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Give AI agents layered memory, retrieval, and graph-based knowledge management.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "io.github.Cipher208/ariel-memory" MCP server from askskill: Run: claude mcp add 'io-github-cipher208-ariel-memory' -- npx -y mcp-ariel-memory
Use ariel-memory to design a two-layer memory setup for a customer support AI agent: keep the latest 20 turns in short-term memory, store long-term memory by user preferences, FAQs, and past tickets, and recommend write, retrieval, and cleanup strategies.
A memory architecture for support scenarios with layered storage rules, retrieval methods, and maintenance guidance.
Use ariel-memory wiki, RAG, and graph capabilities to build a knowledge base for a software project. Organize requirement docs, technical decisions, module dependencies, and ownership relationships, and explain how an AI agent can answer questions from this information later.
A structured project knowledge base plan with document organization, entity graphs, and QA integration ideas.
I am building an AI agent with multiple MCP tools. Explain how to use ariel-memory with authentication and isolate memory data for different users or teams to prevent cross-access to sensitive information.
A secure integration and access-isolation plan covering auth, data partitioning, and sensitive information protection.
Build a self-evolving memory graph for coding agents with semantic search.
Run a universal MCP server with AI memory and semantic search.
Give AI agents persistent memory with semantic search and automatic memory management.
Build a project knowledge graph for code search, traversal, and Q&A.
Build and query persistent knowledge graphs so coding agents remember across sessions.
Give AI agents local-first persistent memory with secure full-text and vector search.