Agentops
Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most
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About
Observability and DevTool platform for AI Agents
AgentOps helps developers build, evaluate, and monitor AI agents. From prototype to production.
Open Source
The AgentOps app is open source under the MIT license. Explore the code in our app directory.
Key Integrations 🔌
| 📊 Replay Analytics and Debugging | Step-by-step agent execution graphs |
| 💸 LLM Cost Management | Track spend with LLM foundation model providers |
| 🤝 Framework Integrations | Native Integrations with CrewAI, AG2 (AutoGen), Agno, LangGraph, & more |
| ⚒️ Self-Host | Want to run AgentOps on your own cloud? You're covered |
Quick Start ⌨️
pip install agentops#### Session replays in 2 lines of code
Initialize the AgentOps client and automatically get analytics on all your LLM calls.
import agentops
# Beginning of your program (i.e. main.py, __init__.py)
agentops.init( )
...
# End of program
agentops.end_session('Success')All your sessions can be viewed on the AgentOps dashboard
Self-Hosting
Looking to run the full AgentOps app (Dashboard + API backend) on your machine? Follow the setup guide in app/README.md:
Agent Debugging
Session Replays
Summary Analytics
First class Developer Experience
Add powerful observability to your agents, tools, and functions with as little code as possible: one line at a time.
Refer to our documentation
# Create a session span (root for all other spans)
from agentops.sdk.decorators import session
@session
def my_workflow():
# Your session code here
return result# Create an agent span for tracking agent operations
from agentops.sdk.decorators import agent
@agent
class MyAgent:
def __init__(self, name):
self.name = name
# Agent methods here# Create operation/task spans for tracking specific operations
from agentops.sdk.decorators import operation, task
@operation # or @task
def process_data(data):
# Process the data
return result# Create workflow spans for tracking multi-operation workflows
from agentops.sdk.decorators import workflowDon't lose this
Three weeks from now, you'll want Agentops again. Will you remember where to find it?
Save it to your library and the next time you need Agentops, it’s one tap away — from any AI app you use. Group it into a bench with the rest of the team for that kind of task and you can pull the whole stack at once.
⚡ Pro tip for geeks: add a-gnt 🤵🏻♂️ as a custom connector in Claude or a custom GPT in ChatGPT — one click and your library is right there in the chat. Or, if you’re in an editor, install the a-gnt MCP server and say “use my [bench name]” in Claude Code, Cursor, VS Code, or Windsurf.
a-gnt's Take
Our honest review
Python SDK for AI agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most . Best for anyone looking to make their AI assistant more capable in devops & monitoring. It's completely free and works across most major AI apps. This one just landed in the catalog — worth trying while it's fresh.
Tips for getting started
Tap "Get" above, pick your AI app, and follow the steps. Most installs take under 30 seconds.
Heads up: this needs an API key to work. You'll get one from the service's website (usually free). The setup guide tells you exactly where.
What's New
Imported from GitHub
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