries in under 1ms. Ships as a single static binary — this package downloads and runs it automatically.
High-quality parsing through tree-sitter AST analysis across 159 languages — producing a persistent knowledge graph of functions, classes, call chains, HTTP routes, and cross-service links. 14 MCP tools. Zero dependencies. Plug and play across 11 coding agents.
Installation
npm install -g codebase-memory-mcp
The binary for your platform is downloaded automatically at install time. Then configure your coding agents:
codebase-memory-mcp install
Restart your agent. Say "Index this project" — done.
Why codebase-memory-mcp
- Extreme indexing speed — Linux kernel (28M LOC, 75K files) in 3 minutes. RAM-first pipeline with LZ4 compression and in-memory SQLite.
- Plug and play — single static binary for macOS (arm64/amd64), Linux (arm64/amd64), and Windows (amd64). No Docker, no runtime dependencies, no API keys.
- 159 languages — vendored tree-sitter grammars compiled into the binary. Nothing to install, nothing that breaks.
- 120x fewer tokens — 5 structural queries: ~3,400 tokens vs ~412,000 via file-by-file search.
- 11 agents, one command —
installauto-detects Claude Code, Codex CLI, Gemini CLI, Zed, OpenCode, Antigravity, Aider, KiloCode, VS Code, OpenClaw, and Kiro. - 14 MCP tools — search, trace, architecture, impact analysis, Cypher queries, dead code detection, cross-service HTTP linking, ADR management, and more.
Supported Platforms
| OS | Architecture |
|---|---|
| macOS | arm64, amd64 |
| Linux | arm64, amd64 |
| Windows | amd64 |
Usage
codebase-memory-mcp install # configure all detected coding agents
codebase-memory-mcp --version
codebase-memory-mcp --help
codebase-memory-mcp update # update to latest release
codebase-memory-mcp uninstall # remove agent configs
CLI Mode
Every MCP tool is also available directly from the command line:
codebase-memory-mcp cli index_repository '{"repo_path": "/path/to/repo"}'
codebase-memory-mcp cli search_graph '{"name_pattern": ".*Handler.*", "label": "Function"}'
codebase-memory-mcp cli trace_call_path '{"function_name": "main", "direction": "both"}'
codebase-memory-mcp cli get_architecture '{}'
MCP Tools
| Category | Tools |
|---|---|
| Indexing | index_repository, list_projects, delete_project, index_status |
| Querying | search_graph, trace_call_path, detect_changes, query_graph |
| Analysis | get_architecture, get_graph_schema, get_code_snippet, search_code |
| Advanced | manage_adr, ingest_traces |
Performance
Benchmarked on Apple M3 Pro:
| Operation | Time |
|---|---|
| Linux kernel full index (28M LOC, 75K files) | 3 min |
| Django full index | ~6s |
| Cypher query | <1ms |
| Trace call path (depth=5) | <10ms |
Full Documentation
See github.com/DeusData/codebase-memory-mcp for the full README including all MCP tools, configuration options, graph data model, and language support details.
License
MIT