PydanticAI is a Python agent framework developed to streamline the creation of production-grade applications utilizing Generative AI. Built by the team behind Pydantic, it aims to bring the ergonomic design and developer-friendly experience of FastAPI to AI application development.
PydanticAI is a Python agent framework designed to make it less painful to build production grade applications with Generative AI.
PydanticAI is a Python Agent Framework designed to make it less painful to build production grade applications with Generative AI.
FastAPI revolutionized web development by offering an innovative and ergonomic design, built on the foundation of Pydantic.
Similarly, virtually every agent framework and LLM library in Python uses Pydantic, yet when we began to use LLMs in Pydantic Logfire, we couldn’t find anything that gave us the same feeling.
We built PydanticAI with one simple aim: to bring that FastAPI feeling to GenAI app development.
Why use PydanticAI
- Built by the Pydantic Team: Built by the team behind Pydantic (the validation layer of the OpenAI SDK, the Anthropic SDK, LangChain, LlamaIndex, AutoGPT, Transformers, CrewAI, Instructor and many more).
- Model-agnostic: Supports OpenAI, Anthropic, Gemini, Deepseek, Ollama, Groq, Cohere, and Mistral, and there is a simple interface to implement support for other models.
- Pydantic Logfire Integration: Seamlessly integrates with Pydantic Logfire for real-time debugging, performance monitoring, and behavior tracking of your LLM-powered applications.
- Type-safe: Designed to make type checking as powerful and informative as possible for you.
- Python-centric Design: Leverages Python’s familiar control flow and agent composition to build your AI-driven projects, making it easy to apply standard Python best practices you’d use in any other (non-AI) project.
- Structured Responses: Harnesses the power of Pydantic to validate and structure model outputs, ensuring responses are consistent across runs.
- Dependency Injection System: Offers an optional dependency injection system to provide data and services to your agent’s system prompts, tools and result validators. This is useful for testing and eval-driven iterative development.
- Streamed Responses: Provides the ability to stream LLM outputs continuously, with immediate validation, ensuring rapid and accurate results.
- Graph Support: Pydantic Graph provides a powerful way to define graphs using typing hints, this is useful in complex applications where standard control flow can degrade to spaghetti code.
Key Features:
Model-Agnostic Support: PydanticAI is compatible with various AI models, including OpenAI, Anthropic, Gemini, Deepseek, Ollama, Groq, Cohere, and Mistral. It also provides a straightforward interface to integrate additional models as needed.
Type Safety: The framework is designed to enhance type checking, ensuring that developers receive informative feedback during development.
Python-Centric Design: PydanticAI leverages Python’s familiar control flow and agent composition, allowing developers to apply standard Python best practices in their AI-driven projects.
Structured Responses: By utilizing Pydantic’s validation capabilities, the framework ensures that model outputs are consistent and reliable across runs.
Dependency Injection System: PydanticAI offers an optional dependency injection system to provide data and services to agents’ system prompts, tools, and result validators, facilitating testing and iterative development.
Streamed Responses: The framework supports streaming LLM outputs continuously, with immediate validation, ensuring rapid and accurate responses.
Example Use Cases:
Bank Support Agent: PydanticAI can be used to build a support agent for a bank, capable of providing customer assistance, assessing risk levels, and performing actions like blocking a customer’s card if necessary. The agent can utilize tools and dependency injection to access customer data and interact with banking systems.
Weather Information Agent: Developers can create an agent that provides weather updates by integrating with external APIs. The agent can process user queries, fetch relevant weather data, and deliver structured responses to users.
SQL Query Generation: PydanticAI can assist in generating SQL queries based on natural language inputs. This is particularly useful for users who need to interact with databases but may not be proficient in SQL syntax.
Flight Booking Assistant: An agent can be developed to assist users in booking flights by understanding user preferences, searching for available flights, and providing booking options. The agent can handle complex workflows and integrate with flight booking APIs.
Retrieval-Augmented Generation (RAG): PydanticAI can be utilized in RAG pipelines to enhance the generation of responses by retrieving relevant information from external sources, ensuring that the AI outputs are both accurate and contextually relevant.
By leveraging PydanticAI, developers can build robust, type-safe, and efficient AI agents tailored to a wide range of applications, benefiting from the framework’s integration with Pydantic’s validation system and its model-agnostic design.