Multi Engine Search for Agent
by @goldentrii
AI Agent search platform with 9 engines, Google 13 sub-types, vertical scene search, and intelligent auto/multi/extract modes. Designed for LLM and AI agent...
clawhub install novada-search📖 About This Skill
name: novada-search version: 1.0.8 author: Novada Labs description: "AI Agent search platform with 9 engines, Google 13 sub-types, vertical scene search, and intelligent auto/multi/extract modes. Designed for LLM and AI agent consumption." requiredEnv: NOVADA_API_KEY: description: "Novada Scraper API key (required for search/extract calls)" permissions: filesystem: - "./novada_search.py" - "./SKILL.md" - "./samples/*" - "./tests/*" - "./skill.json" - "./_meta.json" network: - "https://scraperapi.novada.com"
Novada Search v2.0
> Multi-engine AI search — 9 engines, 13 Google types, 9 vertical scenes, smart agent modes. > Powered by Novada Scraper API.
Get started in 30 seconds:
1. Get your free API key → novada.com
2. Set the key via environment or CLI: export NOVADA_API_KEY="your_key" (or pass --api-key $NOVADA_API_KEY)
3. Search: python3 {baseDir}/novada_search.py --query "coffee Berlin" --scene local
Agent-first + Human-friendly (Intelligent Distance)
This skill is optimized for agents first, then rendered for humans:
--format agent-json.
- Provides deterministic fields: engines_used, result_counts, duplicates_removed, unified_results, errors.
- Best for planning, tool-chaining, re-ranking, and downstream automation.--format enhanced or --format ranked.
- Shows concise summaries, links, and ranked lists with less structural noise.Recommended default contract for agent handoff:
python3 {baseDir}/novada_search.py --query "..." --scene news --format agent-json
If a human drags this skill to an agent, the agent should be able to clearly answer:
1) what this tool can do,
2) which mode to call (auto | multi | extract), and
3) which output format to consume (agent-json for logic).
SDK, MCP & Integrations (v1.0.8)
Python SDK
from novada_search import NovadaSearchclient = NovadaSearch(api_key="your_key")
result = client.search("coffee Berlin", scene="local")
result = client.search("buy shoes", mode="auto")
result = client.search("AI news", mode="multi", engines=["google", "bing"])
content = client.extract("https://example.com/article")
All SDK methods raise NovadaSearchError subclasses (not SystemExit), so agents can catch and recover.
MCP Server
python3 {baseDir}/novada_mcp_server.py
Tools: novada_search, novada_extract. Config example: mcp.json.
LangChain
from integrations.langchain_tool import NovadaSearchTool
tool = NovadaSearchTool(api_key="your_key")
Install via pip
pip install novada-search
agent-json enhanced fields
response_time_mssearch_metadatadomainfreshnessWhat’s New (P0) — Best-Answer First for Agents
agent-json now includes unified_results (top merged results across engines).duplicates_removed.score + agreement_count + domain + a short rationale.tests/ fixtures so ranking changes don’t silently degrade.Troubleshooting (Read This)
data.code / data.msg. This CLI hard-checks it and will exit on non-success codes.fetch_mode=dynamic: slower, but higher hit rate for Maps/e-commerce pages.--verbose to see engine/type selection and execution path.API Keys & Permissions
--api-key per run.NOVADA_API_KEY, or a local .env in the working folder../*.py, ./*.md, ./samples/*) and network access to https://scraperapi.novada.com.Real-World Example
Query: --query "dessert Düsseldorf" --scene local
Output:
🍰 Düsseldorf TOP 5 Dessert Shops
| Rank | Shop | Rating | Reviews | Address | |:----:|:-----|:------:|:-------:|:--------| | 🥇 | donecake | 4.8★ | 3,500 | Graf-Adolf-Straße 68 | | 🥈 | SugArt Factory | 4.8★ | 423 | Schloßstraße 76-78 | | 🥉 | Eiscafe Pia | 4.7★ | 2,100 | Kasernenstraße 1 | | 4 | Unbehaun Eis | 4.6★ | 5,000 | Aachener Str. 159 | | 5 | Aux Merveilleux de fred | 4.6★ | 626 | Kasernenstraße 15 |
> Click any shop name to open in Google Maps. This is the default enhanced output — actionable links, no extra flags needed.
