Integrates vector database capabilities for managing collections, performing semantic searches, and retrieving data with advanced filtering. Enhances AI models with powerful data retrieval features to improve context and performance.

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.

Provides access to Omi conversations and memories through a standardized MCP interface, enabling reading, creating, and managing these elements efficiently within LLM workflows.

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.

Utilizes a scalable knowledge graph built on Elasticsearch to manage and query large datasets, providing persistent memory for AI systems. Supports complete CRUD operations and offers advanced search capabilities for improved data handling in Model Context Protocol applications.

Manage project memory banks remotely and efficiently, enabling streamlined access and manipulation of memory data.

Utilizes a local knowledge graph to enable persistent memory for AI agents, allowing them to create, update, and retrieve personalized user information across chat sessions. Facilitates tailored interactions by managing entities, relations, and observations.

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

Facilitates structured sequential thinking by breaking down complex problems into logical steps, validating reasoning chains, and visualizing thinking pathways. Integrates with a Memory Bank for managing and storing reasoning patterns to enhance problem-solving workflows.

Manage AI assistant's context across sessions by storing, retrieving, and tracking information with remote server support for improved collaboration and persistence.

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 user contexts for LLM interactions by storing and retrieving relevant prompts to ensure continuity in conversations. Provides a RESTful API for context management with support for in-memory storage and TypeScript integration.

Connect and manage your Simplenote notes, enabling actions like creating, updating, and deleting notes directly from LLM applications.

Efficiently caches data between language model interactions to reduce token consumption, enhancing performance and speeding up responses. Integrates seamlessly with any MCP client and language model that uses tokens.

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.

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.

A vector database designed for integrating LLM applications, enabling seamless data retrieval and management through advanced search capabilities and document operations. Supports memory and context enhancement for AI models with efficient embedding functions.

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

A knowledge graph implementation that supports semantic search with a Qdrant vector database, enabling effective graph-based representation of entities and their relations. It includes features for file-based persistence and utilizes OpenAI embeddings for enhanced semantic similarity.

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

Search and retrieve documents from Outline knowledge bases, facilitating access to internal documentation. Supports secure access via interactive credentials and environment variables.

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.

Enables persistent knowledge management through natural language conversations with LLMs by reading and writing to local Markdown files. Facilitates contextual awareness across sessions while maintaining a user-controlled knowledge base with a simple local-first approach.

Manage and enhance memory capabilities for AI assistants within Cursor IDE, enabling seamless storage, recall, and management of information across conversations for more personalized interactions.

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.