OpenLLMetry
TraceloopOpenTelemetry-based instrumentation library for LLM applications. It automatically instruments applications built with LangChain, LlamaIndex, the OpenAI SDK, and other popular frameworks, and exports telemetry data to any OTel-compatible backend, such as Jaeger, Grafana or Datadog. The 'OpenTelemetry for LLMs' approach is standards-based and vendor-neutral, shipping standard OTel instrumentations for LLM providers and vector databases alongside the Traceloop SDK.
Openness
5 high confidence- license
- Apache-2.0(OSI)
- source
- public
- core features withheld
- no
OpenLLMetry is an Apache-2.0 instrumentation library whose SDK is fully open source; it exports to any OpenTelemetry backend, including Datadog and Honeycomb, rather than requiring a paid Traceloop-hosted destination. The optional Traceloop dashboard is simply one export target among many, not a gated core.
- https://github.com/traceloop/openllmetry recorded 2026-08-13
README, read rather than assumed. The badge alt text reads "OpenLLMetry is released under the Apache-2.0 License" and the body states "It's built and maintained by Traceloop under the Apache 2.0 license." On what ships - "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." On the gate question, the vendor's own framing is that "it can be connected to your existing observability solutions - Datadog, Honeycomb, and others", so the published SDK works without any Traceloop-hosted tier and nothing is withheld from it.
- https://pypi.org/project/traceloop-sdk/ recorded 2026-09-29
PyPI page for traceloop-sdk lists License expression "Apache-2.0" and describes the SDK as one that "allows you to easily start monitoring and debugging your LLM execution" built on OpenTelemetry.
- https://www.traceloop.com/docs/openllmetry/introduction recorded 2026-09-29
Introduction page: "OpenLLMetry is an open source project that allows you to easily start monitoring and debugging the execution of your LLM app." and "You can choose to export the traces to Traceloop, or to your existing observability stack." so Traceloop is one optional destination.
Adoption
4 high confidenceAdoption is measured by PyPI downloads of the traceloop-sdk package, the project's own client library, rather than the declared opentelemetry-instrumentation-openai package; both report the same download count. GitHub stars corroborate the reading without adding to it.
- https://github.com/traceloop/openllmetry recorded 2026-08-13
7,376 stars; README still lists supported LLM providers, vector DBs and tested export destinations
- https://pypistats.org/packages/traceloop-sdk recorded 2026-08-13
Downloads last month 2,444,560 for traceloop-sdk (last week 530,018)
Capability
3 high confidenceOpenLLMetry is strong on tracing and integrations but is an instrumentation layer rather than a full evaluation, prompt and dataset suite. Its open-source layer has no native prompt management, evaluation or LLM-as-judge scoring, and no datasets or annotation, which puts it a step behind the broader feature sets of Langfuse, MLflow, Phoenix and Opik.
- https://github.com/traceloop/openllmetry recorded 2026-08-13
README structure - "The repo contains standard OpenTelemetry instrumentations for LLM providers and Vector DBs, as well as a Traceloop SDK", then sections for supported providers, Vector DBs and "Supported (and tested) destinations". No evals, prompt-management or dataset section exists in the OSS repository.
- https://www.traceloop.com/docs/openllmetry/tracing/supported recorded 2026-09-29
Supported page: "A list of the models, vector DBs and frameworks that OpenLLMetry supports out of the box." with tables for LLM Foundation Models, Vector DBs (Chroma DB through Weaviate, nine) and Frameworks. Searched the body for prompt management, dataset, experiment and evaluation: none.
Verified 2026-09-24