LangGraph MultiAgent Network Demo

This is an example of how you can use LangGraph to create a network of LLMs.

Installation

• Download Ollama - https://ollama.com/blog/llama3
• Run the LLMs you wish to using Ollama.
  • For all the models I use:
    • Run ./modelfiles/run.bat for Windows users.
    • Run the individual modelfiles using Ollama.
• Use the package manager pip to install any dependencies using the requirements.txt file (pip install -r requirements.txt)

Use

• Run main.py, which will give you a CLI to talk to this network.

Network Mermaid Diagram

How does this work?

• Learning about Edges is important for this - Check it out here
• Look at main.py and read the code as you follow along.

There are 5 nodes:

• The Start node - "supervisor_routing"
  • The Supervisor which uses Llama3.2 (see the supervisor_routing function)
  • Determines whether the prompt given is about coding, storytelling, or conversation (default)
• The Conversation node - "conversation"
  • Uses Llama3.2 wrapped in a react agent to respond
  • Has a simple tool to roll dice that it can use
• The Coding node - "help_with_coding"
  • Uses CodeLlama to respond to the prompt
  • CodeLlama has been instructed to help with the coding prompt
• The Story node - "tell_a_story"
  • Uses Mistral to respond to the prompt
  • Mistral has been instructed to tell a story based on the prompt
• The End node - Just ends.

Coding example flow:

• The user asks "How do I print the numbers 1 to 10 in Python?"
• The prompt goes to the Start node
  • Is a conditional edge, meaning that it will respond with the name of another node to send the prompt to.
  • In this case, the supervisor returns "help_with_coding"
• The prompt then goes to the Coding node
  • The prompt is processed by the llm in the node and returns the result
• This then moves to the End node, ending the graph.
• The user gets their response.

Conversation example flow:

• The user asks "How are you doing today?"
• The prompt goes to the Start node
  • Is a conditional edge, meaning that it will respond with the name of another node to send the prompt to.
  • In this case, the supervisor returns "conversation" as the prompt does not seem to be about storytelling or coding.
• The prompt then goes to the Conversation node
  • The prompt is processed by the llm in the node and returns the result
• This then moves to the End node, ending the graph.
• The user gets their response.