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

# Test Cases

> Creating and managing test scenarios

# Test Cases

A **Test Case** defines a test scenario for your agent with input, expected behavior, and optional context.

## Quick Start

<CodeGroup>
  ```python Python theme={null}
  test_case = client.test_cases.create(
      name="Refund Request - Valid Order",
      agent_id=agent.id,
      turns=[{
          "input": {"text": "I want a refund for order #1234"},
          "expected_behavior": "Agent should verify order and explain refund process",
          "context": ["Order #1234: $99.99, delivered Dec 1, 2024, Status: Delivered"],
          "ground_truth": "Order #1234 is eligible for refund within 30-day window"
      }],
      tags=["refunds", "customer-support", "happy-path"]
  )
  ```

  ```typescript TypeScript theme={null}
  const testCase = await client.testCases.create({
    name: "Refund Request - Valid Order",
    agentId: agent.id,
    turns: [
      {
        input: { text: "I want a refund for order #1234" },
        expectedBehavior: "Agent should verify order and explain refund process",
        context: [
          "Order #1234: $99.99, delivered Dec 1, 2024, Status: Delivered",
        ],
        groundTruth: "Order #1234 is eligible for refund within 30-day window",
      },
    ],
    tags: ["refunds", "customer-support", "happy-path"],
  });
  ```
</CodeGroup>

## Test Types

### Single-Turn

One question, one response:

```python theme={null}
test_case = client.test_cases.create(
    name="Simple Question",
    agent_id=agent.id,
    turns=[{
        "input": {"text": "What are your store hours?"},
        "expected_behavior": "Provide store hours"
    }]
)
```

### Multi-Turn

Full conversation with multiple exchanges:

```python theme={null}
test_case = client.test_cases.create(
    name="Refund Flow",
    agent_id=agent.id,
    turns=[
        {"input": {"text": "I want a refund"}, "expected_behavior": "Ask for order number"},
        {"input": {"text": "Order #1234"}, "expected_behavior": "Verify and process"}
    ]
)
```

## Additional Options

### Context for RAG

Add retrieved documents for RAG evaluation:

```python theme={null}
turns=[{
    "input": {"text": "Can I return opened items?"},
    "context": ["Policy: Unopened items returnable within 30 days..."]
}]
```

### Ground Truth

Specify factual answers for accuracy checks:

```python theme={null}
turns=[{
    "input": {"text": "What is the capital of France?"},
    "ground_truth": "Paris"
}]
```

### Tags

Organize and filter tests:

```python theme={null}
test_case = client.test_cases.create(
    name="Edge Case",
    tags=["edge-cases", "refunds"]
)

# Run all tests with tag
run = client.runs.create(tags=["refunds"])
```

### Auto-Generate

Generate tests from documents:

```python theme={null}
generated = client.test_cases.generate(
    agent_id=agent.id,
    documents=[{"content": "Policy doc...", "name": "policy.md"}],
    generation_config={"num_test_cases": 10}
)
```

## Next Steps

<CardGroup cols={2}>
  <Card title="Test Runs" icon="play" href="/core-concepts/test-runs">
    Execute your test cases
  </Card>

  <Card title="API Reference" icon="code" href="/api-reference/test-cases/create">
    Full API documentation
  </Card>
</CardGroup>