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Manage persistent AI memory with hybrid search and offline local embeddings.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "HKC Memory Server" yet — see the docs or source repo.
Explain how to integrate HKC Memory Server into my MCP client to store user preferences, task history, and key facts, and provide a recommended flow for memory write, retrieval, and update.
An integration plan covering memory types, invocation timing, and recommended write, retrieval, and update flows.
Design a hybrid retrieval strategy for HKC Memory Server that combines keyword search and semantic vector search to improve relevant memory recall in multi-turn conversations, and explain the best-fit scenarios.
A retrieval strategy recommendation describing how to combine keyword and vector search, rank results, and match use cases.
Describe how the offline-first architecture of HKC Memory Server works, including SQLite storage, the role of local embeddings, and the pros and cons for privacy, offline availability, and deployment.
A clear explanation of the offline-first architecture plus trade-off analysis for privacy, availability, and deployment.
Give AI clients persistent long-term memory with search and organization.
Persistent knowledge-graph memory for MCP with semantic search and version tracking.
Lightweight vector memory for AI agents to store, search, and delete memories.
Give AI knowledge-graph memory with cloud persistence and semantic search.
Enable AI assistants to store, search, and manage persistent semantic memories.
Provide shared cross-session memory storage, retrieval, and governance for MCP AI tools.