Create a new context, either vector or memory, in a Lamatic project to store and retrieve data for AI workflows.
MIT
458,051 tools. Updated 2026-08-14 20:18
"A server for handling vector memory operations" matching MCP tools:
- Load a PDF into memory for session-based operations, making it accessible for redaction and other tools. Use this first to prepare a document for subsequent PDF operations.MIT
- Verify DataBeak server status and resource availability before operations. Returns server health, memory usage, session capacity, and version details to ensure system readiness.Apache 2.0
- Purge a specific visual state with its vector embeddings and perceptual hashes from the cache to reset memory or ensure privacy.MIT
- Retrieve semantically similar memories by providing a memory ID; uses vector similarity to uncover hidden connections.PolyForm Noncommercial 1.0.0
- Get health details for all vector search indexes: document count, memory usage, indexing failures, and percent indexed. Diagnose incomplete indexing or memory growth.MIT
Matching MCP Servers
- Alicense-qualityDmaintenanceA secure vector-based memory server that provides persistent semantic memory for AI assistants using sqlite-vec and sentence-transformers. It enables semantic search and organization of coding experiences, solutions, and knowledge with features like auto-cleanup and deduplication.MIT
- Alicense-qualityCmaintenanceProvides local vector-based semantic memory storage for AI assistants to persist context and decisions across sessions using local embeddings and LanceDB. It enables private semantic search and session handoff capabilities to maintain long-term project context.2525MIT
Matching MCP Connectors
Persistent long-term memory for AI agents: semantic search, knowledge graph, and task canvas.
Cross-session, cross-device memory for your agent: remember and recall notes. No key to start.
- Retrieve MongoDB server metrics including connections, memory, uptime, and operations to monitor database health.MIT
- Generate vector embeddings from text using BAAI BGE models. Useful for custom similarity calculations and manual vector operations.MIT
- Search episodic memory to find past experiences similar to your query using vector similarity.MIT
- Retrieve the vector representation of a specific entity from the knowledge graph memory, enabling semantic search, similarity comparisons, and clustering operations.MIT
- Search memory files using hybrid, lexical, or vector methods to retrieve relevant information from stored data.MIT
- Add a text fragment with optional metadata to a project's vector store, enabling long-term searchable memory.MIT
- Record a conversation or fact with emotional and contextual metadata into persistent memory using Markdown drawers and vector indexes.MIT
- Manage vector embeddings for Cursor10x MCP by performing store, search, update, and delete operations to enhance AI memory retention and recall.MIT
- Retrieve the vector embedding for a specific entity from your knowledge graph memory to enable semantic search and similarity comparisons.MIT
- Streamline development tasks with a universal tool for file operations, command execution, project analysis, notebook management, and vector store interactions. Simplify workflows through a unified interface integrated with Hanzo MCP.MIT
- Store and retrieve temporary data in memory using get, set, delete, and list operations. Data persists only while the server runs.
- Save a new memory entry with vector embedding for semantic search and retrieval. Organize memories by title, content, tags, and topic ID.
- Administer Codex app-server via MCP: manage accounts, config, apps, plugins, skills, environment, search, memory, real-time, and remote-control operations.
- Add a new memory to the LanceDB vector database for semantic storage and retrieval.