KAS Memory

An agent memory system that auto-extracts insights from coding sessions, recalls relevant memories in future conversations, and builds a growing reusable knowledge base - aimed squarely at not repeating the same mistakes across sessions.

View source on GitHub

Key takeaways

• 01

  Auto-extract insights at session end, not mid-flow

• 02

  Recall is proactive: memories surface when relevant, not on demand

• 03

  Coding mistakes are the highest-value memory category

Flows built on this research

Memory & Context

Design a Memory Index Loaded Every Session

Build the pattern behind minimalist memory systems: a compact index file the agent always loads, pointing to detail files it loads on demand.

3 steps · 60-90 minutes

Memory & Context

Cross-Session Project State

Keep long-horizon work coherent across many sessions: externalized project state that any session can load, advance, and hand off.

4 steps · 90-120 minutes