LiteLLM (tracing)

LiteLLM provides a unified Python interface for calling 100+ LLM providers using the OpenAI format. Respan’s LiteLLM instrumentation registers a native LiteLLM callback and sends completion spans through respan-tracing, with optional gateway routing through the OpenAI-compatible Respan endpoint.

Set up Respan

Create an account at platform.respan.ai and grab an API key .

Paste this into your coding agent:


Help me set up Respan tracing with the LiteLLM integration.
Follow https://www.respan.ai/docs/integrations/litellm.md

Setup

Install packages

Terminal


pip install respan-ai respan-instrumentation-litellm litellm

Set environment variables

Terminal


export OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"

OPENAI_API_KEY (or any provider key) is used for LLM requests. RESPAN_API_KEY is used to export traces to Respan.

Initialize and run

Initialize Respan with the LiteLLM instrumentor. Requests go directly to providers; the callback emits canonical chat spans to Respan.

Python


import os

import litellm
from respan import Respan
from respan_instrumentation_litellm import LiteLLMInstrumentor

respan = Respan(
    api_key=os.environ["RESPAN_API_KEY"],
    app_name="litellm-service",
    instrumentations=[LiteLLMInstrumentor()],
)

response = litellm.completion(
    model="gpt-4.1-nano",
    messages=[{"role": "user", "content": "Say hello in three languages."}],
)
print(response.choices[0].message.content)

View your trace

Open the Traces page to see your LiteLLM completions across providers as auto-traced spans.

Configuration

Respan

Parameter	Type	Default	Description
`api_key`	`str \| None`	`RESPAN_API_KEY` env var	Respan API key used for trace export.
`base_url`	`str \| None`	`RESPAN_BASE_URL` env var	Respan API base URL.
`app_name`	`str`	`"respan"`	Service name shown on exported spans.
`instrumentations`	`list`	`[]`	Include `LiteLLMInstrumentor()` to activate LiteLLM tracing.

LiteLLMInstrumentor

Parameter	Type	Default	Description
`include_content`	`bool`	`True`	Capture request messages and assistant response content on chat spans.

Attributes

Set defaults on Respan(...), or override attributes per request with propagate_attributes.

Python


with respan.propagate_attributes(
    customer_identifier="user-123",
    thread_identifier="conv_abc_123",
    trace_group_identifier="litellm-support-flow",
    metadata={"plan": "pro"},
):
    response = litellm.completion(
        model="gpt-4.1-nano",
        messages=[{"role": "user", "content": "Hello!"}],
    )

Attribute	Description
`customer_identifier`	Identifies the end user in Respan analytics.
`thread_identifier`	Groups related messages into a conversation.
`trace_group_identifier`	Groups related traces for search and filtering.
`metadata`	Custom key-value pairs attached to the span.

Async usage

The callback supports async completions automatically.

Python


import litellm
from respan import Respan
from respan_instrumentation_litellm import LiteLLMInstrumentor

respan = Respan(instrumentations=[LiteLLMInstrumentor()])

response = await litellm.acompletion(
    model="gpt-4.1-nano",
    messages=[{"role": "user", "content": "Tell me a joke"}],
)

Multiple providers

LiteLLM’s unified interface means all providers are logged with the same callback.

Python


import litellm
from respan import Respan
from respan_instrumentation_litellm import LiteLLMInstrumentor

respan = Respan(instrumentations=[LiteLLMInstrumentor()])

litellm.completion(model="gpt-4.1-nano", messages=[...])
litellm.completion(model="claude-sonnet-4-5-20250929", messages=[...])
litellm.completion(model="together_ai/meta-llama/Llama-3-70b", messages=[...])

Gateway

See LiteLLM gateway setup to route this integration through the Respan gateway.