Pydantic AI

For developers who appreciate the simplicity and power of FastAPI, building production-grade AI applications can often feel unnecessarily complex. PydanticAI is a Python agent framework designed to change that. Built by the team behind Pydantic, it brings the same ergonomic, type-safe, and intuitive development experience to the world of Generative AI, helping you create robust and reliable AI agents with familiar Python practices.

Key Features

PydanticAI provides a focused, powerful toolset for building AI agents you can trust in production environments.

• ✅ Reliable, Structured Outputs PydanticAI leverages Pydantic models to rigorously validate and structure the output from Large Language Models (LLMs). This means you can define the exact format you need—from simple data types to complex nested objects—and your agent will consistently deliver predictable, type-safe results.

• 🐍 Intuitive, Python-Centric Design You don't need to learn a new paradigm to build with PydanticAI. The framework is designed around standard Python control flow, functions, and async/await syntax. This allows you to apply the same best practices for testing, modularity, and code organization that you already use in your non-AI projects.

• ⚙️ Powerful Dependency Injection Seamlessly provide data, services, or configurations like database connections and user IDs to your agent's tools and prompts. This system, inspired by FastAPI, makes your agents highly modular, significantly easier to test, and simpler to integrate into existing applications.

• 🔍 Seamless Observability with Pydantic Logfire Understand exactly what your agent is doing with one-line integration into Pydantic Logfire. You can instantly trace the entire flow of an interaction—from the initial prompt to tool calls and final validation—making debugging, performance monitoring, and behavior analysis straightforward.

• 🌐 Broad LLM Compatibility PydanticAI is model-agnostic, offering built-in support for leading models from OpenAI, Anthropic, Google (Gemini), Mistral, Cohere, Groq, and more. A simple interface also allows you to easily implement support for other models, giving you the flexibility to choose the best LLM for your task.

How PydanticAI Solves Your Problems:

Here are a couple of practical scenarios where PydanticAI excels:

1. Building a Smart Customer Support Agent Imagine you need a support agent for a bank that can answer questions and take action. With PydanticAI, you can define a SupportOutput Pydantic model that requires the agent to return a specific structure: the advice given, a boolean for whether to block a card, and a numerical risk score. You can then create a customer_balance tool that securely accesses a database via dependency injection. When a user asks, "I lost my card!", the agent can call the necessary tools and returns a perfectly structured, validated response like {support_advice: '...', block_card: True, risk: 8} every time.

2. Developing and Debugging a Complex Workflow You've built an agent that uses multiple tools to answer a query, but it's returning an unexpected result. Instead of adding print statements everywhere, you enable the Pydantic Logfire integration. You immediately get a detailed, real-time trace in your browser showing the initial prompt, the LLM's reasoning for choosing a tool, the exact data passed to that tool, the tool's return value, and the final validated output. This clarity reduces debugging from hours to minutes.

Why Choose PydanticAI?

• Built by the Pydantic Team: PydanticAI is developed and maintained by the same team behind Pydantic, the ubiquitous data validation library for Python. This ensures deep expertise, first-class integration, and a commitment to the principles of type safety and developer ergonomics that millions of developers already rely on.

• Architected for Production, Not Just Prototypes: From its type-safe design and testable components to its built-in observability, every feature in PydanticAI is designed with the end goal of deploying real, maintainable applications. It provides the guardrails needed to move from a "cool demo" to a reliable service.

Conclusion:

PydanticAI bridges the gap between powerful Generative AI models and production-ready applications. By combining the validation power of Pydantic with an intuitive, Pythonic developer experience, it removes friction and introduces a new level of reliability to AI agent development. If you want to build robust, testable, and observable AI agents without the pain, PydanticAI is built for you.