Search the web for a given query using DuckDuckGo. Returns context to the LLM with RAG-like similarity scoring to prioritize the most relevant results.

This tool fetches web search results, scores them by semantic similarity to the query using text embeddings, and returns the top-ranked content as markdown text.

Search on Google for a given query using ddgs. Give back context to the LLM with a RAG-like similarity sort.

Perform deep research across multiple search terms using specified search backends. This tool aggregates results from multiple searches across chosen engines, scores them by relevance, and returns the most relevant content with duplicates removed. Perfect for comprehensive research on a topic.

Available backends: bing, brave, duckduckgo, google, grokipedia, mojeek, yandex, yahoo, wikipedia

USAGE GUIDANCE FOR LLM:

1. Ask the user which backend(s) they prefer, OR

2. Choose appropriate backend(s) based on context:

   • ["duckduckgo"] - Privacy-focused, general search

   • ["google"] - Comprehensive results, best for technical queries

   • ["duckduckgo", "google"] - Maximum coverage (default)

   • ["wikipedia"] - Factual/encyclopedia content

   • ["bing", "google"] - Balanced commercial engines

   • Multiple backends for broader research coverage

3. For specific use cases, consider:

   • deep_research_google() - shortcut for Google-only

   • deep_research_ddgs() - shortcut for DuckDuckGo-only

Perform deep research across multiple search terms using ONLY Google. Aggregates results from multiple Google searches, scores them by relevance, and returns the most relevant content with duplicates removed.

Perform deep research across multiple search terms using ONLY DuckDuckGo. Aggregates results from multiple DuckDuckGo searches, scores them by relevance, and returns the most relevant content with duplicates removed.