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Chanchal Singh
Chanchal Singh

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How to Use AutoGen to Build AI Agents That Collaborate Like Humans

#ai #llm #python #autogen

Curious how AI agents can collaborate like teammates?
Meet AutoGen by Microsoft—an open-source framework that lets you build LLM-based agents. These agents can communicate, make decisions, write code, and complete tasks collaboratively — all within a few lines of Python.

This guide breaks down how AutoGen works, how it compares with other agent frameworks, and how you can start building multi-agent workflows today.

1. Getting Started with AutoGen

AutoGen provides pre-built agent classes that allow LLMs to interact in a structured way. You don't need to build complex orchestration manually — agents handle it for you.

Key components:

  • ChatAgent: Base class for LLM-powered agents that can send and receive messages.
  • AssistantAgent: A helpful agent designed to follow a specific goal or behavior.
  • UserProxyAgent: Acts like a human user; can respond manually or automatically.

These agents engage in turn-based communication — you define their roles, connect them to a GroupChat, and let them work together. You can chain their outputs and create intelligent, decision-making workflows.

Example:
You can create a group chat between a code generator agent and a critique agent. One writes Python code, the other reviews and suggests improvements — no human needed in the loop.

2. Core Architecture

AutoGen is built to coordinate multiple LLM agents in a chat-based setup, allowing them to collaborate on complex tasks. Here's how it's structured:

2.1 Agents (The Brains)

Each agent is like a person with a specific role. Types include:

Agent Class Purpose Example
UserProxyAgent Acts like a human user, gives tasks “Can you summarize this document?”
AssistantAgent General-purpose LLM agent Like ChatGPT with a role
GroupChatManager Coordinates a group of agents Like a manager in a team

You can create custom agents with specific goals and behaviors using prompts.

2.2 GroupChat & GroupChatManager (The Communication Layer)

  • GroupChat: Enables multi-agent conversations.
  • GroupChatManager: Handles message passing, decides who speaks next, and maintains conversation history.

Think of it as a WhatsApp group with rules on who replies when.

2.3 Tools & CodeExecutor (The Muscles)

Agents can use tools to:

  • Execute Python code
  • Call external APIs
  • Access the web, databases, or internal functions

This lets AutoGen agents not just think, but act.

2.4 Configurations (config_list)

This is how you plug in your LLMs:

  • Choose between OpenAI, Azure OpenAI, local LLMs, or Hugging Face models
  • Set temperature, API keys, max tokens, etc.

The config_list is especially useful if you want to switch between OpenAI, Azure OpenAI, HuggingFace models, or your own local models. No need to change your core logic — just update the config.

Example config_list:

config_list = [
    {
        "model": "gpt-4",
        "api_key": "your-key",
        "base_url": "https://api.openai.com/v1"
    }
]
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2.5 Custom Functions & Function Calling

You can write your own Python functions, and agents can:

  • Call them using the tool interface
  • Use results in conversation
  • Chain logic like “if X, then do Y”

Diagram Summary

Autogen Architecture workflow

What Makes AutoGen Special

  • Built-in multi-agent coordination
  • Direct support for function calling
  • Highly customizable workflows
  • Easily scales to complex use cases (e.g. financial analysis, code generation, data pipelines)

The config_list is especially useful if you want to switch between OpenAI, Azure OpenAI, HuggingFace models, or your own local models. No need to change your core logic — just update the config.

3. How AutoGen Compares to Other Agent Frameworks

Let’s see how AutoGen stands against popular frameworks like LangChain, CrewAI, and MetaGPT.

Framework Multi-Agent Support Tool/Code Integration Developer Backing
AutoGen Built-in and native Strong (Python, APIs) Microsoft
LangChain Partial (via tools) Strong Community-supported
CrewAI Modular, goal-driven Limited Community-supported
MetaGPT Yes Yes Open-source

Why choose AutoGen:

  • Clean API for defining and managing multi-agent systems
  • Seamless code execution and tool usage
  • Configurable and modular
  • Actively maintained with solid documentation

4. Models and Personalization

AutoGen supports a variety of LLM providers:

  • OpenAI (GPT-3.5, GPT-4)
  • Azure OpenAI
  • HuggingFace models
  • Local LLMs (e.g., via LMStudio or Ollama)

You can assign custom roles and goals to each agent, tailoring them for specific tasks.
Example use case:

  • “Data Analyst Agent” reads user queries and generates insights
  • “Python Coder Agent” translates those insights into code
  • Together, they automate a business reporting task end-to-end

You can also integrate tools like:

  • Web search APIs
  • File readers
  • Database query engines
  • Custom Python utilities

5. Conclusion

Microsoft AutoGen is a game-changer for developers looking to build intelligent, collaborative AI systems. It combines simplicity with powerful orchestration features, enabling:

  • Multi-agent collaboration
  • Seamless tool and code integration
  • Easy model switching

Whether you're building chatbots, task assistants, coding agents, or autonomous systems — AutoGen gives you a structured way to scale LLM-powered applications.

References & Links

  1. GitHub: https://github.com/microsoft/autogen
  2. Documentation: https://microsoft.github.io/autogen/stable/user-guide/agentchat-user-guide/index.html

I love breaking down complex topics into simple, easy-to-understand explanations so everyone can follow along. If you're into learning AI in a beginner-friendly way, make sure to follow for more!

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