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Building a LangGraph Multi-Agent System in 20 Minutes

Overview

In this post, we'll explore how to build a working multi-agent system with dynamic configuration using LangGraph multi-agent workflows, RAG search, and LaunchDarkly AI Configs. This is the first part of our series on "Chaos to Clarity: Defensible AI Systems That Deliver on Your Goals".

What is a Multi-Agent System?

A multi-agent system (MAS) is a computational paradigm where multiple agents interact with each other to achieve a common goal. In this context, an agent is an autonomous entity that perceives its environment and takes actions to affect it.

LangGraph Multi-Agent Workflows

LangGraph is a lightweight, open-source framework for building multi-agent systems. It provides a simple and intuitive API for defining workflows, which are the core of any MAS. A workflow in LangGraph represents a sequence of steps that an agent must follow to achieve its goal.

Here's an example of a simple workflow:

import langgraph

def greet(name):
    print(f"Hello, {name}!")

def main():
    # Create a new workflow
    wf = Workflow("Greeting")

    # Add steps to the workflow
    wf.add_step(greet, "greet")
    wf.add_step(AskUser("What's your name?"), "ask_name")
    wf.add_step(greet, "greet_again")

    # Run the workflow
    wf.run()

if __name__ == "__main__":
    main()

In this example, we define a simple workflow with three steps: greet, ask_name, and greet_again. The workflow is then run using the run() method.

RAG Search

RAG (Restricted Action Graph) search is an algorithm for planning in MAS. It allows agents to explore their environment and find the most efficient path to achieve their goal.

Here's an example of how to use RAG search with LangGraph:

import langgraph
from rag import RagSearch

def plan():
    # Define the problem space
    ps = ProblemSpace("Greeting")

    # Define the start and goal states
    start_state = State("Hello")
    goal_state = State("Goodbye")

    # Create a RAG search object
    rs = RagSearch(ps, start_state, goal_state)

    # Search for a plan
    plan = rs.search()

    return plan

def main():
    # Get the plan
    plan = plan()

    # Print the plan
    print(plan)

if __name__ == "__main__":
    main()

In this example, we define a problem space and two states: start_state and goal_state. We then create a RAG search object and use it to search for a plan.

LaunchDarkly AI Configs

LaunchDarkly is a feature flag management platform that allows you to manage configuration settings for your application. AI Configs is a module that uses machine learning algorithms to automatically configure the system based on usage patterns.

Here's an example of how to integrate AI Configs with LangGraph:

import langgraph
from launchdarkly import AiConfigs

def configure():
    # Get the current configuration
    config = AiConfigs.get_config()

    # Update the workflow based on the new configuration
    wf = Workflow("Greeting")
    wf.add_step(greet, "greet")
    wf.add_step(AskUser("What's your name?"), "ask_name")
    wf.add_step(greet, "greet_again")

    return wf

def main():
    # Configure the workflow
    wf = configure()

    # Run the workflow
    wf.run()

if __name__ == "__main__":
    main()

In this example, we use AI Configs to update the workflow configuration based on usage patterns.

Best Practices and Implementation Details

When building a multi-agent system using LangGraph, keep in mind the following best practices:

• Modularity: Break down your workflows into smaller, independent modules.
• Reusability: Design your workflows to be reusable across different applications.
• Scalability: Use RAG search to optimize planning for large problem spaces.

Implementation details include:

• Workflow definition: Define your workflows using the LangGraph API.
• RAG search integration: Integrate RAG search with LangGraph using the rag module.
• AI Configs integration: Integrate AI Configs with LangGraph using the launchdarkly module.

By following these guidelines and best practices, you'll be able to build robust and efficient multi-agent systems that deliver on your goals.

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By Malik Abualzait