What problem does it solve?
Pydantic AI removes the friction of building reliable AI agents by providing type-safe structured outputs, dependency injection, and tool calling patterns that work across multiple LLM providers.
Core Features & Use Cases
- Type-safe structured responses: Define
output_type with Pydantic models so the agent returns validated, predictable data for downstream code.
- Tool calling with validation and retries: Register typed tools (with or without context) so the model can execute functions safely and recover from bad inputs using
ModelRetry.
- Production-ready runtime patterns: Use dedicated references for streaming, multi-agent delegation, and durable execution (e.g., DBOS/Temporal) to handle real UX and reliability needs.
Use case: You need an agent that can call tools like database lookups and return a strict JSON schema (e.g., “answer” + “confidence”) while supporting structured UI streaming and safe orchestration across specialized sub-agents.
Quick Start
Ask the agent to: "Use Pydantic AI with a Gemini model to call typed tools and return a validated JSON object with an answer and confidence."
