Hey everyone! Like many of you, I've been increasingly fascinated by the world of AI agents. Recently, I discovered Pydantic AI, and as a long-time user and admirer of Pydantic for data validation in Python, I was immediately intrigued. Learning that the same brilliant team is behind this new framework specifically for building AI agents sparked my curiosity. This blog post is a recount of my initial exploration into Pydantic AI, and I'm excited to share just how surprisingly accessible and powerful it is to get started.

My aim here is to distill my learning experience into a digestible guide, hoping it will help those new to AI agents grasp the fundamental ease and potential of Pydantic AI. If you're looking to dip your toes into agent building, or if you're already familiar with Pydantic and want to extend your Python AI toolkit, read on!

Unpacking Pydantic AI: Why It's Worth Your Attention

So, what exactly is Pydantic AI, and why should you be paying attention? Simply put, Pydantic AI is a Python framework meticulously crafted to streamline and fortify the process of building AI agents. Imagine an AI agent as a smart software entity capable of interacting with its environment, making informed decisions, and executing actions – often powered by the sophisticated reasoning of large language models (LLMs) at its core.

What makes Pydantic AI stand out? Let's delve into its key strengths:

• Built Upon a Rock-Solid Foundation: Originating from the Pydantic team, Pydantic AI inherits the robust design principles and reliability that have made Pydantic a cornerstone for data validation in countless Python projects. This lineage means you're building on a trusted and well-engineered base, ensuring code quality and dependability from the outset.
• Unparalleled Model Flexibility: Pydantic AI boasts impressive model agnosticism, seamlessly supporting a vast ecosystem of LLMs. Whether you prefer the industry giants like OpenAI, Anthropic, and Google Gemini, or lean towards the flexibility of locally hosted models via Ollama, or even enterprise solutions like Azure OpenAI, Pydantic AI has you covered. This adaptability is crucial, allowing you to select the optimal model based on your specific project requirements, performance needs, and budgetary constraints. No more vendor lock-in – choose the best tool for the job, effortlessly.
• Type Safety at its Core: For developers who value clean, maintainable code (like myself!), Pydantic AI's emphasis on type safety is a major win. By leveraging Python's type hinting, Pydantic AI helps you catch potential errors during development, rather than at runtime. This proactive approach significantly improves code robustness, reduces debugging time, and makes your agent code far easier to understand and maintain over the long term.
• Embracing the Pythonic Way: Pydantic AI is intentionally designed to feel natural and intuitive for Python developers. It leverages familiar Python control flow, syntax, and best practices, avoiding the introduction of convoluted custom syntax or unnecessary complexities often found in other frameworks. This Python-centric approach significantly lowers the learning curve and allows you to apply your existing Python expertise directly to AI agent development.
• Powerful Dependency Injection System: While it might sound like a complex concept, Pydantic AI's optional dependency injection system is a game-changer for building modular, testable, and scalable agents. It allows you to cleanly inject data and services into your agent's system prompts, tools, and result validators. This is invaluable for tasks like unit testing, A/B testing different configurations, and fostering iterative, eval-driven development workflows. Dependency injection promotes loosely coupled components, making your agents more flexible and easier to evolve.

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My Hands-On Experience: Building Simple Pydantic AI Agents

The most effective way to truly grasp a new technology is to get your hands dirty and start building. So, I dove headfirst into Pydantic AI and began constructing some foundational agents. I was genuinely taken aback by how little code was needed to get functional agents up and running – it's truly designed for rapid prototyping and iteration.

Hello World, AI Agent Style: The 5-Line Wonder

My very first agent was, of course, a "Hello World" equivalent. Incredibly, I achieved a working agent in just five lines of Python! Here's the code that sparked my Pydantic AI journey:

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAiModel

model = OpenAiModel(model_name="gpt-4-0125-preview") # Harnessing the power of GPT-4
agent = Agent(model)
result = agent.run_sync("What is the capital of the United States?")

print(result.data)

This concise snippet perfectly illustrates the simplicity of Pydantic AI. We import the necessary Agent class and the OpenAiModel for OpenAI integration. We instantiate an OpenAiModel specifying the “gpt-4-0125-preview” model (you’ll need your OpenAI API key configured separately, of course). Then, we create an Agent instance, passing in our chosen model. Finally, the agent.run_sync() method sends the prompt “What is the capital of the United States?” to the LLM and retrieves the synchronous result. Printing result.data displays the agent’s response – likely “Washington, D.C.” – a truly rewarding “Hello World” moment!

Shifting Gears: Local Models with Ollama

One of Pydantic AI’s most compelling features is its model versatility. Keen to explore this, I decided to test running a model locally using Ollama. Ollama is a fantastic tool that simplifies running open-source LLMs directly on your own machine. Switching to Ollama in Pydantic AI was remarkably straightforward:

from pydantic_ai import Agent
from pydantic_ai.models.ollama import OllamaModel

model = OllamaModel(model_name="llama3:2b", base_url="http://localhost:11434/v1") # Utilizing Llama 3 2B locally
agent = Agent(model)
result = agent.run_sync("What is the capital of the United States?")

print(result.data)

The change is minimal! We simply swap the import to OllamaModel and instantiate it accordingly, specifying the model_name (“llama3:2b” in this case) and the base_url pointing to your running Ollama instance. The rest of the agent code remains identical. This seamless transition underscores Pydantic AI’s commitment to model agnosticism.

