I’ve been thinking a lot about how AI systems can work together like a well-coordinated team. Recently, I built a multi-agent LLM system that handles complex tasks by dividing them among specialized agents. This approach has transformed how I think about AI applications. Let me show you how to build something similar.

Have you ever wondered what happens when AI agents start collaborating? Multi-agent systems use multiple specialized models working together. Each agent focuses on a specific task. This makes the whole system more efficient and reliable. Think of it like having a team where each member has their own expertise.

Setting up the environment is straightforward. I start by creating a virtual environment to keep dependencies organized. Here’s how I do it:

python -m venv multi_agent_env
source multi_agent_env/bin/activate
pip install langchain langchain-openai redis chromadb fastapi

Why use multiple agents instead of one powerful model? Specialization allows each agent to excel in its area. A research agent can focus on gathering information. An analysis agent processes data. A reporting agent creates summaries. This division of labor prevents any single point of failure.

The core architecture uses a central coordinator. This coordinator manages task distribution and agent communication. I design it to handle multiple agents efficiently. Here’s a basic setup for the coordinator:

class Coordinator:
    def __init__(self):
        self.agents = {}
        self.task_queue = asyncio.Queue()
    
    def register_agent(self, name, agent):
        self.agents[name] = agent
    
    async def assign_task(self, task):
        await self.task_queue.put(task)

How do agents remember past interactions? Persistent memory is crucial. I use Redis for short-term memory and ChromaDB for long-term storage. This ensures conversations continue seamlessly across sessions. Here’s a simple memory implementation:

import redis
r = redis.Redis(host='localhost', port=6379, db=0)

def store_conversation(session_id, message):
    r.rpush(f"conversation:{session_id}", message)

def get_conversation(session_id):
    return r.lrange(f"conversation:{session_id}", 0, -1)

Building individual agents involves defining their roles and tools. I create each agent with a specific system prompt. This guides their behavior and expertise. For example, a research agent might have tools for web search and data retrieval.

What happens when agents need to share information? I implement a message broker using Redis pub/sub. This allows real-time communication between agents. It’s like giving them a shared workspace where they can post updates and requests.

Custom tools extend agent capabilities. I integrate external APIs for specific functions. Here’s a tool for fetching weather data:

from langchain.tools import BaseTool
import requests

class WeatherTool(BaseTool):
    name = "get_weather"
    description = "Fetches current weather for a city"
    
    def _run(self, city: str):
        response = requests.get(f"https://api.weather.com/{city}")
        return response.json()

Deploying to production requires error handling and monitoring. I add retry logic for API calls and set up logging. Performance optimization involves caching frequent queries and using async operations.

Have you considered how to scale this system? I design it to handle increasing loads by adding more agents. The coordinator balances the workload. Monitoring tools track performance metrics and alert me to issues.

One challenge I faced was agent coordination. Sometimes agents would duplicate work. I solved this by implementing a task registry. Each agent checks this registry before starting a new task.

What about security? I ensure all external API calls use secure connections. Sensitive data is encrypted before storage. Access controls limit what each agent can do.

Testing is essential. I create unit tests for each agent and integration tests for the whole system. This catches issues early and ensures reliability.

In my experience, starting simple and iterating works best. Begin with two or three agents. Add more as needed. Keep code modular for easy updates.

I’d love to hear about your experiences with multi-agent systems. What challenges have you faced? Share your thoughts in the comments below. If you found this helpful, please like and share this article with others who might benefit from it.