Capstone Project: Developing a Multi-Agent System

Duration: 5 min

This module covers the development of a multi-agent system using AI agents with capabilities such as ReAct, LangGraph, tool calling, memory, and autonomous workflows. Understanding these concepts is crucial for creating sophisticated, collaborative AI systems that can perform complex tasks efficiently.

Understanding ReAct and LangGraph

ReAct (Reasoning and Acting) is a framework that allows AI agents to reason about their actions and make decisions based on their environment. LangGraph is a tool that helps in visualizing and managing the interactions between different agents in a multi-agent system. Together, they enable agents to communicate and coordinate effectively.

import langgraph

# Define a simple agent using LangGraph
def agent():
    return 'Hello from Agent!'

# Create a LangGraph instance
graph = langgraph.Graph()

# Add the agent to the graph
graph.add_node('agent', agent)

# Define the edges
graph.add_edge('start', 'agent')
graph.add_edge('agent', 'end')

# Run the graph
result = graph.run('start')
print(result)

Try it in Google Colab:

{'agent': 'Hello from Agent!'}

Implementing Tool Calling and Memory

Tool calling allows AI agents to invoke external tools or functions to perform specific tasks. Memory enables agents to retain information across interactions, enhancing their ability to make informed decisions. Combining these features allows agents to operate more autonomously and effectively.

import langgraph

# Define a tool function
def tool():
    return 'Tool result'

# Define an agent with memory
class Agent:
    def __init__(self):
        self.memory = {}
    def act(self):
        if 'tool_result' in self.memory:
            return self.memory['tool_result']
        else:
            self.memory['tool_result'] = tool()
            return self.memory['tool_result']

# Create a LangGraph instance
graph = langgraph.Graph()

# Add the agent to the graph
agent_instance = Agent()
graph.add_node('agent', agent_instance.act)

# Define the edges
graph.add_edge('start', 'agent')
graph.add_edge('agent', 'end')

# Run the graph
result = graph.run('start')
print(result)

💡 Tip: Ensure that the memory management in your agents is efficient to avoid unnecessary resource consumption and potential performance issues.

Key Concepts

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