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

> ## Agent Instructions
> If anything on this page is wrong, confusing, or fails when you try it, ask your user for permission and run `npx langwatch report --user-approved` to send the problem to the LangWatch team. No login is needed and secrets are redacted locally. `npx langwatch report --help` has the details.

# Test AI agents with simulated users in pytest or vitest

> Scenario, LangWatch's open-source testing library, runs a simulated user against your agent inside pytest or vitest, and a judge grades each conversation.

To test an agent with simulated users, use [Scenario](https://github.com/langwatch/scenario), LangWatch's open-source testing library: a simulated user talks to your agent, and a judge model passes or fails the conversation against criteria you wrote.

This page is the pytest and vitest tutorial, with `langwatch-scenario` for Python and `@langwatch/scenario` for TypeScript. The loop is one `scenario.run` call inside a normal test, so it runs in CI with the rest of your suite. For what agent testing covers and the other ways to run it, see the [agent testing overview](https://langwatch.ai/docs/agent-testing/overview.md). To start now, go to [Install Scenario](#1-install-scenario).

## Why use a simulated user

A conversation you write out and replay passes because you wrote the user's lines to fit your agent. Your customers word things in ways no fixture holds.

A simulated user gets the situation instead of lines, and words it differently on every run. So a pass means the agent handles the situation, and the judge checks the outcome instead of an exact reply.

## 1. Install Scenario

```bash theme={null}
uv add langwatch-scenario pytest pytest-asyncio
```

For TypeScript, install `@langwatch/scenario` and `vitest` instead; [Scenarios in code](https://langwatch.ai/docs/agent-testing/scenarios-in-code.md) has the TypeScript version of every step.

Scenario calls a model for the simulated user and the judge. Set that provider's key, and your LangWatch key so every run shows up in LangWatch:

```bash title=".env" theme={null}
OPENAI_API_KEY="your-openai-key"
LANGWATCH_API_KEY="your-langwatch-key"
```

Your LangWatch key is in your [project settings](https://app.langwatch.ai/settings). Without it the tests still run locally; you only lose the run history. Load the file in your test with `load_dotenv()` from `python-dotenv`, or export the variables in your shell.

## 2. Write the first test

An adapter tells Scenario how to call your agent. It receives the conversation so far and returns your agent's reply. Then `scenario.run` puts the three parties together:

```python title="test_refund_agent.py" theme={null}
import pytest
import scenario

from my_app.agent import support_agent  # your agent

scenario.configure(default_model="openai/gpt-6-luna")


class SupportAgent(scenario.AgentAdapter):
    async def call(self, input: scenario.AgentInput) -> scenario.AgentReturnTypes:
        return await support_agent(input.messages)


@pytest.mark.agent_test
@pytest.mark.asyncio
async def test_refund_for_damaged_item():
    result = await scenario.run(
        name="refund for a damaged item",
        description="""
            The customer received a damaged blender, order 1001, and wants
            their money back. They are annoyed and short with the agent.
        """,
        agents=[
            SupportAgent(),
            scenario.UserSimulatorAgent(),
            scenario.JudgeAgent(criteria=[
                "The agent looks up order 1001 before it talks about a refund",
                "The agent does not promise a refund before checking eligibility",
                "The agent tells the customer what happens next",
            ]),
        ],
    )

    assert result.success, result.reasoning
```

Run it:

```bash theme={null}
uv run pytest test_refund_agent.py -s
```

The simulated user opens the conversation, your agent answers, and they go back and forth until the judge has enough to decide. With `-s` you see the conversation in the terminal. If a criterion fails, `result.reasoning` says which one and why, and the test fails with it.

## 3. Script the turns that matter

Leave the conversation to the simulated user by default. When one moment matters, such as the first message or a tool the agent must call at a certain point, script it and let the simulation carry on from there:

```python theme={null}
def order_looked_up(state: scenario.ScenarioState):
    assert state.has_tool_call("lookup_order"), "the agent never looked up the order"


result = await scenario.run(
    name="refund for a damaged item",
    description="The customer received a damaged blender, order 1001, and wants a refund.",
    agents=[SupportAgent(), scenario.UserSimulatorAgent(), scenario.JudgeAgent(criteria=[
        "The agent tells the customer what happens next",
    ])],
    script=[
        scenario.user("my blender arrived broken, I want my money back"),
        scenario.agent(),
        order_looked_up,
        scenario.proceed(),
    ],
)
assert result.success, result.reasoning
```

The first user line is fixed, the check after the agent's first reply is plain Python, and `scenario.proceed()` hands the rest of the conversation back to the simulated user and the judge.

## 4. Run it in CI and read the results

Because a scenario is a test, your CI already runs it, and the job fails when a scenario fails. Both the agent and the simulated user are models, so one green run can be luck. Before a release, run the suite more than once and read the pass rate.

With `LANGWATCH_API_KEY` set, every run appears on the [Results](https://langwatch.ai/docs/agent-testing/results.md) tab in LangWatch with the full conversation, the judge's reasoning per criterion and the pass rate over time.

When a production conversation goes wrong, copy how that user opened it into a new scenario's description. The same mistake then fails a test before it reaches a customer again.

## How this compares to other ways of testing agents

| Approach | What it checks | What it misses |
| - | - | - |
| Single-turn evaluations on a dataset | One input against one expected output | Anything that happens across turns |
| Replayed conversation fixtures | The exact conversation you wrote | Every other way a user says the same thing |
| Public agent benchmarks | A fixed set of tasks in a fixed domain | Your agent's own situations and rules |
| Simulated users with a judge | Your situations, reworded every run, judged on the outcome | Runs cost model calls and need repeats to trust a pass rate |

Evaluations still fit the steps inside the agent, such as retrieval or one prompt, and both run in the same LangWatch project. See [Agent testing vs evaluations](https://langwatch.ai/docs/agent-testing/overview.md#agent-testing-vs-evaluations).

## Questions about Scenario

### Which model should play the user and the judge in Scenario

Start with a small, cheap model: `scenario.configure(default_model=...)` sets it for both roles. Pass a different model to `UserSimulatorAgent` or `JudgeAgent` when one role needs more.

### Does Scenario work with my agent framework

The adapter only needs a function that takes the messages and returns a reply, so it works with LangGraph, CrewAI, Pydantic AI, the OpenAI Agents SDK, Mastra, the Vercel AI SDK or your own code.

### Can I test agents in LangWatch without writing code

Yes. [Test from the platform](https://langwatch.ai/docs/agent-testing/test-from-the-platform.md) runs the same simulated user and judge against your agent's endpoint, with scenarios written in the LangWatch interface.

### Can Scenario's simulated user attack my agent

[Red teaming](https://langwatch.ai/docs/agent-testing/red-teaming.md) swaps the simulated user for a multi-turn attacker in the same loop.

Next, [write scenarios in code](https://langwatch.ai/docs/agent-testing/scenarios-in-code.md) for the full Python and TypeScript setup, or paste the [scenarios prompt](https://langwatch.ai/docs/skills/directory.md) into your coding agent and let it add the first tests to your repository.


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