AgentOps

AgentOps: See What Your AI Agents Actually Do (And What They Cost)

Your AI agent failed. Was it the prompt? A bad tool call? A hallucinated step in a 12-step chain? Standard logging gives you a wall of text. AgentOps gives you a visual timeline of every decision, every LLM call, and every tool interaction your agent made. Then it lets you rewind to any point and replay the sequence. That's the core pitch, and it works.

Session Replay and Debugging

The standout feature is session replay with step-by-step execution graphs. When an agent fails at step 8 of a 15-step workflow, you jump to step 7, see exactly what context the agent had, what the LLM returned, and what tool call followed. No more reconstructing failures from log files.

For multi-agent systems, this gets more valuable. When Agent A hands off to Agent B, and Agent B makes a bad decision, you can trace the exact information that passed between them. Developers building with CrewAI, AG2, or LangGraph report this as the feature that sold them.

LLM Cost Tracking

AgentOps tracks tokens and costs in real time across 400+ LLMs and providers. Running a Claude agent alongside a GPT-4 agent alongside a local Llama model? AgentOps shows you the cost of each call, each agent, and each workflow. The spend visualization breaks down where your money goes.

This matters because multi-agent systems burn tokens in ways that are hard to predict. A single agent might make 3 LLM calls. A CrewAI workflow with 5 agents might make 30. Without cost tracking, your API bill becomes a surprise.

Open Source and Self-Hostable

AgentOps is open-source under the MIT license. The entire app — dashboard and API backend — is available on GitHub for self-hosting on AWS, GCP, or Azure. Teams that need data sovereignty or want to avoid SaaS dependencies can run AgentOps on their own infrastructure. The hosted version at app.agentops.ai remains available for teams that prefer managed service.

Framework Integrations

AgentOps provides native integrations with CrewAI, AG2 (AutoGen), Agno, OpenAI Agents SDK, LangChain, LangGraph, and CamelAI. The Python SDK uses decorators (@session, @agent, @operation) to instrument your code with minimal changes — often just two lines to get started.

How It Compares

LangSmith offers similar observability but ties more closely to the LangChain ecosystem. AgentOps is framework-agnostic. Helicone focuses on LLM proxy and cost tracking without agent-specific debugging. Langfuse provides open-source observability with self-hosting options. Weights & Biases tracks ML experiments broadly but doesn't understand agent-specific patterns like tool calls and handoffs.

AgentOps's sweet spot: teams running multi-agent systems across multiple LLM providers who need both cost visibility and debugging without locking into a specific framework.

What Real Users Say

Developers on Reddit's r/AI_Agents highlight the unified monitoring for multi-platform agents as the key value. CrewAI users specifically mention the framework integration as a major draw. The platform reports powering thousands of engineers building reliable agents. Common asks include expanded documentation for advanced features and more language SDK support beyond Python.