Multi Engine Search for Agent

Automation

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.

Install

openclaw skills install novada-search

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:

Get your free API key → novada.com
Set the key via environment or CLI: export NOVADA_API_KEY="your_key" (or pass --api-key $NOVADA_API_KEY)
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:

Agent layer (machine logic)
- Use --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.
Human layer (readability)
- Use --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:

what this tool can do,
which mode to call (auto | multi | extract), and
which output format to consume (agent-json for logic).

SDK, MCP & Integrations (v1.0.8)

Python SDK

from novada_search import NovadaSearch

client = 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_ms
search_metadata
per-result domain
per-result freshness

What’s New (P0) — Best-Answer First for Agents

Unified Best Answer: agent-json now includes unified_results (top merged results across engines).
Dedup that Agents Love: aggressive URL normalization + multi-engine merging; exposes duplicates_removed.
Explainable Scoring: each unified result has score + agreement_count + domain + a short rationale.
Regression Guardrail: added tests/ fixtures so ranking changes don’t silently degrade.

Troubleshooting (Read This)

Novada may return HTTP 200 even on failure: the real error is in JSON data.code / data.msg. This CLI hard-checks it and will exit on non-success codes.
Cloud/Vercel IPs may be blocked (402): validate from your production egress IP before shipping; request server-to-server allowlisting if needed.
Local/Shopping default to fetch_mode=dynamic: slower, but higher hit rate for Maps/e-commerce pages.
Debugging: add --verbose to see engine/type selection and execution path.

API Keys & Permissions

NOVADA_API_KEY is required. Either export it (recommended for deployments) or pass --api-key per run.
The CLI no longer scans home directories for secrets; it only checks CLI flag, NOVADA_API_KEY, or a local .env in the working folder.
Declared permissions: filesystem (./*.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>:

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 <name>:

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|multi|extract>:

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,duckduckgo

# Colon 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:

Run with structured output:

python3 {baseDir}/novada_search.py --query "..." --scene news --format agent-json > results.json

Feed 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 <name>:

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”

统一最佳答案：agent-json 新增 unified_results（多引擎合并后的 Top 结果）。
强力去重：URL 归一 + 多引擎聚合；并输出 duplicates_removed。
可解释评分：每条 unified 结果带 score + agreement_count + domain + rationale（为什么排前）。
回归测试：新增 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

在 novada.com 申请 NOVADA_API_KEY。
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）。
运行示例：python3 {baseDir}/novada_search.py --query "coffee Berlin" --scene local。

常见问题｜踩坑

Novada HTTP 常年 200，真实错误在 JSON data.code / data.msg，脚本已内建校验。
云服务器 / Vercel IP 可能被封（402），上线前先在目标 IP 做 Step 1.6 验证。
local / shopping 场景默认 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

Layer 1 引擎层：google / bing / yahoo / duckduckgo / yandex / youtube / ebay / walmart / yelp，Google 额外 13 个子类型（shopping/local/news/...）。
Layer 2 场景层：shopping、local、jobs、academic、video、news、travel、finance、images，根据场景组合多引擎并定义合并策略。
Layer 3 Agent 模式：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

需要稳定输出 → 显式指定 --scene 或 --mode multi，避免 auto 误判。
需要被别的 Agent 调用 → 优先 --format agent-json，字段与 Tavily 兼容。
线上引用时建议直接传 --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）。
发布时请确保 registry metadata 与本包的 requiredEnv.NOVADA_API_KEY、permissions 保持一致（避免扫描器判定 metadata mismatch）。