Open Source Cursor Alternatives for Local AI Coding

Continue is the best starting point for most developers who want an open-source Cursor alternative that can run models locally. It works as an extension for VS Code and JetBrains IDEs, supports local model providers such as Ollama, and offers chat and code-completion features without requiring a hosted coding service. Choose a different option if you want a fully separate editor, a terminal-first workflow, or stronger built-in project-wide agent features.

What to Look For

“Local AI coding” can mean that prompts and source code stay on your computer, but the details depend on the tool and its configuration. Check that the assistant is actually connected to a local model provider; an open-source editor or extension can still send requests to a cloud service if you select one. Also review telemetry and extension settings if keeping code local is a requirement.

• Model compatibility: Confirm the tool supports your chosen local runner, such as Ollama or a compatible local server, and that the model is suitable for coding. Hardware, model size, and context length affect speed and quality.
• Workflow: Decide whether you need inline completions, chat, code edits across multiple files, terminal commands, or all of these. A tool that excels at chat may not offer the best autocomplete.
• Editor fit: VS Code and JetBrains users may prefer an extension; others may want a dedicated editor or terminal interface.
• Privacy and control: Look for clear provider settings, local model configuration, and project-level controls. “Open source” alone does not guarantee that every request remains local.
• Setup and cost: The software may be free, while local inference still needs memory and processing power. Some products also offer optional paid cloud services.

Continue

Best overall for an existing IDE. Continue is an open-source coding assistant available as an extension for VS Code and JetBrains IDEs. It supports chat and autocomplete, and can connect to local models through providers such as Ollama. Its configurable approach makes it a practical way to try local coding assistance without switching editors.

Strengths: Keep your familiar editor, choose among supported models and providers, and configure different models for different tasks. It is a good fit for developers who want control over where inference happens.

Trade-offs: You may need to configure providers, models, and context settings yourself. Results depend heavily on the model and machine; local autocomplete can feel slower or less capable than a polished cloud-backed service. Best for: VS Code or JetBrains users who want an adaptable, local-first assistant.

Tabby

Best for self-hosted code completion. Tabby is an open-source coding assistant focused on code completion, with a self-hosting approach that suits individual machines and teams running their own infrastructure. It is worth considering when inline suggestions are the main need and you want to manage the service yourself.

Strengths: Its emphasis on completion keeps the core use case straightforward, while self-hosting gives administrators more control over deployment and data flow.

Trade-offs: It is not simply a drop-in replacement for every Cursor feature: conversational editing and autonomous, multi-step agent workflows may not match what you expect from a full coding environment. Setup can also be more involved than installing one extension. Best for: Developers or teams prioritizing private, self-managed code completion.

Continue’s Open-Source Alternatives: Aider

Best for terminal-based, multi-file work. Aider is an open-source command-line coding assistant that works with a Git repository and can connect to local models through supported providers. You describe a change in the terminal, review the proposed edits, and use your normal Git workflow to inspect the result.

Strengths: Its repository-aware workflow is useful for changes spanning files, and Git makes it easier to review and undo edits. It suits developers who prefer explicit instructions and a terminal over editor-side chat panels.

Trade-offs: It is not a graphical editor, and local-model setup and command-line conventions add a learning curve. Large tasks can be challenging for smaller models, so review changes and run tests rather than assuming an edit is correct. Best for: Git-comfortable developers who want a terminal-first coding partner.

OpenHands

Best for agent-style development experiments. OpenHands is an open-source platform for AI software development agents. It can be configured to use different model providers, including local setups where supported, and is aimed at delegating broader tasks rather than only suggesting the next line.

Strengths: Its agent-oriented workflow can explore a repository and carry out a sequence of development actions. It is a useful option for people evaluating open-source alternatives to cloud coding agents.

Trade-offs: This is a more involved environment than an inline editor assistant. Local compatibility, performance, and setup depend on the model and deployment configuration, and agent changes need careful review. Best for: Developers comfortable with configuring tools who want to experiment with repository-level agents.

Continue’s Open-Source Alternatives: Zed

Best for a fast, separate code editor. Zed is an open-source code editor with AI features and support for configuring model providers. Depending on your setup and the current provider integrations, it can be used with local inference rather than relying exclusively on hosted services.

Strengths: It offers an editor-first experience, so coding and AI assistance live in one application. It may appeal to developers who are willing to move away from VS Code and prefer a purpose-built editor.

Trade-offs: Confirm that the specific local provider and AI workflow you need are supported before migrating. Its ecosystem and interaction model differ from VS Code, and local model quality remains a separate consideration. Best for: Developers seeking a standalone editor and willing to adapt their setup.

How to Choose

Start with budget and hardware. The software in this category is available as open source, but running a capable model locally is not cost-free in practical terms: it uses your computer’s memory and processing power. If your machine struggles with the model you want, try a smaller model, accept slower responses, or consider a hosted provider—but hosted use may send code or prompts outside your device.

Match the tool to the task. For VS Code or JetBrains chat and autocomplete, start with Continue. For self-managed inline completion, assess Tabby. For terminal-driven edits across a Git project, try Aider. For agent experiments, look at OpenHands. For an editor replacement, evaluate Zed and its current local-provider support.

Test with a small real task. Check completion speed, how well the model understands your project, and whether the assistant can use the context you expect. Review provider configuration and privacy settings before opening a sensitive repository. Keep changes in version control and inspect diffs, especially when an agent can modify several files or run commands.

Expect trade-offs. Local use can improve control over data flow, but it does not automatically mean better privacy if a cloud provider remains configured. Smaller local models may be less capable than larger hosted models, while larger models can require more resources. The best choice depends on whether you value IDE integration, completions, agent behavior, or simplicity most.

Frequently Asked Questions

Can these tools work without an internet connection?
Often, if the application, model, and dependencies are already installed and configured locally. Initial downloads and updates generally require internet access; check each tool’s setup requirements.

Is Continue fully local?
It can use local models, but locality depends on your selected provider and configuration. Verify the active model endpoint and any telemetry settings rather than assuming every setup stays on-device.

Which option is closest to Cursor?
Continue is the most natural first try for people who want AI assistance inside an existing IDE. No option is a feature-for-feature match for every Cursor workflow.

Do I need a powerful computer?
It depends on the model. Smaller models can run on more modest hardware but may be less capable; larger models typically need more memory and can respond more slowly on limited hardware.

Can I use my own codebase for context?
Many of these tools can work with project files, but context selection and indexing vary. Review the tool’s documentation and settings to understand which files are included and what is sent to the model.

Bottom Line

Start with Continue if you want an open-source, local-model option inside VS Code or JetBrains without changing your editor. Choose Tabby for self-hosted completions, Aider for terminal-and-Git work, OpenHands for agent experiments, or Zed if you want a separate editor. Check provider settings, test your own coding tasks, and review every generated change: local models and agent capabilities vary substantially.

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