How far should an AI follow your memory graph?
One of the hardest decisions in Second Brain v2 was deciding how much related context recall should include.
Imagine asking:
"What did we decide about the Product Hunt launch?"
A normal search might find the launch plan.
A graph-aware system could also find:
the positioning decision connected to it
feedback that changed the launch date
the demo you created afterward
a previous community post that performed well
an older decision that has since been replaced
That can be genuinely useful. It can also become noisy very quickly.
Second Brain handles this by ranking direct matches first, then expanding through connected memories with distance decay. The further away a memory is, the more evidence it needs to appear.
I want the graph to recover context you forgot existed, without making every answer feel like a tour of your entire database.
Where would you set the default?
Direct matches only
Direct matches plus one connection
Follow several connections when confidence is high
Let the AI decide each time
Second Brain v2 is live today, and this is one of the areas where I would especially value feedback.
Replies
me would start with one connected memory. users could expand further only when they need extra background and supporting details.
That's the current default. One hop keeps it tight without pulling in things you didn't ask for.
That's the direction. The hops parameter is already configurable per MCP call. Next step is surfacing it as a user-facing setting rather than a developer option.
Hi. At MemSoph.ai we are also concerned about the memory of our AI assistants. We chose to give the users full control on what is being remembered, and whether to reinforce the memories or prune them out.
@memsoph User control is the right foundation. Second Brain keeps superseded memories linked in the graph rather than erasing them, so reinforce and prune are explicit choices with the history intact. Good to see MemSoph.ai building from the same premise.
I would probably choose direct matches plus one connection as the default. It gives me enough background while keeping the answer quick and useful.
@john__milton That's the current default. One hop adds just enough related context without pulling in things two or three degrees out.
option 3 feels right to me too, confidence-gated depth over a fixed hop count. one edge case I'm curious about - when the same memory is reachable through two separate connection paths (say, through both the launch decision and a person mentioned in both), does that count as reinforcing evidence and rank it higher, or does it just take whichever single path scored best and ignore the other? multiple independent paths converging on the same memory feels like a stronger relevance signal than distance alone would capture
I’d probably default to direct matches plus one connection.
The value of memory is not showing everything related. It is surfacing the one forgotten context that changes the decision. If the graph expands too far by default, the user has to do the filtering again, which defeats the point.
I’d default to direct matches + one strong connection. Memory is useful when it brings back the context that changes the decision, not when it shows every related thing it can find. the best AI memory should feel like judgment, not search results with extra steps.
For a default setting, I’d lean towards #2 (Direct matches plus one connection). As a founder, I usually just need the immediate answer and maybe the most recent pivot. Anything deeper than one connection tends to add mental friction when I'm just trying to move fast.