Transform Claude's Hidden Memory Into Interactive Knowledge Graphs Universal tool to visualize any Claude user's memory.json in beautiful interactive graphs. Transform your Claude Memory MCP data into stunning interactive visualizations to see how your AI assistant's knowledge connects and evolves over time. Enterprise teams using Claude lack visibility into how their AI assistant accumulates and organizes institutional knowledge. Claude Memory Viz provides zero-configuration visualization that automatically finds memory files and displays 72 entities with 93 relationships in real-time force-directed layouts. Teams can filter by entity type, search across all data, and explore detailed connections through rich tooltips. The technical implementation supports Claude's standard NDJSON memory format, automatically detecting and color-coding entity types from personality profiles to technical tools. Node size reflects connection count, while adjustable physics parameters enable optimal spacing for large knowledge graphs. Built with Cytoscape.js for performance optimization. Built with the philosophy "Solve it once and for all," the tool works for any Claude user with zero configuration. The visualizer automatically searches common memory file locations, provides demo data fallback, and offers clear guidance when files aren't found. Integration requires just git clone and one command execution. This matters because AI memory has been invisible to users, creating trust and accountability gaps in enterprise AI deployment. When teams can visualize how their AI assistant organizes knowledge, they gain insights into decision-making patterns and can optimize their AI collaboration strategies. 👩‍💻https://lnkd.in/e__RQh_q | 10 comments on LinkedIn

🚀 Introducing RAG-Anything: All-in-One RAG System! ⚡ LightRAG + Multi-Modal = RAG-Anything 🔗 Get started today: https://lnkd.in/gF3D8rnc 📦 Install: pip install raganything No more switching between multiple tools or losing critical visual information! With RAG-Anything, you get ONE unified solution that understands your documents as completely as you do ✨ 🌟 What makes RAG-Anything innovative: - 🔄 End-to-End Multimodal Pipeline: Complete workflow from document ingestion and parsing to intelligent multimodal query answering. - 📄 Universal Document Support: Seamless processing of PDFs, Office documents (DOC/DOCX/PPT/PPTX/XLS/XLSX), images, and diverse file formats. - 🧠 Specialized Content Analysis: Dedicated processors for images, tables, mathematical equations, and heterogeneous content types. - 🔗 Multimodal Knowledge Graph: Automatic entity extraction and cross-modal relationship discovery for enhanced understanding. - ⚡ Adaptive Processing Modes: Flexible MinerU-based parsing or direct multimodal content injection workflows. - 🎯 Hybrid Intelligent Retrieval: Advanced search capabilities spanning textual and multimodal content with contextual understanding. 💡 Well-suited for: - 🎓 Academic research with complex documents - 📋 Technical documentation processing - 💼 Financial report analysis - 🏢 Enterprise knowledge management

Official Implementation of "Affordable AI Assistants with Knowledge Graph of Thoughts" - spcl/knowledge-graph-of-thoughts

An open-source Python framework for real-time, graph-based knowledge in Neo4j

How LLMs can used in a knowledge extraction and visualization pipeline

🧱Building Knowledge Graphs with LLM Graph Transformer A deep dive into LangChain’s implementation of graph construction with LLMs If you want to try out… | 32 comments on LinkedIn