AI Observability by OpenObserve provides OpenTelemetry-native tracing, session replays, and automated evaluations for AI agents and LLMs within a unified observability stack.
What the product does and how it is positioned
AI Observability by OpenObserve is an LLM and agent monitoring solution that ingests OpenTelemetry-native telemetry to trace model calls, tool executions, and sub-agent workflows.
The platform correlates AI application traces with underlying system metrics, logs, and datastores, enabling teams to evaluate response quality and troubleshoot agent failures using SQL and PromQL.
Source-supported ways to use the product
Engineering teams can visualize agent call trees and session replays to identify execution loops, tool failures, and latency bottlenecks.
Teams can evaluate live LLM requests against relevance, hallucination, bias, and toxicity benchmarks using LLM-as-judge or remote scorers.
OpenObserve tracks agentic applications by rendering an Agent Graph that visualizes interactions among sub-agents, foundation models, external tools, and datastores. Color-coded borders indicate error rates and degraded pathways across dependencies.
The session debugging interface allows engineers to replay multi-turn interactions. Users can inspect token distribution, tool call outputs, latency hotspots, and direct hops into underlying distributed traces.
Checks to run with your own material and workflow
What was checked and when
Answers based on the source-checked product record
No re-instrumentation is necessary if applications already emit standard OpenTelemetry gen_ai spans or follow OpenInference conventions. It supports frameworks such as LangChain, CrewAI, and LlamaIndex.
Online evaluation jobs score incoming spans, traces, or full sessions using remote HTTP scorers or LLM-as-judge with a selected model provider, comparing outputs against configurable thresholds.
OpenObserve can be deployed as a fully managed cloud service across four regions, self-hosted via a single Rust binary, or configured to store data in customer-owned object storage buckets.
Telemetry within OpenObserve, including LLM traces, infrastructure metrics, and logs, can be queried across the unified correlation engine using SQL and PromQL.
Production traces can be routed into annotation queues for manual scoring alongside automated evaluators, and reviewed traces can be converted into evaluation datasets for future testing.
