使用方法
- 复制安装命令，在自己的智能体环境安装前先检查源码包。
- 从原始文档核对所需工具、凭据和支持的宿主。
- 说明输入材料与期望交付物，并对照来源检查输出。
npx skills add https://github.com/livekit/agent-skills --skill debugging-livekit-agents开始前需了解
- 安装不包含模型订阅、API 额度或访问文件的权限。
- 仓库星数和安装量反映活跃情况，不等于独立质量或安全测试。
按源仓库内容呈现，保留标题、案例、代码、表格、链接以及原文引用的演示图片。
Debugging a LiveKit agent live
lk agent debugger runs the user's agent locally as a background process 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.
By default the session is text: speech is off and nothing goes to a LiveKit room, so each turn is fast. When the bug lives in the audio path, start the session in audio mode instead (see Audio mode).
Before the first use, confirm the command exists and read its help:
lk agent debugger --helpThe 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:
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 stopEach 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
saylines 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.
Audio mode
Text mode skips STT and TTS, so it can't show a name heard as a different spelling, digits the STT spells out as words, a turn that ends too early, or anything from a realtime model that only takes audio. Where the CLI supports it, starting the session with audio (check start --help) speaks each say with LiveKit Inference TTS into the agent's microphone input, and the agent runs its full audio pipeline.
- Use it when the symptom is about hearing, or when the agent uses a speech-to-speech model that
can't run in text mode. Stay in text mode for logic, prompt, and tool bugs: it's faster, and the transcription noise only gets in the way.
- It needs a LiveKit Cloud project. The spoken turns run on LiveKit Inference, so
lkmust
have project credentials. Text mode doesn't need them.
- Compare what was heard with what you sent. Each turn shows the agent's transcript of your
line, and the sent text when the words differ. That difference is often the whole bug: the agent answered correctly to what it heard.
- Write lines the way a caller would say them. Spell out numbers and codes when testing how the
agent handles spoken input, and test both forms if the agent should accept either.
- The mode belongs to the session. Restart keeps it; stop and start again to switch.
- A long silent pause can end a turn early. A turn ends once the agent has answered and stayed
quiet for a moment. If the agent pauses for a long time before finishing (for example, a realtime model waiting on a delegated answer), the rest of its reply shows up in the next turn. Check the chat history before concluding the agent never answered.
Audio mode still can't interrupt the agent mid-reply or tell you how the agent sounds. Use lk agent console or audio simulations for those.
When to use something else
| Situation | Use |
|---|---|
| You want to hear it, or hand the user something to try | lk agent console (mic and speakers, a human at the keyboard) |
| The same failure keeps coming back | A turn-level regression test (testing-livekit-agents) |
| You need whole-conversation outcomes graded at scale | Simulations (running-livekit-simulations) |
| Transcription or endpointing on a turn you're investigating | The debugger in audio mode (above) |
| Interruptions, or speech behavior graded across many conversations | Audio simulations (running-livekit-simulations) |
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
