OpenLLMetry is an open-source observability library that instruments LLM applications with OpenTelemetry traces covering model calls, tool use, and retrieval steps. Developers add one initialization call plus auto-instrumented packages, and every provider request, agent action, and vector-database query lands in standard GenAI spans exportable to any backend.[1][2][3]
Quick verdict
Engineering teams already standardized on OpenTelemetry benefit most from OpenLLMetry, because one free Apache-2.0 library traces LLM providers, agent frameworks, and vector databases without locking analysis to a vendor. The material constraint is scope: it instruments and exports telemetry but provides no hosted dashboards, evals, or prompt management of its own. Teams shipping LLM features inside instrumented services should adopt it as their collection layer, while teams needing turnkey analysis should pair it with a backend platform such as Langfuse. If nobody on the team writes or operates code, neither fits and a finished AI product is the right choice.[1][2][3]
Key features
Single-call instrumentation Single-call instrumentation: initialize the SDK once and supported LLM providers, agent frameworks, and vector databases emit spans automatically.[1][2]
OpenTelemetry-native telemetry OpenTelemetry-native telemetry: traces, metrics, and logs follow standard GenAI semantic conventions instead of proprietary event shapes.[1][2]
Vendor-neutral OTLP export Vendor-neutral OTLP export: send spans to any OpenTelemetry-compatible backend or existing observability stack.[1][2]
30+ documented integrations 30+ documented integrations: cover major model providers, vector databases, and agent frameworks through SDK and auto-instrumentation packages.[1]
Privacy-by-default content tracing Privacy-by-default content tracing: prompts and completions are excluded unless content tracing is explicitly enabled.[1]
Python and JavaScript SDKs Python and JavaScript SDKs: instrument backend services and JS applications with framework-specific extensions.[1][2][3]
No vendor lock-in positioning No vendor lock-in positioning: the documented FAQ confirms use with existing observability stacks rather than a mandatory cloud.[1]
Community-maintained Apache-2.0 codebase Community-maintained Apache-2.0 codebase: about 10.2k stars, 730 forks, and 94 contributors with ongoing releases as observed October 2026.[1][2]
Who will love it
Teams on OpenTelemetry already Teams on OpenTelemetry already: standard spans drop into existing collectors, dashboards, and alerting without a parallel pipeline.[1][2]
Teams spanning many providers Teams spanning many providers: one instrumentation layer covers model APIs, vector databases, and agent frameworks instead of per-vendor logging.[1][2]
Privacy-sensitive deployments Privacy-sensitive deployments: content tracing stays off by default, so prompts and completions never leave the boundary unless enabled.[1]
Who should avoid it
Teams wanting hosted analysis Teams wanting hosted analysis: OpenLLMetry ships no dashboards, evals, datasets, or prompt playground; pair it with a platform or choose one instead.[1][2]
Non-technical teams Non-technical teams: installation, initialization, and exporter configuration assume engineers who own application code.[1]
Teams needing stated SLAs Teams needing stated SLAs: no support tier, uptime commitment, or enterprise contract is documented for the library itself.[1][2][3]
Questions worth asking
What is the best OpenLLMetry alternative with hosted dashboards?
Langfuse is the closest catalogue comparison: an open-source tracing, evaluation, and prompt-management platform that ingests traces from any OpenTelemetry client. Use OpenLLMetry for vendor-neutral collection and Langfuse where hosted analysis, evals, and experiments are required; the two compose rather than substitute.[1][2]
Is OpenLLMetry really no-code?
How much does OpenLLMetry cost?
Sources and editorial review
Source research. Reviewed on October 12, 2026. Written by Sanjib Sah. AI assisted; reviewed by a human editor.
We have not run or tested this product.
- OpenLLMetry official site
official · Observed October 12, 2026
Official homepage: open-source LLM observability on OpenTelemetry; single init; traces, metrics, logs; 30+ integrations; JS/Python SDKs; Apache-2.0; vendor-neutral FAQ.
- OpenLLMetry introduction
official · Observed October 12, 2026
Official docs introduction: built on OpenTelemetry; instruments LLMs, agents, vector DBs with GenAI conventions; OTLP export to any destination; maintained by Traceloop.
- OpenLLMetry getting started
official · Observed October 12, 2026
Getting-started guide: pip install traceloop-sdk with extras; Traceloop.init; tracing disabled and content tracing opt-in by default.
- traceloop/openllmetry repository
official · Observed October 12, 2026
Canonical repo traceloop/openllmetry: JS/Python monorepo, Apache-2.0, about 10.2k stars, 730 forks, 94 contributors, active releases; homepage links openllmetry.com.
Compared with these products
No explicit proprietary baseline is recorded.