LiteLLM (tracing) | Respan Docs

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.

Run npx @respan/cli setup to set up with your coding agent.

Use Respan Gateway

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

Example projects

Example projects root

Setup

Install packages

$ pip install respan-ai respan-instrumentation-litellm litellm

Set environment variables

$ 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.

1 import os
2 
3 import litellm
4 from respan import Respan
5 from respan_instrumentation_litellm import LiteLLMInstrumentor
6 
7 respan = Respan(
8     api_key=os.environ["RESPAN_API_KEY"],
9     app_name="litellm-service",
10     instrumentations=[LiteLLMInstrumentor()],
11 )
12 
13 response = litellm.completion(
14     model="gpt-4.1-nano",
15     messages=[{"role": "user", "content": "Say hello in three languages."}],
16 )
17 print(response.choices[0].message.content)
18 respan.flush()

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.

See the LiteLLM SDK reference for the full API.

Attributes

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

1 with respan.propagate_attributes(
2     customer_identifier="user-123",
3     thread_identifier="conv_abc_123",
4     trace_group_identifier="litellm-support-flow",
5     metadata={"plan": "pro"},
6 ):
7     response = litellm.completion(
8         model="gpt-4.1-nano",
9         messages=[{"role": "user", "content": "Hello!"}],
10     )

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.

1 import litellm
2 from respan import Respan
3 from respan_instrumentation_litellm import LiteLLMInstrumentor
4 
5 respan = Respan(instrumentations=[LiteLLMInstrumentor()])
6 
7 response = await litellm.acompletion(
8     model="gpt-4.1-nano",
9     messages=[{"role": "user", "content": "Tell me a joke"}],
10 )
11 respan.flush()

Multiple providers

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

1 import litellm
2 from respan import Respan
3 from respan_instrumentation_litellm import LiteLLMInstrumentor
4 
5 respan = Respan(instrumentations=[LiteLLMInstrumentor()])
6 
7 litellm.completion(model="gpt-4.1-nano", messages=[...])
8 litellm.completion(model="claude-sonnet-4-5-20250929", messages=[...])
9 litellm.completion(model="together_ai/meta-llama/Llama-3-70b", messages=[...])
10 respan.flush()