agentops
VerifiedObservability platform that tracks, debugs, and optimizes AI agent performance.
What is agentops?
AgentOps serves as a dedicated observability layer for AI agents, capturing detailed execution traces, LLM interactions, and performance metrics in real time. It connects natively with frameworks such as CrewAI, LangChain, AutoGen, and others to surface step-by-step graphs that help identify issues quickly.
Developers initialize the client with a few lines of code to automatically log sessions, track token usage, and visualize costs across foundation models. The open-source codebase allows teams to run the service on their own infrastructure while still benefiting from replay debugging and framework-specific integrations.
The tool targets AI engineers and teams building autonomous agents who need visibility into complex multi-step workflows without adding heavy instrumentation overhead.
Capabilities
What you can build with agentops
Debugging Agent Workflows
Replay full execution graphs to pinpoint where an agent made incorrect decisions or failed to call tools correctly.
Controlling LLM Spend
Monitor cumulative costs per session or project and set alerts when usage approaches budget limits across different model providers.
Evaluating Framework Integrations
Compare agent behavior across CrewAI, LangGraph, or AutoGen by collecting consistent telemetry without changing core application code.
Install agentops
pip install agentopspip install agentops- 1Install the package using pip install agentops.
- 2Obtain an API key from the AgentOps dashboard.
- 3Import agentops at the start of your main script or module.
- 4Call agentops.init() with your API key to begin automatic session tracking.
- 5Run your agent code and review analytics in the web dashboard or self-hosted instance.
Works with
agentops: pros & cons
Pros
- +Broad native support for major agent frameworks reduces integration effort.
- +Open-source MIT license and self-hosting capability give users full control over data.
- +Automatic capture of LLM calls and cost metrics provides immediate visibility.
- +Step-by-step replay graphs simplify debugging of complex agent trajectories.
Cons
- –Requires an external API key for hosted features even in open-source usage.
- –Self-hosting setup may demand additional infrastructure and maintenance.
- –Focuses primarily on observability rather than providing agent-building primitives.
Frequently asked questions
Minimal changes are needed; initialization typically takes two lines and automatically instruments supported frameworks.
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