miii-cli
Local AI coding agent for your terminal. Open-source, offline alternative to Claude Code, Cursor & Copilot — powered by Ollama and any local LLM. Private by default, free forever.
Install / Use
npx skills add maruakshay/miii-cliInstalls into whichever agent you are using.
Other
Other agent config
Quality Score
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AI & Machine LearningSupported Platforms
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View source on GitHubWhat is miii? — a local AI coding agent
miii lives in your terminal and codes alongside you — reading files, writing features, running tests, fixing bugs. The twist: it runs on your hardware, powered by Ollama (or any local OpenAI-compatible server like llama.cpp / LM Studio).
Your code never leaves your disk. There's nothing to log in to. Pull a model, type miii, go. It's the open-source, offline answer to cloud coding assistants like Claude Code, Cursor, and GitHub Copilot.
Install (macOS, Linux, Windows)
macOS / Linux:
ollama pull qwen2.5-coder:14b # any coding model works
Which model should I use?
- Low VRAM (8GB):
qwen2.5-coder:7b(Fast, capable) - Mid VRAM (16-24GB):
qwen2.5-coder:14b(Sweet spot) - High VRAM (48GB+):
qwen2.5-coder:32b(Powerhouse)
curl -fsSL https://raw.githubusercontent.com/maruakshay/miii-cli/main/install.sh | sh
miii
(The installer downloads the pre-compiled binary and adds it to your local path)
Windows (PowerShell):
ollama pull qwen2.5-coder:14b
irm https://raw.githubusercontent.com/maruakshay/miii-cli/main/install.ps1 | iex
miii
Prefer npm? npm install -g miii-agent works on every platform.
Install failing on permissions? Your global npm prefix isn't writable. The installer retries with
sudowhere available; otherwise point npm at a user-owned prefix and re-run:npm config set prefix "$HOME/.npm-global" export PATH="$HOME/.npm-global/bin:$PATH" # add to ~/.bashrc or ~/.zshrc
Then just talk to it:
> refactor the auth module to use async/await
> @src/server.ts add rate limiting to all POST routes
> why are my tests failing in utils/parser.ts
Needs: Node ≥ 18 and Ollama running locally.
Staying up to date
miii checks npm on launch and, when a newer release exists, pulls it in the background — it applies the next time you start. Manual options:
miii update # update now
miii --version # what you're running
Opt out of background updates by adding "autoUpdate": false to ~/.miii/config.json,
or re-run the install script (curl … | sh) any time to update by hand.
Why local-first? Private, free, offline
Most "AI coding tools" are just wrappers around a cloud API — slow, metered, and they ship your private codebase to someone else's server.
| | Cloud agents | miii |
|--------------|-----------------------|------------------------------|
| Your code | Sent to a third party | Never leaves your machine |
| Cost | Per-token billing | Free — runs on your hardware |
| Setup | API keys, accounts | npm i -g miii-agent |
| Offline | No | Yes |
| Latency | Network + queue | Your GPU only |
It doesn't just chat, either — it follows a Plan $\rightarrow$ Act $\rightarrow$ Observe loop:
- Plan: Decomposes the problem into a sequence of concrete steps.
- Act: Calls the necessary tools to gather context or modify code.
- Observe: Verifies the result and adjusts the plan until the goal is met.
Five letters, five ideas
small · simple · smart · strategic · semantic — a tiny codebase you can read in an afternoon, no config ceremony, plans before it acts, and operates on the meaning of your code, not blind text matching.
Features
- 🧪
miii doctor— not every local model can drive an agent. Doctor runs your models through real engineering tasks and tells you which ones actually deliver.miii doctor # grade every installed model miii doctor qwen2.5-coder:7b # grade one - 🖼️ Paste images — copy a screenshot and hit
Ctrl+Vto attach it to your message, or paste an image file path. Great for "why does this UI look broken?" or reading an error screenshot. Needs a vision-capable model (llava,llama3.2-vision,qwen2-vl, …) — text-only models silently ignore the image.ollama pull llava # or llama3.2-vision - 💧 Lossless output spill — that 50K-line test log won't get truncated and leave the model guessing. miii spills the full output to disk and lets the model page through it. Nothing is ever lost.
