Overview
Pydantic AI is an open-source Python framework for building production-grade AI agents and LLM applications. It combines a model-agnostic architecture with Pydantic validation, helping developers define reliable structured outputs while retaining the flexibility to work with different model providers.
The framework supports agents that reason, invoke tools, interact with external systems, and participate in multi-agent workflows. It is released under the MIT license and fits into the wider Pydantic stack alongside Logfire, AI Gateway, and Evals.
Key Features
- Validates structured model outputs with Pydantic for type-safe application data.
- Registers Python functions as agent tools and generates their JSON schemas from type hints and docstrings.
- Streams text, tool calls, reasoning events, and structured data for real-time interfaces.
- Coordinates specialized agents through graph-based multi-agent workflows.
- Builds evaluation datasets, runs evaluations, and tracks model performance with Pydantic Evals.
- Provides model routing and cost controls through Pydantic AI Gateway, with bring-your-own-key and built-in provider options.
Connectivity and Interfaces
Built-in Model Context Protocol support connects agents to external tools and data sources such as file systems, databases, and APIs. For frontend communication, Pydantic AI supports AG-UI and the Vercel AI Data Stream Protocol, allowing applications to deliver agent events as they happen.
Its provider-independent design works with multiple model providers, including OpenAI, Anthropic, and Google. This enables teams to structure agent logic around a consistent framework instead of tying the application to a single model service.
Reliability and Observability
Durable execution support preserves agent progress through transient API failures, application errors, and restarts. Supported durable execution systems include Temporal, DBOS, Prefect, Restate, and AWS Lambda, with compatibility for long-running, asynchronous, and human-in-the-loop workflows.
Pydantic Logfire integration adds visibility into agent runs through tracing, debugging, latency analysis, token-cost tracking, and monitoring of LLM calls. Durable agents retain support for MCP and streaming where the underlying execution platform can return streamed responses.