Pydantic AI is a type-safe, model-agnostic agent framework from the Pydantic team. These exercises cover the Agent class, tool definition with @agent.tool, result_type for structured output, RunContext for dependency injection, and switching between LLM providers.
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What is Pydantic AI and how does it relate to the Pydantic library?
Pydantic AI is an agent framework from the Pydantic team that leverages Pydantic models for type-safe, structured LLM interactions. It supports multiple model providers, defines agents with tools and structured output types, and uses Pydantic's validation throughout to ensure type safety end-to-end.
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In Pydantic AI, how is a tool added to an agent?
Pydantic AI uses the @agent.tool decorator to register Python functions as tools. The function's type annotations and docstring are used to generate the tool schema automatically. When the model calls the tool, Pydantic AI validates the arguments against the type annotations before invoking the function.
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A developer defines agent = Agent(model='openai:gpt-4o', result_type=MyModel). What does result_type control?
The result_type parameter tells Pydantic AI to use structured outputs to ensure the agent's final response conforms to the specified Pydantic model. Pydantic AI automatically sets up the appropriate structured output mechanism for the underlying model provider and validates the result.
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What is the RunContext object in Pydantic AI tools?
RunContext[T] is passed as the first parameter of Pydantic AI tool functions and contains typed deps (dependencies injected at run time, e.g., a database connection), the current messages, and run metadata. Using RunContext allows tools to access shared resources without global state.
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Pydantic AI describes itself as 'model agnostic'. What does this mean practically?
Pydantic AI is model agnostic in that the same agent definition works across providers: change 'openai:gpt-4o' to 'anthropic:claude-3-5-sonnet' or 'google-gla:gemini-1.5-pro' and the agent behaves the same way. Provider-specific details (tool calling format, structured output API) are handled by provider-specific model classes internally.