plugged.in 🔌

🎯 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
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

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🚀 Quick Start

Docker (Recommended - 2 minutes)

Supported Architectures

Plugged.in Docker images are multi-architecture (amd64 and arm64) and will automatically select the correct platform for your system.

Important Notes:

• ✅ Docker Compose automatically pulls the correct architecture - no manual configuration needed
• ✅ Works seamlessly on mixed architectures - run the same docker-compose.yml on any platform
• ✅ No performance penalty - native builds for both AMD64 and ARM64

To verify which platforms are available, run:

docker manifest inspect veriteknik/pluggedin:latest --verbose | jq '.manifests[].platform'

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

# Start with Docker (includes PostgreSQL 18 + pgvector)
docker compose up --build -d

# Visit http://localhost:12005

What's included:

• ✅ PostgreSQL 18 with pgvector extension for vector search
• ✅ 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-app: Main web application (port 12005)
  - pluggedin-postgres: PostgreSQL 18 + pgvector database (port 5432)
  - drizzle-migrate: One-time migration runner (auto-stops)

Volumes:
  - pluggedin-postgres: Database 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.

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🌟 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

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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 (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

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 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

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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+ (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_FROM=noreply@example.com

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: v3.0.0 — Embedded RAG Vector Engine

🎯 What's New in v3.0.0

This is a major architectural shift that eliminates the external FastAPI/Milvus dependency for RAG.

🧠 Embedded RAG Vector Engine (Breaking Change)

• In-process vector search via zvec (RocksDB + HNSW) — zero external dependencies
• Shared vector infrastructure (lib/vectors/) with domain-based collections
• Server-side PDF extraction using unpdf — no external service needed
• Smart text chunking with configurable overlap and separators
• Model-to-dimension validation — detects misconfigured embedding models at startup
• Re-index menu option in library UI for recovering from vector corruption
• Relevance-ordered results with query term highlighting

🔒 Security Hardening

• Filter injection prevention: Field-name allowlist + per-type value validation (UUID/alphanumeric regex)
• Path traversal protection: External ZVEC_DATA_PATH blocked unless explicitly opted in
• Corruption recovery: Collections backed up to .bak before recreation with stats logging
• Idempotent document processing: Prevents orphaned rows on retry

🗑️ Dead Code Removal

• Removed ~750 lines of upload polling infrastructure (no longer needed with synchronous processing)
• Removed UploadProgressContext, UploadProgressToast, upload-status API route

⚠️ Migration Notes

• Docker image changed to pgvector/pgvector:pg18 — back up your database before upgrading
• New env vars: ZVEC_DATA_PATH, EMBEDDING_MODEL, RAG_SEARCH_TOP_K, RAG_CACHE_TTL_MS
• Removed env var: RAG_API_URL
• Run pnpm db:migrate after upgrading (3 new migrations)

View the full changelog at GitHub Releases

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