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AI Observability by OpenObserve

OpenTelemetry-native observability for agents and LLMs

Informations de la fiche

Fiche d’annuaire HiCyou

Répertorié
Site web répertorié
openobserve.ai
Fiche d’annuaire créée
11 sept. 2026
Dernière mise à jour de la fiche
17 sept. 2026

Informations incluses dans la fiche

  • Présentation
  • 6 fonctionnalités clés
  • 4 cas d’usage

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AI Observability by OpenObserve preview

Qu'est-ce que 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.

Fonctionnalités clés

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

Cas d'utilisation

  • 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

Pourquoi les startups ont-elles besoin de cet outil ?

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.

Questions fréquentes

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