OpenAI is reportedly building Astra, a model family designed to work on problems for hours or days

Learn to build a basic multi-agent system that can collaborate to solve complex problems over time, similar to OpenAI's rumored Astra model.

Introduction

In this tutorial, we'll explore how to create a simple multi-agent system that can work together to solve complex problems over extended periods - much like the rumored Astra model from OpenAI. While we won't build a full-fledged AI system, we'll create a foundational framework that demonstrates how multiple agents can collaborate to tackle tasks that would take a single agent much longer to complete.

This tutorial will teach you how to design a basic multi-agent system using Python, where each agent has specific roles and can communicate with others to solve complex problems. We'll build a simple problem-solving scenario where agents work together to find solutions to a multi-step challenge.

Prerequisites

To follow along with this tutorial, you'll need:

A computer with Python 3.7 or higher installed
Basic understanding of Python programming concepts
Some familiarity with object-oriented programming
Internet access to install additional packages

No prior experience with AI or machine learning is required - we'll build a conceptual framework that demonstrates the principles behind multi-agent systems.

Step-by-Step Instructions

Step 1: Set Up Your Python Environment

First, create a new directory for our project and set up a virtual environment to keep our dependencies organized:

mkdir multi_agent_system
 cd multi_agent_system
 python -m venv agent_env
 source agent_env/bin/activate  # On Windows: agent_env\Scripts\activate

This creates an isolated Python environment for our project, preventing conflicts with other Python installations on your system.

Step 2: Create the Basic Agent Class

Let's start by creating a simple agent class that represents each individual agent in our system:

class Agent:
    def __init__(self, name, role):
        self.name = name
        self.role = role
        self.knowledge = []
        self.messages = []

    def add_knowledge(self, knowledge):
        self.knowledge.append(knowledge)

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

    def receive_message(self, sender, message):
        self.messages.append((sender.name, message))
        print(f"{self.name} received from {sender.name}: {message}")

    def think(self, problem):
        # Simple decision making based on available knowledge
        if self.role == "Researcher":
            return f"Researcher {self.name} found relevant information about {problem}"
        elif self.role == "Analyst":
            return f"Analyst {self.name} analyzed the data and found patterns"
        elif self.role == "Coordinator":
            return f"Coordinator {self.name} organized the team's efforts"
        else:
            return f"Agent {self.name} is working on {problem}"

This basic agent class sets up the foundation for our multi-agent system. Each agent has a name, role, knowledge base, and communication capabilities.

Step 3: Create the Multi-Agent System

Now, let's build a system that can manage multiple agents working together:

class MultiAgentSystem:
    def __init__(self):
        self.agents = []

    def add_agent(self, agent):
        self.agents.append(agent)

    def solve_problem(self, problem):
        print(f"\nStarting to solve: {problem}")
        print("\n--- Initial Agent Roles ---")
        for agent in self.agents:
            print(f"{agent.name} - {agent.role}")

        # Simulate the process of agents working together
        for i in range(3):  # Simulate 3 rounds of collaboration
            print(f"\n--- Round {i+1} ---")
            for agent in self.agents:
                # Each agent thinks about the problem
                response = agent.think(problem)
                print(response)
                
                # Agents communicate with each other
                if i == 0:  # First round - agents share initial thoughts
                    for other_agent in self.agents:
                        if other_agent != agent:
                            agent.send_message(other_agent, f"Thought about {problem}: {response}")

        print(f"\n--- Final Solution ---")
        print(f"Problem '{problem}' has been addressed by the team")

This system manages multiple agents and simulates how they would work together to solve a problem over multiple rounds of communication and analysis.

Step 4: Initialize and Test Your Agents

Now let's create some agents and put our system to work:

# Create our agents
system = MultiAgentSystem()

# Create different types of agents
researcher = Agent("Alice", "Researcher")
analyst = Agent("Bob", "Analyst")
coordinator = Agent("Charlie", "Coordinator")

# Add agents to the system
system.add_agent(researcher)
system.add_agent(analyst)
system.add_agent(coordinator)

# Test the system with a complex problem
system.solve_problem("Optimizing supply chain logistics for a global company")

This code creates three agents with different roles and demonstrates how they would work together to tackle a complex problem.

Step 5: Enhance Agent Communication

Let's make our agents more sophisticated by adding better communication and knowledge sharing:

class EnhancedAgent(Agent):
    def __init__(self, name, role):
        super().__init__(name, role)
        self.shared_knowledge = []

    def share_knowledge(self, knowledge):
        self.shared_knowledge.append(knowledge)
        print(f"{self.name} shared knowledge: {knowledge}")
        
        # Notify other agents
        for agent in self.get_all_agents():
            if agent != self:
                agent.receive_shared_knowledge(knowledge)

    def receive_shared_knowledge(self, knowledge):
        if knowledge not in self.knowledge:
            self.knowledge.append(knowledge)
            print(f"{self.name} received shared knowledge: {knowledge}")

    def get_all_agents(self):
        # This would be implemented in the system class
        pass

This enhanced agent can now share knowledge with other agents, simulating how real agents in a system would collaborate and build on each other's findings.

Step 6: Run the Complete System

Let's put everything together in a complete working example:

class EnhancedMultiAgentSystem(MultiAgentSystem):
    def __init__(self):
        super().__init__()
        self.agents = []

    def add_agent(self, agent):
        self.agents.append(agent)
        # Set the system reference in each agent
        for a in self.agents:
            if hasattr(a, 'get_all_agents'):
                a.system = self

    def get_all_agents(self):
        return self.agents

# Run the complete example
enhanced_system = EnhancedMultiAgentSystem()

# Create enhanced agents
enhanced_researcher = EnhancedAgent("Alice", "Researcher")
enhanced_analyst = EnhancedAgent("Bob", "Analyst")
enhanced_coordinator = EnhancedAgent("Charlie", "Coordinator")

# Add agents to the system
enhanced_system.add_agent(enhanced_researcher)
enhanced_system.add_agent(enhanced_analyst)
enhanced_system.add_agent(enhanced_coordinator)

# Test with a complex problem
enhanced_system.solve_problem("Developing a new AI model for medical diagnosis")

This complete example shows how agents can communicate, share knowledge, and work together over time to solve complex problems.

Summary

In this tutorial, we've built a foundational framework for a multi-agent system that demonstrates how multiple AI agents can work together to solve complex problems over extended periods. While this is a simplified model compared to what OpenAI might be building with Astra, it illustrates the core principles:

Each agent has a specific role and capability
Agents can communicate and share information
Multiple agents collaborate to solve problems that would take longer individually
The system can be extended to include more sophisticated decision-making and knowledge management

This framework provides a starting point for understanding how future systems like Astra might work, where agents can persistently work on problems for hours or days, learning from each other and building upon previous findings to arrive at solutions.