# Knowledge Graph

Project Synapse is a revolutionary MCP server that transforms text into an interconnected knowledge network through semantic analysis and knowledge graph technology and autonomously generates insights. It combines Montague semantics and the Zettelkasten method to achieve the cognitive collaboration ability of AI.

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

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

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.

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.

An intelligent tool scheduling system based on FastMCP 2.0. The large model can autonomously call over 26 tools, supporting knowledge graphs, vector search, and real - time progress display, and enabling automation of enterprise knowledge management and business data analysis.

Fodda MCP Server is an AI knowledge graph service based on the Model Context Protocol (MCP), providing expert knowledge graphs from fields such as PSFK retail, beauty, and sports for AI agents to perform operations such as search, traversal, and evidence retrieval.

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

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

A Python in - memory MCP server based on a knowledge graph, using SQLite as the backend to achieve cross - session persistent memory storage and management.

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.

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.

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 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.

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.

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.

The AI Memory Protocol is a versioned, graph - based persistent memory system designed for AI programming agents, implemented using Sphinx - Needs, supporting the creation, search, update of memories, and integration with AI clients through the MCP protocol.

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.

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.

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

Knowledge Graph Generator MCP Server Project

A knowledge graph builder that uses a local AI model to convert text or web page content into structured knowledge graphs, supporting large-scale content processing, real-time visualization, and integration with Neo4j and Qdrant databases.

Memento MCP is a knowledge - graph - based LLM memory system that provides semantic retrieval, context recollection, and time awareness functions, supporting long - term persistent storage and efficient retrieval.

LLM Conversation Memory System Based on Redis Graph

A knowledge graph memory server based on Neo4j, providing efficient storage and retrieval capabilities for AI interactions

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.

This project adds knowledge graph management functionality to the MCP memory server, using the PouchDB database for efficient storage, supporting custom storage paths and timestamp recording.

A knowledge graph memory server implemented in Swift, providing persistent memory functions for large - language models

An MCP server integrating the Vibe Coding method and knowledge graph technology, using the Kuzu graph database for AI-assisted software development, supporting multi-language code analysis and enterprise-level security performance.