PydanticAI — Type-Safe AI Agent Framework

Key Features

• Type safety — Full IDE auto-completion and static type checking for agents, tools, and outputs
• Model-agnostic — OpenAI, Anthropic, Gemini, DeepSeek, Grok, Ollama, and custom models
• Structured outputs — Define output schemas with Pydantic models, validated automatically
• Dependency injection — Clean separation of business logic and AI configuration
• MCP support — Native Model Context Protocol integration
• Streaming — Continuous structured output with immediate validation
• Human-in-the-loop — Flag tool calls for approval before execution
• Observability — Built-in Pydantic Logfire integration for debugging and cost tracking

Example: Bank Support Agent

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

@dataclass
class SupportDeps:
    customer_id: int
    db: DatabaseConn

class SupportOutput(BaseModel):
    support_advice: str = Field(description='Advice for the customer')
    block_card: bool = Field(description='Whether to block the card')
    risk: int = Field(description='Risk level 0-10', ge=0, le=10)

support_agent = Agent(
    'openai:gpt-4o',
    deps_type=SupportDeps,
    output_type=SupportOutput,
    instructions='You are a bank support agent.',
)

@support_agent.tool
async def customer_balance(
    ctx: RunContext[SupportDeps], include_pending: bool
) -> float:
    return await ctx.deps.db.customer_balance(
        id=ctx.deps.customer_id,
        include_pending=include_pending,
    )

With Thinking & Web Search

from pydantic_ai.capabilities import Thinking, WebSearch

agent = Agent(
    'anthropic:claude-sonnet-4-6',
    capabilities=[Thinking(), WebSearch()],
)
result = agent.run_sync('What was the largest meteorite found this year?')