multi-search-engine

Aggregating web search results across multiple engines requires running parallel queries that return a single normalized JSON list. This approach eliminates manual checks across separate engines for source discovery and fact-checking.

To get normalized JSON output from parallel web search queries, use an aggregation tool that unifies per-engine lists into a uniform JSON structure. This structure is suitable for downstream analysis, claim verification, and agent reasoning workflows.

Multi-engine web search continues when API keys are missing by recording per-engine errors and returning best-effort results from available sources. You can also enable strict mode to fail fast if any required search engine API key is unavailable.

You can use BeautifulSoup and HTTPX for HTML scraping search engines within a parallel querying workflow. The tool leverages these dependencies to run HTTP requests and parse results alongside API-driven engines like Brave, Tavily, and SerpAPI.

The best way to verify claims using fact-checking search engines is to run parallel queries across multiple indexes and compare the normalized results. This ensures coverage and redundancy across different languages and sources for reliable verification.

The limitation of strict mode in parallel web search queries is that it fails the entire request fast if any API key is missing. This prevents best-effort behavior, meaning you get no aggregated results if even one configured search engine is unavailable.