pydantic-ai-agent-creation

Type-safe LLM agents using Pydantic ensure validated inputs and outputs through strong typing. PydanticAI agents use deps_type for dependency injection and output_type for structured schemas, guaranteeing runtime validation and predictable agent behavior across sync, async, and streaming modes.

Structure dependencies by defining a typed Deps model that encapsulates all agent requirements—database connections, API clients, configuration. Inject this model via deps_type when instantiating the agent, enabling testable, reusable agent configurations with clear dependency wiring.

PydanticAI agents support both sync and async execution modes. Define your agent with async methods, pass an async deps instance at runtime, and execute with async context. Streaming output is also supported for real-time agent responses.

Specify the output_type parameter when creating an agent using a Pydantic model class. The agent enforces schema validation on all outputs, ensuring responses conform to your defined structure and enabling type-safe downstream processing.

Yes, configurable model settings control LLM behavior—system prompts, instructions, retry policies, and provider-specific parameters. PydanticAI agents accept these configurations during instantiation, enabling fine-tuned control over agent reasoning and reliability.

Create a test-specific Deps instance with mocked or stubbed components, then pass it to the agent during testing. Type-safe dependency injection enables seamless test overrides without modifying production agent code.