Codeembed

Embeds your codebase into a local vector and graph database and exposes it as an MCP tool, giving AI assistants like Claude Code fast semantic search over your code using Graph RAG.

Particularly useful for questions like:

• How is X implemented in this repo?
• Where is X defined or used?
• Does this repo already have X?

For other questions, the agent will fall back to normal lookups. CodeEmbed can improve lookup speed and accuracy, especially for finding existing implementations before writing new ones. Note that the biggest bottleneck in coding agents is LLM thinking and token generation — solid prompts and follow-up questions still matter.

Uses ChromaDB for vector storage, SQLite for graph storage, and either Ollama or OpenAI (including OpenAI models via Azure AI Foundry) for LLM analysis.

Prerequisites

• Python 3.11+
• uv
• One of:
  • Ollama running locally, or
  • An OpenAI API key or Azure OpenAI endpoint

Installation

With Ollama:

uv tool install codeembed

With OpenAI / Azure OpenAI:

uv tool install 'codeembed[openai]'

Supply chain safety: To reduce the risk of newly-published malicious packages, consider adding exclude-newer = "7 days" to your global uv.toml. This prevents uv from installing packages published in the last 7 days.

Manual installation (from source)

git clone https://github.com/robino16/codeembed
cd codeembed

# With Ollama
uv tool install .

# With OpenAI support
uv tool install '.[openai]'

Then run codeembed init inside of your target repository.

Upgrading

uv tool upgrade codeembed

Usage

CodeEmbed is intended to be used within a single project — run all commands from your project root. Each project gets its own local vector database stored in .codeembed/.

Supported file types: .py, .md, .ts, .tsx, .js, .jsx.

1. Initialize (run once in your project root):

codeembed init

Creates a codeembed.toml config and configures your .gitignore. You'll be prompted to select a provider (Ollama or OpenAI) and a model. You'll also be offered the option to automatically configure Claude Code and/or GitHub Copilot.

2. Pre-populate the index:

codeembed embed

Run this before starting the server to pre-populate the index. Searches will return empty results until the first file has been embedded.

CodeEmbed respects your project's .gitignore and also excludes typical environment directories and files (.env, venv, node_modules, etc.) by default.

3. Start the MCP server:

Note: If the MCP server was added to Claude or GitHub Copilot during codeembed init your coding agent will do this step automatically.

codeembed serve

Starts the MCP server.

The serve command will embed your codebase in the background - by default it will scan for changes every 60 seconds. This embedding interval can be configured in codeembed.toml. CodeEmbed will only process modified files.

Configuring OpenAI

If you use the OpenAI provider, credentials are read from environment variables. The recommended approach is a .env file. codeembed init will ask for the path.

Standard OpenAI

OPENAI_API_KEY=...

Optionally override the endpoint (for compatible APIs like vLLM, LM Studio, OpenRouter):

OPENAI_API_KEY=...
OPENAI_BASE_URL=...

Azure OpenAI — API key

AZURE_OPENAI_ENDPOINT=https://<your-resource>.openai.azure.com/openai/v1/
AZURE_OPENAI_API_KEY=...

Azure OpenAI — RBAC / Entra ID (keyless)

Set only the endpoint; CodeEmbed will use DefaultAzureCredential, which automatically tries multiple credential sources in order — service principals (via env vars), workload identity, managed identity, VS Code Azure sign-in, az login, Azure PowerShell, and azd auth login — falling back to an interactive browser window if none are found automatically:

AZURE_OPENAI_ENDPOINT=https://<your-resource>.openai.azure.com/openai/v1/

Add to coding agents

codeembed init will offer to configure these automatically. If you prefer to do it manually:

Claude Code — add to .mcp.json in your project root:

{
  "mcpServers": {
    "codeembed": {
      "command": "codeembed",
      "args": ["serve"]
    }
  }
}

And add to .claude/settings.local.json to enable and pre-approve the tool:

{
  "enabledMcpjsonServers": ["codeembed"],
  "permissions": {
    "allow": ["mcp__codeembed__search"]
  }
}

GitHub Copilot — add to .vscode/mcp.json:

{
  "servers": {
    "codeembed": {
      "command": "codeembed",
      "args": ["serve"]
    }
  }
}

CodeEmbed - add to opencode.json (or opencode.jsonc):

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "codeembed": {
      "type": "local",
      "command": ["uv", "run", "codeembed", "serve"],
      "enabled": true
    }
  }
}

The MCP server exposes a single search(query) tool for semantic search over your codebase.

Contributing

Clone this repo with:

git clone [email protected]:robino16/codeembed.git

cd codeembed
uv sync

Check for dependency conflicts with:

uv pip check

Check for package vulnerabilities with:

uv run pip-audit

(Optional) Add Ruff pre-commit with:

pre-commit install

Update init files:

uv run --no-sync scripts/generate_init_files.py

Run linter:

ruff check . --fix

Run formatter:

ruff format .

Run tests:

uv run --no-sync pytest

Build with:

uv build

Validate build with:

uv run twine check dist/*

--no-sync is required for local dev commands when the MCP server is running, as uv holds a lock that blocks sync operations.

License

MIT — see LICENSE.