How do I install Pluggedin App?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Pluggedin App need an API key?

No required environment variable is listed on this record.

What transport does Pluggedin App use?

Pluggedin App runs locally over stdio.

Pluggedin App MCP サーバー

Q: When was Pluggedin App last checked?

Last checked 2026-10-27. 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.

エージェントを強化

このサーバーを追加

Install from the publisher. Not a PlugMCP endpoint.

PlugMCP で実行

キーとログ付きでコピーをホスト。

No packaged release in the registry yet. Follow the repository README.

RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

PlugMCP で実行

キーとログ付きでコピーをホスト。

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

起動

Server facts

Local

Transport: stdio
License: MIT
Last checked: 2026-10-27

Overview

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.

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

plugged.in 🔌

[!IMPORTANT] Project status (October 2026): open source, community-driven. plugged.in, the hosted service, is moving to a new, separately developed platform. After that move, this repository will no longer run plugged.in. The code stays open source under its current license, and the community is welcome to carry it on: issues, pull requests, forks and new maintainers are welcome, and self-hosting remains supported. The cutover date will be announced here. At the cutover, the current service moves to v1.plugged.in and keeps running there until December 31, 2026. What this means

plugged.in Logo

Turn your AI conversations into permanent organizational memory

🚀 Get Started • 📚 Documentation • 🌟 Features • 💬 Community

🧩 Now Multi‑Arch Ready! Plugged.in Docker images support both `amd64` and `arm64` architectures via a unified manifest. 🧭 v4.0.0 — Hubs, a hosted MCP connector, and a security pass: Workspaces are Hubs now, one Workspace per Hub. `/api/mcp` serves a hosted connector behind an OAuth 2.1 server. Five reported advisories fixed and credited.

🎯 The Problem We Solve

Every day, you have brilliant conversations with AI - strategy sessions with GPT-4, code reviews with Claude, analysis with Gemini. But when you close that chat window, all that knowledge vanishes. This is the "AI knowledge evaporation" problem.

💡 The Solution

plugged.in is the world's first AI Content Management System (AI-CMS) - a platform that transforms ephemeral AI interactions into persistent, versioned, and searchable organizational knowledge.

Think of it as "Git for AI-generated content" meets "WordPress for AI interactions".

✨ What Makes plugged.in Special

🧠 AI Memory That Persists

Your AI conversations become permanent assets. Every document is versioned, attributed, and searchable.

🤝 Multi-Model Collaboration

Claude writes v1, GPT-4 adds technical specs in v2, Gemini refines in v3 - all tracked and attributed.

🔌 Universal MCP Integration

Works with 1,500+ MCP servers. Connect any tool, any AI, any workflow - all through one interface.

🔒 Enterprise-Grade Security

End-to-end encryption, OAuth 2.1, rate limiting, and sandboxed execution for your peace of mind.

📊 Real Platform Statistics

Documents Managed: 90+ (72% AI-generated)
Integrated MCP Servers: 1,568
Active Versioning: Documents with up to 4 iterations
Model Attributions: 17 different AI models tracked
RAG Engine: Embedded zvec (RocksDB + HNSW) — zero external dependencies
Search Performance: Sub-second semantic search with cosine similarity
Security: AES-256-GCM encryption, Redis rate limiting

🚀 Quick Start

Docker (Recommended - 2 minutes)

Self-hosting builds the image locally

The default docker-compose.yml builds the app image from source (./Dockerfile) for your platform. There is intentionally no prebuilt self-host image: the embedded zvec vector engine needs build-time flags/bindings that a generic registry image can't carry, so the image is compiled locally with the right supportedArchitectures configuration.

The first docker compose up --build takes roughly 10–15 minutes (installing dependencies + next build). Subsequent starts are instant — the image is cached. Requirements: Docker with BuildKit (Docker Desktop, or docker buildx) and at least ~8 GB of memory allotted to the Docker engine — next build is memory-hungry and will abort with "JavaScript heap out of memory" on smaller limits (raise it in Docker Desktop → Settings → Resources).

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

# Edit .env and set these three. Compose refuses to start without the last two,
# so there is no way to end up running with a password this repository knows.
#   NEXTAUTH_SECRET=$(openssl rand -base64 32)
#   POSTGRES_PASSWORD=$(openssl rand -hex 32)
#   REDIS_PASSWORD=$(openssl rand -hex 32)
#
# Use hex (or another URL-safe value) for the last two: they are interpolated
# into a postgresql:// / redis:// URL, so @ : / ? # would corrupt it.
# Compose builds DATABASE_URL and REDIS_URL from them — leave those alone.

