Memory Management MCP Repositories

Manage human-agent collaboration by using a sequential scoring system to evaluate and determine the necessity of human intervention in AI operations based on complexity and sensitivity of requests.

Facilitates structured sequential thinking by breaking down complex problems into logical steps, managing reasoning chains, and visualizing thinking pathways. Integrates with a Memory Bank for storing and retrieving thought processes, while providing tools for reasoning validation and analysis.

Provides a memory system for AI applications where information can be stored and retrieved across user sessions. It enables personalized interactions by managing user-specific data efficiently.

This server enables the implementation of persistent memory in AI models through a local knowledge graph, allowing for information retention across chats and an error-learning mechanism via a lesson system.

Store and retrieve memories efficiently using SQLite as the backend. Offers tools for managing memories, including functions to remember, get, list, update, and delete entries, along with command-line inspection capabilities.

Store and retrieve information from AI interactions using a Neo4j backend, facilitating advanced graph querying and memory management. Enhances performance and scalability for complex knowledge graph applications.

Enables persistent memory capabilities for AI interactions, allowing the model to remember user information and enhance personalization through a local knowledge graph that manages entities, relations, and observations.

Provides semantic memory and persistent storage using ChromaDB for long-term memory retention and semantic search capabilities, enhancing context maintenance across conversations.

Enhances Large Language Models with persistent memory capabilities, allowing for the storage, retrieval, and management of memories across conversations. Integrates with the Claude desktop application, supporting various memory types and semantic search.

Leverage a knowledge graph with entities and relations, enabling semantic search capabilities using OpenAI embeddings and Qdrant for data persistence. Supports HTTPS and Docker for streamlined deployment.

Provides persistent memory storage and version tracking for Infrastructure-as-Code components, focusing on Terraform and Ansible resources with relationship mapping.

Efficiently interact with the Figma API, utilizing memory-aware chunking and pagination to manage and process large Figma files. This enables effective handling of extensive design documents and resource-intensive operations.

A high-performance HTTP server utilizing the epoll model for efficient connection and task management, supporting event-driven architecture and timer management for handling inactive connections. Includes implementations of memory allocation, a simple proxy server, and a basic shell for process management and signal handling.

Track courses, assignments, and exams while optimizing study sessions and monitoring progress through a structured knowledge graph. Manage deadlines and priorities effectively within an academic context.

A server toolset for monitoring and managing remote servers, providing functionalities for system checks, service status updates, network diagnostics, and security audits. It includes features for memory information retrieval, system load monitoring, and process management.

Manage tasks with persistent memory to maintain project context and streamline workflow execution using intelligent planning and execution modes. The server integrates seamlessly with FastMCP-compatible clients for enhanced task and context management.

Enables AI agents to store and retrieve historical interactions and persistent data on a decentralized memory layer. Facilitates conversation management by saving and fetching messages, ensuring continuity and traceability of interactions.

Captures and organizes development context, tracking code changes and user interactions across multiple projects. Provides persistent memory for a more effective coding experience.

Analyze memory dumps using natural language queries to facilitate forensic investigations, reducing the need for technical expertise while accelerating the analysis process and improving cybersecurity responses.

Provides session-based memory management for chat applications using an efficient knowledge graph to search and retrieve information from multiple sources, including uploaded files.

Store and retrieve user-specific memories to maintain context and make informed decisions based on past interactions using a simple API. Features relevance scoring to enhance memory management with user preferences.

Assists with code-related tasks by exploring codebases, managing file contents, and providing insights through summarization. Facilitates interactive communication for effective code manipulation.

Offers a project-centric database for managing contextual information throughout development workflows. Facilitates seamless retrieval and recall of project context, enhancing productivity and fostering intelligent code connections.

Memento is a knowledge graph memory system that facilitates semantic search and contextual recall, enabling LLM applications to manage and retrieve information with temporal awareness. It offers a persistent and adaptive long-term memory structure through ontological entity nodes.

Leverage high-performance vector search and efficient knowledge storage to manage entities and relations. Provides semantic search capabilities and secure token-based authentication for connecting to remote libSQL databases.

Persistently remembers user information across chats using a local knowledge graph, allowing connections between different entities and their relationships. Facilitates the storage and retrieval of observations linked to specific entities for improved contextual interactions.

Long-term memory storage for LLMs that maintains project context across sessions, enabling efficient retrieval and recall of past interactions and decisions via semantic search. Organizes memories by type, tags, and relationships for streamlined management.

Manage conversation context and a personal knowledge base for AI applications with efficient APIs. Create, update, and query user data while handling dialogue and knowledge management seamlessly.

Enables persistent memory for AI models using a local knowledge graph, allowing them to remember user information across chats. Customizable memory paths enhance the management of stored knowledge.

Store and recall important information for agents, manage memory through context item and namespace functionalities, and facilitate semantic search capabilities for relevant context retrieval.