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
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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.
Deep Analysis
vs LangSmith/Langfuse: purpose-built for AI agents with session replay, execution graphs, and native multi-framework support (CrewAI, AG2, OpenAI Agents SDK)
⚡ Capabilities
- • Session replay analytics for AI agents
- • LLM cost tracking across providers
- • Step-by-step agent execution graphs
- • Decorator-based span hierarchy (session/agent/operation/workflow)
- • Native framework integrations (CrewAI, AG2, LangChain, Camel)
- • Self-hosted deployment option
- • MCP server support
🔗 Integrations
✓ Best For
- ✓ Monitoring and debugging AI agent systems in production
- ✓ Teams needing LLM cost visibility across multiple providers
✗ Not Ideal For
- ✗ Building agents from scratch (use with an agent framework)
- ✗ Simple single-call LLM applications
Languages
Deployment
⚠ Known Limitations
- ⚠ Requires API key for cloud dashboard
- ⚠ Self-hosting requires infrastructure setup
- ⚠ Focus on observability, not agent building
- ⚠ Some integrations still in early stage
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