OpenLLMetry
OpenTelemetry-based LLM observability
Overview
OpenLLMetry is an open-source LLM observability framework built on OpenTelemetry — works with any backend (Honeycomb, Datadog, Jaeger). The Traceloop product is the commercial managed version.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 20 May 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
Use cases
What you can produce with OpenLLMetry
- Instrument an LLM application's OpenAI or Anthropic calls with a few lines of initialisation code and start emitting traces immediately.
- Send LLM traces to an existing observability backend such as Datadog, New Relic, Honeycomb, Grafana or Sentry over standard OTLP.
- Capture token usage, cost-relevant metrics and latency for every model call to debug slow or expensive prompts.
- Trace multi-step pipelines built with LangChain, LlamaIndex or CrewAI end to end, including intermediate steps.
- Instrument vector database queries against Pinecone, Chroma and others alongside the LLM calls they feed.
- Use native SDKs in Python, TypeScript, Go or Ruby to fit the instrumentation into your existing service code.
- Upgrade to Traceloop's managed platform for hosted dashboards, monitoring and alerting on the same OpenLLMetry data.
ASEAN Perspective
OpenLLMetry in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
OpenLLMetry by Traceloop is an open-source observability layer for LLM applications built on OpenTelemetry standards, auto-instrumenting popular frameworks (LangChain, LlamaIndex, OpenAI, etc.) to trace prompts, completions, latency and cost. Its big strength is being standards-based and vendor-neutral — traces can flow to any OTel-compatible backend rather than locking you into one platform.
It suits engineering teams already invested in OpenTelemetry who want LLM tracing without a proprietary SDK. Caveats: it is the instrumentation layer, not a full analytics product — for dashboards and evals you either use Traceloop's paid platform or wire up your own backend; and as an LLMOps tool it competes with richer all-in-one offerings. Free/open-source core, good docs, global/English.
What people say
OpenLLMetry is Traceloop's open-source observability layer for LLM applications, built as a set of extensions on top of OpenTelemetry rather than a proprietary SDK. That design choice is exactly why developers like it: traces of prompts, completions, token usage and latency are emitted as standard OTLP spans, so they flow into whatever backend you already run, including Datadog, New Relic, Honeycomb, Grafana and Sentry. The project has grown steadily since its 2023 launch and had passed 7,200 GitHub stars in recent roundups of open-source LLM observability tools.
Praise in developer write-ups and comparison guides focuses on the low-friction instrumentation, often just a couple of lines to initialise, and the breadth of coverage: 20-plus model providers including OpenAI, Anthropic, Gemini, Bedrock and Ollama, vector databases like Pinecone and Chroma, and frameworks such as LangChain, LlamaIndex and CrewAI, with SDKs in Python, TypeScript, Go and Ruby. Being vendor-neutral is repeatedly cited as the reason teams pick it over closed-protocol rivals, since there is no lock-in to a single dashboard.
The honest caveats: OpenLLMetry is plumbing, not a product. It ships no UI of its own, so you need an existing observability backend or Traceloop's commercial managed platform to actually look at the data. Teams also note the usual pains of a fast-moving space: instrumentations occasionally lag behind provider API changes, and OpenTelemetry's generative-AI semantic conventions are still settling, so span attributes can shift between versions.
It fits engineering teams that already run OpenTelemetry infrastructure and want LLM calls traced alongside the rest of their stack without adopting another vendor. Teams that want an out-of-the-box evaluation and prompt-analytics dashboard with no setup are better served starting with a hosted platform, whether Traceloop's own or a competitor like Langfuse.
Summary of public user & expert reviews, compiled by RECATOOLS.
About this listing
This entry was compiled from publicly available data including OpenLLMetry's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with OpenLLMetry unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
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