Pydantic Ai
AI Agent Framework, the Pydantic way
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What is Pydantic Ai?
Pydantic AI is a Python agent framework designed to help you quickly, confidently, and painlessly build production-grade applications and workflows with Generative AI. Built by the team behind Pydantic Validation (used by OpenAI SDK, Anthropic SDK, LangChain, LlamaIndex, and many more), it brings the FastAPI feeling to GenAI app and agent development. The framework is model-agnostic, supporting virtually every model provider including OpenAI, Anthropic, Gemini, DeepSeek, Grok, Cohere, Mistral, Perplexity, Azure AI Foundry, Amazon Bedrock, Google Cloud, Ollama, LiteLLM, Groq, and many more.
Key features include full type-safety for moving errors from runtime to write-time, seamless observability through Pydantic Logfire integration for debugging and cost tracking, powerful evals for systematically testing agent performance, and extensible design with composable capabilities. It supports MCP (Model Context Protocol), A2A (Agent2Agent), human-in-the-loop tool approval, durable execution for handling API failures, streamed outputs with immediate validation, and graph support for complex applications. The framework also includes dependency injection for testing and eval-driven development.
Pydantic AI is ideal for Python developers building production-grade AI agents who value type safety, structured outputs, and clean ergonomic APIs. It targets engineers who want to avoid the pitfalls of messy agent code and need reliable validation of LLM outputs. The framework is open source under MIT license and is particularly suited for teams already using Pydantic in their stack.
Pydantic Ai pricing
Pricing model: Freemium
Pydantic AI is completely free and open source under MIT license. There is no separate paid plan for Pydantic AI itself. Pydantic Logfire, the observability platform that integrates with Pydantic AI, has separate pricing: Personal plan is free with 10M logs/spans/metrics, Team plan is $49/mo with 5 seats included, Growth plan is $249/mo with unlimited seats, and Enterprise plans are custom-priced with SSO, custom retention, and SLAs. Logfire's free tier includes all features and is sufficient for personal projects and proof of concept.
Pydantic Ai pros
- Built by the Pydantic team with strong community trust
- Model-agnostic with support for 20+ providers
- Fully type-safe, moving errors from runtime to write-time
- Seamless Pydantic Logfire integration for observability
- Structured outputs guaranteed through Pydantic validation
- Open source under MIT license
- Powerful evals for systematic agent testing
- Human-in-the-loop tool approval for production safety
- Durable execution handles API failures and restarts
- Streamed outputs with immediate validation
- Graph support prevents spaghetti code in complex apps
- Dependency injection system for testing
- Native MCP and A2A protocol support
- Python-centric design leveraging familiar control flow
- Composable capabilities bundle tools and hooks
Pydantic Ai cons
- Python-only framework, no JavaScript/TypeScript support
- Very new framework still in early development
- Smaller ecosystem compared to mature frameworks like LangChain
- Documentation still developing
- Not yet production-proven at scale
- Tool calling can be hard for smaller models
- JSON schema uses more tokens leading to higher costs
- Prompt engineering experience needs improvement
Frequently asked questions about Pydantic Ai
What is Pydantic AI?
Pydantic AI is a Python agent framework designed to help you quickly, confidently, and painlessly build production-grade applications and workflows with Generative AI. It brings the FastAPI feeling to GenAI app development with type safety, structured outputs, and validation built on Pydantic.
Which LLM providers does Pydantic AI support?
Pydantic AI supports OpenAI, Anthropic, Gemini, DeepSeek, Grok, Cohere, Mistral, Perplexity, Azure AI Foundry, Amazon Bedrock, Google Cloud, Ollama, LiteLLM, Groq, OpenRouter, Together AI, Fireworks AI, Cerebras, Hugging Face, GitHub, Heroku, Vercel, Nebius, OVHcloud, Alibaba Cloud, and SambaNova. Custom model support is also available.
Is Pydantic AI free?
Yes, Pydantic AI is completely free and open source under the MIT license. The framework itself has no paid plans. Pydantic Logfire, the observability product that integrates with it, has its own separate pricing tiers.
How does Pydantic AI differ from LangChain?
Pydantic AI focuses on type safety and validation, while LangChain emphasizes breadth of integrations. Pydantic AI is more opinionated about correctness and provides stronger guarantees through Pydantic validation. It's designed for developers who want the FastAPI experience in GenAI.
What is the dependency injection system used for?
The dependency injection system provides a type-safe way to pass data, connections, and logic into agents. It's especially useful for customizing agent behavior, running unit tests, and eval-driven iterative development. Dependencies are carried via RunContext.
Does Pydantic AI support streamed outputs?
Yes, Pydantic AI provides the ability to stream structured output continuously with immediate validation, ensuring real-time access to generated data while maintaining type safety.
What is human-in-the-loop tool approval?
Human-in-the-loop tool approval lets you flag that certain tool calls require approval before they can proceed. This can depend on tool call arguments, conversation history, or user preferences, adding production safety without workflow complexity.
What is durable execution in Pydantic AI?
Durable execution enables building durable agents that preserve progress across transient API failures, application errors, or restarts. It handles long-running, asynchronous, and human-in-the-loop workflows with production-grade reliability.
Does Pydantic AI support graphs?
Yes, Pydantic AI includes Pydantic Graph support, providing a powerful way to define graphs using type hints. This is useful in complex applications where standard control flow can degrade to spaghetti code.
Do I need to know Pydantic to use Pydantic AI?
Basic Pydantic knowledge is helpful but not required. The framework includes good documentation and examples for getting started. Since Pydantic AI is built by the Pydantic team, it leverages Pydantic Validation for structured outputs and tool argument validation.