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Pydantic AI

Auto-instrument Pydantic AI for type-safe agents.

Risicare automatically instruments Pydantic AI for type-safe agent development.

Installation

pip install risicare[pydantic-ai]
# or
pip install risicare pydantic-ai

Version Compatibility

Requires pydantic-ai >= 0.1.0.

Auto-Instrumentation

import risicare
from pydantic_ai import Agent
 
risicare.init()
 
agent = Agent(
    "openai:gpt-4o",
    system_prompt="You are a helpful assistant."
)
 
# Automatically traced
result = agent.run_sync("Hello!")

What's Captured

FeatureDescription
Agent RunsFull run/run_sync/run_stream calls
Model CallsUnderlying LLM API calls
Tool CallsFunction tool executions
Result ValidationPydantic model validation
DependenciesInjected dependencies

Span Hierarchy

pydantic_ai.agent.run/{name} (AGENT kind)
├── openai.chat.completions.create (provider span, natural child)
└── openai.chat.completions.create (provider span, natural child)

Provider Spans

Pydantic AI instrumentation creates agent/framework-level spans. Underlying LLM calls (e.g., OpenAI, Anthropic) are traced separately by provider instrumentation, giving you both framework-level and LLM-level visibility.

Structured Outputs

Type-safe outputs are captured:

from pydantic import BaseModel
 
class CityInfo(BaseModel):
    name: str
    country: str
    population: int
 
agent = Agent(
    "openai:gpt-4o",
    result_type=CityInfo
)
 
result = agent.run_sync("Tell me about Paris")
# result.output is CityInfo, schema captured in span

Tools

Tool executions are traced:

from pydantic_ai import Agent, RunContext
 
agent = Agent("openai:gpt-4o")
 
@agent.tool
def get_weather(ctx: RunContext, location: str) -> str:
    """Get weather for a location."""
    return f"Sunny in {location}"
 
# Tool calls appear as child spans
result = agent.run_sync("What's the weather in Paris?")

Dependencies

Dependency injection is captured:

from dataclasses import dataclass
 
@dataclass
class Deps:
    user_id: str
    api_key: str
 
agent = Agent("openai:gpt-4o", deps_type=Deps)
 
@agent.tool
def get_user_data(ctx: RunContext[Deps]) -> str:
    return f"Data for {ctx.deps.user_id}"
 
result = agent.run_sync(
    "Get my data",
    deps=Deps(user_id="123", api_key="secret")
)
# Dependencies are captured (excluding secrets)

Streaming

async with agent.run_stream("Write a story") as response:
    async for chunk in response.stream():
        print(chunk, end="")

Multiple Models

# OpenAI
agent = Agent("openai:gpt-4o")
 
# Anthropic
agent = Agent("anthropic:claude-3-sonnet-20240229")
 
# Gemini
agent = Agent("gemini-1.5-pro")
 
# Each model is traced with correct provider

Next Steps

LlamaIndex

RAG framework

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All Frameworks

View all supported frameworks

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