Open source Cursor alternatives: Continue, Void, Zed, Aider and Cline

What does Cursor do that you need to replace?

Cursor is a fork of VS Code with AI built into every surface: inline completion, a chat panel that understands your codebase, multi-file edits you review as diffs, and an agent mode that runs commands. Replacing it means deciding which of those surfaces you actually use.

Most developers lean on two: chat with codebase context and agentic multi-file edits. Autocomplete is a separate problem, covered by Copilot-style tools, and many open source setups handle it with a different component.

The big structural difference with open source tools is model choice. You bring an API key or a local model, and you pay the provider directly instead of a bundled subscription.

Write down what you would miss on day one. That list is your evaluation checklist for every tool below.

Which open source Cursor alternatives are worth trying?

Roo Code, a fork of Cline, is also popular and adds configurable modes. Forks move quickly, so compare recent commit activity rather than trusting an old review.

Extension, editor or terminal agent: which shape fits you?

Extensions are the lowest-risk move. You keep keybindings, themes and language tooling, and add Continue or Cline alongside them. If the AI part disappoints, you uninstall one extension.

A new editor is a bigger bet. Zed is fast and designed with AI and collaboration in mind, but moving means rebuilding your setup. Void keeps VS Code compatibility, which lowers switching cost if you liked Cursor’s shape.

Terminal agents suit people who live in the shell. Aider works directly with your Git repository, commits its changes with descriptive messages and makes it easy to undo a bad edit. It pairs well with any editor.

• Stay in VS Code or JetBrains: start with Continue, add Cline for agent tasks.
• Want an AI-first editor with open code: try Void or Zed.
• Prefer Git-centric, reviewable changes: Aider in a terminal next to your editor.
• Work across several editors: a terminal agent avoids editor lock-in.

Which models should you connect?

All of these tools accept commercial APIs and OpenAI-compatible endpoints, and most can use Ollama or another local runtime. Agentic editing is demanding: the model must follow instructions precisely, produce valid diffs and call tools reliably.

In practice, strong hosted models give the best results for multi-file agent work. Local models are useful for chat about private code and for autocomplete, where smaller specialised coding models perform well.

Many developers mix them: a local model for completion, a hosted model for agent tasks. The tools make this a configuration choice rather than a product switch.

Whatever you choose, keep the model configuration in one place per project. When results change after an update, you need to know whether the tool or the model changed.

How to choose step by step

• Write down the three AI actions you use most in Cursor today.
• Decide whether your code may leave your machine; if not, plan for local models and accept weaker agent results.
• Install one extension-based option first, because it costs nothing to try.
• Run the same real task in two tools: a bug fix touching several files is a good test.
• Compare the diffs, the number of corrections you made and the tokens used.
• Commit to one tool for a week before judging; muscle memory biases the first day.

How much does it cost to run?

The tools are free, so the cost is model usage. With pay-per-token APIs, chat is cheap and agent loops are the expensive part, because each step resends a lot of context. Set spending limits on your provider account before handing an agent a large task.

Local models remove per-token costs but need hardware. A machine with a good GPU or plenty of unified memory can serve a coding model for one developer. For a team, a shared inference server often makes more sense than many powerful laptops.

Track spend per project for the first few weeks. It quickly shows which kinds of tasks are worth handing to an agent and which are cheaper to do by hand.

Where do these tools break? Common mistakes

A clean Git state before every agent task is the single cheapest safety habit. Aider enforces it by design; with the others, make it a routine.

• Letting an agent run shell commands without approval on a machine with production credentials.
• Feeding the whole repository as context, which raises cost and often lowers answer quality.
• Accepting large diffs without review because the summary sounded right.
• Using a small local model for agent mode and blaming the tool for poor results.
• Skipping project rules files, so the agent ignores your conventions every session.
• Working on uncommitted changes, which makes it hard to undo an agent’s edits.

How do you extend and roll out these tools?

Several of these tools support the Model Context Protocol, which lets the agent use external tools such as a database, an issue tracker or documentation search through MCP servers. That is how you give an agent project knowledge without pasting it into every prompt.

Treat MCP servers like any dependency: read what each one can do, prefer read-only access where possible and keep secrets out of shared configs. RepoLoot’s catalog tracks MCP servers alongside coding agents, which helps when you want vetted building blocks rather than random repositories.

Because they are open source, these agents are also building blocks. Teams script Aider in CI to apply routine changes, such as dependency bumps or lint fixes, and open pull requests for human review.

Extension-based tools can be customised with slash commands, prompt files and MCP servers tailored to one codebase. That turns a general assistant into a specialist that knows your deployment scripts, test commands and naming rules.

Keep such automations narrow. An agent that edits one kind of file for one purpose is easy to review; a general agent with write access to everything is not.

• Agree which model providers are approved and store keys in a shared secret manager, not in personal config files.
• Commit a rules file to each repository so every tool and developer gets the same conventions.
• Require that agent changes go through normal code review, the same as human changes.
• Track token spend per team for the first month to spot runaway agent loops early.
• Collect examples of good and bad agent output and share them in a short internal guide.

How do the tools compare on everyday workflow?

Feature lists look similar on paper, so compare how each tool behaves during a real task. The points below are where developers most often notice differences after the first week.

Frequently asked questions

Is there an open source fork of Cursor?

Cursor itself is closed source, but Void is an open source VS Code fork aiming at a similar AI-first experience. Continue and Cline add comparable features to regular VS Code, and Zed is a separate open source editor with built-in AI features.

Can I use local models with these Cursor alternatives?

Yes. Continue, Cline, Aider, Zed and Void can all connect to local runtimes such as Ollama or other OpenAI-compatible servers. Local models work well for chat and autocomplete; complex multi-file agent tasks generally go better with stronger hosted models.

Which alternative is best for large refactors?

Aider and Cline are both built for multi-file changes. Aider keeps every change as a Git commit, which makes large refactors easy to review and revert. Cline shows diffs and asks before running commands. Either way, break big refactors into smaller tasks.

Do these tools send my code to a third party?

Only to the model provider you configure. With a hosted API, the relevant code context goes to that provider under its terms. With a local model through Ollama or similar, code stays on your machine. Check each tool’s telemetry settings as well.