Introduction to AgentBuilder | Automate your Agents with AutoGen

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AI Summary

Summary: Agent Builder Introduction

• Concept: Agent Builder automates the creation of agents for specific tasks and selects the best ones for execution.
• Process:
  1. Configuration setup with config list and llm config.
  2. Generation of agents saved into a JSON file.
  3. Creation of an AgentBuilder class instance.
  4. Calling build from library function.
  5. Execution of the task by selecting the best agents.
• Implementation Steps:
  1. Create a new project in an IDE (e.g., PyCharm Community Edition).
  2. Install necessary libraries (PiPi autogen and chroma DB).
  3. Create a utility Python file with tasks, system prompts, and agent names.
  4. Create a main Python file to:
     • Import modules and set up variables.
     • Define functions to generate agents and start tasks.
     • Execute functions and save results to JSON files.
  5. Run the project and observe the creation and selection of agents for a given task.
• Execution Details:
  • The system generates agents, selects the best ones, and executes a task (e.g., creating a Python function to reverse a string).
  • The process involves building configurations, generating system messages, and managing group chats.
  • Results are saved to JSON files for review.
• Additional Notes:
  • Docker may be required; if not present, set Auto gen _ Docker to false.
  • The system may not use all generated agents, indicating room for improvement.
  • The concept allows AI to handle more tasks, including agent selection.
• Conclusion:
  • Agent Builder is a promising tool that automates agent creation and task execution.
  • The tutorial provides a basic understanding of how to use Agent Builder.
  • Future improvements are expected to enhance the system’s efficiency.