Pydantic
Type-safe Python framework for building LLM-powered agents with structured outputs, function tools, streaming, and first-class observability via Logfire. Model-agnostic, production-ready integrations, and optional durable execution with Temporal, Prefect, DBOS, or Restate.
Overview
Define Pydantic models for inputs and outputs, configure an agent with your provider, and register typed tools. Stream tokens, tool calls, and reasoning to the UI while Logfire captures traces, costs, and failures. Iterate quickly with evaluations, then harden flows with durable execution.
Capabilities
Pydantic AI fits platform teams standardizing agent infrastructure, application engineers shipping user-facing assistants, and data/ML practitioners who require evaluable, typed outputs. It suits MLOps groups that need observability, governance, and cost controls, and backend teams orchestrating complex multi-agent workflows. Teams adopting typed Python stacks, or already using Pydantic in microservices, benefit from consistent schemas and easier integration. It’s also a strong option for startups seeking open-source foundations with clear upgrade paths to production systems.
- Validates and enforces structured outputs with Pydantic models across all model providers.
- Streams tokens, tool calls, and reasoning events to frontends in real time.
- Connects agents to external tools and data via the Model Context Protocol.
- Integrates with Logfire for traces, costs, latency analysis, and production debugging.
- Supports durable execution through Temporal, Prefect, DBOS, and Restate integrations.
Highlights
Who It’s For
Install with uv add pydantic-ai (or pip). Configure Pydantic Logfire for tracing and optionally enable instrumentation for automatic metrics. Define a Pydantic BaseModel for outputs, initialize an Agent with your chosen provider, and register typed tools using decorators. Run synchronously or asynchronously, stream events to your UI, and connect MCP servers for external resources. Add Pydantic Evals to create datasets and track performance, then integrate Temporal, Prefect, DBOS, or Restate if you need durable workflows. Documentation covers streaming, MCP, evaluations, and gateway-driven routing.
Type-safe agents with structured outputs, observability, and durable execution for production.
Getting Started
Pydantic AI unifies typed schemas, operational visibility, streaming, external tool access, and optional durability in a cohesive Python framework. It minimizes glue work while improving reliability and governance through Logfire and Gateway. With native evaluations and open-source licensing, it’s an efficient, pragmatic path from prototype agents to stable, scalable production systems.
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Pydantic
Pydantic is the team behind the widely adopted Python data validation library and the Pydantic Stack. They focus on type safety, developer productivity, and operational visibility, shipping production tooling like Logfire, AI Gateway, and Evals for reliable, scalable AI systems.
Pydantic AI is a type-safe Python framework for building production-grade agents and LLM applications. It enforces validated, structured outputs using Pydantic models, while remaining model-agnostic and compatible with your preferred providers. The framework integrates natively with Pydantic Logfire for deep observability—spanning traces, token costs, latency, and error debugging. Agents can call Python tools via typed decorators, stream reasoning and tool events to frontends, and connect to external systems through the Model Context Protocol. Optional durable execution patterns are available through integrations with Temporal, Prefect, DBOS, and Restate, allowing long-running and resilient workflows. Together with the Pydantic AI Gateway for routing and Pydantic Evals for testing, teams get a cohesive, open-source stack that scales from prototype to production.
pydantic.dev/pydantic-ai
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