M4A

AutoGen

Microsoft framework for building multi-agent conversational systems

open-sourceproductionpythonmicrosoftmulti-agentopen-sourcecode-generation
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Memory Types

episodic, conversation

Integrations

openai, azure, anthropic, ollama


Overview


AutoGen is Microsoft's open-source framework for building multi-agent conversational systems. It enables the creation of LLM applications using multiple agents that can converse with each other to solve tasks. AutoGen is particularly strong for code generation, debugging, and complex problem-solving scenarios requiring iterative refinement.


The framework introduces the concept of "conversable agents" that can be customized with different capabilities, tools, and LLM configurations. Agents can execute code, use tools, and collaborate through structured conversations, making AutoGen powerful for automated software development and data science workflows.


Key Features


  • **Conversable Agents**: Agents communicate through structured conversations
  • **Code Execution**: Built-in safe code execution environment
  • **Human-in-the-Loop**: Easy integration of human feedback
  • **Group Chat**: Multiple agents participate in group conversations
  • **Auto-Reply**: Configurable automatic response mechanisms
  • **Tool Integration**: Agents can use custom tools and functions
  • **Customizable Workflows**: Flexible agent interaction patterns
  • **Multi-LLM Support**: Use different LLMs for different agents

    • When to Use AutoGen


    AutoGen is ideal for:

  • Automated software development and code generation
  • Complex problem-solving requiring multiple perspectives
  • Data science workflows with iterative analysis
  • Applications needing code execution capabilities
  • Research and experimentation with multi-agent systems
  • Tasks benefiting from human-AI collaboration

    • Pros


  • Strong Microsoft backing and support
  • Excellent for code generation use cases
  • Built-in code execution environment
  • Flexible agent conversation patterns
  • Active development and research
  • Good documentation with examples
  • Open-source and free
  • Integration with Azure OpenAI

    • Cons


  • Steeper learning curve than simpler frameworks
  • Primarily Python-focused
  • Can be complex to debug multi-agent interactions
  • Resource-intensive with multiple agents
  • Less opinionated than specialized frameworks
  • Smaller community than LangChain
  • Documentation could be more comprehensive

    • Pricing


  • **Open Source**: Free, MIT license
  • **Azure Integration**: Azure OpenAI costs apply
  • **Self-Hosted**: Free to deploy anywhere

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