⚡ SLMCode

A coding harness that loves SLMs — and works with any LLM.
Plan → atomic tasks → parallel specialists → self-critic → learn
Powered by GoLangGraph · defaults to oMLX · plug in Ollama, OpenAI, OpenRouter, vLLM, …

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🌅 The pitch

LLMs are incredible. Coding with them — inside a well-adapted harness — feels like magic.

And the industry noticed. Claude Code, Antigravity, Pi, and a growing wave of specialized coding agents were all designed around frontier models: huge context windows, strong tool-calling, and enough judgment to survive messy repos.

That is fantastic… until you run out of tokens. And eventually, you will.

Then you try the same harness on an SLM — a 7B–30B local model — and the magic evaporates. The model wanders. JSON breaks. Context overflows. Reviewers hallucinate green lights.

SLMCode exists to fill those gaps — and to stay useful when you plug a bigger model back in.

It is a public baseline for reaching the same quality of outcome with small models (sometimes with longer passes and extra feedback loops) — motivated by a personal need to ship with SLMs over the summer, offline, private, and cheap.

Fork it. Break it. Point it at whatever LLM you have. Push the idea further. 🚀

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📦 Install in one line

macOS / Linux / WSL

curl -fsSL https://raw.githubusercontent.com/UnicoLab/smlcode/main/scripts/install-remote.sh | bash

System-wide:

curl -fsSL https://raw.githubusercontent.com/UnicoLab/smlcode/main/scripts/install-remote.sh | bash -s -- --system

Windows (PowerShell)

irm https://raw.githubusercontent.com/UnicoLab/smlcode/main/scripts/install.ps1 | iex

Homebrew

brew install --formula https://raw.githubusercontent.com/UnicoLab/smlcode/main/Formula/slmcode.rb

Full matrix (CMD, pin versions, uninstall): docs/INSTALL.md

slmcode version
slmcode doctor
cd your-project && slmcode init && slmcode

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🔌 Any LLM, really

SLM-first defaults. Generic harness underneath.

You have…	Try…
Apple Silicon local	provider=omlx (default)
Ollama	--provider ollama --model qwen2.5-coder:14b
LM Studio / vLLM	--provider lmstudio --endpoint http://127.0.0.1:1234/v1
OpenAI / Groq / DeepSeek / Mistral	built-in presets
OpenRouter / corporate gateway	any name + --endpoint + API key

slmcode run --provider ollama --model qwen2.5-coder:14b \
  --endpoint http://127.0.0.1:11434 "fix the flaky test"

export SLMCODE_PROVIDER=openrouter
export SLMCODE_MODEL=anthropic/claude-3.5-sonnet
export SLMCODE_API_KEY=…
slmcode run -v "…"

Deep dive: docs/PROVIDERS.md

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🎯 Why this loop exists

🐘 Large-model habit	🐭 SLMCode approach
Stuff the repo into chat	Incremental .slmcode/*.md memory
One free-form agent	Plan → atomic tasks → specialists
Re-scan every turn	Reuse CONTEXT/MEMORY; skip deep explore
Hope the model self-corrects	Reviewer ↔ corrector + multipass
Opaque progress	Live CLI + Studio stream
Burn tokens until it sticks	Early-exit streams, lean packs, speculative cancel

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✨ Highlights

• 🧭 Planning + atomic split sized for ~30B models (works for larger ones too)
• 🗂️ Coordinator + live kanban board
• 🧩 14 specialists (explorer, docs, architect, worker/deep, reviewer, corrector, tester, …)
• 🔁 Self-critic loop with auto-correct retries
• 🧠 Evolving CONTEXT / MEMORY / skills flywheel
• ⚡ Token-stream early-exit + SLM JSON repair
• 🖥️ Premium TUI + offline Studio GUI (http://127.0.0.1:7420)
• 🔐 Shell permission modes: allow | ask | deny
• 📦 Stacks — slmcode stack apply omlx-local|deepseek|… (global + optional per-agent pins)
• 🔑 Auth store — .slmcode/auth.json + Studio/TUI /auth (keep keys out of config)
• 🔎 Model catalog — find_models + Studio costs / enabled models

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🧬 Pipeline

query → skills → context → explore|reuse → [docs] [architect]
      → plan → split → coordinator → parallel execute
      → review/correct → learn → test → memory → evolve skills

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🚀 Quick start

cd your-project
slmcode init
# edit .slmcode/PROJECT.md

slmcode                      # premium TUI
slmcode run -v "add validation to the login handler"
slmcode board
slmcode studio               # http://127.0.0.1:7420

Useful knobs:

slmcode stack list
slmcode stack apply omlx-local          # or: deepseek, openai, ollama-local, …
slmcode run --think-passes 2 --parallel 3 --retries 2 "…"
slmcode run --agent explorer "Where is auth handled?"
slmcode run --skill atomic-coding "Refactor helpers"
slmcode config set dry_run false

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⌨️ CLI cheat sheet

Command	Purpose
init / doctor / config	Workspace + provider health
stack / agent	Apply model stacks; inspect agent pins
run -v	Full pipeline + live stream
tui / bare slmcode	Premium interactive TUI
chat	Classic REPL
board / watch	Colored kanban
studio	GUI + SSE API
update	Refresh install (binary or source)

TUI: /compact, /models, /mcp, /auth, /schema, /sessions, /stats, /permission, /agents, /stop, /resume.

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📚 Docs

Premium + playful site (MkDocs Material → GitHub Pages): ☀️ unicolab.github.io/smlcode

Section	Pages
🚀 Getting started	📦 Install · ⏱️ Quick start · 🧠 Concepts · 🔌 Providers
📘 Handbook	🧭 Guide · 🖥️ TUI · 🦋 Skills · 🎨 Studio · 🧩 Agents · 🧪 Recipes
📚 Reference	⌨️ CLI · ⚙️ Config · ✅ Testing · ❓ FAQ
🔧 Internals	🏗️ Architecture · 🤝 Contributing

Local preview: make docs-serve → http://127.0.0.1:8000 — bring snacks. 🍿

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🧪 Develop

git clone https://github.com/UnicoLab/smlcode.git && cd smlcode
make tidy && make lint && make test
make docs-build              # MkDocs strict build
make install-system          # build from source onto PATH

import "github.com/UnicoLab/slmcode/pkg/harness"

h, _ := harness.New("/path/to/project")
_ = h.Init()
res, err := h.Run(ctx, "refactor pkg/auth")

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🤝 Contributing

Public baseline on purpose. Bring better prompts, tighter gates, smarter scheduling, new specialists, and evals — especially ones that make small models more reliable.

1. Fork & branch
2. make lint && make test
3. Conventional commits (feat:, fix:, docs:, …)
4. Open a PR

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📜 License

MIT — use it, remix it, ship with it.

Made with ♥ by UnicoLab
_{Summer coding with SLMs should feel like a superpower, not a compromise. ☀️}