# Build the app image and start the full stack (PostgreSQL 18 + pgvector,
# Redis, one-shot migration, then the app):
docker compose up --build -d

# Follow startup (migration runs first, then the app becomes healthy):
docker compose logs -f pluggedin-app

# Visit http://localhost:12005

Useful commands:

docker compose ps                       # service status
docker compose run --rm pluggedin-migrate   # re-run database migrations
docker compose down                     # stop (keeps data)
docker compose down -v                  # stop + wipe all volumes

Production / hosted deployments use a different file — this docker-compose.yml is the self-host default, not the production stack:

docker-compose.production.yml — pulls the prebuilt veriteknik/pluggedin image (the hosted/cloud build)

infra/docker-compose.yml — the Traefik + SOPS production stack used by plugged.in

What's included:

✅ PostgreSQL 18 with pgvector extension for vector search
✅ Redis 7 for rate limiting / caching
✅ Embedded zvec vector engine for RAG (no external Milvus/Qdrant needed)
✅ Next.js 15 web application with optimized production build
✅ Persistent volumes for database, uploads, vectors, and logs
✅ Health checks and automatic restarts

Docker Architecture:

Services:
  - pluggedin-migrate:  One-shot DB migration; reuses the app image, then exits
  - pluggedin-app:      Main web application (host port 12005 → container 3000),
                        starts only after migration completes successfully
  - pluggedin-postgres: PostgreSQL 18 + pgvector database (port 5432)
  - pluggedin-redis:    Redis 7 (port 6379)

Volumes:
  - pluggedin-postgres: Database data (persistent)
  - pluggedin-redis:    Redis append-only data (persistent)
  - zvec-data:          Vector collections (persistent)
  - app-uploads:        User uploaded files (persistent)
  - app-logs:           Application logs (persistent)
  - mcp-cache:          MCP package cache (persistent)

Upgrading from older versions:

# If upgrading from PostgreSQL 16 or earlier
# Option 1: Fresh start (data loss)
docker compose down -v && docker compose up --build -d

# Option 2: Migrate existing data
docker exec pluggedin-postgres pg_dump -U pluggedin pluggedin > backup.sql
docker compose down -v
docker compose up -d
docker exec -i pluggedin-postgres psql -U pluggedin -d pluggedin < backup.sql

Cloud Version

Visit plugged.in for instant access - no installation required.

🌟 Key Features

📚 Document Management & RAG

Embedded Vector Search: In-process zvec engine (RocksDB + HNSW) — no external services needed
Automatic Indexing: Upload PDFs or text, automatically chunked, embedded, and indexed
Version Control: Track every change with Git-style history
Model Attribution: Know which AI contributed what
Semantic Search: Query your knowledge base with natural language
Re-index Recovery: One-click re-indexing from the library UI for corrupted vectors

🔧 MCP Server Hub

1,500+ Integrations: Connect to any MCP-compatible tool
Advanced Multi-Select Filtering: Filter by multiple package types simultaneously (npm, PyPI, Docker, MCPB, NuGet)
Smart Search: Intelligent filtering with real-time result counts and validation
Auto-Discovery: Find and configure servers from GitHub, npm, Smithery
Registry Integration: Claim and manage servers with GitHub credentials
Unified Interface: One API key, all your tools
Tool Prefixing: Automatic namespace management prevents conflicts
OAuth Support: Server-side OAuth handling for MCP servers

🎮 Interactive Playground

Test Any Model: Claude, GPT-4, Gemini, and more
Live Debugging: See real-time MCP interactions
RAG Integration: Use your documents as context
Custom Instructions: Per-server configuration
Extensive Logging: Detailed debugging capabilities

🔔 Real-Time Intelligence

Activity Tracking: Monitor all MCP operations
Email Notifications: Stay informed about important events
Trending Analytics: See what tools are popular
Audit Logs: Complete activity history
Bidirectional Notifications: Send, receive, mark as read

🔐 Security First

End-to-End Encryption: AES-256-GCM for all sensitive data
Per-Profile Encryption: Isolated encryption keys per workspace
OAuth 2.1: Modern authentication flows
Sandboxed Execution: Firejail isolation on Linux
Redis Rate Limiting: Advanced DDoS protection with fallback
LRU Cache: Memory-efficient caching with automatic eviction
Enhanced Password Security: Bcrypt cost factor 14 (16,384 iterations)
Dynamic CSP Nonces: Cryptographically secure Content Security Policy
Security Headers: HSTS, X-Frame-Options, X-Content-Type-Options

