PydanticAI — Typed AI Agents in Python

Overview

PydanticAI is a Python agent framework from the Pydantic team that brings the same type-safety and validation guarantees as Pydantic to LLM-based applications. It supports structured outputs (validated with Pydantic models), dependency injection for testability, streamed responses, multi-turn conversations, and tool use — across OpenAI, Anthropic, Google Gemini, Groq, Mistral, and Ollama. Use this skill when building production AI agents, chatbots, or LLM pipelines where correctness and testability matter.

When to Use This Skill

• Use when building Python AI agents that call tools and return structured data
• Use when you need validated, typed LLM outputs (not raw strings)
• Use when you want to write unit tests for agent logic without hitting a real LLM
• Use when switching between LLM providers without rewriting agent code
• Use when the user asks about Agent, @agent.tool, RunContext, ModelRetry, or result_type

How It Works

Step 1: Installation

bash
pip install pydantic-ai

# Install extras for specific providers
pip install 'pydantic-ai[openai]'       # OpenAI / Azure OpenAI
pip install 'pydantic-ai[anthropic]'    # Anthropic Claude
pip install 'pydantic-ai[gemini]'       # Google Gemini
pip install 'pydantic-ai[groq]'         # Groq
pip install 'pydantic-ai[vertexai]'     # Google Vertex AI

Step 2: A Minimal Agent

python
from pydantic_ai import Agent

# Simple agent — returns a plain string
agent = Agent(
    'anthropic:claude-sonnet-4-6',
    system_prompt='You are a helpful assistant. Be concise.',
)

result = agent.run_sync('What is the capital of Japan?')
print(result.data)  # "Tokyo"
print(result.usage())  # Usage(requests=1, request_tokens=..., response_tokens=...)

Step 3: Structured Output with Pydantic Models

python
from pydantic import BaseModel
from pydantic_ai import Agent

class MovieReview(BaseModel):
    title: str
    year: int
    rating: float  # 0.0 to 10.0
    summary: str
    recommended: bool

agent = Agent(
    'openai:gpt-4o',
    result_type=MovieReview,
    system_prompt='You are a film critic. Return structured reviews.',
)

result = agent.run_sync('Review Inception (2010)')
review = result.data  # Fully typed MovieReview instance
print(f"{review.title} ({review.year}): {review.rating}/10")
print(f"Recommended: {review.recommended}")

Step 4: Tool Use

Register tools with @agent.tool — the LLM can call them during a run:

python
from pydantic_ai import Agent, RunContext
from pydantic import BaseModel
import httpx

class WeatherReport(BaseModel):
    city: str
    temperature_c: float
    condition: str

weather_agent = Agent(
    'anthropic:claude-sonnet-4-6',
    result_type=WeatherReport,
    system_prompt='Get current weather for the requested city.',
)

@weather_agent.tool
async def get_temperature(ctx: RunContext, city: str) -> dict:
    """Fetch the current temperature for a city from the weather API."""
    async with httpx.AsyncClient() as client:
        r = await client.get(f'https://wttr.in/{city}?format=j1')
        data = r.json()
        return {
            'temp_c': float(data['current_condition'][0]['temp_C']),
            'description': data['current_condition'][0]['weatherDesc'][0]['value'],
        }

import asyncio
result = asyncio.run(weather_agent.run('What is the weather in Tokyo?'))
print(result.data)

Step 5: Dependency Injection

Inject services (database, HTTP clients, config) into agents for testability:

python
from dataclasses import dataclass
from pydantic_ai import Agent, RunContext
from pydantic import BaseModel

@dataclass
class Deps:
    db: Database
    user_id: str

class SupportResponse(BaseModel):
    message: str
    escalate: bool

support_agent = Agent(
    'openai:gpt-4o-mini',
    deps_type=Deps,
    result_type=SupportResponse,
    system_prompt='You are a support agent. Use the tools to help customers.',
)

@support_agent.tool
async def get_order_history(ctx: RunContext[Deps]) -> list[dict]:
    """Fetch recent orders for the current user."""
    return await ctx.deps.db.get_orders(ctx.deps.user_id, limit=5)

@support_agent.tool
async def create_refund(ctx: RunContext[Deps], order_id: str, reason: str) -> dict:
    """Initiate a refund for a specific order."""
    return await ctx.deps.db.create_refund(order_id, reason, ctx.deps.user_id)

# Usage
async def handle_support(user_id: str, message: str):
    deps = Deps(db=get_db(), user_id=user_id)
    result = await support_agent.run(message, deps=deps)
    return result.data

Step 6: Testing with TestModel

Write unit tests without real LLM calls:

python
from pydantic_ai.models.test import TestModel

def test_support_agent_escalates():
    with support_agent.override(model=TestModel()):
        # TestModel returns a minimal valid response matching result_type
        result = support_agent.run_sync(
            'I want to cancel my account',
            deps=Deps(db=FakeDb(), user_id='user-123'),
        )
    # Test the structure, not the LLM's exact words
    assert isinstance(result.data, SupportResponse)
    assert isinstance(result.data.escalate, bool)

FunctionModel for deterministic test responses:

python
from pydantic_ai.models.function import FunctionModel, ModelContext

def my_model(messages, info):
    return ModelResponse(parts=[TextPart('Always this response')])

with agent.override(model=FunctionModel(my_model)):
    result = agent.run_sync('anything')

Step 7: Streaming Responses

python
import asyncio
from pydantic_ai import Agent

agent = Agent('anthropic:claude-sonnet-4-6')

async def stream_response():
    async with agent.run_stream('Write a haiku about Python') as result:
        async for chunk in result.stream_text():
            print(chunk, end='', flush=True)
    print()  # newline
    print(f"Total tokens: {result.usage()}")

asyncio.run(stream_response())

Step 8: Multi-Turn Conversations

python
from pydantic_ai import Agent
from pydantic_ai.messages import ModelMessagesTypeAdapter

agent = Agent('openai:gpt-4o', system_prompt='You are a helpful assistant.')

