Top 5 open source AI assistants to run locally in 2026

• What you will learn: five open source tools to use programming AI on your computer.
• Who it is for: people who want privacy or want to test AI without relying only on the cloud.
• Result: a model running locally and an agent talking to it.
• Basis of analysis: official repositories and documentation, with no own performance testing.

Running AI locally means the model stays on your computer. Your project code does not need to leave your machine.

The trade off is that local models tend to be smaller than cloud models. For simple tasks, they work well. For larger requests, you can mix local and cloud.

A 2026 change: Continue.dev stopped being developed. We cover everything in the text about the alternatives to Continue.dev after the project ended.

What you need before you start

• A computer with enough memory for the chosen model.
• VS Code or Antigravity IDE.
• A test project in Git.

1. Ollama: the engine for local models

Ollama downloads and runs open models on your computer. The other four on the list can use it as a model source.

After installing from the official site, run in the terminal:

$ ollama run qwen2.5-coder

This downloads the model and opens a chat. See also our guide to running local LLMs with Ollama.

2. OpenCode

OpenCode is an open source agent that runs in the terminal, as an app or as an extension. It accepts several providers, including local models.

Install with curl -fsSL https://opencode.ai/install | bash, run opencode and use /connect to choose the provider.

3. Cline

Cline is an open source extension for VS Code, with Apache 2.0 license. It plans, edits files and asks for your approval on actions.

In the settings, you can point Cline to Ollama and use a local model.

4. Aider

Aider is an open source coding assistant that runs in the terminal. It works directly with Git and makes a commit for each change.

This makes it easy to undo any step. The documentation shows how to connect Aider to Ollama.

5. OpenHands

OpenHands became a control center for agents you host. According to the official repository, it runs locally by default.

It can run the OpenHands agent itself or others, like Claude Code, Codex and OpenCode.

Left out: Roo Code

Roo Code had its repository archived and no longer receives updates. Prefer the active options above.

Sign in: local and subscription together

Local models do not require sign in. For larger tasks, you can add a cloud agent with your subscription, like Claude Code with a Pro or Max account, without a key. See our guide on how to install and use Claude Code in VS Code.

Practical flow: create, review and publish

1. Create

Write the request as if you were talking to a colleague:

Request: "Create a tip calculator with value and percentage fields. Before you change anything, show me the plan."

Read the plan, approve it or ask for adjustments. Then the agent writes the code and runs the project to check.

2. Review

Review the changes before accepting. The Source Control tab in the editor shows each line changed.

Ask also: "Review what you did and explain in simple language." If something breaks, paste the error in the chat and ask for a fix.

3. Publish

Git is the project version history. A pull request, or PR, is a request to merge changes into the main project after review.

1. Ask: "Create a branch called calculadora-gorjeta and make a commit with a clear message."
2. Ask: "Push the branch to GitHub and open the PR."
3. Review the PR on GitHub and merge it.

The same flow in bridgecode, with separate roles

In bridgecode, which accepts Ollama as a provider, you can mix local and cloud in the same team:

• Planner: reads the request and breaks it into small tasks.
• Executor: writes the code for each task.
• Reviewer: checks everything and flags errors before the PR.

Each agent works on a separate copy of the project, so one does not interfere with the other. You approve the plan at the start and the PR at the end.

For example, a local model handles simple tasks and a cloud agent reviews.

Common mistakes and how to avoid them

Downloading a model that's too large

Start with a small model. If your computer slows down, switch to a smaller one.

Expecting the same results as the cloud

Local models are smaller. Break requests into small parts.