Pydantic AI

Python agent framework for building production-grade GenAI applications with the "FastAPI feeling".

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When to Use

• Building AI agents with structured output
• Need type-safe, IDE-friendly agent development
• Require dependency injection for tools
• Multi-model support (OpenAI, Anthropic, Gemini, etc.)
• Production observability with Logfire
• Complex workflows with graphs

Installation

See references/installation.md for full/slim install options and optional dependency groups. Requires Python 3.10+.

Release Highlights (1.63.0 → 1.65.0)

• Outputs: template=False on PromptedOutput / NativeOutput disables schema prompt injection (v1.64.0).
• Files: provider uploads are supported via the new UploadedFile object (v1.65.0).
• Google Gemini: added gemini-3.1-flash-lite-preview model support (v1.65.0).
• Stability: fixes for UI adapter run_id, Google streaming error wrapping, MCP race hardening, and parallel tools sibling-task cancellation.

Quick Start

Basic Agent

from pydantic_ai import Agent

agent = Agent(
    'openai:gpt-4o',
    instructions='Be concise, reply with one sentence.'
)

result = agent.run_sync('Where does "hello world" come from?')
print(result.output)

With Structured Output

from pydantic import BaseModel
from pydantic_ai import Agent

class CityInfo(BaseModel):
    name: str
    country: str
    population: int

agent = Agent('openai:gpt-4o', output_type=CityInfo)
result = agent.run_sync('Tell me about Paris')
print(result.output)  # CityInfo(name='Paris', country='France', population=2161000)

With Tools and Dependencies

from dataclasses import dataclass
from pydantic_ai import Agent, RunContext

@dataclass
class Deps:
    user_id: int

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

@agent.tool
async def get_user_name(ctx: RunContext[Deps]) -> str:
    """Get the current user's name."""
    return f"User #{ctx.deps.user_id}"

result = agent.run_sync('What is my name?', deps=Deps(user_id=123))

Key Features

Supported Models

Best Practices

1. Use type hints — enables IDE support and validation
2. Define output types — guarantees structured responses
3. Use dependencies — inject context into tools
4. Add tool docstrings — LLM uses them as descriptions
5. Enable Logfire — for production observability
6. Use run_sync for simple cases — run for async
7. Override deps for testing — agent.override(deps=...)
8. Set usage limits — prevent infinite loops with UsageLimits

Prohibitions

• Do not expose API keys in code
• Do not skip output validation in production
• Do not ignore tool errors
• Do not use run_stream without handling partial outputs
• Do not forget to close MCP connections (async with agent)

Common Patterns

Streaming Response

async with agent.run_stream('Query') as response:
    async for text in response.stream_text():
        print(text, end='')

Fallback Models

from pydantic_ai.models.fallback import FallbackModel

fallback = FallbackModel(openai_model, anthropic_model)
agent = Agent(fallback)

MCP Integration

from pydantic_ai.mcp import MCPServerStdio

server = MCPServerStdio('python', args=['mcp_server.py'])
agent = Agent('openai:gpt-4o', toolsets=[server])

Testing with TestModel

from pydantic_ai.models.test import TestModel

agent = Agent(model=TestModel())
result = agent.run_sync('test')  # Deterministic output

Embeddings

from pydantic_ai import Embedder

embedder = Embedder('openai:text-embedding-3-small')

# Embed search query
result = await embedder.embed_query('What is ML?')

# Embed documents for indexing
docs = ['Doc 1', 'Doc 2', 'Doc 3']
result = await embedder.embed_documents(docs)

See embeddings.md for providers and settings.

xAI Provider

from pydantic_ai import Agent

agent = Agent('xai:grok-4-1-fast-non-reasoning')

See models.md for configuration details.

Exa Neural Search

import os
from pydantic_ai import Agent
from pydantic_ai.common_tools.exa import ExaToolset

api_key = os.getenv('EXA_API_KEY')
toolset = ExaToolset(api_key, num_results=5, include_search=True)
agent = Agent('openai:gpt-4o', toolsets=[toolset])

See tools.md for all Exa tools.

Links

• Documentation
• Releases
• GitHub
• PyPI