AI Observability by OpenObserve
OpenTelemetry-native observability for agents and LLMs
AI Observability by OpenObserve launched on August 21, 2026 with 317 votes, #74 of 23,280 launches that month and more than 99% of that year's launches.
In plain words
OpenObserve provides observability for AI agents and language models using OpenTelemetry standards. It traces agent sessions across models, tools, services, and datastores to show where time and costs are spent. The platform integrates with existing observability stacks to display LLM traces alongside logs, metrics, and performance data, helping developers identify bottlenecks, detect loops, and attribute costs per token. It is priced per gigabyte rather than per span and can be deployed across multiple cloud regions.
written from the facts on this page · September 2026
From the sources
Your agent cost $40 and took 34 seconds. But why? OpenObserve traces every agent session across models, tools, services, datastores, and user sessions so you can see exactly where time, money, and quality went. Detect loops, run online evals, and follow failures from the LLM call through your backend and database, alongside the logs, traces, and metrics from the rest of your production stack.
Trace every agent, tool call, and model request. Score quality on live traffic and attribute cost per token, OpenTelemetry-native and priced per GB.
Does a similar job
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OpenLLMetry – OpenTelemetry-based observability for LLMs2023 · github.com · ▲154Hey HN, Nir, Gal and Tomer here. We’re open-sourcing a set of extensions we’ve built on top of OpenTelemetry that provide visibility into LLM applications - whether it be prompts, vector DBs and more. Here’s the repo: https://github.com/traceloop/openllmetry. There’s already a decent number of tools for LLM observability, some open-source and some not. But what we found was missing for all of them is that they were closed-protocol by design, vendor-locking you to use their observability platform or their proprietary framework for running your LLMs. It’s still early in the…
Progress AI ObservabilityAug 2026 · telerik.com · ▲168Hi HN! We’re the team behind Fiddler, and we recently built an Agent Cache feature. It works by capturing an LLM request/response pair and, once caching is enabled, serving subsequent matching calls locally so duplicate requests never hit the provider. It’s meant to reduce three pains that show up together in agent development: token cost, feedback-loop latency, and output non-determinism. We’d love to hear your thoughts.
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You don't need to adopt new tools for LLM observability2024 · github.com · ▲102If you've built any web-based app in the last 15 years, you probably used something like Datadog, New Relic, Sentry, etc. to monitor and trace your app, right? Why should it be different when the app you're building happens to be using LLMs? So today we're open-sourcing OpenLLMetry-JS. It's an open protocol and SDK, based on OpenTelemetry, that provides traces and metrics for LLM JS/TS applications and can be connected to any of the 15+ tools that already support OpenTelemetry. Here's the repo: https://github.com/traceloop/openllmetry-js A few months ago we launched…
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