AgentOps helps developers build, test, and deploy AI agents with confidence. The platform provides observability for OpenAI, CrewAI, Autogen, and 400+ LLMs and frameworks. You get session tracking, cost monitoring, error detection, and replay analytics to understand exactly how your agents behave. Works with minimal code integration and supports popular frameworks like Langchain, AG2, and CamelAI.
AgentOps natively integrates with OpenAI Agents SDK (Python and TypeScript), CrewAI, Langchain, Autogen, AG2, and CamelAI. The platform supports 400+ LLMs and frameworks for comprehensive monitoring across your entire agent stack.
Yes. AgentOps is open source under the MIT license, allowing you to explore the codebase and self-host if needed. The source code is available in their GitHub repository.
Session replay lets you review past agent runs, capturing details like LLM calls, tool calls, timing, errors, and more so you can track exactly how an agent behaved and understand why it made certain decisions.
AgentOps tracks performance metrics including latency, error rates, success codes, token usage, and API costs. You get real-time alerts for anomalies, detailed execution traces, and dashboard analytics for trend analysis.
Integration requires minimal code. Initialize the AgentOps client in one line, then use decorators to track agents, operations, and workflows. The SDK automatically captures analytics on LLM calls and agent steps.
Yes. AgentOps is designed to observe interactions and data exchanges in complex, orchestrated AI systems where multiple specialized agents collaborate toward common goals.
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