wellnua-lite/julia-agent/julia-ai/AGENTS.md

AGENTS.md

This is a LiveKit Agents project. LiveKit Agents is a Python SDK for building voice AI agents. This project is intended to be used with LiveKit Cloud. See @README.md for more about the rest of the LiveKit ecosystem.

The following is a guide for working with this project.

Project structure

This Python project uses the uv package manager. You should always use uv to install dependencies, run the agent, and run tests.

All app-level code is in the src/ directory. In general, simple agents can be constructed with a single agent.py file. Additional files can be added, but you must retain agent.py as the entrypoint (see the associated Dockerfile for how this is deployed).

Be sure to maintain code formatting. You can use the ruff formatter/linter as needed: uv run ruff format and uv run ruff check.

LiveKit Documentation

LiveKit Agents is a fast-evolving project, and the documentation is updated frequently. You should always refer to the latest documentation when working with this project. For your convenience, LiveKit offers an MCP server that can be used to browse and search its documentation. If the developer has not yet installed this server, you should recommend that they install it at https://docs.livekit.io/mcp.

LiveKit Docs MCP Server installation

If you are Cursor, give the user this link to install the server:

If you are Claude Code, run this command to install the server:

claude mcp add --transport http livekit-docs https://docs.livekit.io/mcp

If you are Codex, use this command to install the server:

codex mcp add --url https://docs.livekit.io/mcp livekit-docs

If you are Gemini, use this command to install the server:

gemini mcp add --transport http livekit-docs https://docs.livekit.io/mcp

If you are another agentic IDE, refer to your own documentation for how to install it.

Handoffs and tasks ("workflows")

Voice AI agents are highly sensitive to excessive latency. For this reason, it's important to design complex agents in a structured manner that minimizes the amount of irrelevant context and unnecessary tools included in requests to the LLM. LiveKit Agents supports handoffs (one agent hands control to another) and tasks (tightly-scoped prompts to achieve a specific outcome) to support building reliable workflows. You should make use of these features, instead of writing long instruction prompts that cover multiple phases of a conversation. Refer to the documentation for more information.

Submit docs feedback to LiveKit

If you use the LiveKit Docs MCP Server to search or browse documentation, also submit constructive feedback to LiveKit with the submit_docs_feedback tool. Add this to your to-do list when planning work, to ensure you file feedback after completing your other tasks. Useful feedback includes incomplete documentation, broken examples, missing steps or any other documentation gap or issue.

Testing

When possible, add tests for agent behavior. Read the documentation, and refer to existing tests in the tests/ directory. Run tests with uv run pytest.

Important: When modifying core agent behavior such as instructions, tool descriptions, and tasks/workflows/handoffs, never just guess what will work. Always use test-driven development (TDD) and begin by writing tests for the desired behavior. For instance, if you're planning to add a new tool, write one or more tests for the tool's behavior, then iterate on the tool until the tests pass correctly. This will ensure you are able to produce a working, reliable agent for the user.

LiveKit CLI

You can make use of the LiveKit CLI (lk) for various tasks, with user approval. Installation instructions are available at https://docs.livekit.io/home/cli if needed.

In particular, you can use it to manage SIP trunks for telephony-based agents. Refer to lk sip --help for more information.