plugged.in 🔌

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Turn your AI conversations into permanent organizational memory

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

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🧩 Now Multi‑Arch Ready!
Plugged.in Docker images support both amd64 and arm64 architectures via a unified manifest.
🧠 v3.0.0 — Embedded RAG Vector Engine:
RAG now runs fully in-process using zvec (RocksDB + HNSW). No external services needed — document processing, chunking, and semantic search are all built-in.
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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
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)

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 at minimum NEXTAUTH_SECRET (generate one with:
  openssl rand -base64 32
). The bundled PostgreSQL/Redis URLs already work out of the box.
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.

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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

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