PydanticAI

PydanticAI

Python framework for building production-ready AI agents with type safety

⭐ 4.5
free open-source development
#python #ai-agents #type-safety #framework #pydantic #open-source

Overview

PydanticAI is a Python framework specifically designed for building production-ready AI agents with a focus on type safety, structured outputs, and developer experience. Built by the creators of Pydantic, it leverages Python’s type system to provide robust, reliable, and maintainable AI applications with built-in validation and error handling.

Key Features

Type Safety

  • Static Type Checking: Full mypy and PyRight compatibility
  • Runtime Validation: Automatic validation of inputs and outputs
  • Structured Responses: Type-safe model responses with Pydantic models
  • IDE Support: Excellent autocomplete and error detection

Agent Architecture

  • Modular Design: Compose agents from reusable components
  • Dependency Injection: Clean separation of concerns and testability
  • State Management: Persistent and ephemeral state handling
  • Tool Integration: Easy integration of external tools and APIs

Multi-Model Support

  • Model Agnostic: Works with OpenAI, Anthropic, Google, and other providers
  • Unified Interface: Consistent API across different model providers
  • Model Switching: Easy switching between models for different tasks
  • Fallback Support: Automatic fallback to alternative models

Developer Experience

  • Intuitive API: Pythonic and easy-to-understand interface
  • Rich Debugging: Detailed logging and debugging capabilities
  • Testing Tools: Built-in testing utilities for AI agents
  • Documentation: Comprehensive guides and examples

Use Cases

  • Business Automation: Build AI agents for workflow automation
  • Customer Support: Create intelligent support assistants
  • Data Processing: AI-powered data analysis and transformation
  • Content Generation: Structured content creation workflows
  • API Services: Type-safe AI-powered API endpoints
  • Research Tools: Academic and research AI applications

Core Components

Agent Definition

from pydantic_ai import Agent
from pydantic import BaseModel

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

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

Tool Integration

  • Function Tools: Python functions as agent tools
  • External APIs: Integration with web services and databases
  • Custom Tools: Build specialized tools for specific domains
  • Tool Validation: Type-safe tool inputs and outputs

State Management

  • Agent State: Persistent state across conversations
  • Context Handling: Manage conversation context and memory
  • Session Management: Handle multiple concurrent sessions
  • State Persistence: Save and restore agent state

Structured Outputs

  • Pydantic Models: Define response schemas with validation
  • Automatic Parsing: Parse LLM responses into typed objects
  • Error Handling: Graceful handling of invalid responses
  • Retry Logic: Automatic retry on validation failures

Framework Architecture

Core Principles

  • Type Safety First: Everything is typed and validated
  • Production Ready: Built for real-world applications
  • Model Agnostic: Work with any LLM provider
  • Developer Friendly: Prioritize developer experience

Key Classes

  • Agent: Main class for defining AI agents
  • RunContext: Context object for agent runs
  • Tool: Base class for agent tools
  • ModelAdapter: Interface for different LLM providers

Integration Patterns

  • FastAPI Integration: Build AI-powered REST APIs
  • Async Support: Full async/await support for scalability
  • Streaming: Support for streaming responses
  • Batch Processing: Handle multiple requests efficiently

Getting Started

  1. Install PydanticAI: pip install pydantic-ai
  2. Set API Keys: Configure your LLM provider credentials
  3. Define Agent: Create your first type-safe agent
  4. Add Tools: Implement tools for your specific use case
  5. Run and Test: Execute and debug your agent

Advanced Features

Validation and Error Handling

  • Input Validation: Automatic validation of user inputs
  • Output Validation: Ensure LLM responses match expected schema
  • Custom Validators: Build domain-specific validation rules
  • Error Recovery: Graceful handling of validation failures

Testing and Debugging

  • Test Utilities: Built-in tools for testing AI agents
  • Mock Support: Mock LLM responses for deterministic testing
  • Debugging Tools: Detailed logging and tracing capabilities
  • Performance Monitoring: Track agent performance and costs

Production Features

  • Rate Limiting: Built-in rate limiting for LLM providers
  • Caching: Response caching for improved performance
  • Monitoring: Integration with observability tools
  • Error Tracking: Comprehensive error logging and reporting

Community and Ecosystem

  • Open Source: MIT license with active community
  • Documentation: Comprehensive guides and API reference
  • Examples: Real-world examples and tutorials
  • Support: Community support through GitHub and Discord

Integration Examples

FastAPI Integration

from fastapi import FastAPI
from pydantic_ai import Agent

app = FastAPI()
agent = Agent('openai:gpt-4')

@app.post('/chat')
async def chat(message: str):
    result = await agent.run(message)
    return result.data

Custom Tools

from pydantic_ai import Agent, Tool

@tool
def get_weather(city: str) -> str:
    # Implementation here
    return f"Weather in {city}"

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

PydanticAI represents the next generation of AI development frameworks, bringing the reliability and type safety that Python developers expect to the rapidly evolving world of AI applications.

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