> ## 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.

# Overview

> Understanding Playgent's core concepts and data model

# Core Concepts

Playgent uses a simple pipeline for testing and evaluating AI agents:

```
Agent → Test Case → Test Run → Evaluation
```

<CardGroup cols={2}>
  <Card title="Agent" icon="robot" href="/core-concepts/agents">
    Your AI system configuration including provider, credentials, and system
    prompt.
  </Card>

  <Card title="Test Case" icon="list-check" href="/core-concepts/test-cases">
    Input scenarios with expected behaviors, context, and ground truth.
  </Card>

  <Card title="Test Run" icon="play" href="/core-concepts/test-runs">
    Execution of test cases with captured outputs, metrics, and traces.
  </Card>

  <Card title="Evaluation" icon="chart-simple" href="/core-concepts/evaluations">
    Scoring runs with 29 built-in metrics (RAG, safety, agentic, multi-turn).
  </Card>
</CardGroup>

## How They Connect

1. **Agent** - Register your AI system with Playgent
2. **Test Case** - Define scenarios to test against that agent
3. **Test Run** - Execute test cases and capture results
4. **Evaluation** - Score the run's output with chosen metrics

## Quick Example

```python theme={null}
from playgent import Playgent

client = Playgent(api_key="your-api-key")

# 1. Create agent
agent = client.agents.create(
    name="Support Agent",
    provider="openai",
    system_prompt="You are a helpful support agent..."
)

# 2. Create test case
test_case = client.test_cases.create(
    name="Refund Request",
    agent_id=agent.id,
    turns=[{
        "input": {"text": "I want a refund"},
        "expected_behavior": "Ask for order details",
        "context": ["Refunds allowed within 30 days"]
    }]
)

# 3. Run test
run = client.runs.create(test_case_id=test_case.id)
print(f"Run passed: {run.success}")

# 4. Evaluate
evaluation = client.evaluate(
    run_id=run.id,
    scorers=["answer_relevancy", "faithfulness", "bias"]
)
print(f"Evaluation passed: {evaluation.overall_pass}")
```