Prefer asking your IDE? Install the Mintlify MCP Docs Server for AgentOps to chat with the docs while you code: npx mint-mcp add agentops

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Integrate with developer favorite LLM providers and agent frameworks

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Agent Frameworks

AG2

AG2

Agno

Agno

AutoGen

AutoGen

CrewAI

CrewAI

Google ADK

Google ADK

LangChain

LangChain

OpenAI Agents Python

OpenAI Agents (Python)

OpenAI Agents JS

OpenAI Agents (TypeScript)

smolagents

Smolagents

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LLM Providers

Anthropic

Anthropic

Gemini

Google Generative AI

OpenAI

OpenAI

LiteLLM

LiteLLM

IBM

Watsonx

x.AI

x.AI

Mem0

Mem0

Observability and monitoring for your AI agents and LLM apps. And we do it all in just two lines of code… 
import agentops
agentops.init(<INSERT YOUR API KEY HERE>)
… that logs everything back to your AgentOps Dashboard.
AgentOps is also available for TypeScript/JavaScript applications. Check out our TypeScript SDK guide for Node.js projects.
That’s it! AgentOps will automatically instrument your code and start tracking traces. Need more control? You can create custom traces using the @trace decorator (recommended) or manage traces manually for advanced use cases: 
import agentops
from agentops.sdk.decorators import trace

agentops.init(<INSERT YOUR API KEY HERE>, auto_start_session=False)

@trace(name="my-workflow", tags=["production"])
def my_workflow():
	# Your code here
	return "Workflow completed"
You can also set a custom trace name during initialization: 
import agentops
agentops.init(<INSERT YOUR API KEY HERE>, trace_name="custom-trace-name")

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The AgentOps Dashboard

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With just two lines of code, you can free yourself from the chains of the terminal and, instead, visualize your agents’ behavior in your AgentOps Dashboard. After setting up AgentOps, each execution of your program is recorded as a session and the above data is automatically recorded for you. The examples below were captured with two lines of code.

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Session Drilldown

Here you will find a list of all of your previously recorded sessions and useful data about each such as total execution time. You also get helpful debugging info such as any SDK versions you were on if you’re building on a supported agent framework like Crew or AutoGen. LLM calls are presented as a familiar chat history view, and charts give you a breakdown of the types of events that were called and how long they took.

Session Summary

Find any past sessions from your Session Drawer.

Session Drawer

Most powerful of all is the Session Waterfall. On the left, a time visualization of all your LLM calls, Action events, Tool calls, and Errors. On the right, specific details about the event you’ve selected on the waterfall. For instance the exact prompt and completion for a given LLM call. Most of which has been automatically recorded for you.

Session Waterfall

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Session Overview

View a meta-analysis of all of your sessions in a single view.

Session Overview