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Give AI agents persistent memory and a knowledge graph across sessions.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "raggy-mcp" yet — see the docs or source repo.
Explain how to integrate raggy-mcp into my AI agent to persist user preferences, past decisions, and known errors across sessions, and suggest a recommended data structure.
An integration plan with memory types, storage structure suggestions, and invocation flow.
Use raggy-mcp to find why this agent previously rejected a proposal, and summarize related decision records, triggering conditions, and later corrections.
A summary of relevant history explaining the rejection reason, context, and subsequent adjustments.
Design a knowledge graph approach with raggy-mcp for multi-agent collaboration, linking tasks, conclusions, preferences, error cases, and owners.
A knowledge graph design including entities, relationships, sample nodes, and maintenance guidance.
Enable AI agents to read, write, and evolve memory across apps.
Search and add traceable RAG knowledge for each project workspace.
Intelligent RAG tool that chooses between private knowledge and web search.
Give AI agents persistent memory with storage, search, retrieval, and sharing.
Build and query vector knowledge bases for semantic search and RAG workflows.
Use authenticated MCP tools for graph-augmented and hybrid RAG retrieval.