Last week, we merged a fix that solves one of the most frustrating failure modes in conversational AI: the moment when your AI partner 'forgets' everything you just discussed. If you've been using Sabine and noticed it occasionally losing context mid-conversation, this fix is for you.
What Happened
Sabine is built on a conversational memory system backed by Supabase. Every interaction—every question you ask, every response Sabine provides—should be loaded from the database at the start of each turn. That's the contract: full context, every time.
But we discovered a bug where the /invoke endpoint wasn't actually loading conversation history from the database on every call. In certain execution paths, Sabine would start fresh—no memory of what you'd discussed two minutes earlier. The data was safely stored; it just wasn't being retrieved.
Why This Matters
Conversational continuity is table stakes for AI partnership. When you're working with Sabine to plan a project, refine a strategy, or debug a problem, you expect the AI to remember what you said three exchanges ago. Losing that thread breaks trust faster than almost any other failure mode.
This wasn't a data loss issue—your conversation history was never at risk. But from a user perspective, a memory that isn't loaded might as well not exist. The fix ensures that every /invoke call now pulls the full conversation history from the database before generating a response.
The Fix
The patch is straightforward: we added an explicit database query at the beginning of every /invoke turn to load the conversation history for the active session. No caching shortcuts, no assumptions about what's already in memory. Every turn starts with a fresh pull from the source of truth.
This does add a small query overhead to each turn, but conversational continuity is non-negotiable. We're willing to pay a few extra milliseconds per request to guarantee that Sabine never drops context.
What's Next
This fix is live as of commit a7f1be7. If you were experiencing context loss in Sabine conversations, those issues should now be resolved.
Looking ahead, we're working on smarter memory compression for long-running conversations and investigating whether we can reduce the query overhead through intelligent connection pooling. But reliability comes first—once we've validated that this fix holds under production load, we'll optimize.
If you notice any remaining memory issues in Sabine, please reach out. Conversational trust is the foundation of everything we're building.