AgentOps

Observability and debugging platform purpose-built for AI agents, with time-travel replay and cost tracking across 400+ LLMs.

Freemium· Free up to 5,000 events; Pro from $40/mo; Enterprise customAgentsMulti-model

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Best for

Pick AgentOps if you are shipping multi-step or multi-agent systems on CrewAI/AutoGen/LangChain and need proper traces, replays, and cost visibility.

Skip if

Skip it if you only make single-shot LLM calls - a generic LLM logger or your existing APM will do the job with less overhead.

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.

Editor's take

AgentOps is the most agent-native observability tool we've tested, and the time-travel replay alone justifies wiring it in once your agent has more than two tool calls. The free tier is generous enough to prove value before you commit, and the open-source SDK reduces lock-in worry.

— The AI Tool Bible editorial team