Pydantic AI Documentation Skill

What is Pydantic AI?

Pydantic AI is a production-grade Python agent framework for building type-safe, dependency-injected Generative AI applications. It supports multiple LLM providers, structured outputs via Pydantic models, and composable multi-agent patterns.

Doc: https://ai.pydantic.dev/index.md

Core Concepts

1. Agent Instantiation

from pydantic_ai import Agent

agent = Agent(
    'openai:gpt-4o',          # model string: provider:model-name
    system_prompt='Be helpful.',
)
result = agent.run_sync('What is the capital of France?')
print(result.output)

For full constructor parameters, run methods, and streaming: load references/AGENT.md.

2. Function Tools (`@agent.tool`)

from pydantic_ai import Agent, RunContext

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

@agent.tool
def get_user_name(ctx: RunContext[str]) -> str:
    """Return the current user's name."""
    return ctx.deps

result = agent.run_sync('What is my name?', deps='Alice')

Use @agent.tool_plain when you don't need RunContext. For tool registration, return types, and retries: load references/FUNCTION_TOOLS.md.

3. Dependency Injection (`RunContext`)

from dataclasses import dataclass
from pydantic_ai import Agent, RunContext

@dataclass
class MyDeps:
    api_key: str
    user_id: int

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

@agent.tool
async def fetch_data(ctx: RunContext[MyDeps]) -> str:
    return f'User {ctx.deps.user_id}'

For RunContext fields, injection into system prompts and output validators: load references/DEPENDENCIES.md.

4. Structured Output

from pydantic import BaseModel
from pydantic_ai import Agent

class CityInfo(BaseModel):
    city: str
    country: str

agent = Agent('openai:gpt-4o', output_type=CityInfo)
result = agent.run_sync('Where were the 2012 Olympics held?')
print(result.output)  # CityInfo(city='London', country='United Kingdom')

For union types, plain scalars, output_validator, and partial validation: load references/OUTPUT.md.

Additional Topics

For these topics, load the named reference file or follow the doc link — no implementation code is provided here.

Topic	Reference file	Doc link
Message history / multi-turn conversations	`references/MESSAGES.md`	https://ai.pydantic.dev/message-history/index.md
Model / provider setup (all providers)	`references/MODELS.md`	https://ai.pydantic.dev/models/overview/index.md
Toolsets (`FunctionToolset`, composition)	`references/TOOLS_AND_TOOLSETS.md`	https://ai.pydantic.dev/toolsets/index.md
MCP server integration	`references/MCP.md`	https://ai.pydantic.dev/mcp/client/index.md
Multi-agent applications	doc link only	https://ai.pydantic.dev/multi-agent-applications/index.md
Graphs (pydantic-graph)	doc link only	https://ai.pydantic.dev/graph/index.md
Evals (pydantic-evals)	doc link only	https://ai.pydantic.dev/evals/index.md
Durable execution	doc link only	https://ai.pydantic.dev/durable_execution/overview/index.md
Retries	doc link only	https://ai.pydantic.dev/retries/index.md
Testing (`TestModel`, `override`)	doc link only	https://ai.pydantic.dev/testing/index.md
Logfire integration	doc link only	https://ai.pydantic.dev/logfire/index.md
Builtin tools	doc link only	https://ai.pydantic.dev/builtin-tools/index.md
Streaming	doc link only	https://ai.pydantic.dev/agent/index.md

Agent Behavior Rules

Default to this file — answer from core concepts first; load only the specific references/<CONCEPT>.md relevant to the user's question when more depth is needed.
Never fabricate API details — always end with "For details, see: <URL>" using a link from the official index above.
No implementation code for non-core topics — return a doc link only for topics listed in the Additional Topics table.
Prefer specificity — route to the most specific page (e.g., models/anthropic/index.md) when the user's question targets a specific provider, not the overview.
Out of scope — do not debug user code passively, do not generate full production agent implementations, do not answer questions unrelated to the Pydantic AI ecosystem.

pydanticai-docs