Official code repository for "Designing Multi-Agent Systems: Principles, Patterns, and Implementation for AI Agents" by Victor Dibia.
Learn to build effective multi-agent systems from first principles (from scratch) through complete, tested implementations.
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As the AI agent space evolves rapidly, clear patterns are emerging for building effective multi-agent systems. This book focuses on identifying these patterns and providing practical guidance for applying them effectively.
What makes this approach unique:
- Fundamentals-first: Build from scratch to understand every component and design decision
- Complete implementations: Every theoretical concept backed by working, tested code
- Framework-agnostic: Core patterns that transcend any specific framework
- Production considerations: Evaluation, optimization, and deployment guidance from real-world experience
The book is organized across 4 parts, taking you from theory to production:
| Chapter | Title | Code | Learning Outcome |
|---|---|---|---|
| Ch 1 | Understanding Multi-Agent Systems | - | Understand when multi-agent systems are needed |
| Ch 2 | Multi-Agent Patterns | - | Master coordination strategies (workflows vs autonomous) |
| Ch 3 | UX Design Principles for Multi-Agent Systems | - | Build intuitive agent interfaces |
| Chapter | Title | Code | Learning Outcome |
|---|---|---|---|
| Ch 4 | Building Your First Agent | picoagents.agent, 01_basic_agent.py |
Create agents with reasoning, tools, memory |
| Ch 5 | Building Multi-Agent Workflows | picoagents.workflow/ |
Build deterministic multi-agent systems from scratch |
| Ch 6 | Autonomous Multi-Agent Orchestration | picoagents.orchestration/, 02_roundrobin_orchestration.py, 03_ai_orchestration.py |
Implement autonomoous multi-agent patterns including GroupChat patterns, LLM and Plan Based Orchestration (Magentic One) |
| Ch 7 | Multi-Agent Frameworks | - | How to evaluate and choose the right multi-agent framework |
| Chapter | Title | Code | Learning Outcome |
|---|---|---|---|
| Ch 8 | Evaluating Multi-Agent Systems | Evaluation framework | Measure and improve agent performance |
| Chapter | Title | Code | Learning Outcome |
|---|---|---|---|
| Ch 12 | Multi-Perspective Information Processing | Complete case study | Deploy production multi-agent systems |
# Clone the repository
git clone https://github.com/victordibia/designing-multiagent-systems.git
cd designing-multiagent-systems
# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install the picoagents library
cd picoagents
pip install -e .
# Set up your API key
export OPENAI_API_KEY="your-api-key-here"In this book, we will cover the fundamentals of building multi-agent systems, and incrementally build up the Agents abstractions shown below:
from picoagents import Agent, OpenAIChatCompletionClient
def get_weather(location: str) -> str:
"""Get current weather for a given location."""
return f"The weather in {location} is sunny, 75°F"
# Create an agent
agent = Agent(
name="assistant",
instructions="You are helpful. Use tools when appropriate.",
model_client=OpenAIChatCompletionClient(model="gpt-4o-mini"),
tools=[get_weather]
)
# Use the agent
response = await agent.run("What's the weather in Paris?")
print(response.messages[-1].content)# Run basic agent example
python picoagents/examples/01_basic_agent.py
# Try autonomous orchestration
python picoagents/examples/02_roundrobin_orchestration.pyThe picoagents/ directory contains a complete multi-agent framework built from scratch to demonstrate every concept in the book:
picoagents/
├── agents.py # Core Agent implementation (Ch 4)
├── workflow/ # Explicit control patterns (Ch 5)
├── orchestration/ # Autonomous control patterns (Ch 6)
├── examples/ # Complete chapter implementations
└── tests/ # Comprehensive test suite
"Designing Multi-Agent Systems: Principles, Patterns, and Implementation for AI Agents"
This repository implements every concept from the book. The book provides the theory, design trade-offs, and production considerations you need to build effective multi-agent systems.
Questions or feedback about the book or code? Please open an issue.
@book{dibia2025multiagent,
title={Designing Multi-Agent Systems: Principles, Patterns, and Implementation for AI Agents},
author={Dibia, Victor},
year={2025},
github={https://github.com/victordibia/designing-multiagent-systems}
}