🏢 Hub & Workspace Management

Multi-Hub Architecture: Organize projects into separate hubs
Workspace Isolation: Each hub contains multiple isolated workspaces
Smart Hub Switching: Automatic workspace selection when switching hubs
Data Scoping: Hub-level and workspace-level data isolation
Current Hub Display: Dashboard shows active hub for context awareness

📋 Clipboard & Memory System

Named Entries: Store key-value pairs with semantic names (e.g., customer_context, last_analysis)
Indexed Entries: Array-like storage with auto-incrementing indices for ordered data
Stack Operations: Push/pop functionality for LIFO workflows and pipelines
Rich Content Support: Store text, JSON, images (base64), and any content type up to 2MB
TTL Expiration: Optional time-to-live for automatic cleanup of temporary data
Visibility Controls: Private, workspace, or public visibility per entry
Model Attribution: Track which AI model created each clipboard entry
MCP Integration: Access clipboard via 6 built-in MCP tools from any AI client

🏗️ Architecture

plugged.in Architecture

Architecture Overview

plugged.in acts as the central hub connecting various AI clients, development tools, and programming languages with your knowledge base and the broader MCP ecosystem. The architecture is designed for maximum flexibility and extensibility.

🟢 Production-Ready Integrations (Solid Lines)

MCP Proxy Interface

The MCP Proxy serves as a unified gateway that aggregates multiple MCP servers into a single interface:

Claude Code - Official Anthropic CLI for Claude with native MCP support
Cline - VS Code extension for AI-assisted development
LM Studio - Local model execution with MCP integration
Claude Desktop - Anthropic's desktop application

How it works: Each AI client connects via STDIO, receiving access to all your configured MCP servers through one connection. The proxy handles:

Tool prefixing to prevent namespace conflicts
OAuth authentication for MCP servers
Activity logging and notifications
Unified error handling

SDK Support - Multi-Language Integration

Direct programmatic access through official SDKs:

JavaScript/TypeScript SDK (@pluggedin/sdk) - Full-featured SDK for Node.js and browser
Python SDK (pluggedin-sdk) - Pythonic interface for AI workflows
Go SDK (pluggedin-go) - High-performance Go implementation

Use Cases:

// Create documents programmatically
const doc = await client.documents.create({
  title: "API Analysis",
  content: "...",
  source: "api"
});

// Query RAG knowledge base
const results = await client.rag.query("How do we handle auth?");

Platform Core Features

The plugged.in web platform provides:

Knowledge Base (Embedded RAG)
- In-process vector search via zvec (RocksDB + HNSW) — no external services
- Automatic PDF/text chunking, embedding, and indexing
- Project-scoped isolation with per-field filter validation
- Sub-second cosine similarity search
Document Store
- Version control for AI-generated content
- Multi-model attribution tracking
- Content hash deduplication
- Support for uploads, AI-generated, and API sources
MCP Registry
- 1,500+ curated MCP servers
- GitHub verification and claiming
- Install tracking and trending
- Auto-discovery from npm, GitHub, Smithery
Tools Management
- Discover tools from all connected servers
- Test tools in interactive playground
- Custom instructions per server
- Real-time debugging logs

🟡 In Development (Dashed Lines)

Native Connectors

Direct integrations bypassing the MCP Proxy for enhanced performance:

Plugged.in Connector for Claude Desktop - Native plugin architecture
Plugged.in Connector for ChatGPT - OpenAI plugin integration
Mobile App - iOS and Android native apps

Why Native Connectors?

Faster response times (no proxy overhead)
Richer UI integration
Platform-specific features
Offline capabilities

Advanced Memory System

Persistent memory across sessions:

Knowledge + Memory boxes in the diagram represent:
- Session continuity across different AI clients
- Automatic context injection based on conversation history
- Cross-model memory sharing (Claude remembers what GPT discussed)
- Smart context pruning to stay within token limits

Example Workflow:

Discuss architecture with Claude Desktop → Memory saved
Switch to ChatGPT connector → Previous context automatically available
Mobile app accesses same conversation history
RAG provides relevant documents automatically

📊 Data Flow Example

User Request Flow:

1. User asks question in Claude Desktop
2. MCP Proxy receives request
3. Proxy checks RAG for relevant context
4. Combines context + user question
5. Routes to appropriate MCP servers
6. Aggregates responses
7. Logs activity to database
8. Returns enriched response to user

Document Creation Flow:

