Show HN: Core – open source memory graph for LLMs – shareable, user owned

However, keeping a tight, constrained context turns out to actually be pretty important for correct LLM results (https://www.dbreunig.com/2025/06/22/how-contexts-fail-and-ho...).

Do you have a take on how we reconcile the tension between these objectives? How to make sure the model has access to relevant info, while explicitly excluding irrelevant or confounding factors from the context?

that's the exact problem we've been solving! Context bloat vs. memory depth is the core challenge.

our approach tackles this by being selective, not comprehensive. We don't dump everything into context - instead, we:

- use graph structure to identify truly relevant facts (not just keyword matches) - leverage temporal tracking to prioritize current information and filter out outdated beliefs - structure memories as discrete statements that can be included/excluded individually the big advantage? Instead of retrieving entire conversations or documents, we can pull just the specific facts and relevant episodes needed for a given query.

it's like having a good assistant who knows when to remind you about something relevant without overwhelming you with every tangentially related memory.

the graph structure also gives users more transparency - they can see exactly which memories are influencing responses and why, rather than a black-box retrieval system.

ps: one of the authors of CORE