AgentOps

What is AgentOps?

AgentOps is observability for AI agents. You instrument your agent with a lightweight SDK, and it records what actually happened during a run: every LLM call, every tool invocation, and the sequence of decisions that led somewhere useful or somewhere wrong. That timeline is the product. It also tracks cost and surfaces errors, so a run that quietly burned tokens on a retry loop is visible instead of invisible. It supports agents built on CrewAI, AutoGen, LangChain, and hand-rolled setups, which matters because agent code tends to be assembled from parts rather than bought whole. Session replay is the piece that changes day-to-day work: rather than re-running a nondeterministic agent and hoping the bug reappears, you open the recorded session and read what the agent saw and chose at each step.

Who is AgentOps for?

This becomes necessary the moment an agent moves from your laptop to something other people depend on. If you have ever had an agent work five times and fail the sixth with no idea why, that is the exact problem AgentOps addresses. It fits teams debugging flaky multi-step workflows, anyone trying to find where token spend is actually going, and anyone who needs a record of agent behavior for review. It is overkill for a single-prompt script or a one-off automation you run by hand and watch finish. It is also not a build tool: you still need a framework or your own code to create the agent. AgentOps only tells you what it did.

How does AgentOps compare?

CrewAI is what you build with, AgentOps is what you watch through, and plenty of teams run both. Letta is likewise a framework for building stateful agents rather than a monitoring layer, so comparing them on features misses the point. AgentForce is the contrasting model: Salesforce keeps agent construction, testing, and oversight inside one closed platform, so its customers get an equivalent of this built in and lose the ability to use it elsewhere. AgentOps assumes the opposite world, where you picked your own framework and models and now need one place to see runs across them. Choose it when your stack is assembled rather than bought.

Compare with: CrewAI, Letta, AgentForce

AgentOps pricing

The catalog records AgentOps as freemium. Its own description notes a free tier covering a limited number of sessions, with paid plans intended for production monitoring. That shape is normal for observability tooling and it means the evaluation path is easy: instrument a project, watch real runs, and decide whether the visibility is worth paying for before any commitment. The thing to watch as usage grows is session volume, since that is the axis these products usually meter on. Confirm current limits on the plan page.