Overview
This guide walks you through integrating SigNoz with traceloop(OpenLLMetry) to gain visibility into your LLM and AI applications. By combining traceloop's specialized LLM instrumentation with SigNoz's observability platform, you can monitor and analyze traces from your AI workloads.
Prerequisites
- A SigNoz Cloud accountwith an active ingestion key orSelf Hosted SigNoz instance - Internet access to send telemetry data to SigNoz Cloud
- Python 3.10+ with
traceloop-sdk
installed - For Python:
pip
installed for managing Python packages - For this example: An OpenAI API key. You can get it from OpenAI platform
Integrate SigNoz with traceloop
For more information on getting started with traceloop in your Python environment, refer to the traceloop Quickstart Guide. For more information on integrating SigNoz with traceloop, refer to the traceloop SigNoz Guide.
Step 1: Install the necessary packages in your Python environment.
pip install \
traceloop-sdk \
openai
Step 2: Create an example LLM application (using OpenAI in this example)
from traceloop.sdk import Traceloop
import os
from openai import OpenAI
Traceloop.init(app_name="<service_name>")
client = OpenAI()
chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": "What is SigNoz?",
}
],
model="gpt-3.5-turbo",
)
print(chat_completion.choices[0].message.content)
<service_name>
is the name of your service
Step 3: Run your application with env variables
Run your application with the following environment variables set. This configures OpenTelemetry to export traces to SigNoz.
TRACELOOP_BASE_URL=https://ingest.<region>.signoz.cloud:443 \
TRACELOOP_HEADERS="signoz-access-token=<your-ingestion-key>" \
<your_run_command>
<region>
: YourSigNoz Cloud region<your-ingestion-key>
: Your SigNozingestion key- Replace
<your_run_command>
with the actual command you would use to run your application. In this case we would use:python main.py
View Traces in SigNoz
Your AI usage should now automatically emit traces.
You should be able to view traces in Signoz Cloud under the traces tab:
When you click on a trace in SigNoz, you'll see a detailed view of the trace, including all associated spans, along with their events and attributes.
Troubleshooting
If you don't see your telemetry data:
Verify network connectivity- Ensure your application can reach SigNoz Cloud endpoints** Check ingestion key**- Verify your SigNoz ingestion key is correct** Wait for data**- OpenTelemetry batches data before sending, so wait 10-30 seconds after making API calls** Try a console exporter**— Enable a console exporter locally to confirm that your application is generating telemetry data before it’s sent to SigNoz
Setup OpenTelemetry Collector (Optional)
Setup OpenTelemetry Collector (Optional)
What is the OpenTelemetry Collector?
Think of the OTel Collector as a middleman between your app and SigNoz. Instead of your application sending data directly to SigNoz, it sends everything to the Collector first, which then forwards it along.
Why use it?
Cleaning up data— Filter out noisy traces you don't care about, or remove sensitive info before it leaves your servers.** Keeping your app lightweight**— Let the Collector handle batching, retries, and compression instead of your application code.** Adding context automatically**— The Collector can tag your data with useful info like which Kubernetes pod or cloud region it came from.** Future flexibility**— Want to send data to multiple backends later? The Collector makes that easy without changing your app.
See Switch from direct export to Collector for step-by-step instructions to convert your setup.
For more details, see Why use the OpenTelemetry Collector? and the Collector configuration guide.
Related integrations
Use the same OpenTelemetry pipeline for the frameworks and providers you instrument:
Instrument LLM apps with OpenLIT- a one-line OpenTelemetry SDK covering GenAI, vector DB, and GPU telemetryInstrument LLM apps with Langtrace- an OpenTelemetry-native SDK that exports LLM spans straight to SigNozMonitor Hermes with OpenTelemetry- trace long-running agent sessions, skill execution, and subagent runsMonitor Mistral AI with OpenTelemetry- track Mistral model latency, token usage, and error ratesQwen observability with OpenTelemetry- trace Qwen model calls with latency and token usage
Browse all LLM observability integrations to instrument the rest of your stack.