How do I install Ragstack?

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 Ragstack need an API key?

The publisher lists these environment variables: RAGSTACK_GRAPHQL_ENDPOINT, RAGSTACK_API_KEY.

What transport does Ragstack use?

Ragstack runs locally over stdio.

When was Ragstack last checked?

Last checked 2026-11-09. Search, chat, upload, and scrape a serverless RAGStack knowledge base on AWS.

Ragstack MCP server

Power your agent

Add this server

The publisher's own command. Not a PlugMCP endpoint.

Add to Cursor Add to VS Code Run on PlugMCP

Host a copy with our keys and logs.

Local command

uvx ragstack-mcp==0.1.4

Credentials from the publisher

RAGSTACK_GRAPHQL_ENDPOINT — Your RAGStack GraphQL API URL (from Dashboard → Settings)
RAGSTACK_API_KEY — Your RAGStack API key (from Dashboard → Settings)

Claude Code has no install link for a local command. Copy the command below and run it in a terminal.

shClaude Code

1RAGSTACK_GRAPHQL_ENDPOINT=<value> RAGSTACK_API_KEY=<value> claude mcp add io.github.hatmanstack-ragstack -- uvx ragstack-mcp==0.1.4

Run in a terminal.

jsonClaude Desktop

1{2  "mcpServers": {3    "io.github.hatmanstack-ragstack": {4      "command": "uvx",5      "args": [6        "ragstack-mcp==0.1.4"7      ],8      "env": {9        "RAGSTACK_GRAPHQL_ENDPOINT": "<value>",10        "RAGSTACK_API_KEY": "<value>"11      }12    }13  }14}

claude_desktop_config.json

Local · pypiPublisher installStep-by-step: add this in Claude

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

Run on PlugMCP

Host a copy with our keys and logs.

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.

Launch

Server facts

Local

Transport: stdio
Version: 0.1.4
API key

Tools

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

FAQ

Questions

☁️ Fully serverless architecture (Lambda, Step Functions, S3, DynamoDB)
🧠 NEW Amazon Nova multimodal embeddings for text and image vectorization
📄 Document processing & vectorization (PDF, images, Office docs, HTML, CSV, JSON, XML, EML, EPUB) → stored in managed knowledge base
🎬 NEW Video/audio processing - transcribe speech with AWS Transcribe, searchable by timestamp
💬 AI chat with retrieval-augmented context and source attribution
📎 Collapsible source citations with optional document downloads
⏱️ NEW Media sources with timestamp links - click to play at exact position
🔍 Metadata filtering - auto-discover document metadata and filter search results
🎯 Relevancy boost for filtered results - prioritize matches from metadata filters
🔄 Knowledge Base reindex - regenerate metadata for existing documents with updated settings
🗑️ Document management - reprocess, reindex, or delete documents from the dashboard
🌐 Web component for any framework (React, Vue, Angular, Svelte)
🚀 One-click deploy
💰 $7-10/month (1000 docs, Textract + Haiku)

Live Demo

Environment	URL	Credentials
Base Pipeline	dhrmkxyt1t9pb.cloudfront.net	`[email protected]` / `Guest@123`
Project Showcase	showcase-htt.hatstack.fun	Login as guest

Base Pipeline: The core document processing tool - upload, OCR, and query documents.

Project Showcase: See RAGStack powering a real application.

Quick Start

Option 1: One-Click Deploy (AWS Marketplace)

REPO IS IN ACTIVE DEVELOPMENT AND WILL CHANGE OFTEN

Deploy directly from the AWS Console - no local setup required:

Subscribe to RAGStack on AWS Marketplace (free, not required - If subscribed Lambda roles auto-accept Bedrock model agreements on first invocation)
Click here to deploy
Enter a stack name (lowercase only, e.g., "my-docs") and your admin email
Click Create Stack (deployment takes ~10 minutes)

After deployment:

Check your email for the temporary password (from Cognito)
Go to CloudFormation → your stack → Outputs tab to find the Dashboard URL (UIUrl)

Option 2: Deploy from Source

For customization or development:

Prerequisites:

AWS Account with admin access
Python 3.13+, Node.js 24+
uv (Python package manager)
AWS CLI, SAM CLI (configured)
Docker (for Lambda layer builds)

git clone https://github.com/HatmanStack/RAGStack-Lambda.git
cd RAGStack-Lambda

# Install dependencies
uv sync

# Deploy (defaults to us-east-1 for Nova Multimodal Embeddings)
python publish.py \
  --stack-name my-docs \
  --admin-email [email protected]

Option 3: Nested Stack Deployment

Deploy RAGStack as part of a larger CloudFormation stack. See Nested Stack Deployment Guide for details.