# First turn
result1 = agent.run_sync('My name is Alice.')
history = result1.all_messages()

# Second turn — passes conversation history
result2 = agent.run_sync('What is my name?', message_history=history)
print(result2.data)  # "Your name is Alice."

Examples

Example 1: Code Review Agent

python
from pydantic import BaseModel, Field
from pydantic_ai import Agent
from typing import Literal

class CodeReview(BaseModel):
    quality: Literal['excellent', 'good', 'needs_work', 'poor']
    issues: list[str] = Field(default_factory=list)
    suggestions: list[str] = Field(default_factory=list)
    approved: bool

code_review_agent = Agent(
    'anthropic:claude-sonnet-4-6',
    result_type=CodeReview,
    system_prompt="""
    You are a senior engineer performing code review.
    Evaluate code quality, identify issues, and provide actionable suggestions.
    Set approved=True only for good or excellent quality code with no security issues.
    """,
)

def review_code(diff: str) -> CodeReview:
    result = code_review_agent.run_sync(f"Review this code:\n\n{diff}")
    return result.data

Example 2: Agent with Retry Logic

python
from pydantic_ai import Agent, ModelRetry
from pydantic import BaseModel, field_validator

class StrictJson(BaseModel):
    value: int

    @field_validator('value')
    def must_be_positive(cls, v):
        if v <= 0:
            raise ValueError('value must be positive')
        return v

agent = Agent('openai:gpt-4o-mini', result_type=StrictJson)

@agent.result_validator
async def validate_result(ctx, result: StrictJson) -> StrictJson:
    if result.value > 1000:
        raise ModelRetry('Value must be under 1000. Try again with a smaller number.')
    return result

Example 3: Multi-Agent Pipeline

python
from pydantic_ai import Agent
from pydantic import BaseModel

class ResearchSummary(BaseModel):
    key_points: list[str]
    conclusion: str

class BlogPost(BaseModel):
    title: str
    body: str
    meta_description: str

researcher = Agent('openai:gpt-4o', result_type=ResearchSummary)
writer = Agent('anthropic:claude-sonnet-4-6', result_type=BlogPost)

async def research_and_write(topic: str) -> BlogPost:
    # Stage 1: research
    research = await researcher.run(f'Research the topic: {topic}')

    # Stage 2: write based on research
    post = await writer.run(
        f'Write a blog post about: {topic}\n\nResearch:\n' +
        '\n'.join(f'- {p}' for p in research.data.key_points) +
        f'\n\nConclusion: {research.data.conclusion}'
    )
    return post.data

Best Practices

• ✅ Always define result_type with a Pydantic model — avoid returning raw strings in production
• ✅ Use deps_type with a dataclass for dependency injection — makes agents testable
• ✅ Use TestModel in unit tests — never hit a real LLM in CI
• ✅ Add @agent.result_validator for business-logic checks beyond Pydantic validation
• ✅ Use run_stream for long outputs in user-facing applications to show progressive results
• ❌ Don't put secrets (API keys) in Agent() arguments — use environment variables
• ❌ Don't share a single Agent instance across async tasks if deps differ — create per-request instances or use agent.run() with per-call deps
• ❌ Don't catch ValidationError broadly — let PydanticAI retry with ModelRetry for recoverable LLM output errors

Security & Safety Notes

• Set API keys via environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.) — never hardcode them.
• Validate all tool inputs before passing to external systems — use Pydantic models or manual checks.
• Tools that mutate data (write to DB, send emails, call payment APIs) should require explicit user confirmation before the agent invokes them in production.
• Log result.all_messages() for audit trails when agents perform consequential actions.
• Set retries= limits on Agent() to prevent runaway loops on persistent validation failures.

Common Pitfalls

• Problem: ValidationError on every LLM response — structured output never validates

Solution: Simplify result_type fields. Use Optional and default where appropriate. The model may struggle with overly strict schemas.

• Problem: Tool is never called by the LLM

Solution: Write a clear, specific docstring for the tool function — PydanticAI sends the docstring as the tool description to the LLM.

• Problem: RunContext dependency is None inside a tool

Solution: Pass deps= when calling agent.run() or agent.run_sync(). Dependencies are not set globally.

• Problem: asyncio.run() error when calling agent.run() inside FastAPI

Solution: Use await agent.run() directly in async FastAPI route handlers — don't wrap in asyncio.run().

Related Skills

• @langchain-architecture — Alternative Python AI framework (more flexible, less type-safe)
• @llm-application-dev-ai-assistant — General LLM application development patterns
• @fastapi-templates — Serving PydanticAI agents via FastAPI endpoints
• @agent-orchestration-multi-agent-optimize — Orchestrating multiple PydanticAI agents

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.