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pydantic-ai-testing

Test PydanticAI agents with TestModel, FunctionModel, VCR cassettes, and inline snapshots.

Updated Feb 17, 2026
Search Tags:#testing#llm#unit test#mocking#pydantic-ai#vcr
One-click install
npx skills add https://github.com/javierhbr/random-poc --skill pydantic-ai-testing-javierhbr
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pydantic-ai-testing
Source: https://github.com/javierhbr/random-poc/tree/main/custom-skills/beagle-main/plugins/beagle-ai/skills/pydantic-ai-testing
Command: npx skills add https://github.com/javierhbr/random-poc --skill pydantic-ai-testing-javierhbr

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill simplifies the process of testing PydanticAI agents by providing robust tools for mocking LLM responses, recording API interactions, and asserting outputs deterministically.

Core Features & Use Cases

  • Deterministic Testing: Use TestModel to simulate LLM responses without actual API calls, ensuring consistent test results.
  • Custom Logic: Implement complex testing scenarios with FunctionModel for full control over model behavior.
  • API Interaction Recording: Employ VCR cassettes to record and replay actual LLM API calls, enabling reliable testing of real-world interactions.
  • Inline Snapshots: Automatically manage and assert expected outputs against evolving test results.
  • Use Case: When developing a new PydanticAI agent, use TestModel to write unit tests that verify its core logic without incurring API costs or waiting for external services.

Quick Start

Use the pydantic-ai-testing skill to run a basic agent test with TestModel.

Frequently Asked Questions about pydantic-ai-testing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I test PydanticAI agents without making real LLM API calls?▼

Test PydanticAI agents without real LLM API calls by using TestModel to simulate deterministic responses, ensuring consistent unit test results without incurring external service costs.

How do I mock custom LLM behavior for complex PydanticAI testing scenarios?▼

Mock custom LLM behavior in PydanticAI testing by implementing FunctionModel, which provides full control over model logic to simulate complex testing scenarios and verify agent behavior.

Can I record and replay actual LLM API interactions for PydanticAI tests?▼

Record and replay actual LLM API interactions for PydanticAI tests by employing VCR cassettes, enabling reliable testing of real-world API interactions during development workflows.

What's the best way to assert evolving LLM outputs in PydanticAI unit tests?▼

Assert evolving LLM outputs in PydanticAI unit tests by using inline snapshots, which automatically manage and verify expected outputs against changing test results.

Do I need additional libraries to run PydanticAI agent tests?▼

You need the pydantic-ai and inline-snapshot libraries to run comprehensive PydanticAI agent testing capabilities, including deterministic simulation and inline snapshot management.

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