Building a Multi-Agent System: A Friendly Guide for Beginners

Imagine you want a smart assistant that can do many things for you like searching the web, analyzing financial data, or even drawing charts all in one place. But instead of one super-complicated brain, you have a team of specialists (agents). Each agent is great at one thing.

This is what a multi-agent system does: it uses many agents working together, overseen by a supervisor, to answer your questions or perform tasks efficiently.

What is a Multi-Agent System?

• Agents: Independent units that do specific jobs. For example, a Web Search Agent finds information online, a Financial Agent analyzes stock data, and a Code Agent writes and runs code like plotting graphs.
• Supervisor Agent: The team leader. It listens to your request and decides which agent should handle it next. It manages the workflow so the right expert answers at the right time.
• Conversation State: Keeps track of your messages and agents’ responses, so the system remembers what was said.

Why Use a Multi-Agent System?

• Specialization: Each agent focuses on what it knows best, improving accuracy and speed.
• Scalability: You can add new agents to handle new tasks without overloading one system.
• Flexibility: Agents can hand off tasks to each other smoothly.

How Does the Multi-Agent System Work? (Simplified Flow)

You ask a question or make a request.

Supervisor analyzes your request and decides:

• “This is a web search task, so let’s call the Web Search Agent,” or
• “This needs financial data, so call the Financial Agent,” or
• “This requires a plot, so call the Code Agent.”

The chosen agent processes your request and returns the answer.

Supervisor checks if the task is done:

• If yes, the system stops and shows you the result.
• If no, supervisor decides the next agent to continue the work.

Key Concepts Explained

Concept What It Means Example

Agent A mini-expert that does one type of job Web Search Agent finds articles

Supervisor The team leader deciding which agent to call next Chooses Financial Agent for stocks

Node A point in the workflow representing an agent Web Search node handles queries

Edge Connection between nodes (who calls who next) Web Search → Supervisor

State Memory of the conversation history Keeps track of past messages

Conditional Edges Supervisor’s decision path based on agent output If next=CodeAgent then call CodeAgent

Graph The entire workflow agents as nodes, edges connecting them Supervisor decides the flow

Tools and Libraries Used

• Python: Programming language to glue everything together.
• Langchain (or custom logic): For managing agents and workflow.
• Models (e.g., GPT-3.5 Turbo): For natural language understanding.
• APIs: For web search, financial data (like Polygon), or plotting (matplotlib).
• Gradio: To create a simple, interactive web app interface.

Step-By-Step: Building a Simple Multi-Agent System

1. Setup Your Environment

• Install Python 3.x.
• Install required packages:
• Get API keys for OpenAI (for GPT models), financial data APIs (Polygon, Alpha Vantage), and web search APIs if needed.

2. Define Your Agents

Each agent is a function or class that receives a user input, processes it, and returns an output.

Example:

def web_search_agent(query):
    # Call a web search API or model
    return f"Web search results for '{query}'."

def financial_agent(query):
    # Call financial API to get stock info
    return f"Financial data for '{query}'."

def code_agent(query):
    # Generate Python code to plot data
    return "Generated plot code." 

3. Create a Supervisor Agent

This agent decides which agent to use based on the user query or task status.

def supervisor_agent(query):
    if "stock" in query.lower():
        return "FinancialAgent"
    elif "plot" in query.lower():
        return "CodeAgent"
    else:
        return "WebSearchAgent" 

4. Build a Simple Workflow Graph

Imagine your agents as points (nodes) connected by arrows (edges). The supervisor routes requests to agents and receives answers.

workflow = {
    "start": "Supervisor",
    "Supervisor": supervisor_agent,
    "WebSearchAgent": web_search_agent,
    "FinancialAgent": financial_agent,
    "CodeAgent": code_agent
} 

5. Run the Workflow

You start at the supervisor, who routes your query:

def run_workflow(query):
    current_agent_name = "Supervisor"
    while current_agent_name != "FINISH":
        if current_agent_name == "Supervisor":
            current_agent_name = workflow[current_agent_name](query)
        else:
            output = workflow[current_agent_name](query)
            print(f"Output from {current_agent_name}: {output}")
            current_agent_name = "FINISH" 

6. Create a Simple Web Interface with Gradio

import gradio as gr

def multi_agent_system(query):
    current_agent = "Supervisor"
    if "stock" in query.lower():
        agent = financial_agent
    elif "plot" in query.lower():
        agent = code_agent
    else:
        agent = web_search_agent
    return agent(query)

gr.Interface(fn=multi_agent_system, inputs="text", outputs="text",
             title="Multi-Agent System Demo",
             description="Ask anything and see which agent handles it!").launch() 

RESULTS for Websearch, Fin Data and Plot

Common Questions

Q: Can multiple agents respond simultaneously? Usually, one agent answers at a time as decided by the supervisor. But you can design systems to gather multiple responses and combine them if needed.

Q: How does the system remember conversation context? It stores past messages in the state, so agents have context to make better decisions.

Q: What if the supervisor picks the wrong agent? A: You can improve the supervisor’s logic or use AI models to better classify the query.

Use Cases

• Personal assistant: Answers questions, fetches data, and visualizes info dynamically.
• Financial advisor: Combines stock data, news, and charts.
• Customer support: Routes different types of queries to specialized bots.
• Education: Helps learners get information, solve problems, or get explanations.

Summary

A multi-agent system is like a team of specialists coordinated by a supervisor to provide intelligent responses and perform complex workflows. It uses nodes, edges, and state to manage conversation flow, and libraries like Gradio to make it interactive.

By building small, specialized agents and connecting them, you can create powerful, flexible assistants that do more than one task well.