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OpenLLMetry

by Traceloop

Open-source LLM observability SDK using OpenTelemetry standards — automatically instrumenting APAC LLM calls from OpenAI, Anthropic, LangChain, and LlamaIndex as standard OTel spans that integrate with existing APAC observability platforms (Jaeger, Grafana Tempo, Datadog, Honeycomb).

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AIMenta verdict
Decent fit
4/5

"OpenTelemetry-based LLM observability SDK — APAC engineering teams use OpenLLMetry to instrument LLM calls with standard OTel traces, sending LLM spans to existing APAC observability backends without vendor lock-in."

Features
6
Use cases
1
Watch outs
3
What it does

Key features

  • Auto-instrumentation: APAC two-line setup patches OpenAI/Anthropic/LangChain clients
  • OTel standard: APAC LLM traces in standard OpenTelemetry span format
  • Any backend: APAC route to Jaeger/Tempo/Datadog/Honeycomb/Grafana
  • Multi-framework: APAC LangChain/LlamaIndex/DSPy/Haystack support
  • Token tracking: APAC per-call prompt/completion token count attribution
  • Open-source: Apache 2.0 — APAC no vendor dependency for core instrumentation
When to reach for it

Best for

  • APAC engineering teams with existing OpenTelemetry observability infrastructure who want to add LLM observability without adopting a separate LLM-specific platform — particularly APAC organizations that prefer vendor-neutral standards and want LLM traces in the same backend as their application and infrastructure traces.
Don't get burned

Limitations to know

  • ! Raw OTel traces require APAC additional work to build LLM-specific dashboards
  • ! Less opinionated than dedicated platforms (Langfuse, Braintrust) for LLM workflows
  • ! APAC semantic conventions still evolving — breaking changes possible across versions
Context

About OpenLLMetry

OpenLLMetry is an open-source LLM observability SDK by Traceloop — implementing OpenTelemetry semantic conventions for LLM calls, automatically instrumenting APAC applications using OpenAI, Anthropic, Cohere, LangChain, LlamaIndex, and other LLM frameworks as standard OTel traces. APAC engineering teams with existing OpenTelemetry-based observability infrastructure use OpenLLMetry to add LLM observability without adopting a separate LLM-specific platform.

OpenLLMetry's auto-instrumentation patches LLM client libraries at import time — APAC applications add two lines of initialization code and every subsequent LLM API call is automatically traced as an OTel span with inputs, outputs, model parameters, token counts, and latency. No manual instrumentation of individual LLM calls is required, and the spans emit in OpenTelemetry format compatible with any APAC OTel-compatible backend.

OpenLLMetry's OTel compatibility means APAC engineering teams route LLM traces to their existing observability infrastructure — Jaeger or Grafana Tempo for trace storage, Prometheus for metrics, Grafana for dashboards, and Datadog or Honeycomb for teams using commercial APM. APAC organizations that have invested in building unified observability infrastructure don't need a separate LLM-specific platform to see LLM traces alongside their application traces.

OpenLLMetry integrates with Traceloop's managed platform for APAC teams that want LLM-specific dashboards and features beyond raw OTel — team members who prefer a dedicated LLM observability UI can connect to Traceloop's SaaS layer while maintaining the OTel standard transport. This gives APAC teams flexibility to start with open-source and optionally layer managed features without changing instrumentation.

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