Who is aura-code for?

Developers comfortable with the terminal, Node 18+ and API keys, who want to choose or run their own models.

What can you build with aura-code?

Refactor a module by describing the change in plain English and letting the agent edit and verify it. Run a coding agent against a local Ollama model so your code stays on your machine. Compare cloud models on the same task by switching providers through one tool. Collect your sessions as structured training examples with the :mine command, for a later fine-tune. Write your own skills, plugins or MCP servers as plain files and connect them to the agent.

DusanCar-sudo /

aura-code

Terminal coding agent that remembers past sessions and works with 31 model providers

47 stars
1 stars since 3 Oct
TypeScript
Updated 4 days ago
Some experience
Coding agents & dev tools

Open on GitHub (opens in a new tab) Website (opens in a new tab)

Aura Code is a command-line coding agent. You describe a task in plain English. It reads your code, plans, makes changes, runs commands to verify them, and reports back. It is model-agnostic: you pick the provider, or point it at a local model through Ollama or LM Studio.

It keeps lessons between sessions in ~/.aura/memory/ on your computer, such as what broke last time and what your build needs. An optional layer called Archimedes tries each task with a small local model first. It tracks how often that model succeeds on similar tasks, and a larger cloud model takes over when the local one is unproven or fails a check.

The README contrasts Aura with fixed coding-agent products: skills, plugins and MCP servers are ordinary files you can edit. Besides the terminal UI it offers a web studio, a Telegram bot, and a lean chat mode called Gazelle that skips tool schemas to save context. The project is young, and the token-saving figures come from the authors' own measurements.

What it does

Autonomous coding loop
It reads files, edits code, runs shell commands, verifies the result and retries. You can type while it works, and your note is folded into the next turn.
Persistent memory
Lessons and project context are saved locally and read back in later sessions. The README says this memory never leaves your machine.
Local-first routing (Archimedes)
A small Ollama model attempts tasks first. A cloud model steps in when the local one has a weak track record or fails a verification check. The default local model is qwen2.5-coder:1.5b.
31 routing targets, 4 transports
Works with DeepSeek, Claude, GPT, Gemini, GLM, Ollama, OpenRouter and more. Most providers share one OpenAI-compatible transport, so adding one needs a base URL, a key variable and a model-id rewrite.
Gazelle chat mode
A conversational path with no tool schemas or project context. The README measures 356 tokens per turn against 9,367 for the full coder mode.

What you could build

Refactor a module by describing the change in plain English and letting the agent edit and verify it.
Run a coding agent against a local Ollama model so your code stays on your machine.
Compare cloud models on the same task by switching providers through one tool.
Collect your sessions as structured training examples with the :mine command, for a later fine-tune.
Write your own skills, plugins or MCP servers as plain files and connect them to the agent.

Get started

Install the package globally with npm, set an API key for your chosen provider (the example uses DeepSeek), then run aura with a task in quotes. Node 18 or newer is required.

npm install -g aura-code

export DEEPSEEK_API_KEY=sk-...

aura 'refactor the auth module to use JWT'

Commands are copied from the project's README. Read it on GitHub for the full setup.