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pydantic-ai

Build type-safe AI agents with typed agent frameworks — structured outputs via data models, dependency injection, tool validation, and testable agent code. Use when agent reliability matters and you want types enforcing contracts.

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# Type-Safe Agents with Typed Frameworks

Dynamic agent code fails at runtime in ways types could have caught: malformed tool arguments, 
wrong output shapes, missing dependencies. Typed agent frameworks bring data models, dependency 
injection, and structured validation to agent building — so contracts are checked by the type 
system, not by hope.

## Overview

The pattern: define your agent's world as types. Tool inputs and outputs are data models with 
validation. The agent's dependencies (API clients, database handles) are injected through a typed 
context rather than globals. Model outputs are parsed into models, with retries on validation 
failure. The result is agent code you can test like normal code — unit-test tools, type-check the 
wiring, and mock dependencies — instead of integration-testing everything through the model.