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bee

a minimal coding agent harness

"I'm not a bot. I'm a bee. There's a difference."

Install

curl -fsSL https://raw.githubusercontent.com/elhenro/bee/main/install.sh | sh

or: go install github.com/elhenro/bee/cmd/bee@latest

then run bee, type /model, choose oMLX, Ollama, OpenRouter, etc. and pick a model. Local or hosted, your choice.

What is it?

🧠 Coding agent — writes code, runs tests, commits changes

⚡ Pure Go — single static binary, no runtime deps

📦 Skills — `bee <name>` subcommands, one binary, one PATH

🔬 Works everywhere — Ollama local to OpenRouter, tiny models to frontier

Local LLMs

bee is built to work well with Ollama and oMLX so you can run locally on your own hardware and keep full control. No API keys, no rate limits, no data leaving your machine.

On macOS, oMLX works best. Native Apple Silicon acceleration with prompt caching keeps things fast and memory-efficient.

I run Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-mlx-8bit (34.32 GB) very reliably on a MacBook M3 Max 64 GB, with Qwen3-VL-4B-Instruct-MLX-4bit (2.90 GB) for vision support. bee handles vision automatically for models that only do text.

Settings for the Huihui-Qwen model: temperature 0.7, top-p 0.85, top-k 20, KV-cache quantization 8-bit.

Other models that work well:

gemma-4-12B-it-4bit — 10.26 GB

gemma-4-12B-it-8bit — 11.87 GB

gemma-4-12B-it-assistant-bf16 — 837 MB

Qwen3-Coder-Next-4bit — 41.78 GB

Qwen3.6-27B-8bit — 9.02 GB

Qwen3.6-35B-A3B-4bit — 19.03 GB

Qwen3.6-35B-A3B-8bit — 29.68 GB

Get involved

GitHub Releases