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Provide local memory storage, retrieval, and auto read-write for MCP LLMs.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "membank" yet — see the docs or source repo.
Connect this MCP tool to my AI assistant and make it remember: I prefer concise answers, code examples in Python, and any terminology corrections should be stored as long-term memory. Explain what memory fields to save and the call flow.
A memory write plan with preferences, corrections, structured fields, and an automatic save workflow.
Design a call flow so the AI first retrieves historical decisions related to 'database selection' and 'caching strategy' from membank, then generates advice based on those memories while avoiding conflicts with past conclusions.
A workflow with semantic retrieval steps showing how to read relevant memories and use them for consistent responses.
Create memory rules for a team project assistant: after each retrospective, write problems encountered, solutions, final decisions, and follow-up notes into the local SQLite memory store, and suggest a recommended memory taxonomy.
A project-retrospective memory taxonomy, write guidelines, and a reusable record template for later retrieval.
Manage remote AI memory banks for persistent context and project knowledge.
Provide portable long-term memory storage and recall for AI agents across sessions.
Build persistent, semantically searchable memory for codebases via natural language queries.
Provide AI assistants with persistent memory, full-text search, and knowledge graph storage.
Provide centralized persistent memory, validation, and MCP integration for LLM workflows.
Give AI agents persistent memory and semantic retrieval across conversations.