AgentOps review: Observability, replay debugging and cost tracking for AI agent developers

What AgentOps does

AgentOps is a developer platform for observing AI agents and LLM applications. It records what an agent does during a run, including LLM calls, tool use and interactions between several agents, and shows these events visually so engineers can trace problems. Integration is through an SDK installed with pip, and the site lists support for OpenAI, CrewAI, Autogen and 400+ LLMs and frameworks.

For debugging, it offers replay of past agent runs, which the vendor describes as time travel debugging with point in time precision. A data trail of logs, errors and prompt injection attacks is kept from prototype through production. The platform also counts tokens and tracks spend across several agents, and the homepage mentions fine-tuning specialized models on saved completions.

Pricing is shown on the homepage; no separate pricing page was found.

• Visual tracking of LLM calls and tool use
• Multi-agent interaction tracing
• Replay of agent runs
• Logs, errors and prompt injection data trail
• Token counts
• Agent spend tracking
• SDK integrations with agent frameworks
• Self-hosting on Enterprise

Our take

AgentOps suits engineers who already build agents with common frameworks and want a record of runs, replay for debugging and a view of token spend in one place. The free Basic plan gives a way to try it, but it stops at 5,000 events, so active projects will likely move to Pro. Enterprise needs a conversation with the vendor. We did not test the product; this summary rests on the homepage only.