Master AgentOps with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Sign up for a free account at agentops.ai and obtain your API key Install the SDK in your project with pip install agentops or npm install agentops Add two lines to your application: import agentops and call agentops.init(api_key) Run your agent and visit the AgentOps dashboard to view session replays and telemetry Add agentops.end_session('Success') or agentops.end_session('Fail') to mark session outcomes
💡 Quick Start: Follow these 1 steps in order to get up and running with AgentOps quickly.
Explore the key features that make AgentOps powerful for enterprise agents workflows.
Just two lines: import the AgentOps SDK and call the init function with your API key. The SDK automatically instruments supported frameworks.
AgentOps supports a wide range of frameworks including CrewAI, AutoGen, LangChain, OpenAI Agents SDK, Cohere, Mistral, LiteLLM, and AG2. Custom agents can be instrumented via the Python or TypeScript SDK.
Yes. The entire AgentOps platform is open source under the MIT license and can be self-hosted for teams that need full data control or air-gapped deployments.
AgentOps is framework-agnostic and open source, while LangSmith is tightly integrated with the LangChain ecosystem. AgentOps emphasizes agent-level observability with time travel debugging, whereas LangSmith focuses on LLM chain tracing and evaluation.
Time travel debugging lets you replay an agent session step by step, examining every LLM call, tool invocation, and decision point in sequence to diagnose exactly where and why a workflow succeeded or failed.
Yes, the free tier includes up to 5,000 events per month with core observability features. The Pro plan starts at $40/month for higher volumes, and Enterprise pricing is available by contacting sales.
Now that you know how to use AgentOps, it's time to put this knowledge into practice.
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Tutorial updated March 2026