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Launching the open-source gateway

Langfuse

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 .

Paste this into your coding agent:

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

Setup

Install packages

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

Set environment variables

Terminal
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.

Python
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

ParameterTypeDefaultDescription
api_keystr | NoneNoneFalls back to RESPAN_API_KEY env var.
base_urlstr | NoneNoneFalls back to RESPAN_BASE_URL env var.
instrumentationslist[]Plugin instrumentations to activate (e.g. LangfuseInstrumentor()).
customer_identifierstr | NoneNoneDefault customer identifier for all spans.
metadatadict | NoneNoneDefault metadata attached to all spans.
environmentstr | NoneNoneEnvironment tag (e.g. "production").

Attributes

In Respan()

Set defaults at initialization — these apply to all spans.

Python
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.

Python
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
        ...
AttributeTypeDescription
customer_identifierstrIdentifies the end user in Respan analytics.
thread_identifierstrGroups related messages into a conversation.
metadatadictCustom key-value pairs. Merged with default metadata.