- 🔒 Permission-gated tools — you approve what the agent can touch; "always" approvals persist. File tools are confined to your working directory.
- 📄
MIII.md— drop one in your repo to teach miii your conventions, build/test commands, and do's & don'ts. Same idea asCLAUDE.md, read every turn.
Going deeper
<details> <summary><strong>Built-in tools</strong></summary>| Tool | Function |
|------|----------|
| read_file | Read any file in your workspace |
| write_file | Create new files |
| edit_file | Precise string-level edits, whitespace-tolerant |
| glob | Pattern-match files across the project |
| grep | Regex search across files |
| run_bash | Execute shell commands |
File tools (read_file, write_file, edit_file) reject ../ traversal and absolute paths outside the workspace. run_bash is not path-confined — its only boundary is the permission prompt, so review commands before approving (especially "always"). Saved rules live in ~/.miii/permissions.json.
| Key | Action |
|-----|--------|
| Enter | Send prompt |
| @filename | Attach file to context |
| Ctrl+V | Paste clipboard image (needs a vision model) |
| /models | Switch active model |
| /clear | Reset conversation |
| Esc | Stop generation or tool run |
| Ctrl+O | Toggle full tool output |
| Ctrl+C | Quit |
Settings live in ~/.miii/config.json, created on first run:
{
"model": "qwen2.5-coder:14b",
"ollamaHost": "http://localhost:11434",
"effort": "medium"
}
effort (low | medium | high) controls temperature and limits.
miii talks to any OpenAI-compatible local server too. Start llama-server, then point a named provider at it:
{
"model": "qwen2.5-coder-14b",
"provider": "llamacpp",
"providers": {
"llamacpp": { "type": "openai", "baseUrl": "http://localhost:8080" }
}
}
Switch at launch with miii --provider llamacpp. Any openai-type provider on localhost counts as local — no key, no cloud.
When a tool result exceeds the inline budget (~10K bytes), the full output is written to ~/.miii/output/<id>.txt. Only a head + tail preview is inlined, with a pointer:
[command output truncated: 5184 lines / 412900 bytes.
Full output at ~/.miii/output/9f3a1c.txt — read it with
read_file offset/limit to see the elided middle.]
The model pages through the middle with ranged read_file reads. Spill files are garbage-collected after 24 hours.
Project Architecture:
src/
├── agent/ # The core reasoning loop
├── tools/ # Implementation of read/write/bash
├── terminal/ # UI and input handling
└── config/ # Settings and provider logic
git clone https://github.com/maruakshay/miii-cli.git
cd miii-cli
npm install
npm run dev
npm run build # production build
npm run typecheck # type-check src + eval
npm run eval # regression gate (powers `miii doctor`)
To run your local working tree against the global miii:
npm run build && npm link # restore later with: npm install -g miii-agent
</details>
FAQ
Does miii work without internet? Yes. Once you've pulled a model with Ollama, miii runs fully offline. No network calls, no account, no cloud.
Is my code sent anywhere? No. Every file read, edit, and model inference happens on your machine. Your codebase never leaves your disk.
Which model is best for coding?
Depends on VRAM: qwen2.5-coder:7b (8GB), qwen2.5-coder:14b (16–24GB, the sweet spot), qwen2.5-coder:32b (48GB+). Run miii doctor to grade your installed models on real engineering tasks.
How is miii different from Claude Code, Cursor, or GitHub Copilot? Claude Code, Cursor, and Copilot are cloud services — metered, account-gated, and they ship your code to a third-party server. miii is open-source, free, and runs entirely on your hardware. Same terminal-agent workflow as Claude Code, but on your own local model.
How is it different from Continue.dev? Continue.dev is an IDE extension. miii is a standalone terminal agent — no editor required — with a Plan → Act → Observe loop, permission-gated tools, and lossless output spill built in.
Do I need a GPU? No, but it helps. Smaller models run on CPU; a GPU makes larger models fast enough for real work.
Status
MVP. Core agent loop is stable; actively refining tool execution, streaming, and the permission model. PRs welcome — fork it, break it, improve it.
License
MIT © maruakshay
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