Open Source AI Coding Assistants for Local Models

Continue is the best starting pick for most developers who want an open-source coding assistant that works with local models. It integrates into familiar editors, supports local model providers such as Ollama, and offers chat and code-completion workflows. Choose Aider instead if you prefer a terminal-based tool that makes changes directly in Git repositories.

What to Look For

“Local” can mean that prompts and source code stay on your machine, but it does not automatically mean every component is open source or that a model runs quickly. Check the assistant’s license, the model provider’s behavior, and where inference happens. If privacy is essential, verify that your editor extension is pointed at a local server and not a cloud fallback.

Model compatibility: Look for support for local inference tools such as Ollama, llama.cpp servers, or LM Studio. Compatibility varies by feature: a model that works for chat may not work well for autocomplete.
Workflow: Inline completions, chat, multi-file edits, terminal commands, and Git integration are different capabilities. Decide which you need rather than choosing by a broad “AI coding” label.
Hardware and speed: Local models use your computer’s memory and processing power. Larger models may need substantial RAM or a compatible GPU; smaller quantized models can be faster but may produce weaker code.
License and control: Check the assistant’s current repository license and the model’s separate license. Open-source assistant software does not make every model open source or permit every use.
Reviewability: Prefer tools that show proposed edits clearly and let you inspect diffs. Generated code can be incorrect, insecure, or inconsistent with project conventions.

Continue: Best All-Round Editor Extension

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 providers, including Ollama. Its configuration approach lets developers choose different models for different tasks, such as a completion model for inline suggestions and a more capable model for chat.

Strengths: It fits into existing editor workflows, is configurable, and supports more than one model provider. You can use it without moving to a separate AI-focused editor. It is a practical way to experiment with local models while keeping your usual project tools.

Trade-offs: Setup and model selection take some work, and results depend heavily on the model and machine. Local autocomplete may feel slower or less accurate than a hosted service. Features and provider configuration can change, so check Continue’s current documentation before following older setup guides.

Best for: Developers who want an open-source extension in their current IDE and a flexible mix of local chat and code completion.

Aider: Best for Terminal-First Git Work

Aider is an open-source command-line coding assistant that works with a Git repository. It can connect to supported local model setups, including Ollama, and propose edits to files in the project. Its conversational workflow is designed around making changes that you can review as Git diffs.

Strengths: Aider works naturally for developers who live in a terminal, makes repository changes rather than limiting the experience to chat, and keeps version control central to review. It can be useful for focused tasks such as implementing a small feature or fixing a bug across related files.

Trade-offs: It is less visual than an IDE extension and expects comfort with command-line work and Git. Local model quality, context limits, and speed can affect how well it handles larger changes. Treat each proposed edit as a draft, not an automatically correct patch.

Best for: Developers who want to direct an assistant from the terminal and inspect changes through Git.

Tabby: Best for Self-Hosted Completion

Tabby is an open-source, self-hosted coding assistant focused particularly on code completion. It can be run on infrastructure you control and connected to supported editor integrations. That makes it worth considering for teams that want a centrally managed service rather than each developer configuring an individual local model.

Strengths: Its self-hosted design gives teams control over where the service runs, and its completion-first focus suits developers who mainly want inline suggestions. Administrators can manage a shared setup instead of relying on each workstation’s hardware.

Trade-offs: Self-hosted does not necessarily mean running entirely on each developer’s laptop: a team may need a separate machine or server. Deployment and maintenance add work, and available models and integrations should be checked against current project documentation.

Best for: Teams or technically confident individuals who want to operate a completion service on hardware they control.

OpenHands: Best for Agent-Style Tasks

OpenHands is an open-source platform for software-development agents. It is intended for tasks that can involve interacting with a codebase and development tools, rather than just answering a question in an editor. It can be configured with model providers, but confirm the current local-model setup and requirements before committing to it.

