building-livekit-agents — скилл для Claude Code: голосовые агенты

---
name: building-livekit-agents
description: 'Builds voice and chat AI agents with LiveKit Agents and LiveKit Cloud. Use when the user asks to "build a voice agent", "create a LiveKit agent", "add voice AI to my app", "implement handoffs", "structure an agent workflow", "my agent is slow / too chatty", "it says it booked but nothing was saved", "make it confirm before committing", "it keeps re-asking things the caller already said", or is writing code against the LiveKit Agents SDK. Covers architecture: designing for latency, keeping context small, splitting a monolithic agent into handoffs and tasks, and designing for voice. Also covers keeping the model in charge of meaning while code owns state, approvals, and effects. For API specifics use reading-livekit-docs. To check behavior use debugging-livekit-agents and testing-livekit-agents.'
license: MIT
metadata:
  author: livekit
---

# Building LiveKit agents

This skill covers how to structure a voice agent. It has no API specifics, because those change;
get them from `reading-livekit-docs`.

It assumes LiveKit Cloud, the recommended path: managed infrastructure, plus **LiveKit Inference**
for models so you don't manage per-provider API keys.

Where the agent runs and which LiveKit the project uses are separate questions. An agent the user
self-hosts (on their own servers instead of LiveKit Cloud's agent hosting) still connects to LiveKit
Cloud and can still use LiveKit Inference. Inference is a LiveKit Cloud feature, so it's only off
the table when the project runs on LiveKit OSS. The architecture advice applies either way; on
LiveKit OSS, models come from each provider's own plugin and API keys.

## Models: default to LiveKit Inference

Use LiveKit Inference for each model that the agent uses: STT, LLM, TTS, or a realtime model. Many
examples, and much of the LiveKit code that you remember, use a provider plugin for each model. Do
not copy that pattern. Inference is better for these reasons:

- **No extra credentials.** Inference uses the LiveKit Cloud credentials the agent already has. A
  plugin needs its own provider account, plus an API key in every environment the agent runs in.
- **Switching models is cheap.** Trying another LLM or voice means changing the model name, not
  adding a dependency and a secret. Expect to switch several times while you tune latency and
  voice.
- **One dependency and one bill**, instead of one per provider.

Use a provider plugin only when:

- the project runs on LiveKit OSS
- the model, voice, or provider feature the user needs isn't available through Inference (check the
  current model list with `reading-livekit-docs`; don't assume from memory)
- the user explicitly wants their own provider account, for example for an existing contract or a
  custom model that isn't provided by Inference

If the code already uses plugins, leave them unless the user asks you to change them. Mention
Inference once as an option.

## Before you write code

1. **Load `reading-livekit-docs`** and look up the APIs you're about to use. Don't write LiveKit
   code from memory.
2. **Confirm the project is connected to a LiveKit Cloud project** (or a LiveKit OSS server):
   `LIVEKIT_URL`, `LIVEKIT_API_KEY`, `LIVEKIT_API_SECRET`, usually in `.env`. The CLI can set these
   up.
3. **Decide the workflow shape before writing the first agent class** (see "Structure" below).
   Splitting a monolith into handoffs later is much more work than starting with two agents.
4. **Plan how you'll verify it.** Decide now whether you'll use `debugging-livekit-agents` (drive a
   real conversation), `testing-livekit-agents` (assert on turns), or both, because it affects how
   you factor the code.

## How voice changes the requirements

A voice agent is more than a chat agent with a speaker attached. These constraints drive most
design decisions:

**Latency.** Users expect a reply within a few hundred milliseconds. Context size, tool count,
whether a tool call sits on the critical path, and whether responses stream all add to or save from
that budget. Plan for network stalls and provider timeouts too; they happen routinely.

**Context size.** A 10,000-token system prompt with 50 tool definitions feels sluggish on any
model, because the model re-reads all of it every turn. Give each phase only the tools it can reach
and the instructions it needs.

**Listening.** Users can't skim or scroll back, and they'll talk over the agent. Long replies are a
bug, silence sounds broken, and interruptions are normal.

## Structure: handoffs and tasks

The usual failure is one agent that does everything. It collects every tool, instruction, and piece
of state until it's slow and unreliable, and by that point splitting it is a rewrite.

**Handoffs** transfer control from one agent to another. Put them at natural conversation
boundaries, like greeting → intake → resolution, or general support → billing specialist. Each
agent then carries only its own tools and instructions. Choose a boundary where the context can be
summarized for the next agent. If the next agent needs everything the previous one had, the
boundary is in the wrong place.

**Tasks** are tightly scoped prompts aimed at one outcome. Use them for discrete operations that
don't need a full agent, or where a focused prompt works better than a general one.

