pydantic-ai-agent-creation
Create PydanticAI agents with type-safe dependencies, structured outputs, and proper configuration. Use when building AI agents, creating chat systems, or integrating LLMs with Pydantic validation.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/existential-birds/beagle --skill pydantic-ai-agent-creation- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads pydantic-ai-agent-creation automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Creating PydanticAI Agents
Quick Start
from pydantic_ai import Agent
# Minimal agent (text output)
agent = Agent('openai:gpt-4o')
result = agent.run_sync('Hello!')
print(result.output) # str
Model Selection
Model strings follow provider:model-name format:
# OpenAI
agent = Agent('openai:gpt-4o')
agent = Agent('openai:gpt-4o-mini')
# Anthropic
agent = Agent('anthropic:claude-sonnet-4-5')
agent = Agent('anthropic:claude-haiku-4-5')
# Google
agent = Agent('google-gla:gemini-2.0-flash')
agent = Agent('google-vertex:gemini-2.0-flash')
# Others: groq:, mistral:, cohere:, bedrock:, etc.
Structured Outputs
Use Pydantic models for validated, typed responses:
from pydantic import BaseModel
from pydantic_ai import Agent
class CityInfo(BaseModel):
city: str
country: str
population: int
agent = Agent('openai:gpt-4o', output_type=CityInfo)
result = agent.run_sync('Tell me about Paris')
print(result.output.city) # "Paris"
print(result.output.population) # int, validated
Agent Configuration
from pydantic_ai import Agent
from pydantic_ai.settings import ModelSettings
agent = Agent(
'openai:gpt-4o',
output_type=MyOutput, # Structured output type
deps_type=MyDeps, # Dependency injection type
instructions='You are helpful.', # Static instructions
retries=2, # Retry attempts for validation
name='my-agent', # For logging/tracing
model_settings=ModelSettings( # Provider settings
temperature=0.7,
max_tokens=1000
),
end_strategy='early', # How to handle tool calls with results
)
Running Agents
Three execution methods:
# Async (preferred)
result = await agent.run('prompt', deps=my_deps)
# Sync (convenience)
result = agent.run_sync('prompt', deps=my_deps)
# Streaming
async with agent.run_stream('prompt') as response:
async for chunk in response.stream_output():
print(chunk, end='')
Instructions vs System Prompts
# Instructions: Concatenated, for agent behavior
agent = Agent(
'openai:gpt-4o',
instructions='You are a helpful assistant. Be concise.'
)
# Dynamic instructions via decorator
@agent.instructions
def add_context(ctx: RunContext[MyDeps]) -> str:
return f"User ID: {ctx.deps.user_id}"
# System prompts: Static, for model context
agent = Agent(
'openai:gpt-4o',
system_prompt=['You are an expert.', 'Always cite sources.']
)
Common Patterns
Parameterized Agent (Type-Safe)
from dataclasses import dataclass
from pydantic_ai import Agent, RunContext
@dataclass
class Deps:
api_key: str
user_id: int
agent: Agent[Deps, str] = Agent(
'openai:gpt-4o',
deps_type=Deps,
)
# deps is now required and type-checked
result = agent.run_sync('Hello', deps=Deps(api_key='...', user_id=123))
No Dependencies (Satisfy Type Checker)
# Option 1: Explicit type annotation
agent: Agent[None, str] = Agent('openai:gpt-4o')
# Option 2: Pass deps=None
result = agent.run_sync('Hello', deps=None)
Verification gates
Run these in order before depending on an agent in production code:
- Smoke run — Execute
agent.run_sync('Reply with OK.')(orawait agent.run(...)in async code). Pass: the call completes without raising andresult.outputis present. - Structured output — If you set
output_type, prompt for a response that should satisfy the schema. Pass:result.outputis an instance of your Pydantic model; repeated validation failures mean tightening instructions orretries, not adding features yet. - Dependencies — If you set
deps_type, callrun/run_syncwithdeps=of that type. Pass: the invocation type-checks and completes (or fails only for model/API reasons, not a missing or wrongdepsvalue).
Decision Framework
| Scenario | Configuration |
|---|---|
| Simple text responses | Agent(model) |
| Structured data extraction | Agent(model, output_type=MyModel) |
| Need external services | Add deps_type=MyDeps |
| Validation retries needed | Increase retries=3 |
| Debugging/monitoring | Set instrument=True |
FAQ
What does the pydantic-ai-agent-creation skill do?
Create PydanticAI agents with type-safe dependencies, structured outputs, and proper configuration. Use when building AI agents, creating chat systems, or integrating LLMs with Pydantic validation.
How do I install pydantic-ai-agent-creation?
Run npx skills add https://github.com/existential-birds/beagle --skill pydantic-ai-agent-creation in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/pydantic-ai-agent-creation/ (personal) or .claude/skills/pydantic-ai-agent-creation/ (project).
Which AI agents can use pydantic-ai-agent-creation?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is pydantic-ai-agent-creation safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains pydantic-ai-agent-creation?
It is published by existential-birds in existential-birds/beagle. It has 218 recorded installs.
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