The demand for AI-powered coding tools has exploded, and now open source alternatives can now rival commercial solutions such as cursors in terms of functionality, flexibility and privacy. If you are looking for a powerful, cost-effective and open source code assistant, consider these top choices for 2025:
1. ZED
ZED is a high-performance, open source code editor designed for collaboration between humans and AI. Built by the creators of Atom and Electron, it offers seamless multiplayer editing, AI back, and a sleek Dark UI. It runs smoothly on Mac, Linux, and Windows and is optimized with Rust and GPU acceleration.
2. Pelle
Pearai combines your favorite AI models such as GPT-4, Claude and its own internal models – forming a single intuitive editor. Its purpose is to maximize encoding speed, fix errors and innovation. Pearai’s all-in-one editing ensures that you don’t have to juggle multiple tools while providing powerful AI chat and timely features.
3. Cody
Cody is ideal for developers who deal with large or complex code bases. It works like an experienced team member – can answer questions about your entire project, write new code, mark errors, and provide insightful suggestions directly where you work. Major businesses have already relied on Cody to increase productivity.
4. blank
Arguably, Void is the most cursor-like open source solution, but it is strongly focused on privacy and control. Based on VS code, it allows you to self-service AI models, keep all your code completely local, and enjoy rich AI chats, code suggestions, and compatibility with VS code themes, perfect for developers who care about security and flexibility.
5. continue
Continue is a flexible open source AI assistant extension for popular edits such as VS Code and JetBrains. It supports integration with multiple AI models (Claude, GPT-4, etc.), allowing you to build custom assistants and enable editing AI-driven code chat and autocomplete. Ideal for customizing workflows and maximizing developer traffic.
6. Tabi
Tabby is a self-hosted open source encoding assistant. It utilizes advanced machine learning to obtain context-aware suggestions that can be run directly on the machine. Tabby makes privacy a priority, without sending data to third parties. It fits seamlessly into your coding workflow and is perfect for individual developers and teams.
7. Pythagora
Pythagora turns the idea into the smallest fussy backend code. Its open source AI helps you describe what you need, write initial code and integrate directly with the stack. Pythagora empowers new and experienced developers with the ability to be fast prototyping and come to life quickly.
8. assistant
Aider is a terminal-based AI assistant that blends deeply with your workflow and git. Supports over 100 languages, it helps you write, modify and debug code through natural language conversations – perfect for people who like command line tools and need strong code understanding.
9. ROO code
ROO code stands out by providing multi-file AI editing, proxy workflows, and powerful privacy options. It converts VS code into a smart IDE, allows AI agents to reason in the entire code base instead of a single file, and provides advanced offline capabilities for security-conscious organizations.
| tool | Open source | AI support | Privacy/Self-host | Editing integration | Notable features |
|---|---|---|---|---|---|
| ZED | Yes | Yes | Yes | The country’s | Rusty core, fast, collaborative |
| Pelle | Yes | Yes | Yes | The country’s | Multi-ai, inline chat |
| Cody | Yes | Yes | Yes | Various | Complex projects, Q&A |
| blank | Yes | Yes | Full | Based on code | Privacy, vs. Code Theme |
| continue | Yes | Yes | Yes | VS Code/Jet Bridge | Custom AIS, productivity |
| Tabi | Yes | Yes | Full | Various | Local model, recommended |
| Pythagora | Yes | Yes | Yes | Various | Backend code generation |
| assistant | Yes | Yes | Yes | terminal | git fusion, NLU |
| ROO code | Yes | Yes | Full | VS Code | Multi-file AI, agent workflow |
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Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padua. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels in transforming complex data sets into actionable insights.
