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Introduction:
For months, I treated Pydantic as nothing more than a glorified JSON schema validator — a way to force LLM outputs into a predictable shape and move on. Then I discovered that the Pydantic team had built an entire agent framework on top of their own validation layer, and everything changed. Pydantic AI is not just another entry in the crowded agent framework landscape; it is a type-safe, model-agnostic Python framework designed to bring the same ergonomic, “just works” feeling to GenAI development that FastAPI brought to web APIs.
Learning Objectives:
- Understand the architectural distinction between using Pydantic for output validation versus building full agentic workflows with Pydantic AI
- Learn how to define type-safe agents with structured inputs, outputs, and dependency injection
- Master tool calling patterns that keep business logic deterministic while letting LLMs handle narration and reasoning
- Pydantic vs. Pydantic AI: Two Sides of the Same Coin
Pydantic is a Python library for data validation. You define what your data should look like using Python classes, and Pydantic ensures any data you receive — from an API, a form, or an LLM — actually matches that shape, converting types where it can and raising clear errors where it can’t. If you tell Pydantic a field called `age` should be an integer and you pass in the string "25", Pydantic converts it to `25` for you. Pass in "twenty-five", and it throws a validation error immediately.
Pydantic AI takes that same idea and applies it to building actual AI agents — not just cleaning up their outputs afterward. It gives you a proper `Agent` class where you attach a model (OpenAI, Anthropic, Groq, Gemini, and dozens more), a system prompt, instructions, tools the model can call, and a required output schema — all with the same type safety Pydantic is known for.
The bigger unlock: agents built this way can call your own Python functions as tools mid-conversation, with the same type safety Pydantic is known for — turning an LLM from something that just talks into something that can actually act.
2. Installing and Setting Up Pydantic AI
Getting started takes seconds. Pydantic AI requires Python 3.9 or higher. Install it using your preferred package manager:
Using uv (recommended) uv add pydantic-ai Using pip pip install pydantic-ai For observability features pip install pydantic-ai[bash]
Pydantic AI V2.0 reached stable release on June 23, 2026, after seven betas. The V2 release introduces a harness-first design with capabilities as a core primitive — a single composable unit that bundles an agent’s tools, hooks, instructions, and model settings.
- Building Your First Agent: The Number Guessing Game
Let’s examine a practical implementation: a number-guessing game where an LLM hosts the game and reacts playfully, but never decides the outcome itself — a Python tool function handles all the actual logic.
The architecture is elegant in its separation of concerns:
1. A secret number between 1 and 100 is randomly chosen and stored in a `GameState` object
2. The player types guesses at a prompt
- Each guess is sent to an LLM agent, which calls a `check_guess` tool with the number
- The tool — not the model — decides whether the guess is correct, too high, too low, or out of attempts
- The model replies with a short, playful message based on what the tool told it
Here’s how you’d structure the core agent:
from pydantic_ai import Agent from pydantic import BaseModel from typing import Optional class GameState(BaseModel): secret: int attempts: int max_attempts: int = 7 game_over: bool = False class GuessResult(BaseModel): correct: bool message: str attempts_remaining: int Define the tool function — this is where the real logic lives def check_guess(state: GameState, guess: int) -> GuessResult: if state.game_over: return GuessResult( correct=False, message="Game already over!", attempts_remaining=0 ) state.attempts += 1 if guess == state.secret: state.game_over = True return GuessResult( correct=True, message="You got it! Amazing!", attempts_remaining=state.max_attempts - state.attempts ) elif guess > state.secret: return GuessResult( correct=False, message="Too high! Try again.", attempts_remaining=state.max_attempts - state.attempts ) else: return GuessResult( correct=False, message="Too low! Keep going.", attempts_remaining=state.max_attempts - state.attempts )
The key insight: the LLM never knows the secret number. It only receives the result of the tool call and narrates accordingly. This pattern — LLM as narrator, Python as referee — is a powerful blueprint for building reliable AI applications where accuracy matters.
- Tools and Function Calling: The Heart of Actionable Agents
Pydantic AI inspects function signatures to build tool schemas automatically; docstrings become tool descriptions. Tools can return any Pydantic-serializable JSON content.
To register a tool with your agent:
from pydantic_ai import Agent, RunContext
agent = Agent(
model='openai:gpt-4o',
instructions='You are a helpful assistant with access to real data.'
)
@agent.tool
async def get_order_status(ctx: RunContext, order_id: str) -> dict:
"""Look up the current status of an order by its ID."""
