agentops
Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks including CrewAI, Agno, OpenAI Agents SDK, Langchain, Autogen, AG2, and Ca
Overview
AgentOps is a comprehensive observability and development platform for AI agents, providing Python SDK for monitoring, cost tracking, and benchmarking. It offers end-to-end observability from prototype to production, helping developers build, evaluate, and monitor AI agents effectively. The platform integrates seamlessly with major AI frameworks including CrewAI, OpenAI Agents SDK, Langchain, Autogen, and AG2. With over 5400 GitHub stars, AgentOps has become a popular choice for developers working with AI agents. The platform provides detailed insights into agent performance, LLM usage costs, and operational metrics. As an open-source tool under MIT license, it offers transparency and community-driven development. AgentOps addresses the critical need for visibility in AI agent workflows, enabling developers to identify bottlenecks, optimize costs, and ensure reliable agent performance in production environments.
Pros
- + Comprehensive integration ecosystem supporting major AI frameworks like CrewAI, OpenAI Agents SDK, Langchain, and Autogen
- + Open-source under MIT license with active community development and regular updates
- + Complete observability suite covering monitoring, cost tracking, and benchmarking from prototype to production
Cons
- - Limited to Python ecosystem, which may not suit developers using other programming languages
- - Requires integration setup with each agent framework, potentially adding complexity to existing workflows
Use Cases
- • Monitoring production AI agent performance and identifying bottlenecks in agent workflows
- • Tracking and optimizing LLM usage costs across different agent frameworks and models
- • Benchmarking agent performance during development and comparing different agent implementations