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pydantic-ai-common-pitfalls

Debug common PydanticAI agent pitfalls including tool decorator, dependency, and validation errors.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps developers identify and resolve common errors and misunderstandings when working with PydanticAI agents, streamlining the debugging process.

Core Features & Use Cases

  • Tool Decorator Guidance: Clarifies correct usage of @agent.tool and @agent.tool_plain with RunContext.
  • Dependency Management: Addresses issues with missing or mismatched dependency types at runtime.
  • Output Validation: Provides strategies for handling Pydantic validation failures and complex output types.
  • Async/Sync Best Practices: Highlights common mistakes in mixing asynchronous and synchronous code.
  • Model Configuration: Guides on setting up API keys and using valid model strings.
  • Streaming Handling: Explains correct patterns for consuming streaming responses.
  • Debugging Tools: Offers tips on enabling tracing, capturing messages, and inspecting model responses.

Quick Start

Use the pydantic-ai-common-pitfalls skill to understand why my agent is failing to recognize the RunContext in a tool.

Frequently Asked Questions about pydantic-ai-common-pitfalls

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

FAQPage Schema
Why does my PydanticAI agent fail to recognize RunContext in a tool?▼

PydanticAI tool decorator errors occur when @agent.tool and @agent.tool_plain are used incorrectly. Matching the decorator to your RunContext needs ensures the agent passes dependencies correctly at runtime.

How do I fix dependency type mismatches in PydanticAI at runtime?▼

Dependency mismatches in PydanticAI happen when runtime types do not align with defined expectations. Verifying that your injected dependencies match the expected types resolves these runtime errors.

How do I handle Pydantic validation failures for complex output types in PydanticAI?▼

Pydantic validation failures for complex output types require validating the response structure against the defined schema. Adjusting the output model ensures the agent's response passes validation checks.

What causes async and sync issues when developing PydanticAI agents?▼

Async and sync issues in PydanticAI agents arise from mixing asynchronous and synchronous code incorrectly. Following best practices for async execution prevents blocking operations and runtime conflicts.

How do I debug PydanticAI agents using tracing and message capture?▼

Debugging PydanticAI agents involves enabling tracing and capturing messages to inspect model responses. These tools expose the internal processing logic and help identify where the agent fails.

What is the correct pattern for consuming streaming responses from a PydanticAI agent?▼

Consuming streaming responses from a PydanticAI agent requires using the correct async iteration patterns. Properly handling the stream prevents incomplete outputs and connection errors.

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