pluggedin-app

Overview

plugged.in is an AI Content Management System that transforms ephemeral AI interactions into persistent, versioned, and searchable organizational knowledge. It acts as a central hub connecting various AI clients with your knowledge base and the broader MCP ecosystem. The platform features an embedded RAG vector engine for document processing and semantic search, multi-model collaboration tracking, and universal MCP integration with over 1,500 servers.

Try asking AI

After installing, here are 5 things you can ask your AI assistant:

• create_documentcreate_document(title: string, content: string, source?: string)

  Create a new document with version tracking and model attribution

  Try:Create a document called 'Project Analysis' with the content we discussedTry:Save this research as a new document titled 'Market Findings'
• query_ragquery_rag(query: string, limit?: number)

  Query the RAG knowledge base with semantic search

  Try:Find documents related to our authentication systemTry:Search for information about the latest API changes
• re_index_vectorsre_index_vectors(document_id?: string)

  Trigger re-indexing of document vectors for corrupted or missing embeddings

  Try:Re-index all document vectorsTry:Fix the vectors for the 'API Documentation' document
• list_clipboard_entrieslist_clipboard_entries(visibility?: 'private' | 'workspace' | 'public')

  List all stored clipboard entries with optional filtering by visibility

  Try:Show me all workspace clipboard entriesTry:List all public clipboard items
• get_clipboard_entryget_clipboard_entry(name: string)

  Retrieve a specific clipboard entry by name

  Try:Get the 'customer_context' clipboard entryTry:Show me the 'last_analysis' data
• set_clipboard_entryset_clipboard_entry(name: string, value: any, visibility?: 'private' | 'workspace' | 'public', ttl?: number)

  Store or update a clipboard entry with optional expiration and visibility settings

  Try:Save this analysis as 'current_analysis'Try:Store the config with 1-hour expiration
• pop_clipboard_entrypop_clipboard_entry(name: string)

  Remove and return the last entry from a named clipboard stack

  Try:Pop the last task from the 'todo' stackTry:Remove and return the latest analysis
• push_clipboard_entrypush_clipboard_entry(name: string, value: any)

  Add a new entry to a named clipboard stack

  Try:Push this task to the 'todo' stackTry:Add the new analysis to the history stack
• list_mcp_serverslist_mcp_servers(types?: string[], search?: string)

  Discover and list available MCP servers with advanced filtering options

  Try:Show all Python MCP serversTry:Find MCP servers related to data analysis
• test_mcp_servertest_mcp_server(server_id: string, tool_name: string, args?: any)

  Test a specific tool from an MCP server in the interactive playground

  Try:Test the 'query_postgres' tool from the analytics serverTry:Try the 'generate_report' function
• get_mcp_toolsget_mcp_tools(server_id: string)

  Retrieve all tools available from a specific MCP server

  Try:Show all tools from the GitHub integration serverTry:List available functions in the analytics server
• log_mcp_activitylog_mcp_activity(server_id: string, tool_name: string, request: any, response: any)

  Log detailed MCP interactions for debugging and analytics

  Try:Log this API call to the database serverTry:Record the interaction with the file system tool

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Installation

git clone https://github.com/VeriTeknik/pluggedin-app.git
cd pluggedin-app
cp .env.example .env

docker compose up --build -d

{
  "mcpServers": {
    "pluggedin": {
      "command": "docker",
      "args": ["run", "--rm", "-i", "veriteknik/pluggedin:latest", "mcp"]
    }
  }
}

FAQ

What platforms does plugged.in support?

plugged.in supports both amd64 and arm64 architectures via Docker, with automatic platform detection. It works with Claude Desktop, Cline, LM Studio, and other MCP-compatible clients.

How does plugged.in handle data storage and security?

The platform uses PostgreSQL 18 with pgvector for database storage and embedded zvec for vector search. All sensitive data is encrypted with AES-256-GCM, with per-profile encryption keys. It also supports OAuth 2.1 authentication.

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