Langfuse | Respan Docs

Langfuse is an open-source LLM observability platform. The Respan instrumentor patches Langfuse’s OTLP exporter to redirect traces to Respan, so your existing @observe decorators and langfuse.trace() calls continue to work unchanged. You also get gateway routing through the OpenAI-compatible Respan endpoint.

Set up Respan

Create an account at platform.respan.ai and grab an API key. For gateway, also add credits or a provider key.

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

Example projects

Python examples

Tracing

Gateway

Setup

Install packages

pip install respan-ai respan-instrumentation-langfuse langfuse openai

Set environment variables

export OPENAI_API_KEY="YOUR_OPENAI_API_KEY"
export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"
export LANGFUSE_PUBLIC_KEY="YOUR_LANGFUSE_PUBLIC_KEY"
export LANGFUSE_SECRET_KEY="YOUR_LANGFUSE_SECRET_KEY"

OPENAI_API_KEY is used for LLM requests. RESPAN_API_KEY is used to export traces to Respan. Langfuse keys are still required for the SDK to initialize.

Initialize and run

LangfuseInstrumentor must be activated before importing langfuse. The instrumentor patches the OTLP exporter at import time.

from respan import Respan
from respan_instrumentation_langfuse import LangfuseInstrumentor
# Activate BEFORE importing langfuse
respan = Respan(instrumentations=[LangfuseInstrumentor()])
from langfuse.decorators import observe
from openai import OpenAI
client = OpenAI()
@observe()
def generate_joke():
    response = client.chat.completions.create(
        model="gpt-4.1-nano",
        messages=[{"role": "user", "content": "Tell me a joke about AI"}],
    )
    return response.choices[0].message.content
@observe()
def joke_pipeline():
    return generate_joke()
print(joke_pipeline())

View your trace

Open the Traces page to see your Langfuse traces in Respan.

Configuration

Parameter	Type	Default	Description
`api_key`	`str \| None`	`None`	Falls back to `RESPAN_API_KEY` env var.
`base_url`	`str \| None`	`None`	Falls back to `RESPAN_BASE_URL` env var.
`instrumentations`	`list`	`[]`	Plugin instrumentations to activate (e.g. `LangfuseInstrumentor()`).
`customer_identifier`	`str \| None`	`None`	Default customer identifier for all spans.
`metadata`	`dict \| None`	`None`	Default metadata attached to all spans.
`environment`	`str \| None`	`None`	Environment tag (e.g. `"production"`).

Attributes

In Respan()

Set defaults at initialization — these apply to all spans.

from respan import Respan
from respan_instrumentation_langfuse import LangfuseInstrumentor
respan = Respan(
    instrumentations=[LangfuseInstrumentor()],
    customer_identifier="user_123",
    metadata={"service": "chat-api", "version": "1.0.0"},
)

With propagate_attributes

Override per-request using a context scope.

from respan import Respan, propagate_attributes
from respan_instrumentation_langfuse import LangfuseInstrumentor
respan = Respan(instrumentations=[LangfuseInstrumentor()])
from langfuse.decorators import observe
@observe()
def handle_request(user_id: str, question: str):
    with propagate_attributes(
        customer_identifier=user_id,
        thread_identifier="conv_abc_123",
        metadata={"plan": "pro"},
    ):
        # your Langfuse-traced code here
        ...

Attribute	Type	Description
`customer_identifier`	`str`	Identifies the end user in Respan analytics.
`thread_identifier`	`str`	Groups related messages into a conversation.
`metadata`	`dict`	Custom key-value pairs. Merged with default metadata.