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AI Observability by OpenObserve

OpenTelemetry-native observability for agents and LLMs

Eintragsdetails

HiCyou-Verzeichniseintrag

Gelisted
Gelistedes Ziel
openobserve.ai
Verzeichniseintrag erstellt
11.09.2026
Letzte Aktualisierung des Eintrags
17.09.2026

Im Eintrag enthaltene Informationen

  • Überblick
  • 6 Kernfunktionen
  • 4 Anwendungsfälle

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AI Observability by OpenObserve preview

Was ist AI Observability by OpenObserve

AI Observability by OpenObserve is an OpenTelemetry-native observability tool for AI agents and LLM applications. It traces every agent session across models, tools, services, datastores, and user sessions, showing where time, cost, and quality are lost. Teams can detect loops, run online evaluations on live traffic, attribute cost per token, and follow failures from the LLM call through backend and database alongside logs, traces, and metrics from the rest of the production stack.

Hauptfunktionen

End-to-end tracing of agent sessions across models, tools, services, and datastores
OpenTelemetry-native instrumentation
Cost attribution per token and per request
Detection of agent loops and anomalous behavior
Online evaluations (evals) scored on live traffic
Correlation of LLM calls with backend logs, traces, and metrics

Anwendungsfälle

  • Diagnosing why an AI agent is slow or expensive
  • Debugging agent failures across the full stack, from LLM call to database
  • Monitoring quality of LLM outputs on production traffic
  • Tracking and attributing AI cost across models and tools

Warum brauchen Startups dieses Tool?

Startups building AI agents and LLM features need visibility into cost, latency, and output quality without stitching together multiple monitoring tools. AI Observability by OpenObserve combines agent tracing, online evals, and cost attribution with full-stack observability in one OpenTelemetry-native platform, helping small teams debug faster and control AI spend.

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