The definitive guide to building multi-agent AI systems using Microsoft's AutoGen framework version 0.6.1
Author: James Karanja Maina
This repository contains the complete guide to building multi-agent AI systems using Microsoft's AutoGen framework version 0.6.1. The book covers the entire framework from basic concepts to advanced production deployments, with a focus on practical implementation and best practices.
AutoGen v0.6.1 introduces a sophisticated, modular architecture designed for scalable multi-agent systems:
The book is organized into three main parts with additional dedicated chapters:
- Chapter 1: Introduction to AutoGen - Core concepts and framework overview
- Chapter 2: Assistant Agents and MCP - Building conversational agents
- Chapter 3: Agent Types and Streaming - Understanding different agent patterns
- Chapter 4: AutoGen Core Framework - Event-driven architecture fundamentals
- Chapter 5: Code Execution and Tools - Dynamic programming and tool integration
- Chapter 6: Web Integration and Advanced Tools - External service integration
- Chapter 7: Orchestration Patterns - Basic coordination strategies
- Chapter 8: Advanced Orchestration - Complex multi-agent workflows
- Chapter 9: Memory and Context Management - Persistent conversations and RAG
- Chapter 10: Graph Flows and Optimization - Advanced workflow patterns
- Chapter 11: Advanced Multi-Agent Systems - Sophisticated coordination patterns
- Chapter 12: Human-in-the-Loop Integration - Interactive workflows and feedback
- Chapter 13: Performance and Deployment - Optimization and production readiness
- Chapter 14: Enterprise Deployment - Scalable production systems
- Chapter 15: Testing and Evaluation - Quality assurance frameworks
- Chapter 16: Advanced AI Integration - Cutting-edge capabilities and future patterns
AutoGen v0.6.1 introduces a revolutionary modular architecture with four main components:
| Component | Description | Key Features |
|---|---|---|
| 🧠 AutoGen Core | Event-driven framework for building scalable multi-agent systems | Async-first design, Event handling, Message routing |
| 💬 AgentChat | High-level API for building conversational agents | Team orchestration, Conversation management, Built-in patterns |
| 🔌 Extensions | Implementations that interface with external services | Model integrations, Tool connectors, External APIs |
| 🎨 AutoGen Studio | Web-based UI for prototyping agents without writing code | Visual builder, Testing interface, Deployment tools |
All code examples are meticulously organized in this directory by chapters, with each example including:
- ✅ Detailed comments and explanations
- 🔧 Practical, runnable implementations
- 📝 Step-by-step guidance
- 🎯 Real-world use cases
-
Install Dependencies:
pip install -r requirements.txt
-
Navigate to Examples:
cd chapter1/ -
Run Your First Agent:
python 01_basic_agent.py
- Python 3.10+
- AutoGen v0.5+ packages:
autogen-core- Core event-driven frameworkautogen-agentchat- High-level conversational APIautogen-ext- Extensions and integrationsautogenstudio(optional) - Visual development environment
Working with async/await patterns throughout the framework for optimal performance.
Understanding the Core event-driven programming model for scalable systems.
- RoundRobinGroupChat - Sequential agent collaboration
- SelectorGroupChat - Intelligent agent selection
- Swarm - Dynamic agent coordination
- GraphFlow - Directed workflow orchestration
Working with various model providers through Extensions for maximum flexibility.
Prototyping agents with the intuitive visual interface.
Working seamlessly with text, images, audio, and other modalities.
Implementing scalable, distributed agent systems for enterprise needs.
autogen_blueprint/
├── chapter1/ # Basic agents and concepts
├── chapter2/ # Assistant agents and MCP
├── chapter3/ # Agent types and streaming
├── chapter4/ # Core framework concepts
├── chapter5/ # Code execution and tools
├── chapter6/ # Web integration and tools
├── chapter7/ # Orchestration patterns
├── chapter8/ # Advanced orchestration
├── chapter9/ # Memory and context
├── chapter10/ # Graph flows and optimization
├── chapter11/ # Advanced multi-agent systems
├── chapter12/ # Human-in-the-loop patterns
├── chapter13/ # Performance and deployment
├── chapter14/ # Enterprise deployment
├── chapter15/ # Testing frameworks
├── chapter16/ # Advanced AI integration
└── utils/ # Shared utilities and configurations
Start here if you're new to AutoGen or multi-agent systems. Learn the fundamentals and build your first agents.
