$ loading_
Give AI agents persistent memory, searchable knowledge, and automatic consolidation.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "clude-mcp" yet — see the docs or source repo.
Design a long-term memory setup for my AI assistant using clude-mcp. It should support persistent storage, importance-based memory writes, semantic retrieval, and scheduled consolidation of duplicate or low-value memories. Output the system architecture, memory write rules, retrieval flow, and sample configuration.
A practical long-term memory integration plan with architecture, memory policies, and sample configuration.
Use clude-mcp to plan a project knowledge graph that links requirements, APIs, decision logs, owners, and past issues. Explain the node and relationship design, how to extract memories from chats and documents, and how an agent can use it to answer context-aware questions.
A project knowledge graph design covering structure, extraction methods, and question-answering usage.
Design an automatic memory consolidation strategy for clude-mcp to reduce noisy memories, merge duplicates, preserve high-value context, and prevent the agent from forgetting key preferences. Include trigger conditions, importance thresholds, consolidation rules, and monitoring metrics.
A memory consolidation optimization plan with rules, thresholds, and evaluation metrics.
Centralize memory, tools, and logic for leaner prompts and scalable AI agents.
Give AI agents persistent memory and semantic retrieval across conversations.
Enable AI clients to remember user preferences across conversations with vector search.
Manage persistent agent memories across global or repository-specific scopes.
Give AI agents persistent memory, recall, and context management across sessions
Give AI assistants persistent memory with vector search, relationships, and access control.