Writing Scenarios

The scenario file format: turns, expectations, assertions, and text vs audio modes.

A scenario is a YAML file describing a scripted conversation and the events you expect your agent to emit. This page covers the full format. If you haven’t run a scenario yet, start with the quickstart.

Anatomy of a scenario

name: multi_turn # required: the eval's name

judge: # optional: judge modality and LLM (defaults shown below)
  eval:
    service: ollama
    model: gemma2:9b

turns: # required: the conversation, in order
  - user: "My name is Alex, and I'm planning a trip to Italy."
    expect:
      - event: response
        eval: "acknowledges the user's message (the name Alex and/or the trip to Italy)"

  - user: "Remind me — what's my name and where am I going?"
    expect:
      - event: response
        eval: "recalls that the user's name is Alex and the destination is Italy"

Each turn optionally sends a user utterance (user:) and lists the events expected in response (expect:). Expected events must arrive in the order listed, but the agent may emit other events in between, so you don’t have to enumerate everything it does.

A turn without a user: field is observation-only: the harness just waits for the expected events. This is how you test agent-first behavior like an on-connect greeting:

turns:
  # No user input: just wait for the agent to speak first.
  - expect:
      - event: response
        eval: "the bot opens the conversation with a greeting or an offer to help"

Events

Scenarios assert on a small set of semantic events, mapped from the RTVI messages the agent emits:

Event	Meaning
response	The agent’s reply. In audio mode this is a transcription of the agent’s actual synthesized speech; in text mode it resolves to llm_response. Prefer this for content checks.
llm_response	The LLM’s text output for the turn. Available in both modes.
tts_response	The text the TTS reports speaking, one segment at a time. Audio mode only.
llm_started	The LLM began generating a response.
function_call	The LLM called a function.
user_transcription	The agent’s STT finalized a transcription of the user. Audio mode only.
user_started_speaking	The agent’s VAD detected the start of user speech. Audio mode only.
user_stopped_speaking	The agent’s VAD detected the end of user speech. Audio mode only.

Use response for the agent’s reply unless you have a reason not to. It’s modality-agnostic: the same scenario judges LLM text in text mode and the transcription of real spoken audio in audio mode, so one file covers both.

Assertions

Each entry in expect: names an event and, optionally, asserts on its content or timing.

Semantic judging with eval

The eval: field is a natural-language criterion that the event’s text must satisfy, decided by the judge LLM:

- user: "What's 2 plus 2?"
  expect:
    - event: response
      eval: "the response says the answer is four"

The judge sees the whole conversation so far, so it can resolve terse or context-dependent replies (like “That’s four”). It also understands that audio-mode responses come from a speech-to-text pass and judges intended meaning rather than exact spelling, so “for” transcribed instead of “four” still passes.

The judge handles interim replies gracefully: if the agent says “Let me check on that.” before the real answer, the harness keeps accumulating response text and re-judges until the criterion is met or the time budget runs out.

eval: only makes sense on the agent’s text output (response, llm_response, tts_response).

Substring checks with text_contains

For exact content, text_contains does a plain substring check, with no judge round-trip:

- user: "What is the capital of France?"
  expect:
    - event: response
      text_contains: "Paris"

Latency budgets with within_ms

within_ms bounds how long after the turn’s user send the event may arrive. All of a turn’s expectations share that one anchor:

- user: "What is the capital of France?"
  expect:
    - event: llm_started
      within_ms: 2000 # the LLM must start responding within 2s
    - event: response
      text_contains: "Paris"

When omitted, an expectation defaults to a generous 60 second budget (configurable with --timeout), so timing is only asserted when you ask for it.

Because every deadline is measured from the send, time spent matching earlier expectations counts against later ones. In the example above, if llm_started arrives at 1.5 seconds, the response (with the default 60 second budget) has 58.5 seconds left, and a turn that stalls completely fails within a single budget rather than one per expectation.

