Knowledge Graph AI Agent Skills

English | 中文 Zero-dependency MCP memory server for AI agents — persistent, searchable, local-first, single binary. - No infra to babysit. Single Go binary. No Docker, no Node.js runtime, no cloud account, no API keys. brew install in 30 seconds. - Memory stays with the project. Stored in .aimemo/ next to your code — commit it to git or add it to .gitignore. Switch branches; memory follows the dire

🚀 Try ApeRAG Live Demo - Experience the full platform capabilities with our hosted demo ApeRAG is a production-ready RAG (Retrieval-Augmented Generation) platform that combines Graph RAG, vector search, and full-text search with advanced AI agents. Build sophisticated AI applications with hybrid retrieval, multimodal document processing, intelligent agents, and enterprise-grade management feature

A Model Context Protocol (MCP) server that implements the Zettelkasten knowledge management methodology, allowing you to create, link, explore and synthesize atomic notes through Claude and other MCP-compatible clients. The Zettelkasten method is a knowledge management system developed by German sociologist Niklas Luhmann, who used it to produce over 70 books and hundreds of articles. It consists

Open-source memory backend for multi-agent systems. Agents store decisions, share causal knowledge graphs, and retrieve context in 5ms — without cloud lock-in or API costs. Works with LangGraph · CrewAI · AutoGen · any HTTP client · Claude Desktop Key capabilities for agent pipelines: - Framework-agnostic REST API — 15 endpoints, no MCP client library needed - Knowledge graph — agents share causal

The memory system for AI coding agents. Decisions, lessons, and context that persist across sessions. mcp-name: io.github.omega-memory/omega-memory AI coding agents are stateless. Every new session starts from zero. - Context loss. Agents forget every decision, preference, and architectural choice between sessions. Developers spend 10-30 minutes per session re-explaining context that was already e