Agentops

By providing comprehensive visibility into every event that occurs during agent execution including LLM API calls, tool invocations, memory operations, multi-agent interactions, and cost accumulation AgentOps gives AI engineering teams the diagnostic clarity they need to understand, improve, and maintain AI systems that are too complex to debug through traditional logging and inspection approaches.

The platform integrates with the most widely adopted AI agent frameworks and LLM providers out of the box, including CrewAI, Autogen, AG2, OpenAI Agents SDK, LangChain, CamelAI, and more than 400 LLMs and agent frameworks.

This broad compatibility means engineering teams can add AgentOps observability to their existing agent implementations without rewriting code around a proprietary framework a critical adoption advantage for teams that have already invested in building agents on established ecosystems.

Session replay with point-in-time precision is one of AgentOps' most powerful debugging capabilities.

Engineers can rewind and replay any agent execution session to examine exactly what happened at each decision point what inputs the LLM received, what outputs it produced, which tools were called, what data was retrieved from memory, and how the agent reasoned through its task.

This replay capability transforms the opaque black box of AI agent behavior into a transparent, inspectable execution trace that reveals root causes of failures and unexpected behaviors.

Cost tracking and spend management are built into the AgentOps monitoring layer, providing real-time visibility into LLM API costs as they accumulate across agents and sessions.