Pydantic AI Complete Guide 2026: Type-Safe AI Agent Development Framework

In 2026's AI Agent development ecosystem, Pydantic AI stands out with its unique type-safe philosophy and clean API design. As the latest work from the Pydantic team, it perfectly combines the rigor of data validation with the flexibility of LLMs, becoming the preferred framework for building production-grade AI applications.

Why Choose Pydantic AI?

Core Advantages

Type Safety as First-Class Citizen. Unlike frameworks like LangGraph and CrewAI, Pydantic AI is built from the ground up on Pydantic's type system. This means:

✅ Automatic validation of function parameters and return values
✅ Structured LLM output, avoiding parsing errors
✅ IDE autocomplete and type checking
✅ Significantly reduced runtime errors

Clean API Design. No need to learn complex state machines or graph theory concepts, build powerful Agents with pure Python code.

Native Streaming Support. Built-in SSE and streaming responses, easily implement typewriter effects.

Multi-Model Compatible. Supports OpenAI, Anthropic, Google, Ollama, and any OpenAI API-compatible models.

Quick Start

Installation

pip install pydantic-ai

Basic Example: First Agent

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel

# Initialize model
model = OpenAIModel('gpt-4o')

# Create Agent
agent = Agent(model)

# Run conversation
result = agent.run_sync('Explain quantum entanglement in high school level language')
print(result.data)

Structured Output

from pydantic import BaseModel
from pydantic_ai import Agent

class WeatherResponse(BaseModel):
    city: str
    temperature: float
    condition: str
    humidity: int

# Agent with structured output
agent = Agent(
    'openai:gpt-4o',
    result_type=WeatherResponse
)

result = agent.run_sync('What is the weather in London?')
print(result.data)
# WeatherResponse(city='London', temperature=18.5, condition='Cloudy', humidity=72)

Core Concepts

1. Agent

The fundamental building block:

from pydantic_ai import Agent

# Simple agent
agent = Agent('openai:gpt-4o')

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

# Agent with custom configuration
agent = Agent(
    'openai:gpt-4o',
    system_prompt='You are an expert.',
    retries=3,
    timeout=30.0
)

2. Tools

Extend agent capabilities:

from pydantic_ai import Agent, Tool

# Define tool
@Tool
def get_weather(city: str) -> str:
    """Get current weather for a city."""
    return f"Sunny, 25°C in {city}"

# Agent with tools
agent = Agent(
    'openai:gpt-4o',
    tools=[get_weather]
)

result = agent.run_sync('What is the weather in Paris?')

3. Dependencies

Inject dependencies into tools:

from dataclasses import dataclass
from pydantic_ai import Agent, Tool, RunContext

@dataclass
class Config:
    api_key: str
    database_url: str

@Tool
async def search_db(ctx: RunContext[Config], query: str) -> str:
    """Search database with authentication."""
    # Use ctx.deps.api_key
    # Use ctx.deps.database_url
    return "Search results"

config = Config(api_key="secret", database_url="postgresql://...")
agent = Agent(
    'openai:gpt-4o',
    tools=[search_db],
    deps_type=Config
)

result = agent.run_sync('Find users', deps=config)

4. Message History

Maintain conversation context:

from pydantic_ai import Agent

agent = Agent('openai:gpt-4o')

# First message
result1 = agent.run_sync('My name is Alice')

# Continue conversation
result2 = agent.run_sync('What is my name?', message_history=result1.new_messages())
print(result2.data)  # "Your name is Alice"

Advanced Features

Streaming Responses

from pydantic_ai import Agent

agent = Agent('openai:gpt-4o')

# Async streaming
async def stream_response():
    async with agent.run_stream('Write a poem') as response:
        async for chunk in response.stream_text():
            print(chunk, end='', flush=True)

# Sync streaming
with agent.run_stream('Write a story') as response:
    for chunk in response.stream_text():
        print(chunk, end='', flush=True)

Result Validation

from pydantic import BaseModel, field_validator

class ProductReview(BaseModel):
    rating: int
    title: str
    content: str
    pros: list[str]
    cons: list[str]

    @field_validator('rating')
    @classmethod
    def validate_rating(cls, v):
        if not 1 <= v <= 5:
            raise ValueError('Rating must be between 1 and 5')
        return v

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

result = agent.run_sync('Review the iPhone 16')
print(result.data)

Multi-Step Workflows

from pydantic_ai import Agent

# Define specialized agents
researcher = Agent('openai:gpt-4o', system_prompt='Research topics thoroughly')
writer = Agent('openai:gpt-4o', system_prompt='Write engaging content')
editor = Agent('openai:gpt-4o', system_prompt='Edit and improve content')

