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.

Args: query (str): The search query. Use natural language questions or keywords. Example: "latest developments in quantum computing" num_results (int): Number of initial search results to fetch from DuckDuckGo. More results provide better coverage but increase processing time. Default: 10 top_k (int): Number of top-scored results to include in the final output. These are the most semantically relevant results after scoring. Default: 5 include_urls (bool): Whether to include source URLs in the results. If True, each result includes its URL for citation. Default: True

Returns: Dict: A dictionary with a single key "content" containing the search results. The content is formatted as markdown text with the most relevant information from the top_k web pages. If include_urls is True, each section includes its source URL.

Example: {"content": "# Result 1\n\nContent here...\n\nSource: https://example.com"}

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

Args: query (str): The query to search for. num_results (int): Number of results to return. top_k (int): Use top "k" results for content. include_urls (bool): Whether to include URLs in the results. If True, the results will be a list of dictionaries with the following keys: - type: "text" - text: The content of the result - url: The URL of the result

Returns: Dict of strings containing best search based on input query. Formatted in markdown.

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

Args: search_terms (List[str]): List of search terms to research. Provide multiple related search queries for comprehensive coverage. Example: ["machine learning fundamentals", "neural networks", "deep learning best practices"] backends (List[str] | None): List of search backends to use. Defaults to ["duckduckgo", "google"]. Can include: bing, brave, duckduckgo, google, grokipedia, mojeek, yandex, yahoo, wikipedia. If None, uses default. num_results_per_term (int): Number of results to fetch per search term per backend. top_k_per_term (int): Number of top scored results to keep per search term per backend. include_urls (bool): Whether to include URLs in the results.

Returns: Dict containing aggregated research results from all search terms and specified backends, with duplicates removed.

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.

Args: search_terms (List[str]): List of search terms to research. The LLM should provide multiple related search queries for comprehensive coverage. num_results_per_term (int): Number of results to fetch per search term. top_k_per_term (int): Number of top scored results to keep per search term. include_urls (bool): Whether to include URLs in the results.

Returns: Dict containing aggregated research results from all search terms (Google only), 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.

Args: search_terms (List[str]): List of search terms to research. The LLM should provide multiple related search queries for comprehensive coverage. num_results_per_term (int): Number of results to fetch per search term. top_k_per_term (int): Number of top scored results to keep per search term. include_urls (bool): Whether to include URLs in the results.

Returns: Dict containing aggregated research results from all search terms (DuckDuckGo only), with duplicates removed.