import risicare
from openai import OpenAI
 
risicare.init()
 
client = OpenAI()
 
# This call is automatically traced
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)

Captured Data

For each LLM call, Risicare captures:

Field	Description
`model`	Model name (gpt-4o, claude-3-sonnet, etc.)
`provider`	Provider name (openai, anthropic, etc.)
`prompt_tokens`	Input token count
`completion_tokens`	Output token count
`total_tokens`	Total token count
`cost_usd`	Estimated cost in USD
`latency_ms`	Request duration
`temperature`	Sampling temperature
`max_tokens`	Maximum output tokens
`prompt`	Input prompt (if `trace_content=True`)
`completion`	Output completion (if `trace_content=True`)

Manual Instrumentation

If auto-instrumentation doesn't meet your needs:

from risicare import trace
 
@trace(name="custom-llm-call", kind="llm_call")
def call_custom_llm(prompt: str) -> str:
    # Your custom LLM call
    return response

You can also set provider and model attributes manually:

from risicare import get_tracer
 
tracer = get_tracer()
 
with tracer.start_span("custom-llm", kind="llm_call") as span:
    span.set_attribute("llm.provider", "custom")
    span.set_attribute("llm.model", "my-model")
    response = call_llm(prompt)

Next Steps

Select a provider to see detailed integration guides:

OpenAI Integration

Complete OpenAI setup guide

Learn more

Anthropic Integration

Complete Anthropic setup guide

Learn more

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