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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.
Provide persistent, explainable, MCP-native memory for AI agents.
Build a self-evolving memory graph for coding agents with semantic search.
High-performance memory system for AI agents with layered memory, RAG, and versioning.
Provide encrypted persistent memory for AI agents with secure erasure controls.
Give AI coding agents persistent semantic memory with embedding-based retrieval.
Give AI agents persistent memory with semantic search and automatic memory management.