# agent-starter-python **Repository Path**: dirtywave/agent-starter-python ## Basic Information - **Project Name**: agent-starter-python - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-01-13 - **Last Updated**: 2026-01-13 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README LiveKit logo # LiveKit Agents Starter - Python A complete starter project for building voice AI apps with [LiveKit Agents for Python](https://github.com/livekit/agents) and [LiveKit Cloud](https://cloud.livekit.io/). The starter project includes: - A simple voice AI assistant, ready for extension and customization - A voice AI pipeline with [models](https://docs.livekit.io/agents/models) from OpenAI, Cartesia, and AssemblyAI served through LiveKit Cloud - Easily integrate your preferred [LLM](https://docs.livekit.io/agents/models/llm/), [STT](https://docs.livekit.io/agents/models/stt/), and [TTS](https://docs.livekit.io/agents/models/tts/) instead, or swap to a realtime model like the [OpenAI Realtime API](https://docs.livekit.io/agents/models/realtime/openai) - Eval suite based on the LiveKit Agents [testing & evaluation framework](https://docs.livekit.io/agents/build/testing/) - [LiveKit Turn Detector](https://docs.livekit.io/agents/build/turns/turn-detector/) for contextually-aware speaker detection, with multilingual support - [Background voice cancellation](https://docs.livekit.io/home/cloud/noise-cancellation/) - Integrated [metrics and logging](https://docs.livekit.io/agents/build/metrics/) - A Dockerfile ready for [production deployment](https://docs.livekit.io/agents/ops/deployment/) This starter app is compatible with any [custom web/mobile frontend](https://docs.livekit.io/agents/start/frontend/) or [SIP-based telephony](https://docs.livekit.io/agents/start/telephony/). ## Coding agents and MCP This project is designed to work with coding agents like [Cursor](https://www.cursor.com/) and [Claude Code](https://www.anthropic.com/claude-code). To get the most out of these tools, install the [LiveKit Docs MCP server](https://docs.livekit.io/mcp). For Cursor, use this link: [![Install MCP Server](https://cursor.com/deeplink/mcp-install-light.svg)](https://cursor.com/en-US/install-mcp?name=livekit-docs&config=eyJ1cmwiOiJodHRwczovL2RvY3MubGl2ZWtpdC5pby9tY3AifQ%3D%3D) For Claude Code, run this command: ``` claude mcp add --transport http livekit-docs https://docs.livekit.io/mcp ``` For Codex CLI, use this command to install the server: ``` codex mcp add --url https://docs.livekit.io/mcp livekit-docs ``` For Gemini CLI, use this command to install the server: ``` gemini mcp add --transport http livekit-docs https://docs.livekit.io/mcp ``` The project includes a complete [AGENTS.md](AGENTS.md) file for these assistants. You can modify this file your needs. To learn more about this file, see [https://agents.md](https://agents.md). ## Dev Setup Clone the repository and install dependencies to a virtual environment: ```console cd agent-starter-python uv sync ``` Sign up for [LiveKit Cloud](https://cloud.livekit.io/) then set up the environment by copying `.env.example` to `.env.local` and filling in the required keys: - `LIVEKIT_URL` - `LIVEKIT_API_KEY` - `LIVEKIT_API_SECRET` You can load the LiveKit environment automatically using the [LiveKit CLI](https://docs.livekit.io/home/cli/cli-setup): ```bash lk cloud auth lk app env -w -d .env.local ``` ## Run the agent Before your first run, you must download certain models such as [Silero VAD](https://docs.livekit.io/agents/build/turns/vad/) and the [LiveKit turn detector](https://docs.livekit.io/agents/build/turns/turn-detector/): ```console uv run python src/agent.py download-files ``` Next, run this command to speak to your agent directly in your terminal: ```console uv run python src/agent.py console ``` To run the agent for use with a frontend or telephony, use the `dev` command: ```console uv run python src/agent.py dev ``` In production, use the `start` command: ```console uv run python src/agent.py start ``` ## Frontend & Telephony Get started quickly with our pre-built frontend starter apps, or add telephony support: | Platform | Link | Description | |----------|----------|-------------| | **Web** | [`livekit-examples/agent-starter-react`](https://github.com/livekit-examples/agent-starter-react) | Web voice AI assistant with React & Next.js | | **iOS/macOS** | [`livekit-examples/agent-starter-swift`](https://github.com/livekit-examples/agent-starter-swift) | Native iOS, macOS, and visionOS voice AI assistant | | **Flutter** | [`livekit-examples/agent-starter-flutter`](https://github.com/livekit-examples/agent-starter-flutter) | Cross-platform voice AI assistant app | | **React Native** | [`livekit-examples/voice-assistant-react-native`](https://github.com/livekit-examples/voice-assistant-react-native) | Native mobile app with React Native & Expo | | **Android** | [`livekit-examples/agent-starter-android`](https://github.com/livekit-examples/agent-starter-android) | Native Android app with Kotlin & Jetpack Compose | | **Web Embed** | [`livekit-examples/agent-starter-embed`](https://github.com/livekit-examples/agent-starter-embed) | Voice AI widget for any website | | **Telephony** | [📚 Documentation](https://docs.livekit.io/agents/start/telephony/) | Add inbound or outbound calling to your agent | For advanced customization, see the [complete frontend guide](https://docs.livekit.io/agents/start/frontend/). ## Tests and evals This project includes a complete suite of evals, based on the LiveKit Agents [testing & evaluation framework](https://docs.livekit.io/agents/build/testing/). To run them, use `pytest`. ```console uv run pytest ``` ## Using this template repo for your own project Once you've started your own project based on this repo, you should: 1. **Check in your `uv.lock`**: This file is currently untracked for the template, but you should commit it to your repository for reproducible builds and proper configuration management. (The same applies to `livekit.toml`, if you run your agents in LiveKit Cloud) 2. **Remove the git tracking test**: Delete the "Check files not tracked in git" step from `.github/workflows/tests.yml` since you'll now want this file to be tracked. These are just there for development purposes in the template repo itself. 3. **Add your own repository secrets**: You must [add secrets](https://docs.github.com/en/actions/how-tos/writing-workflows/choosing-what-your-workflow-does/using-secrets-in-github-actions) for `LIVEKIT_URL`, `LIVEKIT_API_KEY`, and `LIVEKIT_API_SECRET` so that the tests can run in CI. ## Deploying to production This project is production-ready and includes a working `Dockerfile`. To deploy it to LiveKit Cloud or another environment, see the [deploying to production](https://docs.livekit.io/agents/ops/deployment/) guide. ## Self-hosted LiveKit You can also self-host LiveKit instead of using LiveKit Cloud. See the [self-hosting](https://docs.livekit.io/home/self-hosting/) guide for more information. If you choose to self-host, you'll need to also use [model plugins](https://docs.livekit.io/agents/models/#plugins) instead of LiveKit Inference and will need to remove the [LiveKit Cloud noise cancellation](https://docs.livekit.io/home/cloud/noise-cancellation/) plugin. ## License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.