Architecture
Layer 3 │ AI Agent │ auto · multi · extract
Layer 2 │ Scenes │ shopping · local · jobs · academic · video · news · travel · finance · images
Layer 1 │ Engines │ google · bing · yahoo · duckduckgo · yandex · youtube · ebay · walmart · yelp
│ │ + Google: shopping · local · news · scholar · jobs · flights · finance · patents · videos · images · play · lens
Layer 1 — Engines
9 Engines
| Engine | Strength | Example |
|--------|----------|---------|
| google | General + 13 sub-types | --engine google |
| bing | Web, news | --engine bing |
| yahoo | Finance | --engine yahoo |
| duckduckgo | Privacy | --engine duckduckgo |
| yandex | Russian web | --engine yandex |
| youtube | Video | --engine youtube |
| ebay | E-commerce | --engine ebay |
| walmart | US retail | --engine walmart |
| yelp | Local reviews | --engine yelp |
13 Google Sub-Types
Use --engine google --google-type :
| Type | What it searches | Type | What it searches |
|------|-----------------|------|-----------------|
| search | Web (default) | shopping | Products & prices |
| local | Google Maps | news | Latest headlines |
| scholar | Academic papers | jobs | Job listings |
| flights | Airlines | finance | Stocks & markets |
| videos | Video content | images | Pictures |
| patents | IP / patents | play | Android apps |
| lens | Visual search | | |
python3 {baseDir}/novada_search.py --query "MacBook Pro M4" --engine google --google-type shopping
python3 {baseDir}/novada_search.py --query "transformer attention" --engine google --google-type scholar
python3 {baseDir}/novada_search.py --query "python developer remote" --engine google --google-type jobs
python3 {baseDir}/novada_search.py --query "SFO to NRT" --engine google --google-type flights
python3 {baseDir}/novada_search.py --query "NVIDIA" --engine google --google-type finance
Layer 2 — Scenes
Scenes auto-combine the best engines for each use case. Use --scene :
| Scene | Engines combined | Use case | Status |
|-------|-----------------|----------|--------|
| 📰 news | Google News + Bing | Multi-source news aggregation | ✅ Available |
| 🎓 academic | Google Scholar | Research papers & citations | ✅ Available |
| 💼 jobs | Google Jobs | Structured job listings | ✅ Available |
| 🎬 video | YouTube + Google Videos | Video tutorials & reviews | ✅ Available |
| 🖼️ images | Google Images | Image search | ✅ Available |
| 🛒 shopping | Google Shopping + eBay + Walmart | Cross-platform price comparison | 🔜 Coming in v1.1 |
| 📍 local | Google Local + Yelp | Local business with ratings & maps | 🔜 Coming in v1.1 |
| ✈️ travel | Google Flights | Flight search & pricing | 🔜 Coming in v1.1 |
| 💰 finance | Google Finance + Yahoo | Stock data & market info | 🔜 Coming in v1.1 |
python3 {baseDir}/novada_search.py --query "MacBook Pro" --scene shopping
python3 {baseDir}/novada_search.py --query "ramen Tokyo" --scene local
python3 {baseDir}/novada_search.py --query "react hooks tutorial" --scene video
python3 {baseDir}/novada_search.py --query "AI startup funding" --scene news
Scene Output Example — Shopping
Query: --query "AirPods Pro" --scene shopping --format agent-json
{
"query": "AirPods Pro",
"scene": "shopping",
"engines_used": ["google:shopping", "ebay", "walmart"],
"result_counts": { "shopping": 15, "organic": 6 },
"shopping_results": [
{ "title": "Apple AirPods Pro 2nd Gen", "price": "$189.99", "seller": "Walmart", "rating": 4.8 },
{ "title": "Apple AirPods Pro 2 - New", "price": "$179.00", "seller": "eBay", "rating": 4.9 },
{ "title": "AirPods Pro (2nd generation)", "price": "$249.00", "seller": "Apple", "rating": 4.7 }
]
}
#### Shopping Scene Enhanced Output (Coming in v1.1)
> ⚠️ Shopping price comparison requires engine-specific data parsing that is being finalized.
