pydantic-ai-testing

Test PydanticAI agents with deterministic mocking and VCR-based API recording.

Updated Jul 18, 2026

Search Tags:#testing #pytest #llm #mocking #pydantic-ai #vcr

Ready to run

npx skills add https://github.com/arthrod/conejo-skills --skill pydantic-ai-testing-arthrod

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pydantic-ai-testing
Source: https://github.com/arthrod/conejo-skills/tree/main/skills/pydantic-ai-testing
Command: npx skills add https://github.com/arthrod/conejo-skills --skill pydantic-ai-testing-arthrod

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pytest, pydantic-ai, inline-snapshot, and includes references (resource) components.

What problem does it solve?

This skill addresses the complexity of testing LLM-powered agents by providing deterministic mocking, recorded API interactions, and structured output validation.

Core Features & Use Cases

Deterministic Mocking: Use TestModel to simulate agent responses without incurring API costs or latency.
API Recording: Utilize VCR cassettes to capture and replay real LLM interactions for consistent test suites.
Use Case: When building a complex agent, use this skill to verify tool-calling logic and dependency injection without needing to trigger live OpenAI or Anthropic calls every time you run your test suite.

Quick Start

Use the pydantic-ai-testing skill to configure a TestModel for your agent and verify that your tool calls are triggered correctly.

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 live LLM API calls?▼

You can mock LLM responses in PydanticAI testing by configuring a deterministic TestModel, which simulates agent outputs and tool execution without incurring real API costs or network latency.

Can I record and replay LLM API interactions in pytest for reproducible test suites?▼

Yes, you can use VCR-based cassettes to record and replay real LLM API interactions within your pytest suite, ensuring consistent and reproducible agent evaluations across test runs.

How do I verify tool-calling logic and dependency injection in PydanticAI workflows?▼

PydanticAI testing frameworks allow you to verify tool-calling logic and dependency injection patterns by using deterministic model mocking to simulate agent responses and validate tool execution paths.

What is the best way to assert structured LLM agent outputs in pytest?▼

The best way to assert structured LLM agent outputs is by integrating inline snapshot assertions, which capture and validate structured model responses directly within your pytest test files.

Do I need pytest and pydantic-ai installed to use this testing framework?▼

Yes, you need both pytest and pydantic-ai installed, as this framework relies on them to configure deterministic mocking, VCR recording, and inline snapshot assertions for reliable agent evaluation.