What is Langflow Observability?
Langflow observability gives you real-time visibility into your visual AI workflows by collecting traces using OpenTelemetry. Langflow (1.10+) ships with OpenTelemetry built in, so you don't build or install any instrumentation. This guide shows you how to export Langflow's flow and LLM traces to SigNoz by running the official Langflow Docker image with a few environment variables, giving you insight into components, model calls, prompts, and token usage.
With full Langflow observability in SigNoz, you can trace every flow run end to end, correlate LLM spans carrying gen_ai.* attributes, set alerts on latency and errors, and continuously improve the reliability of your Langflow applications.
Prerequisites
- A SigNoz Cloud account with an active ingestion key or Self Hosted SigNoz instance
- Docker installed and running on your system
- Langflow 1.10 or later. Follow the Langflow installation guide if you don't have it yet
Monitor Langflow with OpenTelemetry
Langflow's OpenTelemetry stack is bundled inside the Langflow image, so the integration is entirely configuration based. For more details, refer to the Langflow documentation.
Step 1: Run Langflow with telemetry enabled
Replace <your-ingestion-key> and the region in the URL, then run the official Langflow image:
docker run -d --name langflow -p 7860:7860 \
-e TRACELOOP_API_KEY=signoz \
-e TRACELOOP_BASE_URL=https://ingest.<region>.signoz.cloud:443 \
-e TRACELOOP_HEADERS="signoz-ingestion-key=<your-ingestion-key>" \
-e TRACELOOP_TRACE_CONTENT=true \
langflowai/langflow:latest
<region>: Your SigNoz Cloud region (us,eu, orin)<your-ingestion-key>: Your SigNoz ingestion key
Langflow is now exporting traces to SigNoz. Keep the https:// scheme and :443 port. Langflow appends /v1/traces for you, so don't add it yourself.
Most steps are identical. To adapt this guide, update the endpoint and remove the ingestion key header as shown in Cloud → Self-Hosted.
The following environment variables control the export:
| Variable | Required | Value | What it does |
|---|---|---|---|
TRACELOOP_API_KEY | Yes | any non-empty string (e.g. signoz) | Switches Langflow's OTLP tracer on. It is not used to authenticate to SigNoz. |
TRACELOOP_BASE_URL | Yes | https://ingest.<region>.signoz.cloud:443 | Where Langflow sends OTLP data (it posts to .../v1/traces). |
TRACELOOP_HEADERS | Yes | signoz-ingestion-key=<your-ingestion-key> | Authenticates the export to SigNoz. |
TRACELOOP_TRACE_CONTENT | No | true / false (default true) | Include prompt and response text on spans. Set false to keep prompt bodies out of telemetry while still capturing model and token data. |
Prefer docker-compose? Set the same variables in your compose file or a .env file and start Langflow with docker compose up -d. The langflowai/langflow image reads the same TRACELOOP_* variables either way.
Step 2: Generate some telemetry
- Open Langflow at
http://localhost:7860 - Build or open a flow that calls an LLM, and run it
Each run produces spans, including LLM spans carrying gen_ai.* attributes such as the model name and token usage. An idle Langflow instance emits nothing, so a flow must actually run before data appears in SigNoz.
View Langflow Traces in SigNoz
Once configured, your Langflow application automatically emits traces for every flow run.
Traces are available in SigNoz 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. LLM spans carry gen_ai.* attributes (model, token usage) and, when TRACELOOP_TRACE_CONTENT=true, the prompt and response text.
This integration sends Langflow's flow and LLM spans from its native tracer. Full HTTP-request and service-map instrumentation is a separate mechanism and is not covered here.
Langflow Observability Dashboard
You can also check out our custom Langflow dashboard which provides specialized visualizations for monitoring your Langflow usage. The dashboard includes pre-built charts specifically tailored for LLM usage, along with import instructions to get started quickly.
Troubleshooting Langflow Observability
No traces in SigNoz
- Confirm the flow actually ran. An idle Langflow instance emits nothing.
- Verify
TRACELOOP_API_KEYis set to a non-empty value. This switches the tracer on. - Check that the region in
TRACELOOP_BASE_URLmatches your SigNoz account. - OpenTelemetry batches data before sending, so wait 10-30 seconds after running a flow.
Auth errors (401 / 403)
Re-check the ingestion key in TRACELOOP_HEADERS=signoz-ingestion-key=<key>. It must be the exact key from your SigNoz Ingestion Settings, with no extra spaces or quotes.
Endpoint issues
Keep the https:// scheme and :443 port in TRACELOOP_BASE_URL. Langflow appends /v1/traces for you, so don't add it yourself.
Upgrading Langflow
Nothing special is required. The OpenTelemetry stack is bundled inside the Langflow image, so it always matches the version you run. There is no separate agent or instrumentor to update:
docker pull langflowai/langflow:latest # or a specific tag
# recreate the container with the same env vars
For reproducible deploys, pin a specific tag such as langflowai/langflow:1.10.1 instead of latest.
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.
Additional resources:
- Set up alerts for high latency or error rates
- Learn more about querying traces
- Explore log correlation