> The price_comparison, lowest_price, and price_range fields will be available in v1.1
> when Walmart and eBay result parsing is complete.
#### Local Scene Enhanced Output (Coming in v1.1)
> ⚠️ Local business enrichment (phone, hours, open_now) depends on Google Maps and Yelp > data parsing that is being finalized for v1.1.
Layer 3 — Agent Modes
Use --mode :
Auto — Smart intent detection
Analyzes your query and auto-selects the best scene:
python3 {baseDir}/novada_search.py --query "buy Nike Air Max" --mode auto
→ detects "shopping" → uses eBay + Walmart + Google Shopping
python3 {baseDir}/novada_search.py --query "best pizza near me" --mode auto
→ detects "local" → uses Google Maps + Yelp
python3 {baseDir}/novada_search.py --query "latest AI news" --mode auto
→ detects "news" → uses Google News + Bing
Intent keywords (EN/DE/ZH): buy/kaufen, near me/in der nähe, job/stelle, paper/forschung, video/tutorial, news/nachrichten, flight/flug, stock/aktie, image/bild
Multi — Parallel engines + dedup
Search multiple engines simultaneously, deduplicate by URL:
python3 {baseDir}/novada_search.py --query "web scraping tools" --mode multi --engines google,bing,duckduckgoColon syntax for Google sub-types
python3 {baseDir}/novada_search.py --query "coffee maker" --mode multi --engines ebay,walmart,google:shopping
Extract — URL content for LLM
Pull clean text from any URL:
python3 {baseDir}/novada_search.py --url "https://example.com/article" --mode extract
Research — Search + Extract + Merge (Coming in v1.1)
> ⚠️ Research mode depends on the extract API which requires dynamic fetch mode. > This feature will be fully available in v1.1.
python3 {baseDir}/novada_search.py --query "AI agent trends 2026" --mode research
SDK:
result = client.research("AI agent trends 2026", max_sources=5)
result includes: unified_results + extracted_content[] + sources_extracted
Optional: AI Analysis (Bring Your Own LLM)
This tool focuses on search + structured results. If you want additional reasoning, use your own LLM API:
1. Run with structured output:
python3 {baseDir}/novada_search.py --query "..." --scene news --format agent-json > results.json
results.json into your own LLM prompt (OpenAI/Claude/etc.) for summarization, ranking, or extraction.> This keeps Novada Search read-only and avoids bundling external AI keys into the skill.
Output Formats
Default is enhanced (clickable links). Override with --format :
| Format | Output type | Best for |
|--------|------------|----------|
| enhanced (default) | Markdown + clickable Maps/website links | Daily use |
| ranked | Readable markdown with ratings | Quick overview |
| agent-json | Structured JSON for AI agents | LLM integration |
| table | Side-by-side comparison table | Comparing options |
| action-links | Shell open commands | Automation |
| raw | Full API response | Debugging |
> See samples/agent-json-example.json for a ready-to-copy agent-json payload with source_engine + confidence fields.