If you can't say in one sentence what an agent is responsible for, split it.

## Tools

- **Tool descriptions drive behavior.** When an agent calls the wrong tool or calls one at the
  wrong time, check the description before blaming the model. The most common cause is a
  description that doesn't say when to use the tool.
- **Keep tools off the critical path where you can.** Users hear every tool call they wait on as
  latency.
- **Plan for tool failure.** Decide what the agent says when a backend is down or returns nothing.
  An agent that makes up an answer when a tool fails is very hard to catch later.

## The model interprets; your code owns the state

The model reads the conversation and proposes actions. Application code owns the records, the
permission checks, the state transitions, and every external effect. Most agents that "work in
the demo and fail in production" have that line blurred somewhere.

- **Never classify intent with code.** Approval, refusal, correction, cancellation, "next
  Tuesday" — the runtime model interprets those. A regex, a keyword list, or a phrase whitelist
  will be wrong in ways you never test, and adding one as a "conservative" second gate has the same
  defect. Validate *structure* in code (typed dates, enums, required fields); leave *meaning* to
  the model.
- **A tool call is the model's interpretation, not proof it was right.** Keep message provenance,
  version checks, ordering, and business rules in code, where they can be checked.
- **Tools return facts, not sentences.** Compact data, outcomes, and actionable errors; the model
  chooses the wording. Script exact text only when the task mandates a verbatim disclosure.
- **Follow the user, not a form.** Accept facts the caller volunteers together, ask only for what's
  missing or ambiguous, and never demand ritual wording ("say yes to confirm") after a clear answer.
- **One authoritative state object per session**, and keep model-supplied facts separate from
  trusted identity, the clock, ids, and receipts.

## Make every change mean exactly one thing

The costliest agent bugs are mutations that did more or less than the caller meant: "no note for
him" clearing the whole list, a correction that also reset a confirmed field, a re-stated value
that invalidated an approval. Before writing a mutating tool, state its target, what changes, and
what must stay the same — then pair it with the nearest request that must do something *different*.

The rules in short: omission preserves; missing, empty, unknown, and cleared are four different
things; collections get application-issued ids; validate before applying; a scoped negative never
clears a collection; unchanged values are no-ops. When a task requires review before an effect,
approval is a *later real user message* for *that version*, delivery is tracked at the speech
boundary, and success is published only after the write commits. The full treatment — including
closing, output ownership, and how text and audio input take different hook paths — is in
`references/state-and-effects.md`. Read it before building anything that books, edits, confirms, or
ends calls.

## Start with a failing complete-path test

Before expanding the tool surface or polishing the persona, pick one ordinary user goal and drive
it through the real agent to its required effect — the booking exists, the record changed, the
call ended. Write the expected result from the user's request, not from the application's own
export. Then pair it with the first guard that must refuse, because a test that rejects everything
proves nothing about the guard. Keep that pair green while you add everything else.

## Verify before you call it done

Prompt changes break agent behavior as easily as code changes do, and trying it once by hand
doesn't count as verification.

- **While building**, drive conversations with `debugging-livekit-agents`. It runs your agent
  locally, lets you send turns as text or speech, and shows the tool calls behind each reply.
- **Before you call it done**, write tests with `testing-livekit-agents`. At minimum, cover the core
  behavior the user asked for, tool invocation with correct arguments if there are tools, and one
  failure path.
- **Before shipping a change to a live agent**, run simulations with `writing-livekit-scenarios`
  and `running-livekit-simulations`.

If the user asks for no tests, build without them, mention once that you'd recommend them before
production, and move on.

## Common mistakes

- **Starting with one agent "just for now."** You're deciding the structure up front; the
  implementation can still be simple.
- **Putting off latency.** It compounds, and it only gets more expensive to fix.
- **Copying an example you don't understand.** An example shows one pattern. Pasted whole, it brings
  extra context and components you can't explain.
- **Assuming your model knowledge is current.** It isn't. See `reading-livekit-docs`.
- **Installing a provider plugin by reflex.** On LiveKit Cloud, start with LiveKit Inference (see
  "Models" above).
- **Shipping on manual testing alone.** Prompt edits change behavior without any visible error, and
  tests let you find out before users do.

## Related skills

- Facts, APIs, changelogs: `reading-livekit-docs`
- Drive a live conversation while building: `debugging-livekit-agents`
- Turn-level tests: `testing-livekit-agents`
- Whole-conversation testing: `writing-livekit-scenarios`, `running-livekit-simulations`
- State, approvals, commits, delivery, closing: `references/state-and-effects.md`
- Deploying it and keeping it healthy in production: `operating-livekit-agents`