Observability for Fireworks AI with Langfuse
This guide shows you how to integrate Fireworks AI with Langfuse. Fireworks AI’s API endpoints are fully compatible with the OpenAI SDK, allowing you to trace and monitor your AI applications seamlessly.
What is Fireworks AI? Fireworks AI is a platform that provides API access to state-of-the-art open-source and proprietary AI models with OpenAI-compatible endpoints.
What is Langfuse? Langfuse is an open source LLM engineering platform that helps teams trace API calls, monitor performance, and debug issues in their AI applications.
Step 1: Install Dependencies
%pip install openai langfuseStep 2: Set Up Environment Variables
import os
# Get keys for your project from the project settings page
# https://cloud.langfuse.com
os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-..."
os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-..."
os.environ["LANGFUSE_BASE_URL"] = "https://cloud.langfuse.com" # 🇪🇺 EU region
# os.environ["LANGFUSE_BASE_URL"] = "https://us.cloud.langfuse.com" # 🇺🇸 US region
# Set your Fireworks API details
os.environ["FIREWORKS_AI_API_BASE"] = "https://api.fireworks.ai/inference/v1"
os.environ["FIREWORKS_AI_API_KEY"] = "fw_..."Step 3: Use Langfuse OpenAI Drop-in Replacement
from langfuse.openai import openai
client = openai.OpenAI(
api_key=os.environ.get("FIREWORKS_AI_API_KEY"),
base_url=os.environ.get("FIREWORKS_AI_API_BASE")
)Step 4: Run an Example
response = client.chat.completions.create(
model="accounts/fireworks/models/llama-v3p1-8b-instruct",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Why is open source important?"},
],
name = "Fireworks-AI-Trace" # name of the trace
)
print(response.choices[0].message.content)Step 5: See Traces in Langfuse
After running the example, log in to Langfuse to view the detailed traces, including:
- Request parameters
- Response content
- Token usage and latency metrics
Interoperability with the Python SDK
You can use this integration together with the Langfuse SDKs to add additional attributes to the trace.
The @observe() decorator provides a convenient way to automatically wrap your instrumented code and add additional attributes to the trace.
from langfuse import observe, propagate_attributes, get_client
langfuse = get_client()
@observe()
def my_llm_pipeline(input):
# Add additional attributes (user_id, session_id, metadata, version, tags) to all spans created within this execution scope
with propagate_attributes(
user_id="user_123",
session_id="session_abc",
tags=["agent", "my-trace"],
metadata={"email": "user@langfuse.com"},
version="1.0.0"
):
# YOUR APPLICATION CODE HERE
result = call_llm(input)
# Update the trace input and output
langfuse.update_current_trace(
input=input,
output=result,
)
return resultLearn more about using the Decorator in the Langfuse SDK instrumentation docs.
Troubleshooting
No traces appearing
First, enable debug mode in the Python SDK:
export LANGFUSE_DEBUG="True"Then run your application and check the debug logs:
- OTel spans appear in the logs: Your application is instrumented correctly but traces are not reaching Langfuse. To resolve this:
- Call
langfuse.flush()at the end of your application to ensure all traces are exported. - Verify that you are using the correct API keys and base URL.
- Call
- No OTel spans in the logs: Your application is not instrumented correctly. Make sure the instrumentation runs before your application code.
Unwanted observations in Langfuse
The Langfuse SDK is based on OpenTelemetry. Other libraries in your application may emit OTel spans that are not relevant to you. These still count toward your billable units, so you should filter them out. See Unwanted spans in Langfuse for details.
Missing attributes
Some attributes may be stored in the metadata object of the observation rather than being mapped to the Langfuse data model. If a mapping or integration does not work as expected, please raise an issue on GitHub.
Next Steps
Once you have instrumented your code, you can manage, evaluate and debug your application: