pydantic-ai-testing

What This Skill Does

The pydantic-ai-testing skill provides a comprehensive suite of utilities for unit testing and verifying PydanticAI agents. By leveraging TestModel and FunctionModel, developers can decouple their agents from live LLM API providers during the development and CI/CD lifecycle. This skill enables deterministic behavior, allowing you to simulate complex multi-turn conversations, verify structured data output, and mock specific tool call sequences without incurring API costs or latency issues.

Installation

To install this skill, use the OpenClaw command-line interface: clawhub install openclaw/skills/skills/anderskev/pydantic-ai-testing

Use Cases

• Continuous Integration (CI): Run your agent tests in environments without network access to ensure consistent functionality.
• Mocking External APIs: Use FunctionModel to simulate responses from real-world APIs, allowing you to test how your agent handles success, failure, and timeout scenarios.
• Regression Testing: Ensure that changes to your agent logic do not break existing workflows by providing fixed, deterministic outputs that your assertions rely on.
• Structured Output Validation: Validate that your agents correctly map unstructured input to specific Pydantic schemas by using the TestModel structured output configuration.

Example Prompts

1. "Run the unit tests for my agent using the TestModel to ensure the structured response schema remains intact."
2. "Configure a FunctionModel for my weather-agent that mocks a tool call to the weather-api with a temperature of 22 degrees Celsius."
3. "Override my search-agent's model in a pytest function to use TestModel with a custom message payload for regression checking."

Tips & Limitations

• Deterministic Testing: Use the seed parameter in TestModel to generate reproducible random outputs, which helps in debugging non-deterministic agent behavior.
• Context Managers: Utilize agent.override() in your tests to swap production dependencies with mock objects, making it easier to isolate the agent's logic from your database or external service layers.
• Limitations: Remember that while these testing tools provide high fidelity for logic and schema validation, they cannot fully replicate the nuances of actual LLM inference (e.g., specific reasoning styles of GPT-4o versus Claude 3.5). Use these for logic and integration testing, and always perform final validation against live models in a staging environment.