# Knowledge Graph

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

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.

A flexible resource management system that supports managing multiple types of resources (papers, web pages, books, etc.) and integrating with knowledge graphs, providing functions such as unified identification, structured notes, and status tracking.

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.

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.

ATLAS is an intelligent task and knowledge management system built on TypeScript and Neo4j, designed for LLM agents, providing a three - layer management architecture for projects, tasks, and knowledge.

The MCP context manager is a solution for achieving cross - session persistent AI context through a knowledge graph, supporting multi - domain intelligent management.

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

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 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.

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

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.

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.

LLM Conversation Memory System Based on Redis Graph

An intelligent context management system designed for development teams, persistently recording project structure, dependencies, and development progress

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

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

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 a knowledge graph, supporting cross - session memory of user information and learning of error solutions

The Graphiti MCP Server is a multi - project knowledge graph server extension based on the official getzep/graphiti. It supports multi - server single - database configuration, provides a developer - friendly CLI tool, and can build temporary knowledge graphs for each project for AI agents to query through the model context protocol.

A Python memory MCP server based on a knowledge graph, using SQLite as the backend to achieve persistent memory storage and management across conversations.

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

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.

A memory system that enhances AI programming capabilities through multi-type database integration, including vector databases, SQLite, and knowledge graphs, for storing and retrieving code patterns, algorithms, and relationships between programming concepts.

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 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 knowledge graph management server based on the MCP protocol, supporting multiple startup methods and complete knowledge graph operation functions, including experimental short story generation capabilities.

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

An implementation of a persistent memory server based on knowledge graphs, supporting cross - session memory of user information and an error learning system, including core functions such as entity management, relationship establishment, observation recording, and error lessons.

Logseq Knowledge Graph AI Interaction Tool