1. AI generates document via SDK or user uploads PDF/text
2. Content processed, chunked, and sanitized
3. Model attribution recorded
4. Version created in Document Store
5. Chunks stored in PostgreSQL, embeddings indexed in zvec
6. Document searchable immediately via semantic search

🔒 Security Architecture

All connections use:

End-to-end encryption (AES-256-GCM)
Per-profile encryption keys (workspace isolation)
Redis rate limiting with memory fallback
Sandboxed execution (Firejail on Linux)
OAuth 2.1 for external services

📈 Scalability

The architecture supports:

Horizontal scaling of MCP Proxy instances
Database connection pooling for PostgreSQL
Redis caching for frequently accessed data
CDN integration for static assets
Background job processing for heavy operations

📚 Documentation

Visit our comprehensive documentation at docs.plugged.in

For Users

Getting Started - Platform overview and quick start
Installation Guide - Step-by-step setup instructions
Document Library - Managing your AI knowledge base
RAG Knowledge Base - Setting up RAG for AI context
Team Collaboration - Working with your team

For Developers

API Reference - Complete API documentation
API Authentication - API key and authentication guide
Self-Hosting Guide - Deploy your own instance
Docker Deployment - Container-based deployment
Security Overview - Security best practices

MCP Integration

MCP Proxy Overview - Understanding the proxy architecture
MCP Proxy Installation - Setting up the proxy
Custom MCP Servers - Building your own servers

🛠️ Installation Options

Requirements

Node.js 18+ (20+ recommended)
PostgreSQL 15+ (18+ recommended)
Redis (optional, for rate limiting)
Docker & Docker Compose (for containerized deployment)

Environment Variables

Create a .env file with:

# Core (Required)
DATABASE_URL=postgresql://user:pass@localhost:5432/pluggedin
NEXTAUTH_URL=http://localhost:12005
NEXTAUTH_SECRET=your-secret-key # Generate: openssl rand -base64 32

# Security (Required)
NEXT_SERVER_ACTIONS_ENCRYPTION_KEY= # Generate: openssl rand -base64 32

# Features (Optional)
ENABLE_RAG=true
ENABLE_NOTIFICATIONS=true
ENABLE_EMAIL_VERIFICATION=true
REDIS_URL=redis://localhost:6379  # For Redis rate limiting

# RAG / Vector Search
ZVEC_DATA_PATH=./data/vectors     # Path to zvec collection files
EMBEDDING_MODEL=                   # OpenAI model (default: text-embedding-3-small)
RAG_SEARCH_TOP_K=5                 # Chunks returned per query
RAG_CACHE_TTL_MS=60000             # Storage stats cache TTL

# Email (For notifications)
EMAIL_SERVER_HOST=smtp.example.com
EMAIL_SERVER_PORT=587
[email protected]

Manual Installation

# Install dependencies
pnpm install

# Setup database
pnpm db:migrate:auth
pnpm db:generate
pnpm db:migrate

# Build for production
NODE_ENV=production pnpm build

# Start the server
pnpm start

🔌 MCP Proxy Integration

Connect your AI clients to plugged.in:

Claude Desktop

{
  "mcpServers": {
    "pluggedin": {
      "command": "npx",
      "args": ["-y", "@pluggedin/pluggedin-mcp-proxy@latest"],
      "env": {
        "PLUGGEDIN_API_KEY": "YOUR_API_KEY"
      }
    }
  }
}

Cursor IDE

npx -y @pluggedin/pluggedin-mcp-proxy@latest --pluggedin-api-key YOUR_API_KEY

🎯 Use Cases

For Developers

Code Review Memory: Keep AI code reviews across sessions
Documentation Generation: Auto-generate and version technical docs
Bug Analysis Archive: Store AI debugging sessions for future reference

For Teams

Knowledge Base: Build institutional memory from AI interactions
Meeting Summaries: AI-generated summaries with full attribution
Strategy Documents: Collaborative AI-assisted planning with version control

For Enterprises

Compliance Tracking: Full audit trail of AI-generated content
Multi-Model Workflows: Orchestrate different AIs for complex tasks
Secure Deployment: Self-host with complete data control

📊 Why Teams Choose plugged.in

Feature	plugged.in	Traditional AI Chat	MCP Clients Alone
Persistent Memory	✅ Full versioning	❌ Session only	❌ No storage
Multi-Model Support	✅ All models	⚠️ Single vendor	✅ Multiple
Document Management	✅ Complete CMS	❌ None	❌ None
Attribution Tracking	✅ Full audit trail	❌ None	❌ None
Team Collaboration	✅ Built-in	❌ None	❌ Limited
Self-Hostable	✅ Yes	⚠️ Varies	✅ Yes
RAG Integration	✅ Native	⚠️ Limited	❌ None

🤝 Community & Support

GitHub Discussions: Join the conversation
GitHub Issues: Bug reports and feature requests
Reddit: r/plugged_in
Twitter/X: @PluggedIntoAI
Email: [email protected]

Contributing

We love contributions! See our Contributing Guide for details.