Quick example:

Resources:
  RAGStack:
    Type: AWS::CloudFormation::Stack
    Properties:
      TemplateURL: https://ragstack-quicklaunch-public.s3.us-east-1.amazonaws.com/ragstack-template.yaml
      Parameters:
        StackPrefix: 'my-app-ragstack'  # Required: lowercase prefix
        AdminEmail: [email protected]

Web Component Integration

See RAGSTACK_CHAT.md for web component integration guide.

API Access

Server-side integrations use API key authentication. Get your key from Dashboard → Settings.

curl -X POST 'YOUR_GRAPHQL_ENDPOINT' \
  -H 'x-api-key: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{"query": "query { searchKnowledgeBase(query: \"...\") { results { content } } }"}'

Web component uses IAM auth (no API key needed - handled automatically).

Each UI tab shows server-side API examples in an expandable section.

MCP Server (AI Assistant Integration)

Use your knowledge base directly in Claude Desktop, Cursor, VS Code, Amazon Q CLI, and other MCP-compatible tools.

# Install (or use uvx for zero-install)
pip install ragstack-mcp

Add to your AI assistant's MCP config:

{
  "ragstack-kb": {
    "command": "uvx",
    "args": ["ragstack-mcp"],
    "env": {
      "RAGSTACK_GRAPHQL_ENDPOINT": "YOUR_ENDPOINT",
      "RAGSTACK_API_KEY": "YOUR_API_KEY"
    }
  }
}

Then ask naturally: "Search my knowledge base for authentication docs"

See MCP Server docs for full setup instructions.

Architecture

Upload → OCR → Embeddings → Bedrock KB
                                ↓
 Web UI (Dashboard + Chat) ←→ GraphQL API
                                ↓
 Web Component ←→ AI Chat with Sources

Usage

Documents

Upload documents in various formats. Auto-detection routes to optimal processor:

Type	Formats	Processing
Text	HTML, TXT, CSV, JSON, XML, EML, EPUB, DOCX, XLSX	Direct extraction with smart analysis
OCR	PDF, JPG, PNG, TIFF, GIF, BMP, WebP, AVIF	Textract or Bedrock vision OCR (WebP/AVIF require Bedrock)
Media	MP4, WebM, MP3, WAV, M4A, OGG, FLAC	AWS Transcribe → 30s segments → searchable with timestamps
Passthrough	Markdown (.md)	Direct copy

Processing time: UPLOADED → PROCESSING → INDEXED (typically 1-5 min for text, 2-15 min for OCR, 5-20 min for media)

Images

Upload JPG, PNG, GIF, WebP with captions. Both visual content and caption text are searchable.

Web Scraping

Scrape websites into the knowledge base. See Web Scraping.

Video & Audio

Upload MP4, WebM, MP3, WAV, M4A, OGG, or FLAC files. Speech is transcribed using AWS Transcribe and segmented into 30-second chunks for search. Sources include timestamps (e.g., "1:30-2:00") with clickable links that play at the exact position.

Features:

Speaker diarization (identify who said what)
Configurable language (30+ languages supported)
Timestamp-linked sources in chat responses

See Configuration for language and speaker settings.

Chat

Ask questions about your content. Sources show where answers came from.

Documentation

Configuration - Settings, quotas, API keys & document management
Nested Stack Deployment - Deploy as part of larger CloudFormation stack
Image Upload - Image upload and captioning
Web Scraping - Scrape websites
Metadata Filtering - Auto-discover metadata and filter results
Chat Component - Embed chat anywhere
API Reference - GraphQL API documentation
Architecture - System design & API reference
Development - Local dev
Migration - Version migration guide
Troubleshooting - Common issues
Library Reference - Public API for lib/ragstack_common

Development

npm run check  # Lint + test all (backend + frontend)

Deployment Options

Direct Deployment

# Full deployment (defaults to us-east-1)
python publish.py --stack-name myapp --admin-email [email protected]

# Skip dashboard build (still builds web component)
python publish.py --stack-name myapp --admin-email [email protected] --skip-ui

# Skip ALL UI builds (dashboard and web component)
python publish.py --stack-name myapp --admin-email [email protected] --skip-ui-all

# Enable demo mode (rate limits: 5 uploads/day, 30 chats/day; disables reindex/reprocess/delete)
python publish.py --stack-name myapp --admin-email [email protected] --demo-mode

Publish to AWS Marketplace (Maintainers)

To update the one-click deploy template:

python publish.py --publish-marketplace

This packages the application and uploads to S3 for one-click deployment.

Note: Currently requires us-east-1 (Nova Multimodal Embeddings). When available in other regions, use --region <region>.

Acknowledgments

This project was inspired by:

Accelerated Intelligent Document Processing on AWS - AWS Solutions Library reference architecture
docs-mcp-server - MCP server for documentation search