PydanticAI

PydanticAI is a Python framework for building LLM applications and agent-style workflows with typed, validated outputs, tool calling, and model integrations-designed to support correctness and reliability rather than “prompt-only” usage.

Python FrameworkType-safe OutputsOpen-source (MIT)

Overview

What is PydanticAI?

PydanticAI (branded as “Pydantic AI”) is a Python framework for building applications that call LLMs and return validated, structured results instead of untyped text.

At a high level, you define an Agent, connect it to a model provider via documented model adapters, and add tools (functions) plus optional dependencies to control how your app runs and what data it can access.

It’s commonly used to implement reliable “agent-style” flows (tool calls + structured outputs + validation) for production LLM apps-especially where correctness and schemas matter.

Key Features

Agents as a core primitive: Build around the Agent concept and configure how the agent runs against a model provider
Function tools: Define callable tools that the model can invoke as part of an agent run
Dependency injection: Provide run-time dependencies (e.g., clients/config/state) into tools/agents in a structured way
Structured outputs + validation: Support for structured output data, output modes, validation context, and output validators
Streaming support: Streaming support for responses, including structured output patterns
Observability integration with Logfire: Pydantic AI documents integration with Pydantic Logfire for observability (separate product)

Common Use Cases

•Schema-first AI agent workflows where the output must conform to a Pydantic model (e.g., routing, extraction, classification)
•Tool-using assistants that call functions/APIs with dependency injection for safe access to services
•Production LLM apps that need streaming plus validation and observability (via Logfire)

Who It's For

PydanticAI is best for Python developers and teams that want code-first control and type-checked, validated outputs for LLM features-especially when reliability, schemas, and testable behavior matter more than a no-code builder.

How It Works

Main Approach (Python framework)

You build in Python using Pydantic AI’s core concepts (Agents, tools, dependencies, output handling). It’s a framework/library workflow-not a visual automation UI.

Type-safety and Pydantic foundation

Structured output is a first-class part of the docs (structured output data, validators, and custom JSON schema options), letting you validate model outputs rather than trusting free-form text.

Model integrations

Pydantic AI’s docs include a dedicated Models section, indicating it’s designed to work with model providers via documented integrations (rather than being tied to a single vendor).

Key Differentiators

✓Schema/validation-first approach to outputs (structured output + validators) rather than “prompt-only” parsing
✓Explicit dependency injection to keep tools and execution safer/cleaner
✓Tool calling as a documented core concept (Function Tools)
✓Clear separation between framework and observability product (Logfire is separate)

Pricing & Plans

The PydanticAI framework is open-source (MIT). Pricing applies to Pydantic Logfire (a separate observability product); see the official pricing page for current tiers and limits.

View Logfire pricing

Getting Started

Read the official PydanticAI documentation

Quick Start

1Read the “Installation” and “Agents” docs and install Pydantic AI in your Python project
2Create an Agent and choose a model provider from the Models docs
3Add Function Tools and (optionally) Dependencies, then define your output schema/validators

Get Started with PydanticAI

When to Use PydanticAI

PydanticAI vs LangChain

LangChain is a broad, code-first LLM framework ecosystem, while PydanticAI is explicitly centered on typed/validated outputs and a tighter set of core primitives (agents, tools, dependencies, structured outputs).

Learn more about LangChain

PydanticAI vs CrewAI

CrewAI emphasizes “crews” (multi-agent orchestration patterns), whereas PydanticAI’s documentation emphasizes output schemas/validation, tool calling, and dependency injection as core building blocks for agent-style apps.

Learn more about CrewAI

Related Resources

Back to AI Agent Builders

Related Tools

LangChain CrewAI

Last updated: December 2025

Python Agent Framework