Real database or API call here
return {"order_id": order_id, "status": "shipped", "eta": "2026-07-30"}
When the LLM decides it needs order information, it calls `get_order_status` with the appropriate arguments. The agent executes the Python function, gets back real data, and only then replies — so the answer is grounded in your actual system, not the model’s guess.
For advanced use cases, Pydantic AI supports deferred tools, human-in-the-loop patterns, and durable execution. Tools can optionally define a `prepare` function that’s called at every step to customize what gets passed to the model.
5. Dependency Injection: Keeping Agents Clean and Testable
One of Pydantic AI’s standout features is its lightweight dependency injection system. You can pass runtime services — HTTP clients, database connections, user preferences — into prompts, tools, and validators:
from dataclasses import dataclass @dataclass class Dependencies: db_connection: str user_id: str api_key: str agent = Agent( model='anthropic:claude-3-opus', deps_type=Dependencies, instructions='Use the provided dependencies to access user data.' ) @agent.tool async def get_user_profile(ctx: RunContext[bash]) -> dict: ctx.deps gives you typed access to all dependencies return await fetch_profile(ctx.deps.db_connection, ctx.deps.user_id)
This pattern makes agents testable, maintainable, and composable — exactly what you’d expect from a framework built by the Pydantic team.
6. Observability and Production Readiness
Pydantic AI integrates tightly with Pydantic Logfire, an OpenTelemetry-based observability platform for real-time debugging, performance monitoring, and cost tracking. If you already have an OTel-compatible backend, you can use that instead.
The framework also ships with Pydantic Evals for code-first evaluation of agent performance, and an optional AI Gateway for unified access to multiple providers with real-time cost controls.
For teams coming from LangChain, the contrast is stark. Companies like MindsDB and Overjoy have migrated from LangChain to Pydantic AI, citing cleaner codebases, elimination of gigabytes of dependencies, and dramatically simpler debugging.
7. Commands and Troubleshooting
Linux/macOS:
Set up a virtual environment python3 -m venv venv source venv/bin/activate Install with all extras pip install "pydantic-ai[logfire,evals]" Run your agent script python your_agent.py
Windows (PowerShell):
Set up a virtual environment python -m venv venv .\venv\Scripts\Activate.ps1 Install with all extras pip install "pydantic-ai[logfire,evals]" Run your agent script python your_agent.py
Environment variables:
.env file example OPENAI_API_KEY=your_key_here ANTHROPIC_API_KEY=your_key_here GROQ_API_KEY=your_key_here LOGFLIRE_TOKEN=your_token_here optional
Common issues:
- Type errors at runtime: Pydantic AI moves many errors from runtime to write-time. Run `mypy` on your code to catch them early.
- Tool not being called: Ensure your tool function has proper type hints and a docstring — Pydantic AI uses these to build the schema.
- Async/sync mismatches: Use `async` consistently if your tools do I/O. Pydantic AI supports both synchronous and asynchronous execution.
What Undercode Say:
- Pydantic is the validation layer powering virtually every major Python AI framework — from OpenAI SDK to LangChain to LlamaIndex. Using Pydantic AI means going straight to the source instead of using a derivative.
- The LLM-as-1arrator pattern is a production-grade revelation — keeping business logic deterministic in Python while letting the model handle conversation and personality dramatically reduces hallucination risk.
Analysis: The shift from treating Pydantic as a mere validator to using it as the foundation for full agent orchestration represents a maturation in the AI engineering space. We’re moving beyond prompt engineering as the primary discipline toward structured, type-safe, observable agent architectures. The V2.0 release’s capability-based design suggests the framework is evolving toward composability at scale — think FastAPI routers for agents. For teams building production AI applications, the choice between Pydantic AI and frameworks like LangChain increasingly comes down to a preference for simplicity and type safety versus ecosystem breadth.
Prediction:
- +1 Pydantic AI is positioned to become the default agent framework for Python shops already using FastAPI and Pydantic — the learning curve is shallow, and the ergonomics are familiar.
- +1 The capability/harness model introduced in V2.0 will spawn a rich ecosystem of reusable agent components, similar to how FastAPI plugins emerged.
- -1 Teams heavily invested in LangChain’s chain-of-thought and graph abstractions may find the transition non-trivial, though the migration stories from Overjoy and MindsDB suggest the benefits are worth the effort.
- +1 As more organizations adopt OpenTelemetry for observability, Pydantic AI’s native OTel integration becomes a strategic advantage over frameworks that require custom instrumentation.
- +1 The framework’s model-agnostic design means it will continue to thrive regardless of which LLM provider dominates — a hedge against vendor lock-in that enterprise architects will appreciate.
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