Code retrieval engine — hybrid embedding + graph search for LLM context injection.
Project description
codebase-retrieval-context-engine
Code retrieval engine for LLM context via MCP.
Add to Claude Code
claude mcp add codebase-retrieval -e CORBELL_LLM_PROVIDER=google -e GOOGLE_API_KEY=your-google-api-key -e GOOGLE_MODEL=gemini-3.1-flash-lite -e CORBELL_EMBEDDING_MODEL=voyage-4-lite -e VOYAGE_API_KEY=your-voyage-api-key -- uvx codebase-retrieval-context-engine
That's it. The AI agent passes workspace path and triggers index builds automatically.
Build index manually (optional)
uvx codebase-retrieval-context-engine index build
Run from your project root. Env vars (CORBELL_LLM_PROVIDER, GOOGLE_API_KEY, etc.) must be set in your shell.
Environment variables
| Variable | Description |
|---|---|
CORBELL_LLM_PROVIDER |
LLM provider for reranking (google, anthropic, openai) |
GOOGLE_API_KEY |
Google AI API key (supports multiple: key1,key2,key3) |
GOOGLE_MODEL |
e.g. gemini-3.1-flash-lite |
CORBELL_EMBEDDING_MODEL |
voyage-4-lite, voyage-code-3, or gemini-embedding-001 |
VOYAGE_API_KEY |
Voyage AI API key (supports multiple: key1,key2,key3). Add a card to billing to unlock rate limits. |
License
Apache 2.0
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