🚀 Multi-Agent System Using Python
📝 Abstract
This publication presents a lightweight multi-agent system developed in Python. It demonstrates how different specialized agents—Calculator, Search, Summarizer, and Writer—collaborate within an orchestrated environment. The system automatically selects the appropriate tool based on user input, showcasing modular design principles and foundational agentic workflows. This project serves as an introductory example for building and understanding real-world AI agent systems.
🔍 Introduction
Multi-agent systems are increasingly used in modern AI workflows to divide complex tasks among specialized tools. This project illustrates how multiple agents can work together within a simple orchestration setup. Each agent has a dedicated capability, and the orchestrator efficiently routes user queries to the correct agent.
🧠 Architecture & Workflow
The system workflow follows four main steps:
1️⃣ The user submits a query (e.g., calculate 12 / 3, summarize this text, search, or write a paragraph).
2️⃣ The orchestrator analyzes the input.
3️⃣ The appropriate agent is selected automatically.
4️⃣ The agent processes the task and returns the result.
System Architecture
Workflow Visualization
⚙️ Agents & Their Capabilities
1️⃣ Calculator Agent
Performs mathematical operations:
addition
subtraction
multiplication
division
Example:
calculate 5 + 7
2️⃣ Search Agent
Returns mock search results for queries.
This demonstrates how a real-world search agent would behave.
3️⃣ Summarizer Agent
Creates concise summaries by extracting key information from long text.
4️⃣ Writer Agent
Generates a structured written response to general questions or instructions.
🎯 Key Purpose
This project explains core concepts in agent design:
modular tools
orchestration and routing
task delegation
simple automation
It aims to help beginners understand foundational principles before building advanced systems.
🔧 Technologies Used
Python 3
Modular tool-based architecture
Orchestrator for tool selection
Lightweight code structure
🚀 Future Improvements
integrate real APIs
add memory for agents
enhance summarization using NLP
enable conversation history
📥 Conclusion
This lightweight multi-agent system demonstrates how multiple specialized agents can collaboratively solve diverse tasks. The modular design makes it easy to extend and integrate additional capabilities. It serves as a strong foundation for more advanced agentic AI systems.