Introduction to Multi-Agent Systems and Generative AI

In the rapidly evolving landscape of artificial intelligence, multi-agent systems and generative AI have emerged as powerful tools for creating intelligent, adaptive applications. By combining these technologies, we can build real-time systems that leverage the strengths of multiple AI agents working together to solve complex problems.

Let's dive into the world of multi-agent applications powered by generative AI and explore how we can create these systems using Phidata.

Understanding Multi-Agent Systems

Multi-agent systems consist of multiple intelligent agents that interact with each other and their environment to achieve common or individual goals. These agents can be designed to collaborate, compete, or negotiate, depending on the application's requirements.

Key characteristics of multi-agent systems include:

1. Autonomy: Each agent can operate independently.
2. Local views: Agents have limited information about the overall system.
3. Decentralization: There's no central controlling authority.
4. Adaptation: Agents can learn and improve over time.

The Role of Generative AI in Multi-Agent Systems

Generative AI, particularly large language models (LLMs) like GPT-3 or GPT-4, can significantly enhance multi-agent systems by providing:

1. Natural language processing capabilities
2. Knowledge synthesis and generation
3. Adaptive behavior based on context
4. Creative problem-solving abilities

By integrating generative AI into multi-agent systems, we can create more flexible, intelligent, and human-like interactions between agents and with users.

Building Real-Time Multi-Agent Applications with Phidata

Phidata is a powerful framework for building AI-powered applications, including multi-agent systems. Let's walk through the process of creating a real-time multi-agent application using Phidata and generative AI.

Step 1: Setting up the Environment

First, install Phidata and its dependencies:

pip install phidata

Step 2: Defining Agent Roles

In a multi-agent system, each agent has a specific role. Let's create a simple system with three agents:

1. Information Gatherer
2. Analyst
3. Decision Maker

Here's how we can define these agents using Phidata:

from phidata import Agent, LLM

information_gatherer = Agent(
    name="InfoGatherer",
    llm=LLM(model="gpt-3.5-turbo"),
    system_prompt="You are an expert at gathering relevant information from various sources."
)

analyst = Agent(
    name="Analyst",
    llm=LLM(model="gpt-3.5-turbo"),
    system_prompt="You are an expert at analyzing data and identifying patterns and insights."
)

decision_maker = Agent(
    name="DecisionMaker",
    llm=LLM(model="gpt-4"),
    system_prompt="You are an expert at making decisions based on analyzed information and insights."
)

Step 3: Implementing Agent Communication

To enable real-time communication between agents, we can use Phidata's messaging system:

from phidata import Message

def agent_communication(sender, receiver, content):
    message = Message(
        sender=sender.name,
        receiver=receiver.name,
        content=content
    )
    response = receiver.process_message(message)
    return response

Step 4: Creating the Multi-Agent Workflow

Now, let's implement a simple workflow that demonstrates how these agents work together:

def multi_agent_workflow(topic):

# Information Gatherer collects data
    gathered_info = agent_communication(
        information_gatherer, 
        information_gatherer, 
        f"Gather information about {topic}"
    )

# Analyst processes the gathered information
    analysis = agent_communication(
        information_gatherer,
        analyst,
        f"Analyze the following information: {gathered_info}"
    )

# Decision Maker makes a recommendation
    decision = agent_communication(
        analyst,
        decision_maker,
        f"Make a decision based on this analysis: {analysis}"
    )

    return decision

Step 5: Running the Application in Real-Time

To make our multi-agent application run in real-time, we can use asynchronous programming:

import asyncio

async def real_time_multi_agent_app():
    while True:
        user_input = await asyncio.to_thread(input, "Enter a topic (or 'quit' to exit): ")
        if user_input.lower() == 'quit':
            break
        
        result = await asyncio.to_thread(multi_agent_workflow, user_input)
        print(f"Decision: {result}")

asyncio.run(real_time_multi_agent_app())

This setup allows our multi-agent system to continuously accept user input and provide real-time responses.

Enhancing the Multi-Agent System

To further improve our multi-agent application, consider implementing these advanced features:

1. Dynamic Agent Creation: Allow the system to create new agents on-the-fly based on the complexity of the task.

2. Learning and Adaptation: Implement reinforcement learning techniques to help agents improve their performance over time.

3. Conflict Resolution: Develop mechanisms for resolving conflicts when agents disagree on a course of action.

4. Scalability: Design the system to handle a large number of agents efficiently, possibly using distributed computing techniques.

5. User Interaction: Incorporate a user interface that allows real-time interaction with the multi-agent system, enabling users to provide feedback and steer the decision-making process.

By leveraging the power of generative AI and multi-agent systems, we can create intelligent, adaptive applications that can tackle complex real-world problems in real-time. The combination of Phidata's robust framework and the capabilities of large language models opens up exciting possibilities for building next-generation AI applications.