Local MCP memory for coding assistants with SQLite storage, structured knowledge, hybrid search, and no cloud dependency.
Key Features: Local SQLite persistent memory BM25 and semantic hybrid search Namespaces and typed knowledge graph Provides repository-aware documentation context, convention checks, API context, and proposed documentation updates through MCP.
Key Features: Repository-aware AI context Convention discovery with citations Documentation impact analysis Connects Scrivener 3 projects to AI clients for editing, analysis, search, and writing support.
Key Features: Scrivener 3 document access and editing Deterministic writing analysis Keyword and offline semantic search Local MCP memory server with encrypted three-tier storage and cue-based recall for AI coding assistants.
Key Features: Three-tier memory storage: episodic, semantic, procedural AES-256-GCM encryption at rest Multiple recall methods including cue, temporal, structural, and contradiction Indexes repositories into a local code graph for structural search, call tracing, impact analysis, and architecture exploration.
Key Features: Persistent local code knowledge graph Tree-sitter parsing for 162 languages Hybrid LSP semantic resolution Offline MCP memory and task management with semantic recall, knowledge graphs, reminders, and local backups.
Key Features: Local semantic and graph-based memory recall Hebbian strengthening and activation decay Persistent reminders and GTD-style todos Fetches current, version-aware library documentation and code examples for AI coding agents.
Key Features: Documentation and code example retrieval Version-aware documentation queries An MCP server that gives LLMs persistent, searchable semantic memory
Stores searchable project memory, Git history, projects, and issues for MCP-compatible coding assistants.
Key Features: Persistent semantic memory Project and space organization GitHub repository synchronization Hosted memory server for AI agents with feedback-based ranking, recency fading, and multi-client API key support.
Key Features: Memory write, read, list, feedback, stats, and delete APIs Feedback-driven re-ranking of memories based on helpfulness Recency-based fading of recall relevance CLI and hosted MCP endpoint for saving, searching, recalling, and managing user-owned AI memory.
Key Features: Hosted Streamable HTTP MCP endpoint Authentication and credential management Local-first screen and audio capture system exposing indexed desktop history to AI agents via MCP.
Key Features: Continuous local screen and audio recording with OCR and transcription MCP tools for content search, frame context, and element inspection Recommended desktop app setup via Settings > Connections