MCP server providing automated, context-aware documentation and code convention insights for GitHub repos.
Key Features: Compact pre-edit repo context including conventions and gaps Change impact analysis linking code paths to docs and conventions Structured API endpoint context for touched code paths Local-first AI memory server storing knowledge as Markdown files with semantic search and optional cloud sync.
Key Features: Local-first plain text Markdown storage Two-way human and AI file editing Semantic knowledge graph with wikilinks Persistent memory MCP server with knowledge graph, semantic search, cloud sync, and cross-session context management.
Key Features: Persistent storage with SQLite or cloud sync (S3, R2, D1) Hierarchical memory organization with sections and subsections Semantic search using TF-IDF, sentence-transformers, and OpenAI embeddings MCP server providing safe read/write access and comprehensive operations on Obsidian vault notes via MCP.
Key Features: Safe YAML frontmatter parsing and AST-aware updates File operations: read, write, patch, delete, move notes/files Partial reads: get note outlines and specific line ranges Search Claude Code and OpenAI Codex CLI conversations locally with hybrid semantic and keyword retrieval.
Key Features: Hybrid vector and keyword search Claude Code and Codex CLI session indexing Local ONNX embeddings and SQLite storage AI memory layer — one shared, persistent memory across every AI tool you connect.
MCP server providing persistent memory, semantic code search, dependency graphs, and session tracking for AI coding assistants.
Key Features: Semantic, hybrid, keyword, pattern, exhaustive, and refactor code search modes Memory management for events, decisions, docs, runbooks, tasks, todos, diagrams, transcripts Session capture and recall including retroactive capture and plans Persistent personal memory for AI assistants — save, search, and recall across every MCP client.
Search your Obsidian vault to quickly find notes by title or keyword, summarize related content, a…
Apple Developer Documentation with Semantic Search, RAG, and AI reranking for MCP clients
Project-scoped workspace memory that indexes code and docs in Qdrant for hybrid search and persistent AI assistant context.
Key Features: Hybrid dense and sparse search with Reciprocal Rank Fusion Automatic Git project detection and project-scoped collections Tree-sitter semantic code chunking Local code intelligence engine indexing repos into a persistent knowledge graph with fast structural queries across 159 languages.
Key Features: Indexes 159 languages using tree-sitter and Hybrid LSP Supports 15 MCP tools including graph search, tracing, and impact analysis Runs as a single static native binary with zero dependencies