Not to be overshadowed by the many AI announcements from AWS re:Invent this week, Pydantic, the team behind the leading open source Python programming language data validation library, launched PydanticAI, a new agent framework designed to simplify the development of production-grade applications powered by large language models (LLMs).
Currently in beta, PydanticAI brings type safety, modularity, and validation into the hands of developers aiming to create scalable, LLM-driven workflows. As with Pydantic’s primary code, it is open sourced under an MIT License, meaning it can be used for commercial applications and enterprise use cases, which is likely to make it appealing to many businesses — many of them already use Pydantic, anyway.
Already, in the days since PydanticAI launched on December 2, the initial response from developers and those in the machine learning/AI community online has been largely positive, from what I’ve seen.
For example, Dean “@codevore1” wrote on X that PydanticAI looked “promising!” despite being in beta.
Alex Volkov, founder and CEO of video translation service Targum, posted on X a question: “A sort of LangChain competitor?”
Financial economist and quant Raja Patnaik also took to X to state the “new PydanticAI agent framework looks great. Seems to be hybrid between @jxnlco ’s instructor and @OpenAI ’s swarm.“
Agents as containers
At the heart of PydanticAI is its agent-based architecture. Each agent acts as a container for managing interactions with LLMs, defining system prompts, tools, and structured outputs.
The agents allow developers to streamline application logic by composing workflows directly in Python, enabling a mix of static instructions and dynamic inputs to drive interactions.
The framework is designed to accommodate both simple and complex use cases, from single-agent systems to multi-agent applications that can communicate and share state.
Samuel Colvin, creator of Pydantic which originally launched in 2017, earlier alluded to such developments, writing on the Pydantic website: “With Pydantic’s growth, we are now building other products with the same principles — that the most powerful tools can still be easy to use.”
Key features of PydanticAI Agents
PydanticAI agents provide a structured, flexible way to interact with LLMs:
• Model-Agnostic: Agents can work with LLMs like OpenAI, Gemini, and Groq, with Anthropic support planned. Extending compatibility to additional models is made straightforward with a simple interface.
• Dynamic System Prompts: Agents can combine static and runtime-generated instructions, allowing tailored interactions based on application context.
• Structured Responses: Each agent enforces validation of LLM outputs using Pydantic models, ensuring type-safe and predictable responses.
• Tools and Functions: Agents can call functions or retrieve data as needed during a run, facilitating retrieval-augmented generation and real-time decision-making.
• Dependency Injection: A novel dependency injection system supports modular workflows, simplifying integration with databases or external APIs.
• Streamed Responses: Agents handle streamed outputs with validation, making them ideal for use cases requiring continuous feedback or large outputs.
Practical enterprise use cases
The agent framework enables developers to build diverse applications with minimal overhead. For example:
• Customer Support Agents: A bank support agent can use PydanticAI to access customer data dynamically, offer tailored advice, and assess risk levels for security concerns. Dependency injection makes connecting the agent to live data sources seamless.
• Interactive Games: Developers can use agents to power interactive experiences, such as dice games or quizzes, where responses are generated dynamically based on user input and predefined logic.
• Workflow Automation: Multi-agent systems can be deployed for complex automation tasks, with agents handling distinct roles and collaborating to complete tasks.
Designed for devs
PydanticAI emphasizes developer ergonomics and Python-native workflows:
• Vanilla Python Control: Unlike other frameworks, PydanticAI doesn’t impose a new abstraction layer for workflows. Developers can rely on Python best practices while maintaining full control over the logic.
• Type Safety: Built on Pydantic, the framework ensures type correctness and validation at every step, reducing errors and improving reliability.
• Logfire Integration: Built-in monitoring and debugging tools allow developers to track agent performance and fine-tune behavior efficiently.
As an early beta release, PydanticAI’s API is subject to change, but it already shows strong potential for reshaping how developers build LLM-driven systems. The Pydantic team is actively seeking feedback from the developer community to refine the framework further.
PydanticAI reflects the team’s expansion into AI-powered solutions, building on the success of the Pydantic library. By focusing on agents as the core abstraction, the framework offers a powerful yet approachable way to create reliable, scalable applications with LLMs.
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