Came across an early open-source project aiming to fix a big gap in current LLMs: statelessness. Every conversation resets to zero.

LLM-OS tries to give AI systems persistent, evolving memory by treating everything as a memory artifact:

Crystallized tools: repeated patterns auto-convert into executable Python tools (deterministic memory).

Markdown agents: editable behavioral memory.

Execution traces: procedural memory the system can replay/learn from.

Promotion layers: memory flows from user → team → organization via background “crons.”

The idea is that organizations accumulate AI knowledge automatically, and new members inherit it.

Repo: https://github.com/EvolvingAgentsLabs/llmos

Article: https://www.linkedin.com/pulse/what-your-ai-remembered-every...

Curious whether HN thinks persistent AI memory is workable