AgentOps
Python SDK for AI agent monitoring, LLM cost tracking and benchmarking, integrating with most major agent frameworks including CrewAI, LangChain and AutoGen.
- Actively maintained
- Permissive license
Overview
AgentOps gives visibility into what an agent actually did -- traces of its decisions, tool calls and reasoning steps -- plus LLM cost tracking and benchmarking, purpose-built for the multi-step, tool-using nature of agents rather than single LLM calls. It integrates with most major agent frameworks (several already in this catalog) instead of requiring a specific one.
AgentOps is a fit for teams running agents in production who need to debug why an agent took a particular action, track the real cost of running it, and catch regressions -- observability for agent behavior specifically, distinct from general LLM-response evaluation.
- Categories
- AI Agents & Agent Tools
- Languages
- Python
- License
- MIT
Related Projects
LangGraph
MITBuild resilient, stateful agents as graphs -- explicit nodes and edges for control flow, rather than an implicit agent loop, for reliable long-running or multi-step workflows.
Actively maintained
- AI Agents & Agent Tools
Composio
MITPowers 1000+ toolkits, tool search, context management and authentication so AI agents can actually take action, not just plan it.
Actively maintained
- AI Agents & Agent Tools
Letta
Apache-2.0A platform for building stateful agents with advanced memory that learns and self-improves over time, rather than resetting between conversations.
Actively maintained
- AI Agents & Agent Tools
MCP Servers
MITOfficial reference implementations of the Model Context Protocol -- the open standard now adopted across OpenAI, Google, Microsoft and Anthropic for connecting agents to tools and data.
Actively maintained
- AI Agents & Agent Tools
Spotted an error? Suggest an edit on GitHub.