PydanticAI

What is PydanticAI?

PydanticAI is an open-source Python agent framework for building production-grade applications and workflows powered by large language models. It applies Pydantic's data validation and type hints to AI agent development, giving backend teams the same kind of structured, predictable development experience that FastAPI brought to web APIs. The framework is model-agnostic, supporting providers including OpenAI, Anthropic, Google Gemini, Groq, Mistral, Cohere, and others. It is maintained by the Pydantic team and available at github.com/pydantic/pydantic-ai.

Key Features

• Agents as Primary Interface: The Agent class handles all LLM interactions, supporting single-agent workflows and multi-agent patterns including delegation, hand-offs, graph-based control flow, and planning-based "deep agents."
• Function Tools: Models can call custom Python functions for actions or information retrieval, with automatic JSON schema generation derived from type hints and docstrings.
• Capabilities System: A modular extension point that combines tools, lifecycle hooks, instructions, and model settings into reusable units. Built-in capabilities include WebSearch, WebFetch, ImageGeneration, and MCP (Model Context Protocol) integration.
• Multi-Provider Model Support: Connects to providers via vendor-agnostic model classes, with a FallbackModel option for automatic failover when a provider call fails.
• MCP and A2A Protocols: Supports Model Context Protocol for tool server integration and Agent2Agent (A2A) for interoperability between separate agents.
• Human-in-the-Loop: Tool approval workflows let developers gate tool execution based on arguments, call history, or user preferences.
• Pydantic Logfire Integration: Optional observability layer for monitoring, tracing, and evaluating agent runs in production.
• Testing Utilities: Includes TestModel and FunctionModel classes specifically for unit testing agent behavior without live API calls.

Use Cases

• Python teams already using Pydantic: Teams extend their existing data validation stack into LLM application development, maintaining consistent type-safe patterns across the codebase.
• Backend engineers building production agents: Developers who need guaranteed output schemas use PydanticAI's type system to enforce structured responses from LLMs, reducing downstream data handling errors.
• Organizations working with multiple LLM providers: Teams that need to switch between or fall back across providers use the model-agnostic design to avoid tying application logic to any single vendor.
• Developers building compliance-sensitive workflows: Projects requiring observability and evaluation integrate Pydantic Logfire for tracing agent runs and auditing decisions.
• Teams building multi-agent systems: Developers use the multi-agent patterns and A2A protocol support to coordinate agents that delegate tasks, share state, or run in parallel.

Strengths and Weaknesses

Strengths:

• Full type safety through Pydantic validation, catching data errors at the framework level before they reach application logic.
• Vendor-agnostic design with support for a wide range of LLM providers, reducing dependency on any single API.
• Built-in testing utilities (TestModel, FunctionModel) that allow agent logic to be tested without live API calls.
• Modular capabilities system makes it simple to add memory, guardrails, or custom tools without rewriting core agent logic.
• Part of a broader Pydantic ecosystem that includes observability (Logfire) and evaluation tooling.

Weaknesses:

• Incorrect RunContext usage in tool decorators is a frequently reported source of bugs, particularly around dependency injection mismatches.
• Type hint errors, such as mismatched RunContext generics, can produce confusing mypy failures that are not always easy to diagnose.
• Missing or misconfigured API keys raise UserError at runtime; the framework requires either environment variables or explicit api_key arguments to be set correctly.
• Async/sync mismatches cause runtime failures when run_sync() is called inside environments that already have a running event loop, requiring workarounds like nest_asyncio.

Getting Started

PydanticAI is free and open-source. Install it with:

pip install pydantic_ai

Python 3.10 or higher is required. A "slim" install is available for projects that only need support for specific model providers. The full documentation, including quickstart examples and API reference, is at ai.pydantic.dev. The source code and issue tracker are hosted at github.com/pydantic/pydantic-ai. Pydantic Logfire, the optional observability companion, has its own pricing (free personal tier available; paid team plans start at $49/month).

FAQ

What is PydanticAI?

PydanticAI is an open-source Python agent framework for building production-grade applications powered by large language models. It applies Pydantic's data validation and type hints to AI agent development and is maintained by the Pydantic team.

Who created PydanticAI and where can I find it?

PydanticAI is maintained by the Pydantic team and is available at github.com/pydantic/pydantic-ai.

Does PydanticAI use Pydantic?

Yes. PydanticAI applies Pydantic's data validation and type hints directly to AI agent development so developers to enforce structured, typed responses from LLMs.

How do you define an agent in PydanticAI?

The primary interface is the Agent class, which handles all LLM interactions. It supports single-agent workflows and multi-agent patterns including delegation, hand-offs, graph-based control flow, and planning-based deep agents.

What LLM providers does PydanticAI support?

PydanticAI is model-agnostic and supports providers including OpenAI, Anthropic, Google Gemini, Groq, Mistral, Cohere, and others. A FallbackModel option enables automatic failover when a provider call fails.

What is the difference between PydanticAI and LangSmith?

PydanticAI is an agent framework for building LLM-powered applications, while LangSmith is an observability and evaluation platform. PydanticAI has its own optional observability layer through Pydantic Logfire integration for monitoring and tracing agent runs.

How does PydanticAI handle structured output from LLMs?

PydanticAI uses its type system to enforce structured response schemas from LLMs, which reduces downstream data handling errors. Output schemas are derived from type hints and applied automatically during agent runs.

What are Function Tools in PydanticAI?

Function Tools allow LLMs to call custom Python functions for actions or information retrieval. JSON schemas for these tools are generated automatically from type hints and docstrings.

What built-in capabilities does PydanticAI provide?

The built-in capabilities include WebSearch, WebFetch, ImageGeneration, and MCP (Model Context Protocol) integration. Capabilities combine tools, lifecycle hooks, instructions, and model settings into reusable units.

Does PydanticAI support multi-agent workflows?

Yes. PydanticAI supports multi-agent patterns including delegation, hand-offs, graph-based control flow, and the Agent2Agent (A2A) protocol for interoperability between separate agents.

How do you test PydanticAI agents without making live API calls?

PydanticAI includes TestModel and FunctionModel classes specifically for unit testing agent behavior without calling live LLM APIs.

Does PydanticAI support human-in-the-loop workflows?

Yes. PydanticAI includes tool approval workflows that let developers gate tool execution based on arguments, call history, or user preferences.

What observability options does PydanticAI offer?

PydanticAI integrates with Pydantic Logfire as an optional observability layer for monitoring, tracing, and evaluating agent runs in production.