A knowledge graph server for AI agents, built with Neo4j and integrated with Model Context Protocol, enabling dynamic graph management and semantic search.
GPU-accelerated graph visualization and analytics server for Large Language Models that integrates with Model Control Protocol (MCP), enabling AI assistants to visualize and analyze complex network data.
AI-native architecture diagramming for MCP clients (Claude Code, Cursor, Windsurf). The calling agent authors the graph (nodes, groups, edges); Flowgraf validates it, lays it out deterministically with ELK, renders an SVG + Mermaid, and returns a link to a live, editable canvas you can refine by chat, drag, or one-click AI design review. Agent-authored ops — no API key and no LLM cost to the calle
A graph-based MCP server that provides AI coding agents with persistent memory to store patterns, track complex relationships, and retrieve knowledge across sessions. It leverages graph structures to handle temporal queries and relational paths that traditional vector stores often miss.
A Compact Knowledge Graph MCP server providing pre-structured domain knowledge as a routing layer for agent stacks, enabling efficient structural queries (e.g., prerequisites, dependency chains) without hallucinations.