Original Summary

I’ve been working on OpenLTM for a while, and it’s finally stable enough that I’d love to get some real-world feedback. The idea is pretty simple: Coding agents are great at working with what’s currently in context, but once useful information falls out of that context, it’s basically gone. OpenLTM gives them persistent long-term memory. Everything is stored locally in a single SQLite file, with: Semantic search to retrieve relevant memories Autonomous decay based on importance, so old/noisy memories don’t keep polluting the context Fully local storage — no external DB or hosted service required I’ve built integrations for: Claude Code claude plugin marketplace add https://github.com/RohiRIK/OpenLtm claude plugin install openltm OpenCode @rohirik/opencode-ltm Pi https://pi.dev/packages/@rohirik/pi-ltm?name=%40rohirik%2Fpi-ltm Hermes https://hermes-agent.nousresearch.com/docs/plugins/openltm OpenClaw https://clawhub.ai/rohirik/plugins/openclaw-ltm GitHub: https://github.com/RohiRIK/OpenLtm I’d especially love feedback from people who use coding agents heavily. What works? What breaks? What would make this actually useful in your workflow? Issues, criticism, weird edge cases — all welcome. And if you end up finding it useful, a ⭐ on GitHub is always appreciated :)   submitted by   /u/Comfortable_Cat_6207 [link]   [comments]

  • 情报分类：商业与市场研究
  • 分类依据：内容涉及商业、投资或市场动态
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/10/5 13:49:59