# Fork the repo, then:
git clone https://github.com/YOUR_USERNAME/pluggedin-app.git
cd pluggedin-app
pnpm install
pnpm dev

📜 License

MIT License - see LICENSE for details.

🙏 Acknowledgments

Built on top of these amazing projects:

Model Context Protocol by Anthropic
Next.js for the web framework
PostgreSQL for reliable data storage
All the MCP server creators in our community

📝 Release Notes

Latest Release: v4.0.0 — Hubs, a hosted MCP connector, and a security pass

🎯 What's New in v4.0.0

Major by Semantic Versioning: two features were removed and the data model under Hubs changed.

🧭 Workspaces are Hubs (Breaking Change)

Every Workspace is its own Hub, one Workspace per Hub; the Workspace switcher is gone
Existing Workspaces were promoted in place — no row of user data moved
mcp_servers.slug is untouched, so saved instructions naming {slug}__{tool} still resolve

🔌 Hosted MCP connector

/api/mcp serves the connector, with an OAuth 2.1 authorization server in front of it
PKCE, refresh-token rotation with reuse detection, RFC 9728 discovery
Library tools exposed over MCP, behind a Hub boundary a caller cannot step around

🔒 Security

Five advisories fixed and published, each credited to its reporter — see Acknowledgements
Command injection in the pnpm/uv install path and in testMcpConnection
SSRF guards now classify the parsed address rather than matching hostname text, and safeFetch connects to the address it validated instead of re-resolving
Server actions no longer trust a caller-supplied identity

🐳 Deployment

Docker + Traefik + SOPS; secrets are decrypted to tmpfs at deploy time
infra/scripts/deploy.sh is the entry point — running compose by hand skips decryption
Scheduled retention for build artefacts, after a 4 GB-per-push image build filled the disk

🗑️ Removed

Discover / AI Social. The sharing infrastructure underneath it stays

⚠️ Migration Notes

Run pnpm db:migrate after upgrading
Users who hit the duplicate-Hub race will see the second one renamed to "Default Hub 2"; nothing is merged and nothing is deleted

View the full changelog at GitHub Releases

Ready to give your AI permanent memory?

🚀 Start Now • ⭐ Star on GitHub

If this project inspires you, please ⭐ Star it —
stars power the community and keep development momentum alive!

このサーバーを追加

キーとログ付きでコピーをホスト。

Server facts

About Pluggedin App

Tools

Questions

How do I install Pluggedin App?

Does Pluggedin App need an API key?

What transport does Pluggedin App use?

When was Pluggedin App last checked?

Documentation

plugged.in 🔌

🎯 The Problem We Solve

💡 The Solution

✨ What Makes plugged.in Special

🧠 AI Memory That Persists

🤝 Multi-Model Collaboration

🔌 Universal MCP Integration

🔒 Enterprise-Grade Security

📊 Real Platform Statistics

🚀 Quick Start

Docker (Recommended - 2 minutes)

Self-hosting builds the image locally

Cloud Version

🌟 Key Features

📚 Document Management & RAG

🔧 MCP Server Hub

🎮 Interactive Playground

🔔 Real-Time Intelligence

🔐 Security First

🏢 Hub & Workspace Management

📋 Clipboard & Memory System

🏗️ Architecture

Architecture Overview

🟢 Production-Ready Integrations (Solid Lines)

MCP Proxy Interface

SDK Support - Multi-Language Integration

Platform Core Features

🟡 In Development (Dashed Lines)

Native Connectors

Advanced Memory System

📊 Data Flow Example

🔒 Security Architecture

📈 Scalability

📚 Documentation

For Users

For Developers

MCP Integration

🛠️ Installation Options

Requirements

Environment Variables

Manual Installation

🔌 MCP Proxy Integration

Claude Desktop

Cursor IDE

🎯 Use Cases

For Developers

For Teams

For Enterprises

📊 Why Teams Choose plugged.in

🤝 Community & Support

Contributing

📜 License

🙏 Acknowledgments

📝 Release Notes

🎯 What's New in v4.0.0