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:

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:

• 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:

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 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

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

13 Google Sub-Types

Use --engine google --google-type <type>:

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>:

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:

1. Run with structured output:

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

2. 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>:

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

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

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

• 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）

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）。