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 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 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 Agent-agnostic memory backend that preserves continuity between humans and AI over time.
Fetches current, version-aware library documentation and code examples for AI coding agents.
Key Features: Documentation and code example retrieval Version-aware documentation queries Local knowledge base with encrypted vector search, multi-format document indexing, and cross-session memory for Claude Desktop.
Key Features: Hybrid vector and keyword search with reranking Supports 40+ document types including Markdown, CSV, PDF, code Self-maintaining memory with contradiction detection and auto-merge Local memory layer indexing coding agent session histories for fast recall and cross-machine sync over SSH.
Key Features: Zero-dependency single binary, fully local operation Search 3.5 GB of session history in ~1.5 ms Supports multiple coding agents via MCP protocol Local MCP memory server with persistent sessions, offline routing, knowledge graphs, and an auditable token-savings report.
Key Features: Persistent session memory Offline Ollama-based routing Stores project memory, code structure, file content, Git context, and optional read-only Notion data for AI coding sessions.
Key Features: Local project memory over MCP Structured file exploration Git status and repository analysis Searches a Git-backed collection of verified debugging lessons through local or remote MCP tools.
Key Features: BM25 and SAG-Lite lesson search Git-backed failure-recovery knowledge base MCP tools for searching and retrieving lessons 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