About pydanticai-docs
pydanticai-docs is a documentation skill for developers working with the Pydantic AI Python agent framework. It explains core concepts and directs questions to focused reference files, with rules to avoid unsupported API details.
Key features
- 01Agent setup and execution
- 02Function tools and dependency injection
- 03Structured output with Pydantic
- 04Topic-specific reference routing
Use cases
- 01Looking up agent constructor and run concepts
- 02Checking tool registration and dependency patterns
- 03Finding guidance on structured outputs
- 04Routing MCP or provider questions to references
MCP support
The skill lists MCP server integration as an additional topic and directs users to `references/MCP.md` for details.
Install
Copy the command and run it in a terminal.
npx skills add https:/After installing, type/pydanticai-docsin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
references/AGENT.mdDEPENDENCIES.mdFUNCTION_TOOLS.mdMCP.mdMESSAGES.mdMODELS.mdOUTPUT.mdTOOLS_AND_TOOLSETS.mdDetails
From the project READMEPydantic 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.
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.
Agent Behavior Rules
- Default to this file — answer from core concepts first; load only the specific
references/<CONCEPT>.mdrelevant 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.