Top 10 Open Source GitHub Copilot Alternatives (2024 Version)

AI-powered coding assistants have become increasingly popular. GitHub Copilot, developed by GitHub in collaboration with OpenAI, has been at the forefront of this revolution. However, for those seeking open-source alternatives, there are several impressive options available. In this article, we’ll explore the top 10 open-source GitHub Copilot alternatives that you can use to enhance your coding experience.

1. FauxPilot

FauxPilot is an ambitious project that aims to create a locally hosted alternative to GitHub Copilot. It leverages the SalesForce CodeGen models and NVIDIA’s Triton Inference Server with the FasterTransformer backend to provide code suggestions.

GitHub — fauxpilot/fauxpilot: FauxPilot — an open-source alternative to GitHub Copilot server

• One of the standout features of FauxPilot is its ability to run entirely on your local machine, ensuring privacy and eliminating the need for an internet connection.
• This makes it an excellent choice for developers working in secure environments or those concerned about data privacy.

Setting up FauxPilot requires some technical know-how, as you’ll need to have Docker and an NVIDIA GPU with sufficient VRAM to run the model of your choice. The project provides a setup script that guides you through the process of downloading and converting the necessary models.While FauxPilot may not have the polish of GitHub Copilot, it’s a promising open-source alternative that continues to improve with community contributions.

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2. AI Shell

For developers who spend a lot of time in the command line, AI Shell offers a unique and powerful tool.

This CLI converts natural language to shell commands, making it easier for both beginners and experienced developers to navigate complex command-line operations.

• AI Shell is inspired by the GitHub Copilot X CLI but is open source and available for everyone. It uses OpenAI’s API to generate shell commands based on your natural language input, making it a versatile tool for various operating systems and shell environments.
• One of the great features of AI Shell is its ability to explain the generated commands, helping users understand what each part of the command does. This educational aspect makes it an excellent tool for those looking to improve their command-line skills.

Setting up AI Shell is straightforward, requiring only Node.js and an OpenAI API key. The project also supports multiple languages, making it accessible to a global audience.

GitHub — BuilderIO/ai-shell: A CLI that converts natural language to shell commands.

3. Privy

Privy takes a different approach to AI-assisted coding. While it’s described as an open-source alternative to GitHub Copilot, it focuses more on managing user data and providing simple APIs for this purpose.

GitHub — srikanth235/privy: An open-source alternative to GitHub copilot that runs locally.

• The project aims to simplify the process of handling user information in applications, which can be a complex and sensitive task. By providing easy-to-use APIs, Privy allows developers to focus more on their core application logic rather than worrying about the intricacies of user data management.
• While Privy may not offer direct code completion like some other alternatives, its specialized focus on user data management makes it a valuable tool for developers working on applications that require robust and secure handling of user information.

4. Tabby

Tabby is a self-hosted AI coding assistant that aims to provide a local alternative to cloud-based solutions like GitHub Copilot. It’s designed to be fast, privacy-focused, and customizable to fit various development environments.

GitHub — TabbyML/tabby: Self-hosted AI coding assistant

• One of Tabby’s key features is its ability to run entirely on your local machine, ensuring that your code and prompts never leave your system. This makes it an excellent choice for developers working with sensitive or proprietary code.
• Tabby supports multiple programming languages and can be integrated with various IDEs and text editors. It uses machine learning models to provide context-aware code suggestions, helping developers write code more efficiently.

The project is actively developed and has a growing community, which means regular updates and improvements. For those looking for a powerful, local AI coding assistant, Tabby is definitely worth considering.

5. Open Copilot

Open Copilot is an exciting project that aims to create an open-source alternative to GitHub Copilot. While it’s still in the early stages of development, it shows promise as a community-driven solution for AI-assisted coding.

GitHub — MykalMachon/open-copilot: an open-source, llama-powered, GitHub copilot alternative.

• The goal of Open Copilot is to provide a free and open-source tool that can offer code suggestions and completions similar to GitHub Copilot.
• By leveraging open-source language models and community contributions, Open Copilot aims to create a powerful coding assistant that respects user privacy and can be customized to fit individual needs.

6. Codeium.vim

For Vim and Neovim users, Codeium.vim offers a free and ultrafast alternative to GitHub Copilot.

• This plugin brings AI-powered code completion to these popular text editors, enhancing the coding experience for users who prefer a keyboard-centric workflow.
• Codeium.vim is designed to be fast and responsive, providing code suggestions as you type. It supports a wide range of programming languages and can be easily integrated into your existing Vim or Neovim setup.One of the standout features of Codeium.vim is its commitment to being free for individual developers.
• This makes it an attractive option for those who want AI-assisted coding without the subscription costs associated with some other solutions.The plugin also offers various configuration options, allowing users to customize its behavior to suit their coding style and preferences.

GitHub — Exafunction/codeium.vim: Free, ultrafast Copilot alternative for Vim and Neovim

7. Llama Coder

Llama Coder is an innovative project that brings the power of large language models to local development environments. It uses the Llama model, a powerful open-source language model, to provide code suggestions and completions.

