Comment by the_harpia_io
19 days ago
this is really cool - the single binary thing solves a huge pain point I have with OpenClaw. I love that tool but the Node + npm dependency situation is a lot.
curious: when you say compatible with OpenClaw's markdown format, does that mean I could point LocalGPT at an existing OpenClaw workspace and it would just work? or is it more 'inspired by' the format?
the local embeddings for semantic search is smart. I've been using similar for code generation and the thing I kept running into was the embedding model choking on code snippets mixed with prose. did you hit that or does FTS5 + local embeddings just handle it?
also - genuinely asking, not criticizing - when the heartbeat runner executes autonomous tasks, how do you keep the model from doing risky stuff? hitting prod APIs, modifying files outside workspace, etc. do you sandbox or rely on the model being careful?
Hitting production APIs (and email) is my main concern with all agents I run.
To solve this I've built Wardgate [1], which removes the need for agents to see any credentials and has access control on a per API endpoints basis. So you can say: yes you can read all Todoist tasks but you can't delete tasks or see tasks with "secure" in them, or see emails outside Inbox or with OTP codes, or whatever.
Interested in any comments / suggestions.
[1] https://github.com/wardgate/wardgate
this is a clever approach - credential-less proxying with scoped permissions is way cleaner than trying to teach the model what not to do. how do you handle dynamic auth flows though? like if an API returns a short-lived token that needs to be refreshed, does wardgate intercept and cache those or do you expose token refresh as a separate controlled endpoint?
and I'm curious about the filtering logic - is it regex on endpoint paths or something more semantic? because the "tasks with secure in them" example makes me think there's some content inspection happening, not just URL filtering.