How I Made AI Assistants Do My Work For Me: CrewAI

Introduction

Have you ever found yourself about to make a controversial purchase, only to be struck by an unexpected inner dialogue? Questions arise about the item's aesthetic and its fashionable endorsement by a celebrity. In moments like these, our minds alternate between quick, automatic responses and slower, more deliberate reasoning. This interesting phenomenon was highlighted by Daniel Kahneman in his book Thinking, Fast and Slow, where he differentiated between two systems of thinking: System One (fast, subconscious) and System Two (slow, conscious).

Understanding AI Limitations

This distinction is essential for understanding the current limitations of AI systems, particularly large language models (LLMs). These models, while powerful, primarily engage in System One thinking, producing quick predictions without the in-depth analysis characteristic of System Two thinking. Andre Karpati from OpenAI has pointed out that no existing LLM can take time to reflect on complex problems thoroughly. However, innovative thinkers are developing methods to simulate the rational thinking process our brains perform naturally.

The Methods: Tree of Thought Prompting and Agent Systems

Two primary methods have been proposed to enhance AI reasoning. The first is tree of thought prompting, which encourages models to evaluate issues from multiple perspectives, leading to collaborative conclusions. The second involves utilizing platforms like CrewAI, which allows users to create custom agents that can work together to tackle complex tasks effectively. This guide aims to show you how to assemble a team of intelligent AI agents to solve intricate problems efficiently, providing insights on accessing real-world data without breaching privacy.

Setting Up AI Agents

To kickstart our journey, we’ll set up a basic framework using CrewAI to create a team of three agents: a market researcher, a technologist, and a business development expert. Here's how you can do it:

1. Open a terminal in VS Code after creating and activating a virtual environment.
2. Install CrewAI by typing the appropriate commands.
3. Import the necessary modules, including OpenAI’s basic module.
4. Define the roles of the agents, specifying clear goals and backstories for each.
5. Create tasks, ensuring they are specific and result-oriented, and assign them to the respective agents.

For example, one of my agents (the market researcher) was tasked with analyzing market demand for an innovative product idea: decorative plugs for Crocs. The other agents had their respective assignments, providing valuable insights and ultimately culminating in a cohesive business plan.

Enhancing Agent Intelligence

To make AI agents smarter, we can integrate real-world data through built-in tools or create custom tools. This approach allows agents to access pertinent information while executing their tasks. By incorporating a Google search tool, I was able to generate a newsletter summarizing the latest in AI and machine learning innovations based on real-time data.

While the initial results were promising, I realized the importance of the information source. To enhance quality, I developed a custom Reddit scraper to gather insights from the local llama subreddit, ensuring that the content was relevant and up-to-date.

Local Models versus API Calls

Using CrewAI, I also explored the option of running local models to bypass API fees. This approach presents challenges, as many local models struggled to meet the task demands. Through testing various models based on their parameters, I was able to pinpoint which ones performed best in generating relevant outputs.

In summary, I discovered that local models can give great results under specific conditions but expect varied and sometimes unreliable outcomes.

Conclusion

Leveraging CrewAI and assembling a team of AI agents has transformed my approach to handling complex tasks. Whether it’s developing business concepts or creating informative newsletters, AI assistants can enhance productivity and deliver valuable insights.

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Keyword

• AI
• CrewAI
• Large Language Models
• System One
• System Two
• Tree of Thought Prompting
• Custom Agents
• Market Research
• Real-World Data
• Local Models

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FAQ

1. What is CrewAI?
CrewAI is a platform that allows users to create custom AI agents that can collaborate to solve complex tasks.

2. How can I set up AI agents using CrewAI?
You can set up AI agents by creating a virtual environment, installing CrewAI, defining agent roles, and assigning specific tasks to each agent.

3. What is the difference between System One and System Two thinking?
System One thinking is fast, automatic, and subconscious, while System Two thinking is slow, deliberate, and requires conscious effort.

4. How do I make AI agents smarter?
You can enhance AI agents by giving them access to real-world data through built-in tools or by creating custom tools to scrape relevant information.

5. Are local models better than API calls?
While local models can avoid fees and enhance privacy, their performance may vary. Some local models struggle to complete tasks effectively compared to API-based models.