Tag: knowledge-graph

MemoryBear is a next-generation AI memory system mimicking human cognition through perception, extraction, association, and forgetting. Built on Neo4j graph storage with hybrid search and biological memory decay, it outperforms Mem0, Zep, and LangMem with 75% benchmark accuracy.

OpenDocs by ioteverythin converts GitHub repos, Markdown files, and Jupyter Notebooks into 15+ professional formats instantly. Stop wasting hours on manual documentation — automate Word, PDF, PPTX, and AI-ready outputs with one command.

CocoIndex is an open-source incremental data transformation framework with a Rust core that keeps AI agent context fresh. Only process the delta — stop wasting compute on full re-embeddings.

GBrain is Garry Tan's open-source AI agent memory system—17K+ pages, self-wiring knowledge graph, hybrid search, and 34 production skills. Install in 30 minutes, get persistent agent intelligence that compounds overnight.

GitNexus is a zero-server code intelligence engine that indexes codebases into interactive knowledge graphs with built-in Graph RAG agents. Discover how it prevents breaking changes, enables deep codebase analysis, and integrates with Cursor, Claude Code, and Codex through native MCP tools.

Discover Thoth, the local-first AI assistant with knowledge graphs, 30+ tools, voice, vision, and full data sovereignty. Run entirely on your machine via Ollama or add opt-in cloud models.

RAG-Anything is the all-in-one multimodal RAG framework with VLM integration that processes text, images, tables, and equations in a single pipeline. Built on LightRAG by HKUDS, it eliminates fragile tool chains with native cross-modal knowledge graphs and automatic visual reasoning.

Discover how InfraNodus MCP Server transforms text into knowledge graphs for LLMs. Learn installation, 25+ powerful tools, real-world use cases, and advanced techniques to supercharge your AI workflows.

KAG is a revolutionary logical reasoning framework that outperforms traditional RAG by 20-30 points on domain knowledge bases. Learn installation, real code examples, and advanced strategies for building factually accurate AI systems with schema-constrained knowledge graphs and logical form-guided reasoning.