Observability and testing for AI agents, with session replays and cost tracking
AgentOps is an observability platform for AI agents. It records agent sessions - every step, tool call, model response, cost and error - and presents them as a replay you can inspect when something behaves unexpectedly. Alongside tracing it offers evaluation runs, regression testing across prompt or model changes, and spend tracking broken down by agent and session. It integrates with the major agent frameworks through a few lines of code, which is the practical way to find out why an agent took a wrong turn in production rather than guessing from logs.
Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.
agent developers, debugging production agents, cost monitoring and evaluation
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Visibility. Agents fail in ways ordinary logs hide, so a captured replay of steps, tool calls and costs is usually the fastest route to understanding why a run went wrong.
The common Python agent and LLM frameworks are covered through lightweight integrations, and a generic SDK is available for custom code that does not use a framework.
Not necessarily. A single-step prompt is easy to reason about. The value appears once agents chain tools, loop, or run in production where cost and failure modes compound.
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