Introduction to Multi-Agent LLM Systems

Multi-Agent LLM (Large Language Model) Systems involve multiple agents working collaboratively to leverage the capabilities of LLMs. These systems can solve complex problems through communication, negotiation, and decision-making among agents.

2.1 What is a Multi-Agent System?

A Multi-Agent System (MAS) consists of multiple interacting agents that can autonomously solve problems. Agents can be software programs or robots, and they communicate to achieve individual or collective goals.

2.2 What are LLMs?

Large Language Models are AI models designed to understand and generate human language. They can perform tasks such as translation, summarization, and conversation.

2.3 Interaction Mechanisms

• Direct Communication: Agents send messages to each other.
• Shared Knowledge: Agents access a common knowledge base.
• Negotiation: Agents negotiate terms or solutions to conflicts.

3.1 Setting Up Agents

Agents can be implemented using various frameworks such as JADE, SPADE, or even using Python's asyncio library. Below is a simple example of creating two agents using Python:

class Agent:
    def __init__(self, name):
        self.name = name

    def send_message(self, message, recipient):
        print(f"{self.name} sends message to {recipient.name}: {message}")

agent1 = Agent("Agent 1")
agent2 = Agent("Agent 2")

agent1.send_message("Hello!", agent2)
                

3.2 Communication Protocols

Protocols define how agents communicate. Common protocols include FIPA ACL (Agent Communication Language) and custom JSON-based APIs.

• Define clear roles for each agent to avoid conflicts.
• Implement robust communication protocols for effective message passing.
• Use logging to track interactions for debugging and optimization.
• Test agents individually and as a system for integration issues.