AI Agent is so popular, so how can AI teach me to become an AI Agent?

How to create an AI Agent using AI technology?

Article Source: ShenChao

Article Author: SunnyZ

Most people in the Web3 industry are aware of this bull market led by AI and MEME. Many people think it's a bubble or hype, but the current pace of AI development is incomparable to the past. AI Agents have become a hot topic. As a Web3 MKT person, tweeting frequently is common, and I also use chatgpt to help write tweets. Can we take it a step further and let AI teach me how to be an AI agent, automating tweet publishing? So in this article, I will guide you step by step on how to write a simple AI Agent using Python to help us automate tweet publishing.

1. Introduction

AI Agent is an intelligent system that combines machine learning and automation to perform tasks without human intervention. This article will demonstrate how to use AI technology to create an agent that can automatically publish tweets through a practical example.

2. Environment Configuration

Before starting to write code, we need to configure the development environment. Here are the required tools and libraries:

2.1 Install cursor

Cursor is an AI code editor

After installation, it should look like the following figure:

2.2 Install Anaconda【python environment management tool】

This step only needs to be downloaded and continued step by step.

2.3 Installing tweepy [Twitter package in Python]

2.4 Install crewai【ai agent development framework】

2.5 Open a Twitter developer account

The account is as follows: **【

Here, the API_KEY and API_KEY_SECRET of the user are obtained by following the part highlighted in the screenshot.

Similarly, obtain the user's ACCESS_TOKEN and ACCESS_TOKEN_SECRET

Through these four fields:

By calling the API with API_KEY, API_KEY_SECRET, ACCESS_TOKEN, and ACCESS_TOKEN_SECRET, you can establish an account connection and perform various operations such as tweeting and retweeting using your own Twitter account. For specific API permissions and quotas, please refer to the following link.

【

2.6 Establish API Connection for Twitter

The following is a demo of how to use python to operate your own Twitter account to send tweets:

2.7 Create your own Crew

Crewai is a multi-agent framework that can define the role, goal, background story of each agent, the description and output requirements of each task, and the agent specified for the task. Furthermore, it can define the workflow of these tasks to enable collaboration among multiple agents.

Simple examples can be referred to the following link

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2.8 One-click send AI-generated content to Twitter

Combining 2.6 with 2.7 [i.e. the previous two steps], you can send the AI-generated content to Twitter. Here, the code is displayed, and everyone can also see the whole process of using AI to write the agent, step by step, to send out the tweet.

2.8 Run Your Code

Click the button in the upper right corner

The terminal outputs as follows:

2.9 Check if the tweet has been sent successfully

Here, in order to achieve the desired effect, I debugged several times. You can see that from a simple 'automatically published tweet' to a tweet with emojis, it went through 3 rounds of debugging. This is the most difficult part of writing an AI agent, finding out where the problem is: Version 1: Manually typing tweet content. Version 2: Generating content using AI, but did not consider the character limit of Twitter, so only part of the content is presented.

Third edition: AI-generated content with consideration of word count limitations and added requirements for tweet style, so the third tweet will have emojis, which are adjusted by AI based on the overall style of Twitter.

3. Optimize the code according to Twitter's requirements

This step is very important, please pay attention to the following items to meet the final requirements:

The Twitter text is limited to 280 characters, which can be achieved by changing the prompt in the agent and task configurations to meet this requirement.

Free API tweet limit: 17 calls within 24 hours

4. The following is the content Chatgpt taught me

Make sure Python is installed on your computer. You can check if it is installed by using the following command:

If not installed, it can be downloaded and installed from the [Python official website].

4.1 Creating a Virtual Environment

Using a virtual environment can isolate project dependencies and avoid conflicts with other projects. You can create and activate a virtual environment using the following commands:

4.2 Install necessary libraries

We need some libraries to help us interact with the Twitter API and process data. You can use the following command to install these libraries:

Tweepy: Interact with the Twitter API.

OpenAI: Used to invoke AI models to generate content.

4.3 Get Twitter API Credentials

To use the Twitter API, we need to create a developer account and obtain API credentials. Here are the steps:

Visit [Twitter Developer] and log in

Create a new application

Get API Key and API Secret Key

Make sure to keep these credentials as we will be using them in the code.

4.4 Write Code

4.1 Import Library

First, we need to import the required libraries:

4.2 Configuring Twitter API

Next, we need to configure the credentials of the Twitter API:

4.3 Configuring OpenAI API

Similarly, we need to configure the API key for OpenAI:

4.4 Generate Twitter Content

We will create a function to generate Twitter content through the OpenAI model:

4.5 Release Twitter

Next, we create a function to publish the generated Twitter content:

4.6 Main Program

Finally, we integrate all the features into the main program:

4.7 Run Code

Make sure your virtual environment is activated, then run the following command in the command line:

This will generate a tweet about the AI Agent and post it to your account.

In the above tutorial, you can see the results of the three tweets that have been sent and evolved.

8. Thank you

Thanks to AI for generating code for me and automatically sending tweets, thanks to Yu. [TG @rainy_zh] for helping with configuration and debugging, and also thanks to Shenzhen Tide and Metaera for their support. I hope this article can help you understand the working principle of AI Agent. If you have any questions, feel free to ask me on my TG and Twitter: @SunnyZ_Crypto.