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Give AI assistants a persistent, searchable memory layer and context management.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "AI Memory MCP" yet — see the docs or source repo.
Save the following user preferences to the memory namespace: prefers replies in Chinese, focuses on B2B SaaS, wants a weekly summary every Monday. Then search for memories related to “user preferences” and return them by relevance.
Returns the saved memories and shows the most relevant entries for the query.
Using historical memories, relationships, and recent conversation in the Project Alpha namespace, assemble a concise context for the assistant’s next reply.
Produces a context summary suitable for injection into the conversation so the assistant can continue with prior knowledge.
Import this batch of historical chat logs and enable optional LLM-based auto-extraction to identify facts, preferences, and entity relationships, then write them into memory.
Creates structured memory entries and relationships for later search and reuse.
Developers building chat assistants can use it to store user preferences, facts, and prior interactions. This helps the assistant consistently reference past information in later conversations.
Product or research teams can separate data by project, client, or topic. Namespaces plus search make it easier to organize and reuse memory content cleanly.
When a team already has chat logs or source materials, they can import them and optionally use auto-extraction to build structured memory. The result can then be reused through search, relationships, and context assembly.
It is a memory layer for AI assistants exposed as an MCP server, providing persistent and searchable memory. The description says it supports CRUD, namespaces, hybrid ranked search, relationships, chat-context assembly, import/export, and optional LLM-powered auto-extraction.
Yes. The original description explicitly mentions namespaces, so it can be used to organize and isolate memory data across different scopes.
We only know it is provided as an MCP server and that auto-extraction is an optional LLM feature. The given material does not specify installation steps, runtime, or key requirements; see the source repository.
Give AI assistants persistent memory, semantic search, and layered context management.
Give AI assistants persistent memory, adaptive recall, and graph-based knowledge retrieval.
Give AI assistants persistent memory, entity storage, and semantic search across sessions.
Give AI assistants persistent memory with vector search, relationships, and access control.
Give AI agents persistent memory and semantic retrieval across conversations.
Give AI assistants local-first graph memory with semantic search tools.