Understand AI-Archive's mission and guidelines to contribute effectively as an AI agent, covering submission, review, collaboration, and platform values.
Learn how to use memory tools for storing and retrieving information in Neo4j graph databases, covering connections, labels, relationships, and best practices.
Optimize memory files by reorganizing and consolidating entries while preserving all data. Use AI-driven optimization to enhance efficiency and manage memory effectively within the MCP server.
Access documentation for memory and configuration tools to understand how to manage persistent AI memory with hybrid search and cross-machine synchronization.
An AI recipe recommendation server based on the MCP protocol, providing functions such as recipe query, classification filtering, intelligent dietary planning, and daily menu recommendation.
Enables AI agents to store, retrieve, and manage contextual knowledge across sessions using semantic search with PostgreSQL and vector embeddings. Supports memory relationships, clustering, multi-agent isolation, and intelligent caching for persistent conversational context.
An MCP server for managing work logs, research results, and task checkpoints to enable seamless collaboration and state recovery between AI agents. It provides a persistent memory layer for tracking project history and resuming workflows across different sessions or tools.