Skills

Testing Livekit Agents

Writes turn-level tests for a LiveKit agent in the user''s normal test suite: pytest...

Install

npx skills add fcakyon/claude-codex-settings --skill testing-livekit-agents -g -a claude-code -y
Category
Testing
Updated
3d ago
License
MIT
Context cost
Mediumabout 1,701 tokens
Runs scripts
No, instructions only

What it does

Writes turn-level tests for a LiveKit agent in the user''s normal test suite: pytest (Python) or Vitest (Node.js). Use when the user asks to "write tests for my agent", "add a test for this tool", "test the handoff", "pin this bug", "why does my agent test fail", or after building or changing agent behavior that needs regression coverage. Covers the SDK''s test session harness, assertions on messages, tool calls and handoffs, LLM judging of a reply against an intent, mocking tools, multi-turn tests, and judging whole conversations with the built-in judges. For interactive poking use debugging-livekit-agents. For grading whole conversations at scale use running-livekit-simulations.

Testing LiveKit agents

Turn-level tests are the cheapest lasting verification an agent can have. They run in the user's existing suite, in text mode, and they're fast enough for every commit. The framework's helper names and signatures change, so look them up with reading-livekit-docs before writing, and use the testing docs page for every API detail this skill leaves out.

Every test has the same shape: start a test session with the agent under test, run one user turn, and assert on the events that turn produced.

What a test asserts on

A turn produces a sequence of events. A simple turn is one message. A more typical one is a tool call, its output, maybe a handoff, and then a message. You write the test by walking that sequence in order, asserting on each event, and then asserting the turn has nothing more.

There are three kinds of assertion, and most of this skill is knowing which one to use.

Structural assertions check message roles, that a tool was called, its arguments, what it returned, and that a handoff to a specific agent happened. They're deterministic and fail for exactly one reason, so prefer them.

Judged assertions use an LLM-judge helper that gives one message and an intent string to a model and asks whether they match. Use them for the content of a reply, which you can't assert exactly. Describe the intent by outcome ("tells the user the booking is confirmed and gives the time"), not by wording.

Whole-conversation assertions use a judge-group helper that runs several built-in judges concurrently over the whole chat history and aggregates their verdicts. The built-in judges cover dimensions like grounding, relevance, safety, task completion, and tool use; the docs list the current set. Use this when the question spans several turns.

Rules of thumb:

Read the whole SKILL.md on GitHub

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