Open-source observability for your LLM application
🎉 New: Our semantic conventions are now part of OpenTelemetry! Join the discussion and help us shape the future of LLM observability.
Looking for the JS/TS version? Check out OpenLLMetry-JS.
OpenLLMetry is a set of extensions built on top of OpenTelemetry that gives you complete observability over your LLM application. Because it uses OpenTelemetry under the hood, it can be connected to your existing observability solutions - Datadog, Honeycomb, and others.
It's built and maintained by Traceloop under the Apache 2.0 license.
The repo contains standard OpenTelemetry instrumentations for LLM providers and Vector DBs, as well as a Traceloop SDK that makes it easy to get started with OpenLLMetry, while still outputting standard OpenTelemetry data that can be connected to your observability stack. If you already have OpenTelemetry instrumented, you can just add any of our instrumentations directly.
The easiest way to get started is to use our SDK. For a complete guide, go to our docs.
Install the SDK:
pip install traceloop-sdkThen, to start instrumenting your code, just add this line to your code:
fromtraceloop.sdkimportTraceloopTraceloop.init()That's it. You're now tracing your code with OpenLLMetry! If you're running this locally, you may want to disable batch sending, so you can see the traces immediately:
Traceloop.init(disable_batch=True)- ✅ Traceloop
- ✅ Axiom
- ✅ Azure Application Insights
- ✅ Braintrust
- ✅ Dash0
- ✅ Datadog
- ✅ Dynatrace
- ✅ Google Cloud
- ✅ Grafana
- ✅ Highlight
- ✅ Honeycomb
- ✅ HyperDX
- ✅ IBM Instana
- ✅ KloudMate
- ✅ Laminar
- ✅ New Relic
- ✅ OpenTelemetry Collector
- ✅ Oracle Cloud
- ✅ Service Now Cloud Observability
- ✅ SigNoz
- ✅ Sentry
- ✅ Splunk
See our docs for instructions on connecting to each one.
OpenLLMetry can instrument everything that OpenTelemetry already instruments - so things like your DB, API calls, and more. On top of that, we built a set of custom extensions that instrument things like your calls to OpenAI or Anthropic, or your Vector DB like Chroma, Pinecone, Qdrant or Weaviate.
- ✅ OpenAI / Azure OpenAI
- ✅ Anthropic
- ✅ Cohere
- ✅ Ollama
- ✅ Mistral AI
- ✅ HuggingFace
- ✅ Bedrock (AWS)
- ✅ SageMaker (AWS)
- ✅ Replicate
- ✅ Vertex AI (GCP)
- ✅ Google Generative AI (Gemini)
- ✅ IBM Watsonx AI
- ✅ Together AI
- ✅ Aleph Alpha
- ✅ Groq
- ✅ MCP
The SDK provided with OpenLLMetry (not the instrumentations) contains a telemetry feature that collects anonymous usage information.
You can opt out of telemetry by setting the TRACELOOP_TELEMETRY environment variable to FALSE, or passing telemetry_enabled=False to the Traceloop.init() function.
- The primary purpose is to detect exceptions within instrumentations. Since LLM providers frequently update their APIs, this helps us quickly identify and fix any breaking changes.
- We only collect anonymous data, with no personally identifiable information. You can view exactly what data we collect in our Privacy documentation.
- Telemetry is only collected in the SDK. If you use the instrumentations directly without the SDK, no telemetry is collected.
Whether big or small, we love contributions ❤️ Check out our guide to see how to get started.
Not sure where to get started? You can:
- Book a free pairing session with one of our teammates!
- Join our Slack, and ask us any questions there.
- Slack (For live discussion with the community and the Traceloop team)
- GitHub Discussions (For help with building and deeper conversations about features)
- GitHub Issues (For any bugs and errors you encounter using OpenLLMetry)
- Twitter (Get news fast)
To @patrickdebois, who suggested the great name we're now using for this repo!