AgentOps

AgentOps is an observability and monitoring platform built specifically for AI agents rather than traditional applications. It captures traces of what an agent actually did while running: every LLM call, tool invocation, decision branch, and dollar spent, so a developer can replay a session and see exactly where a multi-step agent went off track instead of piecing it together from scattered logs. It works through a single SDK that hooks into frameworks like CrewAI, AutoGen, and LangChain, plus direct support for OpenAI and a long list of other LLM providers, so teams don't need to instrument each framework separately.

It's aimed at engineers moving agents from prototype into production, where failures are harder to reproduce and cost overruns are easy to miss. Features like time-travel debugging (rewinding and replaying an agent run step by step), event-level tracking of tool and LLM calls, and built-in cost monitoring address problems that generic APM tools weren't built for: agents can loop, call the wrong tool, or burn through tokens in ways that only show up when you can see the full execution trail. Session replay and audit trails also help with compliance and post-incident review for teams running unattended or long-running agents.

What sets it apart from general observability tools is that it's purpose-built around agent execution patterns rather than adapted from web app tracing. It has a free tier for smaller projects (a set number of events per month) and paid plans for unlimited events and longer retention, with an enterprise option for self-hosted or on-prem deployment, which has made it a fairly common default choice for teams shipping agent-based products who need to debug behavior rather than just uptime.