"Elm" matching MCP servers:

Enables any LLM to process arbitrarily long contexts through recursive decomposition, without requiring external LLM APIs.

Transforms your local repository into a shared project brain using recursive reasoning and local LLMs to analyze, reason, and remember project architecture.

EMM is an MCP-native server that gives AI agents persistent, local, model-independent memory with context-efficient retrieval and project scoping.

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.

An MCP server that gives coding agents a persistent, chained memory of debugging investigations, tracking what's been tried, ruled out, and solved across sessions and scopes.

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.

Exposes LLM Wiki desktop capabilities via Streamable HTTP transport for MCP clients, enabling project listing, file reading, search, and knowledge graph queries through a secure API.

A self-learning knowledge layer for local LLM chat that answers questions from Markdown files, writes new knowledge back every turn, and provides grounding verdicts.

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

Enables Claude to automatically extract entities and relationships from URLs, PDFs, and YouTube videos to build structured knowledge graphs in Neo4j. It supports custom schemas, academic citation extraction, and community detection for advanced research and content analysis.

Enables LLMs to store, search, and manage memories with hybrid semantic and keyword search using ChromaDB and Neo4j for persistent memory and knowledge graph capabilities.

Enables AI assistants across platforms to share memory, recall facts, preferences, and conversation history, creating a unified cognitive system.

MCP server that enables persistent, hybrid, local memory for LLM agents, with vector + BM25 search, knowledge graph, and policy-driven retention, providing token-budgeted context injection for AI assistants.

A generic MCP server that turns a directory of Markdown wiki pages into a network-reachable knowledge base, queryable and updatable by any MCP-compatible client.

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

Builds a local, citation-aware wiki from an approved project scope and exposes it as an MCP server with a single read-only tool for agents to explore the wiki.

Enables creation of persistent, compounding knowledge bases using Karpathy's LLM Wiki pattern with LLM-maintained markdown wikis. Supports automated ingestion, cross-referencing, synthesis, and linting of sources as an alternative to traditional RAG systems.

Provides persistent memory and semantic file discovery for AI coding agents, enabling them to remember changes and find relevant files across sessions.

Enables AI agents to interact with a persistent knowledge graph backend using MCP tools for reading, searching, and analyzing wiki pages with vector search and graph algorithms.

Enables querying a local wiki through MCP-style tools, including search, Q\&A, and knowledge map, with a web viewer for human browsing.