Let's create a simple conversational agent step by step:
1. Set up your environment:
cd autogen_blueprint/chapter1/
export OPENAI_API_KEY="your_api_key_here" # Replace with your actual API key2. Run the basic agent example:
python 01_basic_agent.py3. Here's what the code does (01_basic_agent.py):
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_agentchat.conditions import MaxMessageTermination
async def main():
# Create a helpful assistant agent
agent = AssistantAgent(
name="helpful_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o-mini"),
system_message="You are a helpful AI assistant. Be concise and friendly."
)
# Start a conversation
result = await Console(
chat_completion_client=agent.model_client
).run_stream(
task="Hello! Can you help me understand what AutoGen is?",
termination_condition=MaxMessageTermination(max_messages=4)
)
if __name__ == "__main__":
asyncio.run(main())4. Expected Output:
🤖 helpful_assistant: Hello! I'd be happy to help you understand AutoGen!
AutoGen is Microsoft's framework for building multi-agent AI systems. Here are the key points:
✨ **What it does:**
- Enables multiple AI agents to collaborate and communicate
- Supports complex workflows with agent coordination
- Provides both high-level and low-level APIs
🏗️ **Key Components:**
- **AgentChat**: Easy-to-use conversational agents
- **Core**: Event-driven architecture for scalability
- **Extensions**: Integrations with external services
- **Studio**: Visual interface for building agents
🎯 **Perfect for:**
- Automated workflows, customer service, research tasks, and more!
Would you like me to explain any specific aspect in more detail?
Conversation completed! ✅
📝 Try These Examples:
chapter1/01_basic_agent.py- Create your first AutoGen agent with simple conversation capabilitieschapter2/01_assistant_agent.py- Build an assistant agent that can help with tasks and questionschapter3/03_tool_assistant_agent.py- Add tool capabilities to make your agent interact with external services
# Quick start sequence:
cd chapter1 && python 01_basic_agent.py
cd ../chapter2 && python 01_assistant_agent.py
cd ../chapter3 && python 03_tool_assistant_agent.pyAdvance to complex orchestration patterns, memory management, and sophisticated multi-agent workflows.
🔧 Build These Systems:
chapter7/06_basic_round_robin.py- Implement round-robin agent coordination for sequential task processingchapter9/06_personal_assistant.py- Create a memory-enabled personal assistant with context retentionchapter11/05_sequential_workflow.py- Build sophisticated multi-agent workflows with handoffs and collaboration
# Intermediate progression:
cd chapter7 && python 06_basic_round_robin.py
cd ../chapter9 && python 06_personal_assistant.py
cd ../chapter11 && python 05_sequential_workflow.pyMaster enterprise deployment, testing frameworks, performance optimization, and cutting-edge AI integration.
🚀 Deploy Production Systems:
chapter13/05_workflow_optimization.py- Implement performance optimization and caching strategies for productionchapter14/02_kubernetes_deployment.py- Deploy scalable multi-agent systems on Kubernetes infrastructurechapter16/02_multi_modal_orchestration.py- Build advanced multimodal agents with vision, audio, and text capabilities
# Advanced production examples:
cd chapter13 && python 05_workflow_optimization.py
cd ../chapter14 && python 02_kubernetes_deployment.py
cd ../chapter16 && python 02_multi_modal_orchestration.pyThis codebase follows the book's progression and maintains high code quality standards:
- All examples are tested and verified
- Code follows Python best practices
- Each chapter builds upon previous concepts
- Real-world applicability is prioritized
- 📖 Full Book on Amazon
- 🔗 AutoGen Official Documentation
- 💻 AutoGen GitHub Repository
- 🎨 Zavora Technologies
This project is licensed under the MIT License - see the LICENSE file for details.
🚀 Ready to build the future of AI? Start with Chapter 1!