Function calls

A function_call expectation asserts that the turn invoked one or more tools. List the expected calls under calls:; they’re matched by name in any order, and the expectation passes once all are found:

- user: "What's the weather in San Francisco? And recommend a restaurant."
  expect:
    - event: function_call
      calls:
        - name: get_current_weather
          args: { location: "San Francisco" }
        - name: get_restaurant_recommendation
    - event: response
      eval: "describes the weather and recommends a restaurant"

args is a subset check: every listed key/value must be present in the call’s arguments, and extra arguments are ignored. A single expected call can use the name:/args: shorthand directly on the expectation, and a bare function_call with neither just asserts that some call happened.

Interruptions

send_after: schedules a turn’s user send relative to a prior event, which is how you script barge-in tests:

turns:
  - user: "Tell me a long, detailed story about the history of Paris."
    expect:
      - event: llm_started

  # Interrupt 2 seconds after the agent starts its long answer.
  - user: "Actually, never mind that — what's the capital of Japan?"
    send_after:
      event: llm_started
      delay_ms: 2000
    expect:
      - event: response
        eval: "the response says the capital of Japan is Tokyo, instead of continuing the Paris story"

Vision turns

A turn may register an image with image: (a path relative to the scenario file). When a vision agent requests a user image during the turn, the eval transport serves it:

turns:
  - user: "What do you see in this image?"
    image: assets/cat.jpg
    expect:
      - event: response
        eval: "the response describes a cat"

Text and audio modes

Two top-level blocks control a scenario’s modalities, and each has its own modality: field:

• user: sets how each turn’s utterance is delivered to the agent: sent as text, bypassing its STT (modality: text), or synthesized into real speech (modality: audio).
• judge: sets what the judge evaluates: the agent’s LLM text, with its TTS skipped (modality: text), or a transcription of its actual spoken audio (modality: audio).

When modality: isn’t specified, or a block is omitted entirely, it defaults to text. The two sides are also independent: you can drive the agent with text while judging its real speech, or speak to it and judge the LLM text.

A scenario with neither block runs entirely in text mode. No audio flows on either side, so this is the fastest and cheapest way to test prompts, conversational logic, and function calling: no audio service cost, and a multi-turn scenario finishes in seconds. The judge LLM is the only service the harness itself needs (Ollama with gemma2:9b by default).

The top-level user: block only configures delivery. Each turn’s user: field is the utterance itself, and is written the same way in both modes.

User input with user:

Text (the default). Each turn’s utterance is sent to the agent as text, bypassing its STT. This needs no configuration; it’s equivalent to:

user:
  modality: text

Audio. Each turn’s utterance is synthesized by a TTS the harness runs and streamed into your agent’s pipeline at real-time cadence, exercising its VAD, turn detection, and STT exactly as a live microphone would. Synthesized audio is cached across runs, so repeated turns don’t re-synthesize. The speech: block (the TTS service and voice) is required:

user:
  modality: audio
  speech:
    service: kokoro # local TTS, no API key, no per-run cost
    voice: af_heart
    sample_rate: 16000

The built-in speech services are kokoro, a local model and the recommended default, and cartesia (HTTP) when you want a cloud voice.

Judging with judge:

Text (the default). The agent’s TTS is skipped automatically, including any on-connect greeting, and the judge evaluates the LLM’s text output. Fast and silent; equivalent to:

judge:
  modality: text

Audio. The agent speaks for real. The harness captures its synthesized audio, transcribes it with the configured STT, and the response event becomes that transcription, so the judge evaluates what a user would actually have heard. This is the true end-to-end check: STT in, LLM in the middle, TTS out. The transcription: block is required:

judge:
  modality: audio
  transcription:
    service: moonshine # STT for the agent's audio (or: whisper)
    model: small-streaming
  eval:
    service: ollama # the judge LLM
    model: gemma2:9b

The built-in transcribers are moonshine and whisper, both local models. When transcription.service: is omitted, it defaults to moonshine.

In either modality, the judge.eval: block selects the judge LLM: ollama (the default, gemma2:9b), openai, or any OpenAI-compatible endpoint via endpoint:.

