MCP servers that ship as code — a repository or a package you install and run locally (usually over stdio). They have no hosted endpoint, so we cannot probe or grade them: instead each one shows its repository, install command, license, stars and when it was last updated.
Collected from GitHub repositories and npm, PyPI, Cargo, Go, Maven, NuGet and RubyGems packages tagged as MCP servers, the official MCP Registry, Docker's MCP catalog, ModelScope, Baidu MCP World, Cline and other public directories; one entry per repository. A server that also offers a hosted endpoint is listed under Remote instead. These entries are not counted in our reachability, verification or readiness statistics.
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CLI and SDK for π — the RuVector shared brain. Share, search, and transfer learning across AI sessions.
npm MIT -
Decentralized AI Memory Network (DAMN) — a persistent learning and memory-sharing infrastructure for autonomous agents. Blockchain + IPFS-based memory layer enabling cross-agent experience reuse and scalable distributed intelligence.
★ 1 -
Mnemoverse Agent Memory for VS Code and Cursor: persistent memory for your coding agent that learns from outcomes, with shared rooms. Browser sign-in, no key to paste. MIT.
MIT ★ 1 -
spine-releasesgithub.com/sokactomas/spine-releasesupdated this year
Passive menu-bar shared brain for Claude Code, Codex and Antigravity: persistent memory, MCP hub, auto-learned skills, and zero-config advisor routing.
NOASSERTION -
Shared memory for AI agents over MCP: one agent contributes what it learned, another one finds it. Seven tools, PostgreSQL with pgvector, PII removal before storage.
MIT ★ 2 -
Self-learning memory for AI agents — experience captured automatically, distilled into lessons overnight, shared across your whole fleet. Works with Hermes, OpenClaw, Claude Code, and Pi.
npm NOASSERTION ★ 3 -
Shared memory for your team’s AI agents. What one agent learns, the others can find. Self-hosted MCP server on Postgres and pgvector, with plugins for Claude Code, Codex, Gemini CLI, Copilot CLI, OpenCode and more.
MIT -
**Persistent memory for AI agents that you can actually read.** Kybase stores what your agent learns as plain Markdown notes with `[[wikilinks]]`, searched by keyword and by meaning. It runs locally: notes live in an embedded Postgres on your machine (`~/.kybase`), with no Docker, no server and no account. **What your agent gets (18 tools)** - **Search** — `search_notes`: hybrid keyword + semantic search that returns the section each excerpt came from - **Read** — `get_note` (whole, windowed or one section), `list_notes`, `list_tags`, `list_folders`, `indexing_status` - **Write safely** — `create_note`, `append_to_note`, `replace_in_note`, `update_note` with conflict protection, `delete_note` / `restore_note` (trash) - **Link** — `get_backlinks`, `get_neighbors`, `get_graph` over the wikilink graph - **Organize** — `create_folder`, `update_folder`, `delete_folder` **Setup** Keyword search works right away. For search by meaning, pick an embedding provider in the settings: local **Ollama** (`ollama pull embeddinggemma`) keeps everything on your machine; **Google** or **OpenAI** need an API key and send note text to that provider. **Your data stays yours** `kybase-mcp export vault.zip` writes every note out as plain Markdown, folders as directories, readable in any editor, Obsidian included. **Want a UI?** The full Kybase app adds a web editor, a knowledge-graph view, share links, and one memory shared by Claude Code, claude.ai and Cursor: [github.com/Kyrzin/kybase](https://github.com/Kyrzin/kybase) Open source, AGPL-3.0.
smithery -
MCP server for Auxilo. Your agent stops solving the same problem twice: auto-extracted learnings, free self-unlocks, and earnings when other agents unlock yours.
npm MIT -
Shared Single-file memory layer for all your agents, sub mili-second RAG over text, photo and video on Apple Silicon.. No Server. No API. One File. Pure Swift
npm Apache-2.0 ★ 810 -
autologygithub.com/Curt-Park/autologyupdated this year
A Claude Code plugin that turns AI-assisted work into a shared living ontology. Captures decisions, conventions, and context autonomously into typed, interlinked knowledge nodes (git-committed markdown). Navigate the ontology as a graph, trace conceptual paths between ideas, and catch doc-code drift before every commit.
MIT ★ 25