Original Summary

My two friends and I are engineering students who kept re-explaining our codebase to Claude Code and Cursor every single session. Every session, from zero. So between classes we built the tool we wanted: a memory layer that gives agents persistent memory, MIT licensed, on PyPI. The hook is what it doesn't need: no vector database, no embedding server, no infrastructure at all. Retrieval is TF-IDF over pure Python stdlib, so it runs in-process with any agent that speaks MCP. pip install, one config line, and your agent remembers your corrections across sessions. It is at about 12,000 downloads now, all organic — zero marketing budget, it has just been Reddit and word of mouth. The part we are proudest of is portable memory: your agent's memories can be exported as a sealed bundle with the proof attached (command, result, exit code) and a chain of custody, then imported by a different agent on a different platform. We tested transfer across four platforms and it works. Honest numbers, because I refuse to hype a side project: memory improved agent task accuracy 78.8 to 85.2 percent at 5 retrieved chunks, 87.2 to 92.8 at 10, and flat beyond that. TF-IDF also misses when vocabulary differs a lot between sessions. The limitations are in the README. Where it stands: 64 stars, and we are three students deciding this month whether next semester goes into this or into shelving it for placements. Stars are the only signal we have that says keep going — if you work with AI agents, the repo is github.com/cognicore-dev/cognicore-env. Happy to answer anything about how it is built.   submitted by   /u/Neither-Witness-6010 [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：2026/9/16 23:22:50