Azure OpenAI Integration: Enterprise-Grade Models

For those working in enterprise environments or leveraging Azure’s AI services, Pydantic AI provides excellent support for Azure OpenAI. Here’s how effortlessly I integrated Azure OpenAI:

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAiModel
from openai import AsyncAzureOpenAI
import os
from dotenv import load_dotenv

load_dotenv()

client = AsyncAzureOpenAI(
    api_key=os.getenv("AZURE_OPENAI_KEY"),
    api_version=os.getenv("AZURE_OPENAI_API_VERSION"),
    azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
)

model = OpenAiModel(client=client, model_name="gpt-4-turbo") # Leveraging GPT-4 Turbo on Azure
agent = Agent(model)
result = agent.run_sync("What is the capital of the United States?")

print(result.data)

Integrating Azure OpenAI involves a few more steps, primarily due to Azure’s authentication requirements. We import AsyncAzureOpenAI from the openai library and use dotenv to securely load Azure OpenAI credentials from environment variables (best practice!). We instantiate an AsyncAzureOpenAI client with your Azure API key, API version, and endpoint. Then, when creating the OpenAiModel, we pass this client object along with the model_name. Again, the core agent logic remains consistent, demonstrating Pydantic AI’s ability to abstract away the complexities of different model providers.

Multi-Agent Collaboration: Orchestrating Different LLMs

This is where Pydantic AI truly began to shine for me. I wanted to explore the potential of multi-agent systems, specifically seeing if I could make two agents, each powered by different LLMs, communicate and collaborate. My experiment involved having an OpenAI agent answer a question and then passing that answer to an Ollama agent for further refinement or processing:

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAiModel
from pydantic_ai.models.ollama import OllamaModel
import os
from dotenv import load_dotenv

load_dotenv()

# OpenAI Agent
openai_model = OpenAiModel(model_name="gpt-4-0125-preview", api_key=os.getenv("OPENAI_API_KEY"))
openai_agent = Agent(openai_model)
openai_result = openai_agent.run_sync("What is the capital of Mexico?")
print(f"OpenAI Agent: {openai_result.data}")

# Get the last message from the OpenAI agent
last_message = openai_result.messages[-1]

# Ollama Agent
ollama_model = OllamaModel(model_name="llama3:2b", base_url="http://localhost:11434/v1")
ollama_agent = Agent(ollama_model)
ollama_result = ollama_agent.run_sync(
    "Tell me about the history of this city.", message_history=[last_message]
)
print(f"Ollama Agent: {ollama_result.data}")

Here, we create two agents: openai_agent using OpenAiModel and ollama_agent using OllamaModel. The openai_agent answers “What is the capital of Mexico?”. Crucially, we extract the last_message from the openai_result.messages list. This message object contains the OpenAI agent’s response. Then, we create the ollama_agent and use agent.run_sync() again, but this time, we pass message_history=[last_message]. This instructs the ollama_agent to consider the previous conversation history (the OpenAI agent’s response) when answering the prompt “Tell me about the history of this city.”. This simple example showcases the powerful concept of agent delegation and communication within Pydantic AI.

Key Learnings and Next Steps on My Pydantic AI Journey

My initial foray into Pydantic AI has been overwhelmingly positive. I’m genuinely impressed by its user-friendliness, incredible flexibility in model selection, and the sheer power it unlocks even for basic agent configurations. My key takeaways so far:

• Extremely Low Barrier to Entry: Getting started with Pydantic AI is remarkably easy, even for individuals relatively new to AI agent development. The straightforward API and clear documentation make it quick to grasp the fundamentals and start building.
• True Model Agnosticism is a Game Changer: The framework’s ability to seamlessly switch between diverse LLMs – from commercial APIs to open-source local models – is a massive advantage. It empowers developers to choose the best model for each specific task and adapt to changing model landscapes without significant code rewrites.
• Multi-Agent Systems Become Tangible: The ease with which you can create and connect multiple agents opens up exciting possibilities for building more complex and sophisticated AI systems. Pydantic AI provides the building blocks for orchestrating intricate agent workflows and collaborative AI solutions.
• A Delightfully Pythonic Development Experience: Pydantic AI’s commitment to a Python-centric approach ensures a smooth and familiar development experience for Python programmers. It feels like a natural extension of the Python ecosystem, making it a joy to work with.

Conclusion: Embrace Pydantic AI and Start Building!

If you’re at all intrigued by the prospect of building AI agents, I wholeheartedly recommend giving Pydantic AI a try. It’s a potent, adaptable, and remarkably developer-friendly framework that makes the agent-building process surprisingly accessible. Whether you’re a seasoned AI expert or just beginning your journey, I believe Pydantic AI will prove to be an invaluable asset in your toolkit.