Debugging Livekit Agents logo

Debugging Livekit Agents

Organization
livekit
debugging-livekit-agents

Drives a multi-turn conversation with a LiveKit agent running locally to see what it does. Use when the user says "test my agent", "try my agent", "does this work", "why did it call that tool", "it says the wrong thing when I ask X", "test this change", or whenever you have edited an agent and need to check how it behaves. Wraps `lk agent debugger`: start the agent in text mode, send turns, read the tool calls, handoffs, errors and logs behind each reply, and restart after an edit. It runs without audio or a LiveKit room at one LLM call per turn, so it is the preferred way for a coding agent to live-test during development, and the default when the user says "test" without naming unit tests or simulations.

Overview

Publisherlivekit
Repositoryagent-skills
Skill namedebugging-livekit-agents
Stars
71
Forks
12
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by livekit on GitHub. Read the source before you install it.

Installation

Install the Debugging Livekit Agents AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/livekit/agent-skills/tree/main/skills/debugging-livekit-agents
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/livekit/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/debugging-livekit-agents .claude/skills/debugging-livekit-agents
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Debugging Livekit Agents in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Debugging Livekit Agents on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Debugging Livekit Agents is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Debugging a LiveKit agent live

lk agent debugger runs the user's agent locally as a background process in text mode and lets you drive a conversation one turn at a time. It's built for coding agents: you play the user, choose each next line based on the last reply, and inspect what the agent did in between.

Speech is off and nothing goes to a LiveKit room, so a turn costs only the agent's own LLM and tool calls. That's cheap enough to use constantly while building.

Before the first use, confirm the command exists and read its help:

bash
lk agent debugger --help

The help is thorough and is the source of truth for subcommands and flags, so this skill doesn't restate them. If the command is missing, the installed CLI predates it. Tell the user to update lk and use testing-livekit-agents until then. Don't guess at an older command's shape.

The loop

Start the agent, say user turns, inspect what happened, edit the code, restart, repeat, and stop when you're done. Roughly:

bash
lk agent debugger start
lk agent debugger say "Hi, what can you do?"
lk agent debugger say "Book me a table for two tonight"
lk agent debugger stop

Each say prints everything the agent did in response (tool calls with arguments and results, handoffs, errors) followed by the reply. Between turns you can look at the agent's chat history, a live event stream, the process logs, and status; --help lists the subcommands.

Restart after every code edit. A running session keeps the old code, and it's easy to lose time debugging behavior the file no longer has.

Debugging with it

  • Read the tool calls as well as the reply. The reply shows what went wrong; the tool call and its arguments usually show why. A wrong argument points to the prompt or the tool description. No call at all usually means the tool description never says when to use it.
  • Interleave the logs when a tool misbehaves. An option shows the agent's log lines under the turn they belong to, so a tool's traceback appears right below the sanitized error the user would have heard. That's the quickest way from symptom to cause.
  • Check the agent's chat history instead of relying on your memory of the conversation. It records what the LLM saw, including instruction and tool changes across handoffs. When behavior seems impossible, the history usually shows the context isn't what you assumed.
  • Reproduce before you fix, then re-run the same turns. Keep the sequence of say lines that triggered the bug so you can compare before and after.
  • Drive whole conversations. Most bugs take several turns to show up: details collected early that get lost, a user changing their mind mid-flow, a handoff that drops context. A single turn won't find them.
  • Script it if a program is deciding the turns. There's machine-readable output and meaningful exit codes. Check --help for the current format instead of assuming field names.

When to use something else

SituationUse
You want to hear it, or hand the user something to trylk agent console (mic and speakers, a human at the keyboard)
The same failure keeps coming backA turn-level regression test (testing-livekit-agents)
You need whole-conversation outcomes graded at scaleSimulations (running-livekit-simulations)
The bug is about speech: turn-taking, interruptions, transcriptionAudio simulations. The debugger is text-only and can't see these

The debugger is for finding bugs interactively. Once you've found one, write a test for it so a later change can't reintroduce it unnoticed.

Related skills

  • Building the agent: building-livekit-agents
  • Pinning a found bug as a test: testing-livekit-agents
  • Whole-conversation checks: writing-livekit-scenarios, running-livekit-simulations
  • Production-only failures (worker processes, providers, shutdown): operating-livekit-agents
  • CLI flags and API facts: reading-livekit-docs

Frequently asked questions

What does the Debugging Livekit Agents AI skill do?

Drives a multi-turn conversation with a LiveKit agent running locally to see what it does. Use when the user says "test my agent", "try my agent", "does this work", "why did it call that tool", "it says the wrong thing when I ask X", "test this change", or whenever you have edited an agent and need to check how it behaves. Wraps `lk agent debugger`: start the agent in text mode, send turns, read the tool calls, handoffs, errors and logs behind each reply, and restart after an edit. It runs without audio or a LiveKit room at one LLM call per turn, so it is the preferred way for a coding agen...

Why use Debugging Livekit Agents on TypingMind?

Because you install it once and use it with any model. Debugging Livekit Agents is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Debugging Livekit Agents in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/livekit/agent-skills/tree/main/skills/debugging-livekit-agents. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Debugging Livekit Agents?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Debugging Livekit Agents?

As many as you like. As long as a model supports skills, you can use Debugging Livekit Agents with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Debugging Livekit Agents AI skill free?

Yes. It is published on GitHub by livekit under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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