AutoGen Graph - I Created AI Agents to Automate Tasks at Reduced Cost
AI Summary
Autogen Graph Overview
- Introduction to Autogen Graph
- Autogen Graph is a system for managing workflows among multiple agents.
- It prevents unnecessary and random conversations between agents, saving time and costs.
- Predefines communication paths and task assignments for efficiency.
- Demonstration Setup
- Three teams (A, B, C) are used in a use case to control workflow.
- The goal is to count the total number of chocolates given to each team.
- Team members can only communicate within their team, and team managers communicate with each other.
- Technical Steps
- Create a Python environment and install necessary packages (
autogen,matplotlib,networkx).- Set up OpenAI API key and create
app.py.- Import required modules and configure the language model.
- Define agents and teams, and assign random chocolate counts to agents.
- Establish communication rules within and between teams using a graph dictionary.
- Optional graph visualization to show chocolate distribution.
- Implement a termination message for when the task is complete.
- Create a group chat with a manager to oversee the communication flow.
- Initiate the process with the first agent and pass messages according to the predefined rules.
- Execution and Results
- Run the code to see the visualization and communication flow.
- Each team calculates their chocolate count and passes the total to the next team.
- The final summary shows the total chocolates for each team and overall.
- Conclusion
- Autogen Graph can be applied to various team-based workflows, such as software development, to improve efficiency and reduce costs.
- The presenter encourages viewers to subscribe for more content on artificial intelligence.
Additional Notes
- The presenter emphasizes the importance of subscribing and liking the video for more AI-related content.
- The example provided is a simple game, but the principles can be applied to real-world team management scenarios.