Show HN: Kumbukum, open source memory infrastructure for teams(kumbukum.com)
kumbukum.com
Show HN: Kumbukum, open source memory infrastructure for teams
https://kumbukum.com/
We built Kumbukum because team knowledge keeps getting scattered across chats, notes, URLs, and AI tools. Kumbukum is an open-source memory infrastructure for teams: a shared layer for notes, memories, URLs, and reusable context that remains inspectable and portable. Add and manage everything in the app or MCP server. Plus, add URLs and notes with a browser extension. Happy to answer questions.
4 コメント
is the bottleneck actually retrieval/search, or just that the underlying data is unstructured to begin with? have you seen a setup where context and decision don't get lost in threads over time, without relying on constant summarization?
With how we build it, the content doesn't really matter. The AI and Typesense embedding model handles many of the relationships, so the AI tools don't need to do all the work. Mostly just calling "search_knowledge".
Most teams do not suffer from a lack of information. They suffer from context being scattered across notes, chats, docs, URLs, tickets, and AI sessions, with no reliable way to retrieve the right piece at the right time. Even a good search is not enough if the underlying memory layer is fragmented or opaque.
We have not seen many setups that preserve context and decisions well over time without ongoing cleanup. Usually, people fall back on some combination of repeated prompting, summaries, manual docs, or isolated memory within a single tool. That works for a while, but it decays.
Our view is that the better approach is not endless summarization. It provides teams with a shared memory layer where notes, decisions, URLs, and reusable context remain inspectable, editable, and portable, so retrieval has something durable to work with in the first place.
Most teams do not suffer from a lack of information. They suffer from context being scattered across notes, chats, docs, URLs, tickets, and AI sessions, with no reliable way to retrieve the right piece at the right time. Even a good search is not enough if the underlying memory layer is fragmented or opaque.
We have not seen many setups that preserve context and decisions well over time without ongoing cleanup. Usually, people fall back on some combination of repeated prompting, summaries, manual docs, or isolated memory within a single tool. That works for a while, but it decays.
Our view is that the better approach is not endless summarization. It provides teams with a shared memory layer where notes, decisions, URLs, and reusable context remain inspectable, editable, and portable, so retrieval has something durable to work with in the first place.