Unlock the Power of AI Agents with the CREI Framework

Updated on Feb 23, 2024

Are you ready to bring your innovative ideas to life? In this tutorial, we will delve into the world of AI agents and how the CREI framework simplifies the process of building powerful applications. By the end, you will have the skills to create your own AI agents and integrate them into applications seamlessly.

What is the CREI Framework?

The CREI (Creative, Responsive, Efficient, Intelligent) framework is a robust tool designed for developing AI agents. It allows developers to combine multiple agents and their tasks to tackle complex challenges effectively. Key features include:

Process Management: Acts as a project manager, delegating tasks to agents.
Simplicity: Easier to use compared to other frameworks, making it accessible for all developers.
Popularity: Over 3,400 stars on GitHub, showcasing its growing community.

Creating a Simple AI Agent with CREI

To get started with the CREI framework, follow these steps to create a simple AI agent:

Set Up Your Environment: Create a Python environment named "CREAI Test".
Install the CREI Package: This package includes all necessary submodules and tools.
Import Required Modules: Use a simple example from the GitHub repository to understand the components.
Define the Agent: Specify the system context, tools, and models for your AI agent.
Create Tasks: Define tasks for the agent and assemble a crew of agents to work together.
Start the Crew: Initiate the crew to complete the tasks and display the results.

Building a Sophisticated AI Agent

Next, let’s explore how to create a more complex AI agent, such as a stock analysis agent:

Clone the Repository: Start by cloning the relevant GitHub repository.
Examine the Code: Understand how the agent utilizes various tools and APIs for stock analysis.
Set System Context: Define the agent as a financial analyst with expertise in stock market analysis.
Define Tasks: Include tasks for gathering news articles, conducting financial analysis, and summarizing findings.
Run the Agent: Execute the stock analysis agent to see its capabilities in action.

Making Your AI Agent Available as an API

To enhance accessibility, you can make your AI agent available as an API:

Create a Server File: Set up a file named "server.py" and configure a FastAPI app.
Add Middleware: Allow cross-origin requests for better integration.
Create API Endpoints: Develop endpoints to handle requests for analysis.
Build a User Interface: Design a simple HTML and JavaScript interface for user interaction.
Trigger Analysis: Allow users to input company names and start the analysis as a background task.

Integrating the AI Agent into a SAS Application

Finally, let’s combine the AI agent API with a SAS application:

Modify Main.py: Adjust the main file to handle API requests.
Create Static Index File: This file will include an input field for company names and a button to trigger analysis.
Handle Form Submission: Use JavaScript to send requests to the API and display results.

Conclusion

In summary, the CREI framework empowers you to create AI agents that can transform your ideas into reality. We explored how to build both simple and sophisticated AI agents, make them available as APIs, and integrate them into SAS applications. With this knowledge, you are now equipped to explore the exciting possibilities of AI agents. Start your journey today!

Highlights:

Explore the True AI Agent Framework
Create Simple AI Agents using CREI
Build Sophisticated AI Agents with Integrated Tools
Make AI Agents Available as APIs
Create SAS Applications Powered by AI Agents

FAQ:

Q: What is the CREI framework?
A: The CREI framework is a simplified tool for creating AI agents, allowing the combination of multiple agents and tasks to solve complex problems.

Q: How do I create a simple AI agent?
A: Set up your environment, install the CREI package, define the agent's context, create tasks, and start the crew to execute them.

Q: Can I build a sophisticated AI agent?
A: Yes, by integrating various tools and APIs, you can create an AI agent capable of performing complex analyses.

Q: How can I make the AI agent available as an API?
A: Set up a FastAPI server, create endpoints, and handle requests to trigger the agent's tasks.

Q: Is it possible to integrate the AI agent into a SAS application?
A: Absolutely! You can create a user interface that communicates with the API, allowing users to perform analyses and view results.

Resources:

Note: The URLs mentioned are for illustrative purposes and may not be actual resources.

Unlock the Power of AI Agents with the CREI Framework