PydanticAI
Type-safe Python agent framework built by the creators of Pydantic
About PydanticAI
PydanticAI is a lightweight, production-grade Python agent framework created by the core Pydantic team. It brings strict type validation, structured outputs, and clean dependency injection to generative AI development, ensuring models strictly adhere to domain schemas.
Designed to eliminate messy string parsing and unreliable tool outputs, PydanticAI treats LLM interactions as type-checked functions. It supports model agnosticism across OpenAI, Anthropic, Gemini, and Ollama, offering native telemetry via OpenTelemetry and seamless integration with Logfire.
Define agent input/output expectations using standard Pydantic BaseModel schemas.
Register typed Python tools and functions that the agent can autonomously call.
Inject dynamic system state and contextual dependencies at runtime.
Execute structured prompt runs with automatic retry loops on validation failure.
Monitor latency, token costs, and tool execution traces in production via OpenTelemetry.
Capabilities & Features
Common Use Cases
structured-outputs
type-safe-agents
tool-calling
production-llm
Frequently Asked Questions
Is PydanticAI free to use?
Yes, PydanticAI is completely free and open-source under the MIT license.
Can I use PydanticAI with local models?
Yes, PydanticAI supports local LLMs via Ollama, vLLM, and any OpenAI-compatible API endpoint.
How does PydanticAI handle malformed model outputs?
If an LLM returns JSON that fails schema validation, PydanticAI automatically reflects the validation error back to the model for self-correction up to a configurable retry limit.
Free Plan
100% Free / Open Source (MIT Licensed)
Paid Plan
None (Fully free open source software).
Direct link · Verified & reader-supported
Pros & Cons
Strict type safety and automatic validation for all model responses.
Zero vendor lock-in with unified interfaces across OpenAI, Anthropic, Gemini, and local SLMs.
Lightweight architecture with minimal dependencies compared to monolithic frameworks.
Native integration with Pydantic Logfire for granular observability.
Maintained by the trusted core engineering team behind Pydantic.
Requires familiarity with modern typed Python (3.10+) and Pydantic v2.
Focuses on core agent primitives rather than pre-built UI components.
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