AgentOps

AgentOps is a developer platform for instrumenting, monitoring, and debugging AI agents in both development and production. It captures LLM calls, tool invocations, and multi-agent interactions as structured events, then surfaces them through a visual timeline with point-in-time replay so you can walk backward through a failed run and see exactly what the agent saw. Token counting, cost tracking, and audit trails come standard, and completions can be exported for fine-tuning.

The pitch is for teams shipping agents on frameworks like CrewAI, AutoGen, and LangChain who have outgrown print-debugging and generic APM. A single Python SDK covers 400+ LLMs and frameworks, so most integrations are a couple of lines. Pricing is genuinely freemium: 5,000 events per month at the free tier, Pro from about $40/month with unlimited events, and Enterprise for on-prem, SSO, and SOC-2/HIPAA/NIST AI RMF compliance. The core SDK is open source with an active GitHub community.

Compared to general LLM tracing tools, AgentOps is opinionated about the agent shape - multi-step reasoning, tool calls, sub-agent handoffs - rather than treating each prompt as an isolated span. That focus is the reason to choose it, and also the reason it may feel overkill if you're just logging single-shot chat completions.