Transform Your Ideas with AI Agents: Learn CREI Framework and Build Powerful Applications
Table of Contents
- Introduction
- Stock Analyzer AI Agent Framework
- Creating a Simple AI Agent
- Creating a Sophisticated AI Agent
- Making the AI Agent Available as an API
- Creating a SAS Application with the AI Agent
- Conclusion
Introduction
In this Tutorial, we will explore the world of AI agents and how they can be used to bring your ideas to life. We will discuss the True AI Agent Framework and its simplicity compared to other frameworks. We will create a simple AI agent using this framework to understand its components better. Next, we will dive into a more complex example to create a sophisticated AI agent with integrated tools. Finally, we will make the complex AI agent available as an API and create a simple user interface to demonstrate its capabilities. By the end of this tutorial, you will have the knowledge and skills to create your own AI agent and even build a SAS application powered by AI agent APIs.
Stock Analyzer AI Agent Framework
The True AI Agent Framework, also known as CREI, is a powerful and Simplified framework for creating AI agents. It allows you to combine multiple agents and their tasks to solve complex problems. The framework introduces the concept of processes, which act as project managers, delegating tasks to agents and defining their interactions. Currently, the process is sequential, but more advanced process types are being developed. CREI has gained popularity among developers, with over 3,400 stars on GitHub. The framework's creator, Qi Jia, also has a YouTube Channel where he shares videos on creating interesting flows with CREI.
Creating a Simple AI Agent
To create a simple AI agent using the CREI framework, we first need to set up our environment. We will create a Python environment named "CREAI Test" and install the CREI Package. The package contains all the necessary submodules and tools for the agent. We will use a simple example provided in the GitHub repository to understand each component more effectively. We import the required modules and create an AI agent, specifying its system context, tools, and models. We then define tasks for the agent and create a crew, which is a combination of agents and tasks. Finally, we start the crew, and the agents work together to complete the tasks. The crew completes the analysis, and the result is displayed.
Creating a Sophisticated AI Agent
In this section, we will explore a more complex example of an AI agent: the stock analysis agent. We will clone the repository and examine the code. The agent utilizes various tools and APIs to perform comprehensive stock analysis. The system context sets the AI agent as a financial analyst with expertise in stock market analysis. The agent accesses tools such as a browser and financial research tools to Gather data. Tasks are defined for collecting and summarizing news articles, conducting financial analysis, and analyzing financial filings. The crew of agents, combined with tasks, works together to provide an investment recommendation based on the analysis. We then run the stock analysis agent to see its functionality in action.
Making the AI Agent Available as an API
To make the AI agent available as an API, we create a new file named "server.py" and set up a FastAPI app. We add Middleware to allow cross-origin requests and create an endpoint for the API. We also create a simple HTML and JavaScript user interface, where users can input the company they want to analyze. The user interface sends a request to the API with the company name, and the API starts the analysis as a background task. Once the analysis is complete, the result is displayed in the user interface.
Creating a SAS Application with the AI Agent
In this final section, we combine the AI agent API with a SAS application. We modify the main.py file to handle the API requests and create a static index file for the user interface. The index file contains an input field for the company name and a button to trigger the analysis. The JavaScript script handles the form submission, sending the request to the API and displaying the analysis result. We demonstrate how the SAS application interacts with the AI agent API, allowing users to perform stock analysis and receive investment recommendations.
Conclusion
In conclusion, AI agents are powerful tools that can bring your ideas to life. The True AI Agent Framework provides a simplified approach to creating AI agents, allowing you to combine agents and tasks to solve complex problems. We have explored the process of creating both simple and sophisticated AI agents using the CREI framework. We have also learned how to make the AI agent available as an API and integrate it into a SAS application. With this knowledge, you are empowered to create your own AI agents and build exciting applications powered by AI. Start exploring the possibilities 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 True AI Agent Framework?
A: The True AI Agent Framework, also known as CREI, is a powerful and simplified framework for creating AI agents. It allows you to combine multiple agents and their tasks to solve complex problems.
Q: How do I create a simple AI agent?
A: To create a simple AI agent using the CREI framework, set up your environment, import the necessary modules, define the agent's system context, tools, and models, create tasks for the agent, and start the crew to work on the tasks.
Q: Can I create a sophisticated AI agent?
A: Yes, you can create a sophisticated AI agent by integrating various tools and APIs into the agent's tasks. This allows the agent to perform complex analysis and provide detailed recommendations.
Q: How can I make the AI agent available as an API?
A: To make the AI agent available as an API, set up a server using a framework like FastAPI, create an API endpoint, and handle the requests to trigger the agent's tasks. You can then communicate with the API to start the analysis and retrieve the results.
Q: Can I integrate the AI agent into a SAS application?
A: Yes, you can integrate the AI agent into a SAS application by creating a user interface that communicates with the API. Users can input the necessary information, trigger the analysis, and display the results within the SAS application.
Resources:
Please note that the URLs Mentioned in the text are for illustrative purposes and may not be actual resources.
