Awesome AI Agents: A Collection of Resources for 150+ AI Intelligent Body Frameworks

General Introduction

Awesome AI Agents is a GitHub open source project maintained by the e2b-dev team that focuses on collecting and organizing resources related to autonomous AI intelligences. The project brings together over 150 AI agents and development frameworks, covering a wide range of application scenarios from coding assistants to data processing agents. It not only provides an exhaustive list, but also supports users to find open-source or closed-source projects through a filtering function, helping developers quickly locate the right tool for their needs. The goal of the website is to provide AI developers with inspiration and practical resources to advance the development of intelligent body technology. Both beginners and professionals can find a wealth of reference material and community support here.

Function List

• Categorization and organization of resources: Organize AI intelligences items by category (e.g., Coding, Productivity, General Intelligences) for a quick overview.
• Open Source vs. Closed Source ScreeningThe AI resource is a resource that supports users to filter open source or commercialized AI intelligences to satisfy different development needs.
• Community Contribution Support: Allows users to keep the list up to date by submitting new smartbody items via Pull Request or form.
• link jumping: Provide links to GitHub or the official website of each Intelligence for users to learn more about it and use it.
• real time update: A regularly updated list reflecting the latest AI intelligences trends and technological advances.

Using Help

Awesome AI Agents is a GitHub-based resource listing page that requires no installation to use. Below is a detailed guide to help users get started quickly and make the most of this repository.

How to access and use

1. Open the web site
   In your browser, type https://github.com/e2b-dev/awesome-ai-agentsYou can access the project's homepage by clicking on the link below. You don't need a GitHub account to view it, but it's recommended to log in if you want to contribute.
2. Browse the list of resources
   • Once the page loads, you'll see a README.md file that contains a list of all the AI intelligences.
   • The list is organized alphabetically and by category, e.g. "Coding Assistant", "Data Processing Intelligence" etc.
   • Each entry includes the project name, a short description, and a jump link (usually to a GitHub repository or official website).
3. Screening for items of interest
   • If you are only interested in open source projects, you can use your browser's search function (Ctrl + F) and type in "open-source" to find a description.
   • Intelligences for specific purposes, such as "coding" or "data processing", can also be quickly located using keyword searches.
4. In-depth knowledge of the program
   • Click on the link after each entry to jump to the corresponding GitHub repository or official website.
   • On the target project page, you can view detailed documentation, code samples or installation instructions. For example, clicking on the "GPT Engineer" link will redirect you to its repository, which shows you how to generate a complete code base based on tips.
5. Participation in community contributions
   • If you find an unlisted AI intelligence, you can click the "Create a pull request" or "fill in this form" links on the page.
   • Pull Request Process::
     1. Click the "Fork" button at the top of the page to copy the repository to your GitHub account.
     2. Modify README.md in a local or online editor to add new items (keep alphabetical order and categorization).
     3. Commit the changes and create a Pull Request and wait for review.
   • Form Submission: Click on the form link to fill in the name, profile and link of the smartbody and submit it to be updated by the maintainer.

Main function operation flow

Find Code Assistant

• Suppose you need an AI intelligence that helps write code:
  1. Open the page, press Ctrl + F and type "coding" or "developer".
  2. Find items such as "Smol" or "Stackwise".
  3. Click on the links to jump to their repositories for features (such as "Automatically Generate Node.js Functions") and installation steps.
  4. Deploy locally or use the VS Code extension according to the guidelines.

Explore Data Processing Intelligence

• If you need to process data:
  1. Search for "data processing" or "analytics".
  2. Find the "TaskWeaver" program and click on it.
  3. Read the documentation to learn about its "Seamless Planning and Execution of Data Analysis Tasks" feature.
  4. Follow the instructions to download the source code and run the example.

Submission of new projects

• An unlisted intelligence (e.g. "Devika") is found:
  1. Click "Create a pull request".
  2. After Forking the repository, add a line to README.md:

     - [Devika](https://github.com/stitionai/devika) - Agentic AI Software Engineer that breaks down instructions and writes code.
     

  3. Submit the Pull Request and wait for the merge.

Featured Functions

• Categorization and organization of resources
  The page organizes the intelligences into categories, such as "Coding Agents", "Productivity Agents", etc. Each category has dozens of items with short descriptions. Under each category are dozens of projects with short descriptions. For example, "GPT Engineer" is described as an "AI intelligence that generates a complete code base based on hints", which visualizes its purpose.
• Community Driver Updates
  The program encourages user participation to ensure fresh content. You can suggest new content by submitting a Pull Request or by contacting the hello@e2b.dev mailbox. The Discord community (linked at the bottom of the page) also provides live discussions.
• Ease of Link Jumping
  Each program name is followed by a hyperlink that can be clicked on to jump to the program. For example, want to try "SuperAGI"? Click on the link and you'll be taken to its repository, where you'll find a full description and deployment guide for "an open source framework for building autonomous AI intelligences".

caveat

• network requirement: A stable network is required to access GitHub, and domestic users may need to go online.
• Language Support: The pages are mainly in English, but the descriptions are concise and can be easily understood with a translation tool.
• Update Frequency: Since it is community maintained, it is recommended to check regularly for the latest resources.

With these steps, users can easily browse, filter, and utilize the resources provided by Awesome AI Agents, whether they are looking for development tools or participating in community contributions, and get started quickly.