Turbocharge ChatGPT: Multi-Agents are the Future of AI (example with CrewAI)

Turbocharge ChatGPT: Multi-Agents are the Future of AI (example with CrewAI)

Introduction

Have you ever wondered how to make ChatGPT even more powerful? Imagine it could answer harder, more thought-provoking questions rather than just provide simple summaries of Google results. Questions like "How should I grow my business?" or "Is this stock going to make me any money?" can be addressed using a multi-agent approach. This is a glimpse into the future of how we will use large language models to solve challenging problems. But, you don’t need to wait; we have what we need to initiate the use of multiple agents now.

This multi-agent system is the first step toward building AI teams capable of achieving what used to require a group of humans. Your AI team can work on complex problems and present you with a detailed and well-researched conclusion. To demonstrate the power of multi-agents, let's create a team of AI agents to analyze stocks and identify winners.

If you enjoy learning about AI and the latest technologies, consider subscribing to the channel and like the video if you want more content on multi-agent large language models (LLMs). Understanding this now will put you ahead before the next version of ChatGPT arrives.

The Concept of Multi-Agent Approach

What is the Multi-Agent Approach?

In our example, we’re going to analyze a stock to determine its investment potential. If you ask ChatGPT about a stock right now, its response will mostly reflect training data, your specific prompt, and perhaps a brief internet search.

For instance, for a question like "Acting as a financial advisor, is Tesla a good investment?", a financial advisor would delve into Tesla’s financial statements, reviewing earnings and hard numbers. However, stock analysis is much more comprehensive than just looking at earnings statements.

A breakthrough announcement, like Tesla discovering nuclear fusion, significantly impacting the stock, won’t be reflected in financial statements. A second ChatGPT, analyzing the financial advisor's report and performing news searches, could offer a more nuanced opinion. Adding a third ChatGPT to review SEC filings for insider trading activities, and a fourth one to track scheduled product launches, enriches the analysis.

The multi-agent approach deploys various agents, each focusing on different aspects of stock analysis, yielding a thorough and accurate report. This makes your decisions more data-driven and precise.

Step-by-Step Tutorial

Introduction to CrewAI

To start with multiple agents, we will use CrewAI. In this article, I'll guide you through creating a team of AI agents to analyze stocks.

You’ll download all the necessary files from the provided link and get acquainted with Google Colab for execution. CrewAI doesn’t sponsor this content; the example is based on their stock analysis tutorial.

Prerequisites

1. Download Files: Obtain the necessary files from the provided link.
2. Setup Google Colab: Utilize Google Colab to run Python scripts.

Tools and API Keys

We will use various APIs for different tools that the agents will need:

• SerpAPI: For Google search results.
• Browserless: For web scraping.
• SEC API: To access SEC filings.
• OpenAI API: For performing natural language processing tasks.

Defining Agents, Tasks, and Tools

1. Tools: Define tools such as browser tools, calculators, search tools, and SEC tools.
2. Agents: Specify the roles, goals, and tools for each agent like financial analysts, research analysts, and investment advisors.
3. Tasks: Assign specific tasks to each agent, such as researching news articles, conducting financial analysis, reviewing SEC filings, and providing investment recommendations.

Running the Program

Use Google Colab to set current directories, initialize API keys, and install necessary libraries. Define the main structure for the AI crew, agents, and tasks. Finally, execute the primary Python script to get the stock analysis report.

Example Output

Once the program completes, it provides a comprehensive investment recommendation, analyzing financial health, market sentiment, insider trading activities, and upcoming events, ultimately concluding whether the stock is a good investment.

Conclusion

Multi-agent systems truly showcase the immense potential of AI for complex problem-solving, especially in financial analysis. This technology enriches insights by combining perspectives from multiple specialized agents.

Keywords

• Multi-agent system
• AI teams
• Financial analysis
• Stock analysis
• CrewAI
• Google Colab
• API keys
• Large language models
• ChatGPT

FAQ

Q: What is a multi-agent approach in AI?

A: A multi-agent approach involves using multiple AI agents, each focusing on different aspects of a problem, to deliver a comprehensive solution.

Q: How can I use CrewAI for analyzing stocks?

A: Download the necessary files, set up Google Colab, acquire and configure API keys, define tools, agents, and their tasks, then execute the script to get a stock analysis report.

Q: Are there any prerequisites for running CrewAI’s stock analysis example?

A: Yes, you need a Google Colab account, appropriate API keys, and basic knowledge of setting up Python scripts.

Q: Is this multi-agent system limited to financial analysis only?

A: No, the multi-agent system can be adapted for various types of complex problem-solving beyond financial analysis.

Q: What are the benefits of using a multi-agent system over a single-agent?

A: Multi-agent systems provide a more thorough and accurate analysis by leveraging specialized agents focused on different aspects of a problem.