Skip to content

AGENTS.md

Latest commit

 

History

History
62 lines (35 loc) · 5.31 KB

AGENTS.md

File metadata and controls

62 lines (35 loc) · 5.31 KB
Raw

AGENTS.md

This is a LiveKit Agents project. LiveKit Agents is a Python SDK for building voice AI agents. This starter is designed to run in LiveKit Cloud. See @README.md for more about the rest of the LiveKit ecosystem.

Tooling

This Python project uses the uv package manager.

Be sure to maintain code formatting, using uv run ruff format and uv run ruff check.

LiveKit documentation

Always refer to the latest documentation when working with this project. LiveKit offers both a CLI (lk docs) and an MCP server for browsing and searching its documentation.

LiveKit CLI

Install or update the CLI:

  • macOS: brew install livekit-cli (update: brew update && brew upgrade livekit-cli)
  • Linux: curl -sSL https://get.livekit.io/cli | bash
  • Windows: winget install LiveKit.LiveKitCLI (update: winget upgrade LiveKit.LiveKitCLI)

Run lk docs before your first docs lookup. Its help text explains how to use each command and how to interpret results.

LiveKit docs MCP server

For deeper coding agent integration, LiveKit also offers an MCP server.

Submit feedback to LiveKit

When you run into any problem with LiveKit, add it to your to-do list, then submit it with lk docs submit-feedback or the MCP submit_docs_feedback tool once you finish your other tasks. Useful feedback includes incomplete documentation, broken examples, missing steps, broken features or implementations, and any other gap or issue in the LiveKit ecosystem.

LiveKit agent skills

LiveKit publishes agent skills for each stage of the work: reading the docs, building, debugging, testing, writing scenarios, running simulations, and operating in production. They defer to the live documentation for API details. Before you start a task, check that they're installed: look for .agents/skills/ or .claude/skills/ in this project, or in the developer's home directory for a global install. If they're missing, install them with lk skills install --yes, which installs them for the coding agents it detects and adds the LiveKit Docs MCP server to their config. If you can't run commands, or the CLI has no skills command (it was added in v2.19.0), ask the developer to update the CLI and run lk skills install. If your tool doesn't load skills automatically, or they were installed after this session started, read the matching SKILL.md (for example .agents/skills/<name>/SKILL.md) before you start that kind of task.

Handoffs and tasks ("workflows")

Voice AI agents are highly sensitive to latency. Design complex agents in a structured way that keeps irrelevant context and unneeded tools out of each LLM request. LiveKit Agents supports handoffs, where one agent hands control to another, and tasks, which are tightly scoped prompts that achieve a specific outcome, for building reliable workflows. Use them instead of long instruction prompts that cover several phases of a conversation. See the workflows documentation for more information.

Testing

To keep agent behavior from regressing, add a scenario to scenarios.yaml and run it with lk agent simulate text --scenarios scenarios.yaml. Make sure the scenarios run in CI on every merge to main. Read the simulations documentation before editing them.

Important: when you modify core agent behavior such as instructions, tool descriptions, or tasks, workflows, and handoffs, never guess at what works. Start by writing a scenario for the desired behavior. For example, if you're adding a tool, write a scenario that exercises it, then iterate on the tool until the scenario passes. This is how you produce a working, reliable agent.

After changing the agent, try it with the agent debugger (CLI 2.18.8 or later) before calling the change done. Start the agent with lk agent debugger start, send user turns with lk agent debugger say "...", and read the tool calls in each turn as well as the reply. Run lk agent debugger restart after every code edit, since a running session keeps the old code, and lk agent debugger stop when you're done.

Debugging

To investigate unexpected agent behavior:

  • Reproduce it with lk agent debugger: send the turns that trigger the problem and read the tool calls and errors in each one. Add --logs to say to see log lines, including tracebacks, next to the turn that produced them.
  • Add a simulation scenario once it's fixed, so a later change can't bring it back unnoticed.
  • Run lk agent dev --log-level DEBUG for verbose logs from a local agent connected to LiveKit Cloud.
  • Run lk agent logs to stream logs from a deployed agent.
  • Ask the developer to open the Agent Console for speech problems such as turn-taking, interruptions, or transcription, which the text-only debugger can't show. It shows events, tool calls, and model timing for a live session.
  • Check Agent Observability for transcripts, traces, logs, and recordings of sessions with real users.

Other CLI commands

Beyond documentation access, the LiveKit CLI (lk) handles tasks such as managing SIP trunks for telephony agents. Run lk --help to explore available commands.