# Knowledge Graph

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 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.

The GraphDB MCP Server provides read-only access to Ontotext GraphDB, supporting SPARQL queries and RDF graph exploration

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

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.

This project adds custom memory paths and timestamp functions to the MCP team's Memory server, supporting the construction of a knowledge graph through LLM to manage interaction memory.

An implementation of the MCP server based on txtai, providing semantic search, knowledge graph, and AI text processing functions.

A persistent memory server based on knowledge graphs that supports remembering user information across sessions and accumulating experience through an error - learning system. It provides functions for entity - relationship management, observation record storage, and error - solution management, and can be integrated into the Cursor MCP client.

An MCP memory server based on the Qdrant vector database, providing knowledge graph and semantic search capabilities.

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.

An implementation of the MCP Server based on txtai, providing semantic search, knowledge graph, and AI text processing functions.

A knowledge graph storage and retrieval service based on Neo4j, implemented as a server for the MCP protocol, supporting database connection configuration through environment variables and providing project - specific knowledge graph management functions.

A Deno-based MCP server template that supports publishing through JSR.io and optional Deno Deploy hosting, including knowledge graph functionality implemented with the Deno KV database.

An implementation of the MCP server based on txtai, providing semantic search, knowledge graph, and AI text processing functions, supporting local knowledge base construction and standardized interface access.

A memory-based knowledge graph management MCP server that provides strict validation and management functions for entities, relationships, and observations

A knowledge graph memory server implemented in Go, providing persistent memory management capabilities across conversations for AI assistants

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.

SourceSage is an efficient MCP server designed for LLMs, used to remember key aspects of codebases, such as logic, style, and standards, and supports dynamic updates and fast retrieval.

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.

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.

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.

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.

An implementation of a knowledge graph management MCP server based on Neo4j, providing standard interfaces for the storage and retrieval of graph databases.

MCP Server Knowledge Graph Memory Project

A student knowledge graph management server that provides structured management and progress tracking of learning resources such as courses, assignments, and exams

A server for managing qualitative research knowledge graphs, providing structured representation and management tools for research projects, participants, interviews, coding, and themes

A Wikidata API service implementation based on the Model Context Protocol, providing entity/property search, metadata extraction, and SPARQL query functions.

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.