Which is the best local AI code assistant in 2026?

Depends on your workflow. Aider is the best terminal-based pair-programmer with auto git commits. Continue is the best VS Code / JetBrains extension with broad LLM support and inline edits. Tabby is the best self-hosted server if you want your team on a single backend with no external API calls.

Do any of these work with Ollama or Mullama?

Yes. All three support any OpenAI-compatible local LLM backend, including Ollama, Mullama, LM Studio, and llama.cpp server. Configuration is usually one base URL and one model name.

For the best experience, yes — a GPU with 12GB+ VRAM for a 7B model or 24GB+ for a 14B model. All three work on CPU, but code completion latency is the limiting factor for inline suggestions.

Can Aider and Continue be used together?

Yes. Aider runs in the terminal and is great for batch refactors, commit messages, and multi-file edits. Continue runs in the IDE and is great for inline autocomplete and chat. Many teams use both.

Aider vs Continue vs Tabby: Local AI Code Assistants in 2026

Aider, Continue, and Tabby are the three most popular open-source AI coding assistants that work with local LLMs in 2026. They differ in workflow: terminal, IDE, or self-hosted server.

Overview

	Aider	Continue	Tabby
Workflow	Terminal pair-programmer	IDE extension	Self-hosted server
Target user	Senior devs, batch refactors	Every dev, day-to-day coding	Teams, enterprises
Autocomplete	✗	✓	✓
Chat	✓	✓	✓
Inline edit	✓ (multi-file)	✓	partial
Multi-file refactor	✓ (best in class)	partial	✗
Auto git commits	✓	✗	✗
Voice input	✗	partial	✗
IDEs	any (terminal)	VS Code, JetBrains	VS Code, JetBrains, Vim
Local LLM	✓ (any OpenAI-compat)	✓ (any OpenAI-compat)	✓ (built-in)
License	Apache 2.0	Apache 2.0	Apache 2.0

When to use each

Choose Aider when…

You live in the terminal and prefer commit-based workflows.
You do multi-file refactors regularly (rename across 20 files, update API calls, etc.).
You want automatic git commits with descriptive messages.
You pair-program with AI like you would with a senior engineer: ask, review the diff, accept or reject.

Choose Continue when…

You live in VS Code or JetBrains and want the GitHub Copilot-style inline experience.
You want autocomplete + chat + inline edit all in one IDE extension.
You are willing to use the OpenAI-compatible endpoint of Ollama, Mullama, or LM Studio.
You want the largest community and the most active development.

Choose Tabby when…

You want a self-hosted server that your whole team uses, with no per-user config.
You need enterprise features like SSO, audit logs, and team-level model access control.
You are okay with Tabby owning the model backend rather than using your existing Ollama / Mullama setup.
You want built-in retrieval over your codebase via Tabby’s indexer.

Code completion

Aider does not do autocomplete — it is a pair-programmer, not a Copilot replacement. Continue and Tabby both do:

Continue is the most configurable. You can route autocomplete to one model, chat to another, and inline edits to a third.
Tabby has the most polished autocomplete UX out of the box, especially for languages with strong LSP support (TypeScript, Python, Go).

Multi-file edits

This is Aider’s strongest feature. Aider reads the whole repo, plans the change, and applies the edit across files in a single commit. It works well for:

Renaming a class across the codebase
Updating an API endpoint signature everywhere it’s called
Refactoring a config format

Continue can do multi-file edits but the UX is less integrated. Tabby does not do multi-file edits — it is a single-file autocomplete + chat tool.

Setup with a local LLM

Aider

# Install
pip install aider-chat

# Configure to use Mullama (or Ollama, or any OpenAI-compatible)
export OPENAI_API_BASE=http://localhost:11434/v1
export OPENAI_API_KEY=not-needed
aider --model mullama://llama3.2:3b

Continue

# ~/.continue/config.json
{
  "models": [
    {
      "title": "Local Llama via Mullama",
      "provider": "openai",
      "model": "llama3.2:3b",
      "apiBase": "http://localhost:11434/v1",
      "apiKey": "not-needed"
    }
  ]
}

Tabby

# Tabby has its own model server; you point it at GGUF files
docker run -d --gpus all -p 8080:8080 \
  -v $HOME/.tabby:/data \
  tabbyml/tabby serve \
  --model Qwen2.5-Coder-1.5B

Decision matrix

Your situation	Use
Terminal-first, senior dev	Aider
Multi-file refactor across the codebase	Aider
VS Code, want inline autocomplete	Continue
JetBrains IDEs	Continue
Team of 5+ devs, want central control	Tabby
Enterprise, need SSO + audit logs	Tabby
Want a single tool, all workflows	Continue
Already in VS Code, want quick win	Continue
Want the best autocomplete UX	Tabby