Open Source Windsurf Alternatives

Written by the team behind Atom and Tree-sitter, Zed is a high-performance, multiplayer code editor. Multiplayer is part of the editor rather than a plugin, so shared editing is not something you bolt on afterwards.

Installation is a direct download or a package from your usual manager, and the same three desktop platforms are supported for building from source. The repository carries per-platform build documentation and a contribution guide for anyone who wants to work on the editor itself.

A few practical details shape what you can expect.

• Desktop platforms: macOS, Linux and Windows, by direct download or local package manager.
• Web: not available yet, with progress tracked in an open discussion.
• Build from source: separate build guides for each supported platform live in the docs directory.
• Licensing: source is GPL-3.0-or-later, with Apache-2.0 components where they are marked as such.
• Dependency licenses: cargo-about checks third party crates automatically, and continuous integration fails when one is unaccounted for.

Zed is developed by Zed Industries, a for-profit company that is hiring. Financial support runs through GitHub Sponsors and goes to the company as general revenue, with no perks or entitlements attached, so sponsoring is a way to fund the work rather than to buy anything.

Warp began as a terminal and now describes itself as an agentic development environment. You use its built-in coding agent, or point it at a CLI agent you already run such as Claude Code, Codex or Gemini CLI.

The client codebase lives in this repository and builds from source through three scripts, one for platform setup, one to build and run, and one that formats, lints and tests before you submit. OpenAI is the founding sponsor of the open source repository, and the agentic management workflows run on GPT models.

Development of Warp itself happens largely in the open, and much of it is done by agents.

• Warp Factories: infrastructure defined in code for cloud software factories, with evals built in.
• Contribution dashboard: watch agents triage issues, write specs, implement changes and review pull requests.
• Readiness labels: maintainers mark issues ready to spec or ready to implement for contributors.
• Agent sessions: click into an active session running in a web-compiled Warp terminal.
• Dual licensing: the UI crates are MIT while the rest of the repository is AGPL v3.

Downloads cover platform-specific installs and a preview build carries experimental features. The project credits dependencies including Tokio, NuShell, Alacritty, Hyper and the Fig completion specs, and contributors gather with maintainers in a public Slack channel.

Aider pairs you with a language model inside the terminal, on a new project or an existing codebase. Edits land in your real files, and every change is committed with a sensible message, so ordinary git tools are enough to diff, manage or undo whatever the model did.

To stay useful in larger projects, Aider builds a map of the entire repository and feeds the relevant parts back as context. More than a hundred languages are supported, among them Python, JavaScript, Rust, Ruby, Go, C++, PHP, HTML and CSS.

The workflow has several entry points beyond typing at the prompt.

• Editor mode: leave a comment in your code from any IDE and Aider picks it up and starts working.
• Linting and testing: your linters and test suite run after each change, and Aider fixes what they report.
• Images and web pages: add screenshots or reference documentation to the chat for visual context.
• Voice to code: ask for features, test cases or bug fixes by speaking rather than typing.
• Copy and paste mode: work through any model's browser chat, with context and edits shuttled back and forth for you.

Model choice is wide. It works best with the strongest coding models from Anthropic, DeepSeek and OpenAI, but connects to almost anything, local models included. Installation is a pip install of a small installer followed by one command, after which you point it at a project directory and supply an API key.

Continue is a coding agent that ships in three forms, a command line interface, a VS Code extension and a JetBrains plugin. The repository is read-only and no longer actively maintained, so anyone picking it up today is working with a frozen codebase rather than a project still moving.

The team closed the work with a final 2.0.0 release across all three clients. That release removed anonymous telemetry, pulled out authentication and squashed remaining bugs, which leaves a version that runs without phoning home or asking anyone to create an account first.

What remains available splits across the distribution channels.

• CLI: published on npm, and the maintainers recommend it over the JetBrains plugin.
• VS Code extension: listed on the Visual Studio Marketplace and on the Open VSX registry.
• JetBrains plugin: distributed through the repository releases, with its source in the extensions directory.
• Documentation: the guides on configuring Continue, how it works and how to customize it are still published.
• Apache 2.0 license: the code stays open for anyone who wants to fork it or build something else on it.

Continue suits developers who want an agent they can read end to end and adapt, and the maintainers say they hope the codebase serves as a foundation for others. Anyone who needs ongoing fixes or support for new models should plan on maintaining a fork of their own.

Kilo Code is an AI coding agent that follows you between editors, running as a VS Code extension, a JetBrains plugin and a command line tool from one project. Pricing is open: you choose from more than 500 models, switch between them mid task, and pay the model provider's rate with no markup, without needing API keys to begin.

Work is divided among specialized agents you switch between, and you can define your own. Code implements and edits, Plan designs architecture before anything is written, Ask answers questions without touching files, Debug traces issues, and Review surfaces problems across performance, security, style and test coverage.

Around those agents sit the tools they use.

• Inline autocomplete: ghost text suggestions accepted with the tab key.
• Self checking: the agent reviews and corrects its own work.
• Terminal and browser control: running commands and automating the web.
• MCP marketplace: a place to find and wire up servers that extend what the agent can do.

Installation covers the VS Code Marketplace, the JetBrains plugin repository and the CLI through npm, Homebrew, the Arch User Repository or prebuilt binaries. For pipelines, an autonomous run flag removes every permission prompt, which the project advises using only in trusted environments. The code is MIT licensed, and the CLI began as a fork of OpenCode.

Tabby is a self hosted coding assistant, offered as an on premises alternative to hosted completion services for teams whose source code is not allowed to leave the building. It is self contained, needing neither a database server nor a cloud service behind it.

Integration is meant to be undemanding. An OpenAPI interface exposes the server to existing infrastructure such as a cloud IDE, and inference runs on consumer grade GPUs, with Apple Metal support for M1 and M2 machines.

The project has grown well past plain autocomplete.

• Repository aware completion: retrieval over the repository, plus declarations from the local language server and recently modified code, feeding each suggestion.
• Answer engine: a knowledge engine for an engineering team, answering from internal data and turning threads into persistent, shareable pages.
• Editor extensions: plugins for VS Code, Vim and IntelliJ, with chat in a side panel, file mentions as context and inline editing.
• Source integrations: self hosted GitHub and GitLab, GitLab merge requests indexed as context, and your own documentation added through REST APIs.
• Administration: an admin interface with team management, secured access, usage reports and LDAP authentication.

The quickest start is a single Docker command that mounts a data directory, exposes port 8080 and names both a completion model and a chat model, while a published model registry lists what is supported. Deployment guides also cover running the server on cloud infrastructure.