Memocore Knowledge Base
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Your guide to Memocore. Memocore is your persistent memory for AI — it saves notes, ideas, and knowledge so they stay with you across chats and tools. Here you'll learn what Memocore does and how it works, discover its features and best tips, and see real ways to use it in everyday work. Browse the articles or search for answers to the most common questions.
Shared project
This is Memocore
Memocore is the memory your AI clients share. Save something once and every assistant you use already knows it — nothing to re-explain, nothing to copy between chats.
Use cases and scenarios
Use Memocore for personal cross-tool memory, shared coding context across IDEs, team knowledge and onboarding, shareable handoff links, and building memory into your own product via the API.
Ways people use Memocore: Personal, across every AI tool: - Keep one memory of your context, preferences and decisions so you never re-brief an assistant when you switch between Claude, ChatGPT, Cursor and others. - A personal second brain: recurring facts, reminders, meeting notes, personal preferences. For coding, across IDEs: - Store project conventions, architecture decisions and coding standards once, so every IDE assistant (Cursor, VS Code, Claude Code) shares the same context. For teams: - One shared memory for the whole team - decisions, docs and context live in shared project memory instead of in someone's head or a buried chat. - Onboarding: hand a new hire a project of shared memos, or a public link to a decision record or checklist. Hand off context with a link: - Turn a project memo into a clean public page for onboarding notes, a decision record, or a reusable prompt - no login required for the reader. Build memory into your own product (via the API): - Customer-support bots that answer from your docs and policies without shipping the whole handbook in every prompt. - Internal copilots that get exactly the company context they need, scoped to the right project. - Retrieval for autonomous agents so they remember across runs and share memory with each other. - RAG without the pipeline - search-by-meaning is built in, no vector store to run.