What is the best way to handle Pydantic validation failures?

Increase the 'retries' parameter in your Agent configuration and refine your instructions to explicitly describe the required JSON structure.

Why am I getting a UserError for RunContext in my tool?

RunContext is only allowed as the first parameter in tools decorated with @agent.tool. If you are using @agent.tool_plain, context is not supported and should be removed.

Can I register tools without using decorators?

Yes, PydanticAI supports raw function registration. You can pass functions directly to the Agent(tools=[...]) argument, and it will automatically detect RunContext from the signature.

How do I prevent 'ModelAPIError' during agent runs?

Ensure your environment variables (like OPENAI_API_KEY) are set, or use defer_model_check=True if you plan to override the model during testing.

Is it safe to use time.sleep() inside an async tool?

No, using blocking calls like time.sleep() will freeze the event loop. Always use await asyncio.sleep() or other non-blocking I/O within async tools.

PydanticAI Debugging & Best Practices

Name: PydanticAI Debugging & Best Practices
Author: existential-birds

byexistential-birds

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데이터 과학 및 ML

Identifies and fixes common implementation errors and architectural pitfalls when building PydanticAI agents.

소개

This skill provides specialized guidance for developers working with the PydanticAI framework to build robust AI agents. It streamlines the debugging process by identifying frequent mistakes such as incorrect RunContext usage in tool decorators, dependency injection mismatches, and problematic async/sync patterns. By applying these best practices and verification workflows, developers can ensure their agents handle validation retries correctly, manage streaming responses efficiently, and maintain proper serialization for production-grade LLM applications.

주요 기능

Troubleshoots response streaming and message history capture issues
Validates RunContext positioning and annotations for context-aware tools
Identifies blocking sync code within asynchronous tool definitions
Detects dependency type mismatches and missing runtime dependencies
Optimizes Pydantic output validation and LLM retry strategies
14 GitHub stars

사용 사례

Debugging UserError exceptions related to tool signatures and decorators
Ensuring proper instrumentation and tracing with Logfire integration
Conducting code reviews for complex PydanticAI agent architectures

소개

주요 기능

Troubleshoots response streaming and message history capture issues
Validates RunContext positioning and annotations for context-aware tools
Identifies blocking sync code within asynchronous tool definitions
Detects dependency type mismatches and missing runtime dependencies
Optimizes Pydantic output validation and LLM retry strategies
14 GitHub stars

사용 사례

Debugging UserError exceptions related to tool signatures and decorators
Ensuring proper instrumentation and tracing with Logfire integration
Conducting code reviews for complex PydanticAI agent architectures