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A local-first MCP memory plane for persistent, user-owned AI context.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "sthayi" yet — see the docs or source repo.
Record my project preferences, key terms, and current progress as reusable memory, and keep using them in future chats.
Creates persistent memory that can be reused across apps.
Organize my working style, writing tone, and reminder rules into user-owned context for all MCP clients.
Outputs a unified personal context for different clients to use.
Save my common workflows, prompt templates, and procedures as skills so I can reuse them in new sessions.
Persists reusable skills and reduces repeated setup.
Developers or researchers switching between AI clients can keep one shared memory set available. It fits workflows that need long-term context without repeating preferences.
When you want to store preferences, terminology, workflows, and skills in one place for different MCP clients to read, this tool acts as a shared memory layer.
It is useful for turning common tasks into reusable skills so AI can follow the same approach later. This helps users with repeated workflows.
It is a local-first MCP server that provides AI clients with a shared, user-owned memory and skills plane. Its goal is to keep memory persistent across apps.
From the description, it is meant for AI clients that support the Model Context Protocol. Installation and setup details are not provided here; see the source repository.
It emphasizes local-first, user-owned data, and sharing the same memory and skills across apps via MCP, rather than working only inside one app.
Local MCP server for utility tools, shared notes, and searchable knowledge
Provide local-first governed AI memory with human approval and optional sync.
Centralize memory, tools, and logic for leaner prompts and scalable AI agents.
Share local memory across AI tools and resume context between sessions.
Give AI agents persistent memory and semantic retrieval across conversations.
Enable AI clients to remember user preferences across conversations with vector search.