Pluggedin App

Name: Pluggedin App
Author: VeriTeknik

@VeriTeknik

About Pluggedin App

The Crossroads for AI Data Exchanges. A unified, self-hostable web interface for discovering, configuring, and managing Model Context Protocol (MCP) servers—bringing together AI tools, workspaces, prompts, and logs from multiple MCP sources (Claude, Cursor, etc.) under one roof.

#pluggedin #mcp #ai-tools

Basic information

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "pluggedin-app": {
      "command": "docker",
      "args": [
        "compose",
        "up",
        "--build",
        "-d"
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Pluggedin App?

Pluggedin App is the world's first AI Content Management System (AI-CMS) that transforms ephemeral AI conversations into permanent, versioned, and searchable organizational memory. It integrates with 1,500+ MCP servers and supports multiple AI models (Claude, GPT-4, Gemini) to store, attribute, and retrieve AI-generated content.

How to use Pluggedin App?

Self‑host via Docker using docker compose up --build -d (requires Docker with BuildKit and ~8 GB RAM for the initial build), or use the cloud version at plugged.in with zero installation. After setup, configure MCP servers, upload documents, and interact via the web UI or SDKs (JavaScript/TypeScript, Python, Go).

Key features of Pluggedin App

AI memory persistence with full versioning and model attribution
Multi‑model collaboration (Claude, GPT‑4, Gemini) in one document
Universal MCP integration with 1,500+ servers
Embedded RAG vector engine (zvec) – no external services
Git‑style document version control
Enterprise‑grade security (AES‑256‑GCM, OAuth 2.1, sandboxed execution)

Use cases of Pluggedin App

Store and search AI‑generated analyses, code reviews, and strategy documents
Track contributions from multiple AI models per document (e.g., Claude v1, GPT‑4 v2)
Unify 1,500+ MCP tools through a single proxy interface
Self‑host a private AI knowledge base with built‑in semantic search
Use the clipboard/memory system for temporary data with TTL and MCP integration

FAQ from Pluggedin App

What is the difference between the self‑hosted and cloud versions?

Self‑hosted builds the app image from source (due to the embedded zvec engine) and requires Docker with BuildKit and ~8 GB RAM. The cloud version at plugged.in is instantly accessible with no installation – a prebuilt production image is used.

What are the system requirements for self‑hosting?

Docker with BuildKit (Docker Desktop or docker buildx), at least ~8 GB of memory allocated to the Docker engine for the next build, and the bundled PostgreSQL 18 with pgvector and Redis 7 images.

How does the RAG engine work?

The RAG engine is an embedded zvec vector store (RocksDB + HNSW) that runs fully in‑process – document processing, chunking, and semantic search all happen inside the application with no external Milvus or Qdrant dependencies.

Can I access Pluggedin App programmatically?

Yes. Official SDKs are available for JavaScript/TypeScript (@pluggedin/sdk), Python (pluggedin-sdk), and Go (pluggedin-go) to create documents, query the RAG knowledge base, and interact with MCP servers via code.

Is there a clipboard or temporary memory system?

Yes. The system supports named key‑value entries with TTL expiration, stack operations (push/pop), and visibility controls (private, workspace, public). It is accessible via 6 built‑in MCP tools from any AI client.