HeySearch

A privacy-respecting metasearch engine that aggregates results from multiple search engines.

Inspired by SearXNG, built for the modern web.

Screenshots

Home	Web Search	Image Search

Why HeySearch over SearXNG?

Both are open-source, self-hosted, privacy-respecting metasearch engines. Here's why HeySearch is the better choice for most people:

	HeySearch	SearXNG
Setup	docker compose up -d — one command, zero config	Requires YAML config, engine tuning, sometimes breaks
UI	Modern, clean React UI with dark mode, background images, image lightbox	Functional but dated — not mobile friendly
AI agent friendly	MCP tool server at /api/mcp (Claude Desktop, Cursor, Continue), format=llm for minimal responses, clean JSON REST API, OpenAPI docs at /docs	API exists but less documented; HTML-heavy responses
Bookmarks	Built-in bookmark manager for results	❌
Search history	Full search history with timestamps, re-run any past query in one click	❌
Usage stats	Built-in analytics dashboard — top queries, click-through rates, engine usage	❌
Background gallery	Beautiful Unsplash/Picsum backgrounds on the home page	❌

TL;DR — If you want something you can run in 30 seconds, looks great, works well on your phone, and exposes a clean API for your AI tools, HeySearch is for you. If you need 70+ search engines and deep customisation, SearXNG has the edge.

Architecture

• Backend: Python / FastAPI — async metasearch orchestrator with retry logic
• Frontend: React + Vite + TypeScript + Tailwind CSS (shadcn theming) — mobile-first UI
• Docker: Multi-stage build (Node frontend build → Python runtime)

Prerequisites

• uv (Python package manager)
• Node.js >= 18
• Python >= 3.12
• Redis (optional — enables search result caching)

Running Locally

1. Quick start

# Without Redis (caching disabled)
just dev

# With Redis
REDIS_URL=redis://192.168.1.2:6399 just dev

2. Docker Compose

The easiest way to run HeySearch with Redis caching in one command:

docker compose up -d

Then open http://localhost:8000. Data and Redis are persisted in named volumes automatically.

3. Docker (standalone)

# Basic — data stored in anonymous volume
docker run -p 8000:8000 ghcr.io/wahyd4/hey-search

# Recommended — mount data directory for persistence
docker run -p 8000:8000 -v ./hey-search-data:/app/data ghcr.io/wahyd4/hey-search

# With Redis
docker run -p 8000:8000 -v ./hey-search-data:/app/data \
  -e REDIS_URL=redis://your-redis:6379 ghcr.io/wahyd4/hey-search

The /app/data volume stores the SQLite database (engine settings, excluded domains, cache config). Mount it to preserve your settings across container restarts.

API Endpoints

Method	Path	Description
GET, POST	/api/search	Search web or images
GET	/api/autocomplete?q=	Autocomplete suggestions
GET	/api/engines	List all search engines
PUT	/api/engines/{name}	Enable/disable an engine
GET	/api/settings	Get app settings (cache TTL)
PUT	/api/settings	Update settings
DELETE	/api/cache	Flush search cache
POST	/api/mcp	MCP tool server (for LLMs)

Search endpoint parameters

Parameter	Default	Description
q	required	Search query string
category	web	web or images
page	1	Page number (1–50)
pageNumber	—	Alias for page (takes precedence when provided)
max_results	—	Hard limit on results returned (1–100)
numResults	—	Alias for max_results
format	—	llm for minimal LLM-friendly response (see below)
imageProxy	—	Client image-proxy preference flag (informational)
safesearch	—	Safe search level: 0 off, 1 moderate, 2 strict
engines	—	Comma-separated engine names to restrict (e.g. google,bing)
image_size	—	large, medium, or small (images only)
sort	default	default, date_asc, or date_desc
date_filter	—	day, week, month, or year

Using with AI Agents / LLMs

HeySearch is designed to be used by LLMs and AI agents. There are two integration methods:

1. MCP Tool Server (recommended)

Model Context Protocol (MCP) is the standard for LLM tool use. Add HeySearch to any MCP-compatible client:

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "heysearch": {
      "url": "http://localhost:8000/api/mcp",
      "transport": "http"
    }
  }
}

Cursor / Continue / VS Code Copilot — add http://localhost:8000/api/mcp as an MCP server URL in the tool settings.

Manual test:

# List available tools
curl -X POST http://localhost:8000/api/mcp \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}'

# Call the search tool
curl -X POST http://localhost:8000/api/mcp \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"search","arguments":{"query":"python async","num_results":3}}}'

Available MCP tools: search, autocomplete

2. REST API with format=llm

For direct API calls from LLM agents, use format=llm to get a minimal, token-efficient response:

# LLM-optimised response — only title, url, snippet, date. No engine noise.
curl "http://localhost:8000/api/search?q=python+async&format=llm&max_results=5" | jq

Response shape:

{
  "query": "python async",
  "category": "web",
  "results": [
    { "title": "...", "url": "https://...", "snippet": "...", "date": "2024-01-15" }
  ],
  "total_results": 5
}

# Restrict to specific engines
curl "http://localhost:8000/api/search?q=rust+programming&engines=brave,google&format=llm" | jq

# Image search with size filter
curl "http://localhost:8000/api/search?q=mountain+landscape&category=images&image_size=large" | jq

# Limit results (hard limit, not a hint)
curl "http://localhost:8000/api/search?q=openai&max_results=3&format=llm" | jq

Interactive API docs (Swagger UI) are available at http://localhost:8000/docs.

Features

See FEATURES.md for the full feature list.

License

Hey Search is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0-or-later).

This project was inspired by and incorporates techniques from SearXNG (AGPL-3.0). See NOTICE for details on third-party attributions.

AGPL-3.0 in plain English: You can use, modify, and deploy this software freely. If you run a modified version as a public network service, you must make your modified source code available to your users.