Plugged.in

What is Plugged.in about?

Plugged.in is a unified platform that aggregates dozens of MCP servers, captures every AI interaction, and stores it as versioned, searchable documents. It acts like a Git‑style content management system for AI‑generated output, offering persistent memory, multi‑model attribution, and semantic RAG capabilities.

How to use Plugged.in?

1. Self‑host: Clone the repository, copy .env.example to .env, and start the stack with Docker Compose (docker compose up --build -d).
2. Cloud: Sign up at https://plugged.in for an instant hosted instance.
3. Connect MCP servers: Use the built‑in MCP Proxy or configure your own servers via the UI. Provide an API key and the platform will auto‑discover tools, manage OAuth flows, and expose them through a single endpoint.
4. Create & query documents: Through the web UI, SDKs (JS/TS, Python, Go) or the interactive playground you can generate, version, and search documents. RAG queries return relevant context in sub‑second latency.

Key Features of Plugged.in

• Persistent AI Memory: Every conversation is stored, versioned, and searchable.
• Multi‑Model Collaboration: Track contributions from Claude, GPT‑4, Gemini, etc., with full attribution.
• Universal MCP Integration: Supports 1,500+ MCP servers; auto‑discovery from GitHub, npm, Smithery.
• RAG Knowledge Base: Semantic search and AI‑augmented Q&A over stored documents.
• Enterprise‑Grade Security: AES‑256‑GCM encryption, per‑profile keys, OAuth 2.1, rate‑limiting, sandboxed execution.
• SDKs & Playground: JavaScript/TypeScript, Python, and Go SDKs; interactive UI for testing models and tools.
• Team Collaboration: Activity tracking, audit logs, email notifications, and real‑time alerts.
• Extensible Architecture: Horizontal scaling, Redis caching, PostgreSQL persistence, Docker‑ready deployment.

Use Cases of Plugged.in

• Developer Workflows: Store code reviews, documentation drafts, and debugging sessions with full history.
• Team Knowledge Bases: Build institutional memory from AI‑powered brainstorming, meeting summaries, and strategy documents.
• Enterprise Compliance: Maintain immutable audit trails for AI‑generated content, meeting regulatory requirements.
• Multi‑Model Pipelines: Orchestrate different AIs for complex tasks (e.g., Claude for design, GPT‑4 for implementation, Gemini for QA) while preserving context across hand‑offs.
• Self‑Hosted SaaS: Companies can run Plugged.in on‑premises to keep all data under their control.

FAQ from Plugged.in

Q: Do I need to run a separate MCP server? A: No. Plugged.in includes an MCP Proxy that aggregates existing MCP servers. You only need to provide API keys for the models you use.

Q: Can I run Plugged.in without Docker? A: Yes. Install Node.js 18+, PostgreSQL, and Redis, then follow the manual installation steps (pnpm install, pnpm build, pnpm start).

Q: How is data secured? A: All sensitive data is encrypted with AES‑256‑GCM. Each workspace gets its own encryption key, and communication uses TLS. Rate limiting and sandboxed execution protect against abuse.

Q: Which AI models are supported? A: Any model that implements the MCP, including Claude, GPT‑4, Gemini, and community‑hosted servers.

Q: How does RAG work? A: Documents are vectorized on ingest; queries are embedded and matched against the vector store, returning the most relevant passages which are then injected into the model prompt.

Q: Is there a free tier? A: The hosted version offers a free tier with limited storage and request limits. Self‑hosting is unrestricted aside from your own infrastructure costs.

plugged.in 🔌

Turn your AI conversations into permanent organizational memory

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

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🎯 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".

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✨ 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.

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📊 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
Search Performance: Sub-second RAG queries
Security: AES-256-GCM encryption, Redis rate limiting

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🚀 Quick Start

Docker (Recommended - 2 minutes)

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

# Start with Docker
docker compose up --build -d

# Visit http://localhost:12005

Cloud Version

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

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🌟 Key Features

📚 Document Management & Versioning

• Version Control: Track every change with Git-style history
• Model Attribution: Know which AI contributed what
• Smart Search: Semantic search across all documents
• Multiple Formats: PDF, Markdown, Code, Images, and more
• Dual Storage Display: View both file and RAG vector storage usage

🔧 MCP Server Hub

• 1,500+ Integrations: Connect to any MCP-compatible tool
• 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

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🏗️ 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:

1. Knowledge Base (RAG)

   • Semantic search across all documents
   • AI-powered question answering
   • Project-scoped isolation
   • Sub-second query performance

2. Document Store

   • Version control for AI-generated content
   • Multi-model attribution tracking
   • Content hash deduplication
   • Support for uploads, AI-generated, and API sources

3. MCP Registry

   • 1,500+ curated MCP servers
   • GitHub verification and claiming
   • Install tracking and trending
   • Auto-discovery from npm, GitHub, Smithery

4. 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:

1. Discuss architecture with Claude Desktop → Memory saved
2. Switch to ChatGPT connector → Previous context automatically available
3. Mobile app accesses same conversation history
4. 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
2. Content processed and sanitized
3. Model attribution recorded
4. Version created in Document Store
5. Vectors generated for RAG
6. Document searchable immediately

🔒 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

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📚 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

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🛠️ Installation Options

Requirements

• Node.js 18+ (20+ recommended)
• PostgreSQL 15+
• 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

# Email (For notifications)
EMAIL_SERVER_HOST=smtp.example.com
EMAIL_SERVER_PORT=587
EMAIL_FROM=noreply@example.com

# Performance (Optional)
RAG_CACHE_TTL_MS=60000  # Cache TTL in milliseconds

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

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🔌 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

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🎯 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

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📊 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

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🤝 Community & Support

• GitHub Discussions: Join the conversation
• GitHub Issues: Bug reports and feature requests
• Reddit: r/plugged_in
• Twitter/X: @PluggedIntoAI
• Email: team@plugged.in

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

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📜 License

MIT License - see LICENSE for details.

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🙏 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

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📝 Release Notes

Latest Release: v2.12.0 - Enhanced Security & Performance

View the full changelog and release notes at docs.plugged.in/releases

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Ready to give your AI permanent memory?

🚀 Start Now • ⭐ Star on GitHub

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