"Understanding the Concept of LLM Context" matching MCP servers:

Saves the user's exact words as immutable, SHA-256-hashed captures before anything else, then derives events, entities, context cards, and causal hypotheses — every fact traceable to its verbatim source. Local-first personal memory for Claude Code, Codex, and any MCP client.

Enables MCP-compatible assistants to connect to a user's workspace, read across boards, notes, checklists, habits, money, meals, chores, and chat, and make scoped additions, changes, or removals with attribution.

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.

Enables an LLM to autonomously maintain a Markdown-based wiki with YAML frontmatter, using hybrid BM25 and local CPU vector search for fully offline retrieval without external APIs or databases.

Enables LLMs to access a user's personal writing context—voice, style, opinions, expertise, projects, and communication patterns—via curated markdown files, helping the LLM match the user's voice when generating written content.

An open-source memory layer that provides persistent project context and architectural history for AI development tools across multiple platforms and sessions. It enables AI assistants to maintain a shared understanding of codebases while integrating directly with services like Notion for documentation management.

Graph-level operations for Karpathy LLM Wiki knowledge bases, enabling LLM agents via MCP and humans via CLI to perform structured graph surgery including node/edge CRUD, wikilink management, index rebuilding, and graph metrics.

Enables LLM clients to read, search, create, update, and delete notes in a self-hosted shared memory bank over MCP, using hybrid vector and full-text search with optional summarization.

Enables Claude to pull curated context from self-hosted Markdown files via a remote MCP connector, with an append-only journal for agent notes manually promoted by the owner.

Enables AI clients to read and write to a user-owned markdown bucket via S3-compatible storage, providing a persistent shared context layer across different AI tools.

Persistent markdown wiki for your AI agent, built on Karpathy's LLM wiki gist.

An MCP server for a plain-markdown LLM wiki vault with YAML frontmatter and Obsidian-style wiki-links, enabling headless full-text search, note reading, backlink discovery, and safe append/create-only writing with path traversal protection and an immutable raw/ directory.

Stores personal context as markdown files and exposes them via MCP for AI assistants to retrieve user preferences, skills, and background.

A KISS self-hosted wiki server for AI agents, providing MCP tools to list, read, search, write, and append notes to Markdown files on the filesystem.

Parses an Obsidian vault and exposes structured context (notes, blocks, tasks, headings) through MCP tools, enabling AI clients to query and reason over local markdown notes.

An MCP server for saving, managing, and enhancing context with pre-processing strategies like clarification, analysis, and search optimization.

Lets AI agents retrieve source-linked passages from a local folder of Markdown notes or an Obsidian vault, optionally following the explicit links between notes, and can also fetch passages from pinned public GitHub files you configure. It returns bounded evidence packets with original text, file paths and hashes so agents can answer from your material rather than generating content themselves.

MCP server for persistent, compounding markdown wikis maintained by LLMs. Enables incremental knowledge base building with interlinked pages, search, and raw source management.

Enables ChatGPT to search, read, and organize your Google Keep notes through read-only MCP tools, including full note retrieval, label listing, recent-note browsing, and task extraction. Notes refresh automatically from your connected account without any local database or write access.

Enables a local-first Codex plugin to maintain durable OKF memory, manage scoped project and personal context, and perform reviewed selective encrypted exports and recovery.