Watches local and SMB folders for settled new files and pushes them into the Renfield knowledge base and Paperless over REST; also provides interactive MCP tools for browsing and on-demand file ingestion.
An MCP server for managing contextual data as markdown files with metadata, enabling agents to save, retrieve, search, and delete contexts using simple CRUD operations.
Privacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.
A portable memory server for AI agents, built for the Model Context Protocol (MCP). It stores durable memories as plain markdown files with YAML frontmatter.
Mason indexes your codebase into a persistent concept map linking features and flows to their implementing files, so AI agents can answer "where is X implemented" without running grep/glob. It also provides pre-edit impact analysis and generates CLAUDE.md files from structured analysis of git history, architectural file sampling, and test mappings.
A portable memory server for MCP that stores durable memories as markdown files, enabling AI agents to create, search, and organize persistent knowledge.
Open-source MCP server giving Claude persistent project memory via plain markdown files. Local-first workspace with 11 tools for projects, tasks, sessions, and notes.
Provides redacted access to a private local knowledgebase for coding agents, allowing them to inspect files while hiding sensitive names and identifiers.
Enables AI assistants to maintain persistent project context across sessions by storing and retrieving structured information in markdown files organized in a memory bank directory.
Provides AI agents with Obsidian vault operations via the official CLI, including reading, writing, searching notes, and managing files, with security and concurrency protections.
A read-only MCP server that provides document awareness for agents by parsing local files into structured profiles, blocks, chunks, and search results, enabling agents to understand and cite document content without dealing with raw file formats.
Stores AI memories as Markdown files for visualization in Obsidian's graph view, allowing users to create knowledge graphs with entities, relations, and observations.