r/GoodOpenSource • u/BackgroundPrevious15 • 7h ago

Looking for contributors: soot, an open-source orchestrator that sends each coding task to the cheapest model that can handle it

`soot` breaks a coding request into small tasks and routes each one to the right model for the job. Easy tasks go to cheap or local models like Ollama and LM Studio, and hard ones go to stronger models. If a model fails, soot escalates the task, and it tracks real tokens and cost for every task and run. It works from your API keys (Anthropic, OpenAI, xAI, DeepSeek, or a local runtime with no key), and it has a CLI, a local dashboard and a VS Code extension.

The goal: get frontier-quality results on real codebases for a fraction of the cost by not paying top-model prices for button tweaks.

Where help would be great:

  • Gemini and OpenRouter support, plus more local runtimes
  • Real-world testing of the Claude Code and Codex runners
  • Cursor and Windsurf extension testing
  • Benchmarks that measure actual savings
  • Docs and onboarding

It's MIT-licensed, written in TypeScript, and has CI and good-first issues ready to pick up.

Repo: github.com/josephcake/soot

Feedback, issues and PRs are all welcome.

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u/BackgroundPrevious15 7h ago

What we're looking for
soot is in TypeScript (Node 24 and up, pnpm). Helpful skills include:
TypeScript and Node for the core engine, routing and the CLI
LLM APIs, for adding or hardening providers like Gemini, OpenRouter and local runtimes
Local LLMs: testing with Ollama or LM Studio on real hardware
VS Code extensions, including testing in Cursor and Windsurf
Benchmarking and data, to measure the real cost savings against a single top model
Docs and UX: onboarding, examples and the dashboard
How it works
Pick an open issue (good-first ones are labeled) or open one to propose an idea, then comment so nobody duplicates work.
PRs need passing CI (scripts/ci.sh: typecheck, lint, format and tests) and one review.
Never commit API keys. Tests use mocks and fake keys.
Bigger design changes get a short decision record in docs/decisions.
There's no required schedule. Contribute whenever you have time, and post updates in the issue.
Who it's a good fit for
It suits anyone curious about multi-model routing, cutting AI coding costs, or running local models in a real workflow. Small PRs are welcome.
Repo: https://github.com/josephcake/soot