Strengths: It offers a more agent-oriented workflow for multi-step tasks and is a useful option for developers exploring how assistants can work with a project beyond generating snippets. Its open-source codebase also gives teams room to inspect and adapt the platform.

Trade-offs: Agent workflows are more complex than inline completion, and local models may struggle with long tasks or tool use. Setup can demand more resources and oversight. Review actions and code carefully, especially when an agent can run commands or modify files.

Best for: Developers who want to experiment with supervised, multi-step coding agents rather than simple editor autocomplete.

Continue.dev CLI: Best for Lightweight Terminal Use

Continue also provides command-line workflows for developers who want to interact with models outside an IDE. Depending on the current release and configuration, it can complement the editor extension and use supported providers, including local ones. Check the project’s documentation for the exact CLI capabilities available now.

Strengths: A command-line interface can fit scripts and terminal-centric work, while keeping the option of using the same broader Continue ecosystem. It may suit developers who want model assistance without switching editors.

Trade-offs: CLI features and setup can change, and a terminal chat interface is not a substitute for a polished multi-file IDE workflow. Confirm that the specific command-line task you need is supported and that requests stay with your chosen local provider.

Best for: Developers who prefer terminal interaction and want to explore Continue beyond its editor extension.

How to Choose

Start with your budget and hardware. The assistant software may be free, but running a model locally has real hardware costs: memory, storage, and sometimes a capable GPU. Try a small model first using a local runner such as Ollama. If responses are too slow or consistently miss the mark, compare a different model or consider running a local service on a stronger machine. Do not assume a model’s parameter count alone predicts its coding quality or speed.

Match the tool to the task. For in-editor chat and completion, begin with Continue. For Git-centered changes from a terminal, try Aider. For a team-managed completion service, assess Tabby. For multi-step agent experiments, look at OpenHands and keep human review in the loop. Installing several tools at once makes it harder to tell whether a problem comes from the model, configuration, or assistant.

Balance privacy, convenience, and performance. Local inference can keep prompts on hardware you control, but verify network behavior, logs, telemetry, and any configured fallback. A self-hosted server can still send data elsewhere if configured to use a remote model. Local operation also means you manage updates and hardware; hosted models are often easier to access and may be stronger, but involve sending requests to a provider.

Test with real work. Try a small, low-risk task in a non-critical branch. Check whether the assistant understands your language and framework, handles the repository’s context, and produces reviewable changes. Keep tests and code review in place; no assistant reliably replaces them.

Frequently Asked Questions

Are these tools free?
Their software is generally available under open-source licenses, but hardware, hosting, and maintenance can cost money. Check each project’s current license and any optional paid services.

Does open source mean my code stays private?
No. Privacy depends on configuration. Confirm that the selected provider is local, disable cloud fallbacks if needed, and review the tool’s network and telemetry settings.

Can I run a coding model on a laptop?
Often, yes, with smaller or quantized models, but speed and quality depend on memory, processor, and GPU. Try a modest model before choosing hardware around a specific setup.

Which option is best for autocomplete?
Start with Continue for an editor-based workflow or Tabby if you want a self-hosted completion service. Test completion quality with your own code and hardware.

Do local models work offline?
They can, once the model and required components are downloaded, provided the assistant is configured not to call remote services. Verify behavior rather than assuming offline operation.

Can these assistants safely edit a whole project?
They can propose multi-file changes, but may introduce bugs or misunderstand requirements. Use version control, inspect diffs, and run relevant tests before accepting changes.

Bottom Line

For most developers seeking an open-source assistant with local-model support, start with Continue: it offers a flexible route into editor-based chat and completion without requiring a new IDE. Pick Aider for terminal-and-Git workflows, Tabby for self-hosted team completion, or OpenHands when you specifically want to explore supervised coding agents. In every case, the right model depends on your hardware and work; test locally, check the project’s current documentation, and review every generated change.

Method: the standard formula for this calculation, rounded as each result states. See how the calculators work or report an error.

More cards in this drawer

The whole Numbers and Powers drawer →