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AI Observability by OpenObserve

Traces agent sessions across models, tools, services, and data stores to expose cost and failures.

Product brief

What AI Observability by OpenObserve does.

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.

Why we selected it

A concrete production layer for teams deploying agents: it connects model behavior to tool, backend, database, cost, latency, and quality signals. It is especially timely as managed agent runtimes make operational trace,

Best for
Teams operating agents in production
Category
Coding
Daily picks
1
First selected
2026-09-10
AI Observability by OpenObserve product preview

Product preview saved with our daily selection

Capability scan

What it can help with.

Only capabilities supported by the product information we collected are listed here.

01

Trace agent sessions

Traces agent sessions across models, tools, services, datastores, and user sessions.

02

Connect operational signals

Shows where time, cost, and quality went, alongside logs, traces, and metrics from the production stack.

03

Detect agent loops

Detects loops in agent activity.

04

Run online evaluations

Supports online evals for agent behavior.

05

Follow failures across systems

Follows failures from an LLM call through backend and database activity.

Best-fit use cases

Investigating why an agent session had high cost or latency.
Tracing a failed agent request from an LLM call into backend and database activity.
Detecting loops in production agent behavior.
Reviewing agent quality alongside logs, traces, and metrics.

FAQ

Before you open it.

What does AI Observability by OpenObserve trace?

It traces agent sessions across models, tools, services, datastores, and user sessions.

Can it help investigate agent failures?

Yes. It follows failures from the LLM call through the backend and database.

What signals can teams review together?

The product describes time, cost, and quality signals alongside logs, traces, and metrics from the rest of a production stack.

Does it support evaluations?

Yes. The description states that it can run online evals.