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Create PydanticAI agents with type-safe dependencies, structured outputs, and proper configuration.
Passed 20 of 22 test cases with the skill, and 11 of 22 without it.
and 9 more in the full report
9 fixed11 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
Write a minimal Python snippet using a lightweight type-safe LLM framework to initialize an agent with OpenAI GPT-4o and run a prompt synchronously to output text.
| Checked | Without skill | With skill |
|---|---|---|
| The agent is initialized using Agent('openai:gpt-4o'). | Passed | Passed |
| The execution uses result = agent.run_sync(...) to run synchronously. | Passed | Passed |
| The resulting output is printed using result.output. | Missed | Passed |
The instructions your model reads when the skill loads.
from pydantic_ai import Agent
# Minimal agent (text output)
agent = Agent('openai:gpt-4o')
result = agent.run_sync('Hello!')
print(result.output) # str
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.
Use Pydantic models for validated, typed responses:
from pydantic import BaseModel
from pydantic_ai import Agent
test cases passed, vs 11 / 22 without
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