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licenseA
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maintenanceLocal-first memory engine for AI-agent teams: private/team/project ACL, associative recall, and federated sync across nodes. One SQLite file, no LLM required.
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2
Apache 2.0
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"Tools and Methods for Controlling Windows Desktop and Managing OS Usage" matching MCP servers:
- Alicense-qualityDmaintenanceAn LLM-native decision tracking system that captures unexpected engineering outcomes as 'pressure events' to build a persistent learning foundation. It enables AI assistants to manage cases, log surprises, and promote recurring insights into global or project-specific knowledge bases.Last updated8MIT
- Alicense-qualityCmaintenancePersistent operational state for AI agents. Decisions, plans, priorities, and patterns that survive across sessions. Not conversation logs; structured project state with enforcement.Last updated2395MIT
- Flicense-quality-maintenanceProvides AI agents with tools to navigate, search, and manage a hierarchical knowledge base of themes, skills, and learning resources with tenant-aware JWT authentication.Last updated
- AlicenseBqualityBmaintenanceProvides context management and todo persistence with AI second opinions from ChatGPT and Claude. Enables saving code snippets, conversations, and todos across sessions with full-text search capabilities.Last updated33MIT
- Alicense-qualityAmaintenanceTheWeave is a markdown-native memory architecture for Claude and any MCP-aware agent. Your agent's memory lives as plain .md files you own: a 5-verb MCP core over the vault, query-driven PageRank retrieval, bi-temporal facts (valid_from / valid_until), and a persona-as-vault model. No database and no embeddings server. The files are the memory, inspectable in your editor and versionable in git.Last updated2Apache 2.0
- Alicense-qualityBmaintenanceEnables searching, reading, and appending change notes in an Obsidian vault through local MCP tools.Last updatedBSD Zero Clause
- Alicense-quality-maintenanceEnables semantic search across Apple Mail, Messages, Calendar, and Contacts on macOS using natural language queries. All processing happens locally with privacy-first vector indexing for fast similarity search.Last updated
- Alicense-qualityAmaintenanceA collection of MCP servers for Apple macOS apps (Mail, Contacts, Notes, Memory, Messages, Calendar, Reminders) enabling AI assistants to read, search, create, and update data via JXA, SQLite, and EventKit.Last updated12MIT
- Alicense-qualityDmaintenanceA MongoDB Atlas-based Notion-style knowledge base management MCP server that supports user workspaces, project management, page CRUD with tree structure, and text search.Last updated28MIT
- Alicense-qualityBmaintenancePersists Claude Code sessions to Notion as team memory, enabling queryable recall of decisions, work state, and project architecture.Last updatedMIT
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- Flicense-qualityFmaintenanceA Model Context Protocol server that provides AI assistants with structured access to your Logseq knowledge graph, enabling retrieval, searching, analysis, and creation of content within your personal knowledge base.Last updated70
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- AlicenseAqualityBmaintenanceEnables local semantic search and management of OKF knowledge bundles via MCP tools, with hybrid BM25 and vector search.Last updated102Apache 2.0
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- AlicenseBqualityDmaintenanceProvides a scalable knowledge graph implementation for Model Context Protocol using Elasticsearch, enabling AI models to store and query information with advanced search capabilities, memory-like behavior, and multi-zone architecture.Last updated201420MIT
- Alicense-qualityBmaintenanceAn MCP server that provides shared memory, kanban board, and agent registry for AI agents to collaborate as a team, with a live dashboard for human oversight.Last updatedMIT
- Alicense-qualityBmaintenanceProvides a durable, Obsidian-compatible knowledge base for agents using markdown notes and wikilinks. Enables agents to store, retrieve, and interlink knowledge persistently, with tools for writing, searching, and managing a graph of notes.Last updated1MIT
- Alicense-qualityBmaintenanceEnables agents to query across all their memory stores (brain, team, reading, code) in one call, returning a token-budgeted, ranked briefing with results interleaved from each source.Last updatedMIT