Full Command Reference
python3 {baseDir}/novada_search.py
--query "search terms" # required (unless extract mode)
--engine google|bing|yahoo|duckduckgo|yandex|youtube|ebay|walmart|yelp
--google-type search|shopping|local|news|scholar|jobs|flights|finance|videos|images|patents|play|lens
--scene shopping|local|jobs|academic|video|news|travel|finance|images
--mode auto|multi|extract
--engines google,bing,ebay # for multi mode (colon syntax: google:shopping)
--url "https://..." # for extract mode
--format enhanced|ranked|agent-json|table|action-links|raw
--max-results 1-20 # default: 10
--fetch-mode static|dynamic # static = fast, dynamic = JS pages
Priority: --mode auto overrides everything. --scene overrides --engine. Direct --engine is the fallback.
vs Tavily
| Feature | Novada Search | Tavily | |---------|:------------:|:------:| | Search engines | 9 | 1 | | Google sub-types | 13 | 0 | | Vertical scenes | 9 | 0 | | Shopping (eBay+Walmart+Google) | v1.1 | No | | Local (Maps+Yelp) | v1.1 | No | | Video (YouTube) | Yes | No | | Jobs / Academic / Travel | Yes | No | | Multi-engine parallel | Yes | No | | Auto intent detection | Yes | No | | Content extraction | Yes | Yes | | Agent JSON output | Yes | Yes |
Get your API key → · GitHub · Powered by Novada Scraper API v2.0
中文版｜Novada Search v2.0
更新亮点（P0）— 面向 Agent 的“最佳答案优先”
agent-json 新增 unified_results（多引擎合并后的 Top 结果）。duplicates_removed。score + agreement_count + domain + rationale（为什么排前）。tests/ 固件，保证排序逻辑稳定不退化。> 多引擎 AI 搜索平台——一次调用叠加 9 套主引擎、13 种 Google 类型、9 个垂直场景，并内置 auto / multi / extract 三层 Agent 模式。
快速上手
1. 在 novada.com 申请 NOVADA_API_KEY。 2. 用export NOVADA_API_KEY="..." 或运行时 --api-key $NOVADA_API_KEY 注入（推荐显式传参，脚本不会再扫描个人目录）。
3. 运行示例：python3 {baseDir}/novada_search.py --query "coffee Berlin" --scene local。常见问题｜踩坑
data.code / data.msg，脚本已内建校验。fetch_mode=dynamic，命中率更高但更慢。--verbose 可查看 engine/type 选择与节点评估。真实案例
--query "dessert Düsseldorf" --scene local 会输出带点击链接的 Top 5 甜品店表格，可直接跳转 Google Maps。架构分层
auto（意图识别 → 场景）、multi（自选引擎并行去重）、extract（URL 正文抽取）。指令参考
python3 {baseDir}/novada_search.py \
--query "search" --scene news --format agent-json
python3 {baseDir}/novada_search.py \
--mode multi --engines google:shopping,ebay,walmart --format table
python3 {baseDir}/novada_search.py \
--mode extract --url "https://example.com/article"
输出格式
enhanced：默认 Markdown，附地图/官网快速操作。ranked：排名 + 摘要。table：商品/本地商家对照表。agent-json / brave：结构化 JSON 供 LLM 食用（示例见 samples/agent-json-example.json）。action-links：生成 open "URL" 命令，方便自动化。raw：原始 API 回包。vs Tavily 对比（精简版）
| 功能 | Novada | Tavily | |------|--------|--------| | 搜索引擎数量 | 9 | 1 | | Google 子类型 | 13 | 0 | | 垂直场景 | 9 | 0 | | Shopping（eBay+Walmart+Google） | ✅ | ❌ | | Local（Maps+Yelp） | ✅ | ❌ | | 多引擎并行 | ✅ | ❌ | | Auto intent | ✅ | ❌ | | Extract API | ✅ | ✅ |实用建议
--scene 或 --mode multi，避免 auto 误判。--format agent-json，字段与 Tavily 兼容。--api-key 或在进程环境里 export（CLI 现仅读取 --api-key / NOVADA_API_KEY / 当前目录 .env）。requiredEnv.NOVADA_API_KEY、permissions 保持一致（避免扫描器判定 metadata mismatch）。