> ## Documentation Index
> Fetch the complete documentation index at: https://developers.agentx.so/llms.txt
> Use this file to discover all available pages before exploring further.

# Simulate Conversation

> A persona-driven simulated user converses with your prompt, model, and tools - judged, recorded, and convertible into regression tests

The [Playground](/evaluation/playground) grid tests single-shot answers; **Simulate conversation** (below its Run button) tests multi-turn behavior. A simulated user - a persona and a goal you write, played by its own model - converses with your current prompt, model, and tools for up to a turn cap (default 6, max 10):

* The simulated user opens the conversation, reacts in character to each agent reply, and ends the conversation itself: `GOAL ACHIEVED` when the agent genuinely got them there, `USER GAVE UP` when the persona would realistically walk away, or `TURN LIMIT REACHED`.
* The agent side of each turn is exactly a Playground run - same model settings, same tools, including simulated tool results for schema-only tools.
* With an Evaluator config selected ("Scored with judge" in the Playground), the finished transcript is scored 0-10 against its criteria (reference-free - a simulated conversation has no expected answer); the result names the judge that scored it.
* Turns stream into the transcript in real time as each exchange completes, and every finished simulation is kept in the Playground's **History** (Runs | Simulations) for later review or re-running.

The same simulation runs from the SDK - useful for scripted persona sweeps (each call blocks
for the whole conversation, one LLM call per turn plus the closing judgment):

```python theme={null}
result = client.evaluations.simulate_conversation(
    model="gpt-4o-mini",
    system_prompt=SYSTEM_PROMPT,
    persona="An impatient customer whose order arrived three weeks late...",
    goal="Get written confirmation of the exact refund amount.",
    max_turns=5,
    tools=[LOOKUP_TOOL],          # optional; simulated results unless an endpoint is set
    agent_name="sim-support-agent",
)
print(result["outcome"], result["sessionId"])   # GOAL_ACHIEVED / USER_GAVE_UP / TURN_LIMIT
```

**Recorded as a real session.** Unless you untick "Record as a session", every turn is written through the normal ingest path under one `sim-<id>` session with `metadata.simulated: true` - each turn a real span tree (the model call and any tool executions as timed child spans, so the trace dialog's Execution Timeline works). That means the simulation shows up in Observe → Sessions like any conversation: the built-in [Session Baseline Judge](/monitor/online-evaluators#session-scoped-evaluators) judges it once idle, session-scoped evaluators score it, and **Add to dataset** works from the session view - so a bad simulated conversation converts directly into regression test cases.