GitHub — ex3ndr/llama-coder: Replace Copilot local AI

• One of the key advantages of Llama Coder is its ability to run entirely locally, ensuring privacy and eliminating the need for an internet connection. This makes it an excellent choice for developers working in secure environments or those concerned about data privacy.
• Llama Coder is designed to be flexible and can be integrated with various development environments. It supports multiple programming languages and can be fine-tuned to specific codebases or coding styles.

While Llama Coder may require more computational resources than some other alternatives, its use of a powerful language model can result in high-quality code suggestions. For developers looking for a local, customizable AI coding assistant, Llama Coder is definitely worth exploring.

8. Clara Copilot

Clara Copilot is an open-source project that aims to create an AI-powered coding assistant similar to GitHub Copilot.

GitHub — badboysm890/clara-copilot: A alternative to Github Copilot for vscode until you get the…

Named after Clara, the project focuses on providing intelligent code suggestions and completions to enhance developer productivity.Clara Copilot is designed to be flexible and can be integrated with various development environments.

• It uses machine learning models to analyze code context and provide relevant suggestions, helping developers write code more efficiently.One of the interesting aspects of Clara Copilot is its community-driven development.
• The project welcomes contributions from developers, allowing the tool to evolve and improve based on real-world usage and feedback.
• While Clara Copilot may not have all the features of GitHub Copilot yet, it represents an exciting open-source alternative that has the potential to grow into a powerful coding assistant.

9. CodeWhisperer

While not strictly open-source, Amazon’s CodeWhisperer deserves a mention as it offers a free tier for individual developers.

What is CodeWhisperer?

• CodeWhisperer is an AI-powered coding companion that integrates with various IDEs and provides real-time code suggestions.
• CodeWhisperer uses machine learning models trained on Amazon’s vast codebase and open-source repositories to generate code snippets and provide intelligent autocomplete suggestions.
• It supports multiple programming languages and can help with tasks ranging from simple code completions to generating entire functions.
• One of the standout features of CodeWhisperer is its focus on security. It includes a security scan feature that can identify potential vulnerabilities in your code, helping developers write more secure applications.

While not fully open-source, CodeWhisperer’s free tier and integration with popular IDEs make it a compelling alternative to GitHub Copilot for many developers.

10. Cursor

Cursor

While not being completely Open Source, Cursor is an AI-powered code editor that has been gaining popularity recently, which you might check it out!

Here are some key features and benefits of Cursor:

1. AI-Powered Coding Assistant: Cursor uses advanced AI models to provide intelligent code suggestions, completions, and even entire function implementations.
2. Chat Interface: One of Cursor’s standout features is its integrated chat interface. You can ask questions about your code, request explanations, or even ask for help with specific coding tasks directly within the editor.
3. Code Refactoring: Cursor can help you refactor your code, suggesting improvements and optimizations to make your codebase more efficient and maintainable.
4. Multi-Language Support: Like Copilot, Cursor supports a wide range of programming languages, making it versatile for different types of projects.
5. Context-Aware Suggestions: Cursor analyzes your entire codebase to provide more accurate and relevant suggestions, taking into account the context of your project.
6. Customization: You can fine-tune Cursor’s behavior to match your coding style and preferences.
7. Privacy-Focused: Unlike some cloud-based solutions, Cursor prioritizes privacy by running locally on your machine, ensuring your code doesn’t leave your system.
8. Regular Updates: The Cursor team actively develops the tool, regularly adding new features and improvements based on user feedback.
9. Free Tier Available: While Cursor offers paid plans for additional features, there’s also a free tier available for individual developers.
10. IDE Integration: Cursor can be integrated with popular IDEs, allowing you to use its features within your preferred development environment.

Cursor has gained popularity among developers for its powerful AI capabilities and user-friendly interface. Many users find it to be more advanced than Tabnine in terms of code understanding and generation. However, as with any tool, it’s recommended to try it out yourself to see if it fits your workflow and coding style.

Conclusion

The world of AI-powered coding assistants is rapidly evolving, and these open-source alternatives to GitHub Copilot demonstrate the innovation and creativity of the developer community.

• While these alternatives may not yet match all the features of GitHub Copilot, they offer unique advantages such as increased privacy, customization options, and the ability to run entirely locally.
• Moreover, being open-source, these projects benefit from community contributions and can rapidly improve over time.
• As AI continues to play an increasingly important role in software development, these open-source alternatives provide developers with more choices and control over their AI-assisted coding tools.

Whether you’re looking for a complete GitHub Copilot replacement or a specialized tool to enhance your workflow, exploring these open-source alternatives can lead to exciting discoveries and improvements in your coding process.Remember, the best tool for you will depend on your specific needs, development environment, and preferences.

Don’t hesitate to try out multiple options to find the one that best fits your workflow. Happy coding!