Built EverFern, an alternative to cowork open-source(LangGraph + Electron). Tested it against Qwen3-8B, looking for feedback

I built EverFern, an open-source (MIT) desktop agent that does computer use, browser control, and file/code tasks, aimed at people who don't want to pay $20–200/mo for Claude Cowork or Manus and send everything to the cloud. Everything runs locally, config and history live in ~/.everfern/store, nothing phones home.

Stack: Next.js/Electron frontend, LangGraph for agent orchestration, works with local models via Ollama/LM Studio or any of 10+ cloud providers if you want to mix and match.

What I've tested so far:

• Ran it against Qwen3-8B locally (quant: [fill in, e.g. Q4_K_M via Ollama]) on [your GPU/VRAM]. It handled multi-step tasks like opening YouTube and playing a specific channel, and a "research the top AI startups" task that needed several tool calls chained together.
• It's noticeably slower and less reliable on 8B than with cloud models, planning steps sometimes need a retry, so don't expect frontier-model reliability at this size. Working on prompt/tool-call tuning to make smaller local models more consistent.

A couple of things I'd call experimental rather than polished:

• Tool synthesis: when it hits a task with no existing tool, it can write and register a new tool at runtime. Gated behind a human-approval step before anything it writes executes, but I'd treat this as early/unstable, not something to trust blind on 8B-class models yet.
• Rollback: destructive shell commands (rm, git reset, etc.) get snapshotted so you can undo, but I haven't stress-tested this against every edge case.

Not claiming it beats Cowork/Manus outright, just that it's a free, auditable, local option if that's what you're after. Genuinely want the sub's opinion on running agentic tool-use on smaller local models, that's been the hardest part to get reliable.

GitHub: https://github.com/Everfern-AI/Everfern