PydanticAI Tutorial

PydanticAI is a Python agent framework developed by the creators of Pydantic, aiming to simplify the development of production-grade applications utilizing Generative AI. Inspired by FastAPI's ergonomic design, PydanticAI seeks to bring a similar experience to AI application development.

Key Features of PydanticAI:

• Model-Agnostic Support: PydanticAI is compatible with various language models, including OpenAI, Anthropic, Gemini, Deepseek, Ollama, Groq, Cohere, and Mistral. It also offers a straightforward interface to integrate additional models.

• Seamless Integration with Pydantic Logfire: The framework integrates effortlessly with Pydantic Logfire, facilitating real-time debugging, performance monitoring, and behavior tracking of AI-driven applications.

• Type Safety: Designed with a focus on type safety, PydanticAI ensures robust type checking, enhancing code reliability and maintainability.

• Python-Centric Design: Leveraging Python's familiar control flow and agent composition, PydanticAI enables developers to apply standard Python best practices in AI projects.

• Structured Responses: Utilizing Pydantic's validation capabilities, PydanticAI ensures consistent and structured model outputs across runs.

• Dependency Injection System: The framework offers an optional dependency injection system, providing data and services to system prompts, tools, and result validators, which is beneficial for testing and iterative development.

• Streamed Responses: PydanticAI supports streaming of LLM outputs with immediate validation, ensuring rapid and accurate results.

• Graph Support: With Pydantic Graph, developers can define graphs using typing hints, aiding in managing complex applications and preventing convoluted control flows.

Getting Started with PydanticAI:

To begin using PydanticAI, install it via pip:

pip install pydantic-ai

Here's a minimal example demonstrating PydanticAI's usage:

from pydantic_ai import Agent
agent = Agent(
    'google-gla:gemini-1.5-flash',
    system_prompt='Be concise, reply with one sentence.',
)
result = agent.run_sync('Where does "hello world" come from?')
print(result.data)

Remember to export  GEMINI_API_KEY in your environment before running the above code.

This script initializes an agent using the Gemini 1.5 Flash model with a concise system prompt. It then synchronously queries the agent, printing the response.

Advanced Usage with Tools and Dependency Injection:

PydanticAI allows for advanced configurations, such as integrating tools and utilizing dependency injection. For example, building a support agent for a bank can involve defining dependencies, result structures, and tools that the agent can use to interact with external systems like databases.

PydanticAI streamlines the process of developing AI applications by providing a structured, type-safe, and Pythonic framework. Its model-agnostic approach, seamless integration capabilities, and focus on best practices make it a valuable tool for developers aiming to build robust AI solutions.

PydanticAI vs. LangChain

PydanticAI and LangChain serve similar purposes but with different design philosophies, so PydanticAI is unlikely to completely replace LangChain. However, it could become a strong alternative for certain use cases, especially for developers who prefer Pythonic, type-safe, and structured AI development.

Comparison: PydanticAI vs. LangChain

Feature	PydanticAI	LangChain
Design Approach	Pythonic, type-safe, and structured	Modular, extensible, and highly flexible
Ease of Use	Simpler, inspired by FastAPI	More complex with a steeper learning curve
Model Agnostic	Yes (supports OpenAI, Gemini, Cohere, etc.)	Yes (supports multiple LLMs)
Tooling	Supports dependency injection, streaming responses, and graph-based workflows	Extensive integrations with memory, chains, and external tools
Best Use Case	Developers who want a lightweight and type-safe AI framework	Users building complex multi-agent workflows
Community & Adoption	Newer, but backed by the Pydantic team	Mature, widely adopted, and used in production

Will PydanticAI Replace LangChain?

• For simple and structured AI apps? 🚀 Yes, PydanticAI could replace LangChain because it offers a cleaner, more Pythonic way to build AI applications.
• For complex agent-based workflows? 🤔 Not yet—LangChain still has better support for memory, chaining, and external tool integrations.

If you already use FastAPI and Pydantic, PydanticAI might feel more natural and integrated into your workflow. But if you're working on multi-step reasoning, autonomous agents, or heavy tool integration, LangChain remains a strong choice.

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