# Knowledge Graph

The Neo4j MCP project connects LLMs with external systems through a standardized protocol to enable natural - language operation of Neo4j databases, knowledge graph storage, and Aura cloud service management.

MCP Server Knowledge Graph Memory Project

An AI memory storage server based on knowledge graphs, providing persistent memory functions across interactions for AI models.

This project uses Docker Compose to set up multiple Model Context Protocol (MCP) services, including Git operations, file systems, PostgreSQL databases, knowledge graph storage, and visual debugging tools, providing a complete local development environment deployment solution.

A Python - based code review system that integrates the programming principles of Martin Fowler and Robert C. Martin and provides AI - assisted code review services.

An implementation of a persistent memory server based on knowledge graphs, allowing Claude to remember user information across different chats. It provides functions for creating, querying, updating, and deleting entities, relationships, and observations, and supports local JSON storage.

Memento is a knowledge graph memory system based on SQLite, providing persistent memory functions, supporting full - text retrieval and semantic search, and realizing intelligent context retrieval through BGE - M3 embedding. It is suitable for technical and creative project management.

The Project MCP Server is a tool for managing project knowledge graphs, providing structured representations of projects, tasks, milestones, resources, and team members to help project managers track progress, manage risks, allocate resources, and make decisions.

An MCP server that connects AI agents with Apache Jena, providing SPARQL query functionality

An MCP server for the AI Memory Engine Cognee, supporting the construction of knowledge graphs from text and searching.

NeoCoder is an AI programming assistance system based on the Neo4j knowledge graph. It enables the interaction between AI assistants and the knowledge graph through an MCP server, providing standardized coding workflows, dynamic instruction manuals, and project memory functions. The system supports multiple operation modes (incarnations), including basic programming, scientific research, decision - making analysis, complex system modeling, knowledge graph management, and code analysis. It stores workflow templates and execution records in Neo4j to ensure the traceability and standardization of the coding process.

MemoryMesh is a knowledge graph server designed for AI models, focusing on text role - playing games and interactive narratives. It helps AI maintain consistent and structured memory in conversations through dynamic schema definition and automatic tool generation, enabling a richer and more dynamic interactive experience.

A knowledge graph service supporting both FastAPI and FastMCP protocols, providing core operation functions of the Qdrant vector database.

The Memory Custom project adds custom memory paths and timestamp functions to the Memory server of the MCP team, optimizing the management of knowledge graphs and interaction tracking.

A persistent memory server based on a knowledge graph, supporting cross - session storage and retrieval of user information

Logseq Knowledge Graph AI Interaction Tool

A persistent memory service implementation based on a knowledge graph that allows Claude to remember user information across chats and builds a memory network through entities, relationships, and observation data.

An MCP server project that stores AI memories as Markdown files and integrates with Obsidian, supporting knowledge graph visualization.

A memory server implementing the Model Context Protocol (MCP), providing persistent memory storage and retrieval functions based on a knowledge graph for large language models.

A fully functional LightRAG MCP server that provides 22 complete tools, covering four major functional modules: document management, query operations, knowledge graphs, and system management, achieving 100% compatible integration with the LightRAG API.

Graphiti MCP Server is an enhanced knowledge graph framework specifically designed for AI agents to build and query time - aware knowledge graphs in dynamic environments. It exposes core functions through the MCP protocol, supports continuous integration of user interactions, enterprise data, and external information, and provides capabilities for incremental updates, efficient retrieval, and precise historical queries.

CortexGraph is an MCP server that provides a human-like memory system for AI assistants. It realizes the natural decay and reinforcement use of memories by simulating the Ebbinghaus forgetting curve, including short-term memory storage, long-term memory archiving, and knowledge graph functions.

Flexible GraphRAG is a flexible platform that supports multi - data source document processing, automatic knowledge graph construction, hybrid search (full - text, vector, graph), and AI Q&A. It includes a FastAPI backend, an MCP server, and various front - end interfaces.

Repo GraphRAG MCP Server is a service based on the MCP protocol that uses LightRAG and Tree - sitter to build a knowledge graph from code and text documents and provides functions such as question - answering and implementation planning.

SmartMemory is an MCP server that provides structured memory for LLMs. It converts conversation content into knowledge graphs through natural dialogue, enabling AI assistants to learn and apply business rules.

The Memory Journal MCP Server is a project context management tool for AI-assisted development, solving the problem of AI context loss across multiple threads and sessions through persistent knowledge graphs and intelligent context recall.

KIP is a graph interaction protocol designed for large language models, connecting LLMs with knowledge graphs and providing capabilities for memory persistence, knowledge evolution, and interpretable interaction.

Penfield is a service that provides persistent memory for AI agents. It supports cross - session storage of decisions, preferences, and context, builds a knowledge graph that grows over time, and is compatible with multiple MCP tools.

An MCP server project for threat information collection, supporting the collection of threat intelligence within a specified time range through command - line tools and integration with the Neo4j knowledge graph.

A general resource management system that supports the management of multiple resource types (papers, web pages, books, etc.) and integrates with the knowledge graph, providing structured notes and status tracking functions.