⚡ Multi-Repository Code Search Engine

Search across multiple code repositories at once, built for AI coding assistants. Connect over MCP and call tools such as search_codebases, get_symbol_definition, get_call_hierarchy and read_file; results are ranked code chunks with repo, file, lines, symbol and caller/callee context. Repositories are registered and indexed with the CLI or the web UI. Specs live in openspec/ (OpenSpec).

Quick Start

Requires Python 3 (standard library only) and Ollama for embeddings.

1. Pull the embedding model:

   ollama pull qwen3-embedding:0.6b

2. Register and index your repositories, from a local directory or a git URL:

   # Local directory
   python3 main.py add my-app /abs/path/to/my-app
   
   # Git repository (add --branch <name> for a non-default branch)
   python3 main.py add nest git@github.com:nestjs/nest.git --type git

3. Check that search works:

   python3 main.py search "login user authenticate"

4. Start the server and keep it running (MCP endpoint and web UI at http://127.0.0.1:8000):

   python3 main.py serve

5. Add the server to your AI assistant's MCP config (Claude Code, Cursor, Windsurf, Antigravity, etc.) and restart the assistant:

   {
     "mcpServers": {
       "multi-repo-code-rag": { "url": "http://127.0.0.1:8000/mcp" }
     }
   }

6. Ask the assistant a question about your code, e.g. "Use multi-repo-code-rag to find where users are authenticated."

Related MCP server: Synapse

MCP

The assistant gets these tools from POST /mcp on the running serve process. Only one process may use the data directory at a time, so CLI commands fail while serve is running.

To require MCP authentication, start the server with --mcp-token <token> (or set MCP_AUTH_TOKEN) and send Authorization: Bearer <token> on every request. When binding a non-localhost host, use a token and put the server behind TLS or an SSH tunnel.

CLI

The CLI manages repositories, groups, dependencies and search. Run python3 main.py --help (or <command> --help) for all commands and options.

Embedding Model

The default model is qwen3-embedding:0.6b (override with --embedding-model or $OLLAMA_EMBEDDING_MODEL). Pull it with ollama pull, or pass --auto-pull to serve / sync. If Ollama is offline, a built-in encoder is used.

• Memory: the model loads on first use and is released after 30s idle. Change this with --keep-alive (0, a duration like 5m, or always), or release it now with python3 main.py unload.

• Changing models: affected repositories are re-embedded automatically, and searches are rejected until that finishes.

Configuration

Flags apply to the CLI and serve; environment variables are the fallback.

Tests

python3 -m unittest discover -s tests -p "test_*.py" -v

Browser UI checks (no dependencies; requires Chrome):

node tests/test_web_repo_sync.mjs                                       # macOS (default Chrome path)
CHROME_BIN=/usr/bin/google-chrome node tests/test_web_repo_sync.mjs     # Linux / custom Chrome path
$env:CHROME_BIN="C:\Program Files\Google\Chrome\Application\chrome.exe"; node tests/test_web_repo_sync.mjs   # Windows (PowerShell)