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Show HN: Aegis Memory v1.2 – We solved "what's worth remembering" for AI agents
Aegis Memory is an open-source, self-hostable memory layer for multi-agent AI systems.
v1.2 adds Smart Memory - a two-stage pipeline that automatically decides what's worth storing:
1. Fast rule-based filter catches obvious noise (greetings, "thanks", etc.)
2. LLM extracts atomic facts only when the filter passes
This saves ~70% of extraction costs while keeping memory high-quality.
Try it in 15 seconds:
pip install aegis-memory
aegis demo
GitHub: https://github.com/quantifylabs/aegis-memory https://github.com/quantifylabs/aegis-memory
Happy to answer questions about multi-agent memory architecture.
- dmarwicke 9mo agomemory voting sounds interesting but does it work? i tried having agents mark useful chunks once, they just marked everything as helpful. accuracy went to shit
- Arulnidhi_k 9mo agoVoting requires context.agents must specify 'why' something was helpful, not just thumbs up/down. This adds friction that reduces noise. Here's the Effectiveness score that is implemented in the project: (helpful - harmful) / (total + 1), so marking everything helpful dilutes the signal rather than inflating it. Along with it gotta pair voting with reflections, agents store "this worked because of X" not just "this worked." May I know what setup you tried.. was it a single agent or multi-agent?
- austinbaggio 9mo agoAre you working with any projects to implement this yet?
- Arulnidhi_k 9mo agoNot yet in production, but actively looking for devs to test it and gain feedback..