Pydantic AI is a Python framework for building LLM agents that return validated, structured outputs. The tutorial covers installation and setup with Google Gemini, defining Pydantic BaseModel schemas for type-safe structured outputs, registering Python functions as agent tools using @agent.tool decorators, and injecting runtime dependencies (like database connections) with full type safety via RunContext. It also covers practical trade-offs including token costs from validation retries, tool-calling overhead, and varying LLM provider support for structured outputs.

17m read timeFrom realpython.com
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Table of contents
Start Using Pydantic AI to Create AgentsReturn Structured, Validated Data With Pydantic ModelsLeverage Your Agent’s Function Calling CapabilitiesInject Runtime Dependencies With Type SafetyBeware of Limitations and GotchasConclusionNext StepsFrequently Asked Questions

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