Pydantic AI
Agent framework with type-safe, model-agnostic design built by the creators of Pydantic
15K+
GitHub Stars
10+
LLM Providers
100%
Type-Safe
Overview
Pydantic AI is a Python agent framework built by the creators of Pydantic, the most widely-used data validation library in Python. It brings the same philosophy of type safety and developer experience to AI agent development. Unlike LangChain's configuration-heavy approach, Pydantic AI uses plain Python with full IDE support, type hints, and Pydantic models for structured outputs. It's model-agnostic, supporting OpenAI, Anthropic, Google Gemini, Groq, Mistral, Ollama, and more through a unified interface.
The Verdict
Who Should Use Pydantic AI?
Best For
- Python devs who value type safety
- Teams already using Pydantic
- Production-grade agent systems
- Multi-model/provider flexibility
- Developers wanting IDE autocomplete
Not Ideal For
- Non-Python developers
- Beginners needing visual builders
- LangChain ecosystem integrations
- No-code/low-code preferences
What's Great
- Full type safety with IDE autocomplete
- Built on battle-tested Pydantic validation
- Model-agnostic: swap providers easily
- Pythonic API, not YAML/config heavy
- Structured outputs via Pydantic models
- Built-in dependency injection
- Streaming support out of the box
- Logfire integration for observability
Watch Out For
- Python-only (no JS/TS support)
- Newer than LangChain, smaller ecosystem
- Fewer pre-built integrations
- Less documentation/tutorials available
- Requires Pydantic knowledge to maximize
Pricing
Open Source
Free
MIT License, full features
Logfire (Observability)
Freemium
Optional tracing & monitoring
View all features & details
Core Features
- Type-safe agent definitions
- Structured outputs with Pydantic
- Tool/function calling
- Dependency injection system
- Streaming responses
- Async-first design
- Result validation
- Retry with exponential backoff
Supported Models
- OpenAI (GPT-4, GPT-4o, o1)
- Anthropic (Claude 3.5, Claude 4)
- Google (Gemini Pro, Gemini Ultra)
- Groq (Llama, Mixtral)
- Mistral AI
- Ollama (local models)
- Azure OpenAI
- Amazon Bedrock
Developer Experience
- Full IDE autocomplete
- Type checking with mypy/pyright
- No YAML configuration
- Plain Python code
- Pytest integration
- Logfire observability
Installation
- pip install pydantic-ai
- Python 3.9+
- MIT License
- Active development
How It Compares
| Feature | Pydantic AI | LangChain | LlamaIndex | CrewAI |
|---|---|---|---|---|
| Type Safety | Full, native | Partial | Partial | Limited |
| Model Agnostic | Yes, unified API | Yes | Yes | Yes |
| Structured Output | Pydantic models | JSON schemas | Pydantic | Basic |
| IDE Support | Full autocomplete | Limited | Limited | Limited |
| Learning Curve | Moderate | Steep | Moderate | Easy |
| Ecosystem Size | Growing | Largest | Large | Medium |
| Config Style | Pure Python | YAML/chains | Python | Python |
| Best For | Type-safe production | Complex pipelines | RAG/search | Multi-agent |
Code Example
from pydantic import BaseModel
from pydantic_ai import Agent
class CityInfo(BaseModel):
name: str
country: str
population: int
agent = Agent(
'openai:gpt-4o',
result_type=CityInfo,
system_prompt='Extract city information from user queries.'
)
result = await agent.run('Tell me about Paris')
print(result.data) # CityInfo(name='Paris', country='France', population=2161000)
# Full type safety: result.data.name has autocomplete!
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