Ragstack

Name: Ragstack
Author: HatmanStack

by HatmanStackversion 0.1.4i★ –iRuns on your computer

Registry nameio.github.HatmanStack/ragstack

Install

Overview

Description from the maker (original English)

Search, chat, upload, and scrape a serverless RAGStack knowledge base on AWS.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: RAGSTACK_GRAPHQL_ENDPOINT, RAGSTACK_API_KEY

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "ragstack": {
      "command": "uvx",
      "args": [
        "ragstack-mcp"
      ],
      "env": {
        "RAGSTACK_GRAPHQL_ENDPOINT": "your-value",
        "RAGSTACK_API_KEY": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add ragstack --env RAGSTACK_GRAPHQL_ENDPOINT=your-value --env RAGSTACK_API_KEY=your-value -- uvx ragstack-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "ragstack": {
      "command": "uvx",
      "args": [
        "ragstack-mcp"
      ],
      "env": {
        "RAGSTACK_GRAPHQL_ENDPOINT": "your-value",
        "RAGSTACK_API_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "ragstack": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "ragstack-mcp"
      ],
      "env": {
        "RAGSTACK_GRAPHQL_ENDPOINT": "your-value",
        "RAGSTACK_API_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

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

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account HatmanStack, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7233 servers in this category
Published to the registry more recently than 2% of the other 7233 servers in this category
Basic checks equal to or better than 100% of the other 7233 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Ragstack safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.4) reached the registry 9 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated 3 days ago

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 44 days ago

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Serverless document and media processing with AI chat. Scale-to-zero architecture — no vector database fees, no idle costs. Upload documents, images, video, and audio — extract text with OCR or transcription — query using Amazon Bedrock or your AI assistant via MCP.

QUESTIONS?

Features

☁️ 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	`guest@hatstack.fun` / `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 admin@example.com

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: admin@example.com

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 admin@example.com

Skip dashboard build (still builds web component)

python publish.py --stack-name myapp --admin-email admin@example.com --skip-ui

Skip ALL UI builds (dashboard and web component)

python publish.py --stack-name myapp --admin-email admin@example.com --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 admin@example.com --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 .

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

Ragstack

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

Features

Live Demo

Quick Start

Option 1: One-Click Deploy (AWS Marketplace)

Option 2: Deploy from Source

Install dependencies

Deploy (defaults to us-east-1 for Nova Multimodal Embeddings)

Option 3: Nested Stack Deployment

Web Component Integration

API Access

MCP Server (AI Assistant Integration)

Architecture

Usage

Documents

Images

Web Scraping

Video & Audio

Chat

Documentation

Development

Deployment Options

Direct Deployment

Skip dashboard build (still builds web component)

Skip ALL UI builds (dashboard and web component)

Enable demo mode (rate limits: 5 uploads/day, 30 chats/day; disables reindex/reprocess/delete)

Publish to AWS Marketplace (Maintainers)

Acknowledgments