Skill: anderskev/pydantic-ai-agent-creation (version 1.0.1)

--- name: pydantic-ai-agent-creation description: 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. --- # Creating PydanticAI Agents ## Quick Start ```python 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: ```python # 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: ```python 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 ```python 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: ```python # 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 ```python # 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) ```python 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) ```python # 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: 1. **Smoke run** — Execute `agent.run_sync('Reply with OK.')` (or `await agent.run(...)` in async code). **Pass:** the call completes without raising and `result.output` is present. 2. **Structured output** — If you set `output_type`, prompt for a response that should satisfy the schema. **Pass:** `result.output` is an instance of your Pydantic model; repeated validation failures mean tightening instructions or `retries`, not adding features yet. 3. **Dependencies** — If you set `deps_type`, call `run` / `run_sync` with `deps=` of that type. **Pass:** the invocation type-checks and completes (or fails only for model/API reasons, not a missing or wrong `deps` value). ## 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` |


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