AgentOps AutoGen AI Agent Monitoring: Step-by-Step Guide

In the video titled ‘Track AutoGen Agents with AgentOps’ by business24_ai, the host provides a step-by-step tutorial on how to use AgentOps to monitor and evaluate AI agents created with the AutoGen framework. The video begins by introducing AgentOps, a tool that helps developers build, evaluate, and monitor AI agents by tracking LLM calls, costs, tokens, latency, and agent interactions. The host explains that AgentOps provides detailed statistics and visual representations of each step in the agent’s workflow.

The tutorial is conducted using Google Colab, which simplifies the setup process by eliminating the need to install Python environments or set up Conda environments. The host provides a link to the Skool community, where viewers can access the Colab notebook and other resources related to the tutorial. The tutorial covers the following steps:

1. Creating API keys for AgentOps and OpenAI.
2. Assigning the API keys in Colab’s secret environment variables.
3. Installing the necessary packages (pyautogen and agentops) in Colab.
4. Initializing AgentOps and configuring AutoGen with a ConversibleAgent and a UserProxyAgent.
5. Running a sample interaction between the agents and monitoring the session using AgentOps.

The host demonstrates how to set up the ConversibleAgent and UserProxyAgent, initialize a chat session, and monitor the interactions between the agents. After the chat session, the host shows how to end the AgentOps session and view the detailed statistics and interactions on the AgentOps dashboard.

The tutorial emphasizes the importance of using AgentOps to evaluate and monitor agent activities to build more reliable AI agents. The host also mentions that future videos will cover the use of tools within agents and provide updated Colab notebooks in the Skool community.

Overall, the video provides a comprehensive guide for beginners to set up and use AgentOps with AutoGen to monitor and evaluate AI agents effectively.