# Chain agents
def create_content(topic: str) -> str:
    # Step 1: Research
    research = researcher.run_sync(f'Research {topic}')

    # Step 2: Write
    draft = writer.run_sync(f'Write article based on: {research.data}')

    # Step 3: Edit
    final = editor.run_sync(f'Edit this: {draft.data}')

    return final.data

article = create_content('AI trends in 2026')

Error Handling

from pydantic_ai import Agent, ModelRetry

agent = Agent('openai:gpt-4o')

@agent.system_prompt
def add_retry_instructions() -> str:
    return 'If you encounter an error, explain what went wrong and suggest a fix.'

try:
    result = agent.run_sync('Complex task')
except ModelRetry as e:
    print(f'Retrying due to: {e}')
    result = agent.run_sync('Simpler version of task')

Use Cases

1. Data Extraction

from pydantic import BaseModel
from pydantic_ai import Agent

class ContactInfo(BaseModel):
    name: str
    email: str
    phone: str
    company: str

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

text = """
John Smith
Email: john@example.com
Phone: +1-555-0123
Works at Acme Corp
"""

result = agent.run_sync(f'Extract contact info: {text}')
contact = result.data
print(contact.email)  # john@example.com

2. Classification

from enum import Enum
from pydantic_ai import Agent

class TicketCategory(str, Enum):
    TECHNICAL = "technical"
    BILLING = "billing"
    GENERAL = "general"
    URGENT = "urgent"

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

result = agent.run_sync('My app keeps crashing!')
print(result.data)  # TicketCategory.TECHNICAL

3. Code Generation

from pydantic import BaseModel
from pydantic_ai import Agent

class CodeSolution(BaseModel):
    language: str
    code: str
    explanation: str
    tests: str

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

result = agent.run_sync('Create a function to sort a list')
print(result.data.code)

Best Practices

1. Define Clear Types

# ✅ Good: Specific types
class User(BaseModel):
    id: int
    name: str
    email: EmailStr
    created_at: datetime

# ❌ Bad: Vague types
class User(BaseModel):
    id: Any
    name: Any
    data: dict

2. Use Descriptive Prompts

# ✅ Good
agent = Agent(
    'openai:gpt-4o',
    system_prompt='''You are an expert Python developer.
    Write clean, well-documented code following PEP 8.
    Include type hints and docstrings.'''
)

# ❌ Bad
agent = Agent('openai:gpt-4o')

3. Validate Outputs

class Response(BaseModel):
    answer: str
    confidence: float

    @field_validator('confidence')
    @classmethod
    def validate_confidence(cls, v):
        assert 0 <= v <= 1, 'Confidence must be between 0 and 1'
        return v

4. Handle Errors Gracefully

from pydantic_ai import ModelHTTPError

try:
    result = agent.run_sync('Task')
except ModelHTTPError as e:
    if e.status_code == 429:
        print('Rate limited, retrying...')
        time.sleep(60)
        result = agent.run_sync('Task')
    else:
        raise

Troubleshooting

Issue 1: Validation Errors

Solution:

# Check type definitions
class Result(BaseModel):
    value: int  # Ensure LLM returns int, not string

# Add validators
@field_validator('value')
@classmethod
def parse_int(cls, v):
    return int(v)

Issue 2: Slow Performance

Solution:

# Use smaller model
agent = Agent('openai:gpt-3.5-turbo')

# Reduce max tokens
agent = Agent('openai:gpt-4o', model_settings={'max_tokens': 500})

# Enable caching
agent = Agent('openai:gpt-4o', cache=True)

Issue 3: Inconsistent Output

Solution:

# Set temperature
agent = Agent('openai:gpt-4o', model_settings={'temperature': 0.0})

# Provide examples
agent = Agent(
    'openai:gpt-4o',
    examples=[
        {'input': 'example1', 'output': 'expected1'},
        {'input': 'example2', 'output': 'expected2'}
    ]
)

Resources

Official Website: https://ai.pydantic.dev
GitHub: https://github.com/pydantic/pydantic-ai
Documentation: https://ai.pydantic.dev/docs
PyPI: https://pypi.org/project/pydantic-ai
Examples: https://github.com/pydantic/pydantic-ai/tree/main/examples

Conclusion

Pydantic AI is a modern, type-safe framework for building AI Agents in 2026. With its clean API, structured outputs, and excellent developer experience, it's an ideal choice for Python developers building production AI applications.

Key Takeaways:

✅ Type-safe by design
✅ Structured outputs with Pydantic
✅ Clean, Pythonic API
✅ Native streaming support
✅ Multi-model compatible

Who Should Use Pydantic AI?

Python developers wanting type safety
Teams building production AI applications
Anyone familiar with Pydantic

Start building type-safe AI agents with Pydantic AI today!