Custom services with factory:

To use a TTS or STT beyond the built-ins, both blocks accept a factory: escape hatch: a dotted path to a callable that receives the block’s mapping and the resolved sample rate, and returns the service. Any extra keys you put in the block are passed through to your factory:

user:
  modality: audio
  speech:
    factory: "my_evals.services.make_tts"
    voice: luna # available to your factory as speech_cfg["voice"]

judge:
  modality: audio
  transcription:
    factory: "my_evals.services.make_stt"

my_evals/services.py

import os

from pipecat.services.fal.stt import FalSTTService
from pipecat.services.rime.tts import RimeHttpTTSService


def make_tts(speech_cfg, sample_rate):
    return RimeHttpTTSService(
        api_key=os.environ["RIME_API_KEY"],
        settings=RimeHttpTTSService.Settings(voice=speech_cfg["voice"]),
        sample_rate=sample_rate,
    )


def make_stt(transcription_cfg, sample_rate):
    return FalSTTService(api_key=os.environ["FAL_KEY"])

The service your factory returns must be a local model or an HTTP-based service. WebSocket-streaming services aren’t supported: they need a running pipeline to manage their connection lifecycle, and keeping them out keeps the evals code simple.

For a fully custom setup (your own caching, a pre-built service instance), construct EvalSpeech or EvalTranscriber directly and inject them through the library.

Sharing config across scenarios

Any value can be pulled from another file with !include, resolved relative to the scenario file. This keeps per-scenario noise down when a whole directory of scenarios shares the same audio setup:

name: capital_question

user: !include user_audio.yaml
judge: !include judge_audio.yaml

turns:
  - user: "What is the capital of Germany?"
    expect:
      - event: response
        eval: "the response says the capital of Germany is Berlin"

Seed the conversation context with context:

By default the harness leaves the bot’s LLM context alone: whatever the bot sets up for itself — for example, a system prompt added in its connect handler — is what the scenario runs against. Provide context: to replace that with messages of your own, which lets a scenario start mid-conversation:

context:
  - role: developer
    content: "The user has already introduced themselves as Alex."
  - role: assistant
    content: "Nice to meet you, Alex! How can I help?"

The harness sends these right after the bot-ready handshake as an LLMMessagesUpdateFrame that replaces the bot’s context wholesale. Omit context: and the harness sends nothing, leaving the bot’s own context in place.

Running scenarios back to back

By default the bot keeps running between scenarios. When a scenario ends its eval connection closes, but the eval transport suppresses the bot’s on_client_disconnected handler, so the pipeline stays up to serve the next scenario. This is what lets pipecat eval run a.yaml b.yaml c.yaml drive a whole list against one bot instance with no reboot between them, which keeps a run fast.

The trade-off is that anything the bot accumulated in one scenario is still there for the next. For results to be independent, each scenario has to start from a clean slate, and clearing that state is split between the harness and your bot:

• Conversation context — seed or clear it per scenario with context:. The harness replaces the bot’s LLM context with the messages you provide (via an LLMMessagesUpdateFrame); without it, the previous conversation carries forward, which is rarely what you want across independent scenarios.
• Application state — counters, flags, cached data, anything your bot holds outside the LLM context. The harness can’t see this, so resetting it is your bot’s job. A common place is the bot’s connect handler, which runs again for each scenario’s connection.

Exercising the disconnect path

Some bots do meaningful work in on_client_disconnected — a goodbye message, session teardown, resource cleanup. Because that handler is suppressed by default, set trigger_disconnect: true on a scenario to fire it when that scenario ends:

name: test_goodbye_on_disconnect
trigger_disconnect: true

turns:
  - user: "Thanks for your help!"
    expect:
      - event: response
        eval: "the agent acknowledges the thanks"
  # on_client_disconnected fires after this turn, so the agent can
  # send a goodbye message or clean up resources.

Bots often cancel their pipeline in on_client_disconnected, so a scenario with trigger_disconnect: true usually ends the bot process — treat it as a terminal run, last in a list.

Enable it for every scenario in a run with pipecat eval run --trigger-disconnect; a scenario’s own trigger_disconnect field still takes precedence. This is independent of --stop-bot, which tears the bot down via an eval-cancel message regardless of the disconnect handler.

Next steps

The Eval Loop

Let a coding assistant write agent code, run evals, and iterate automatically until the agent is better.