crewAI/crewai_tools/tools/arxiv_paper_tool/README.md

ArxivPaperTool

📚 ArxivPaperTool

The ArxivPaperTool is a utility for fetching metadata and optionally downloading PDFs of academic papers from the arXiv platform using its public API. It supports configurable queries, batch retrieval, PDF downloading, and clean formatting for summaries and metadata. This tool is particularly useful for researchers, students, academic agents, and AI tools performing automated literature reviews.

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Description

This tool:

• Accepts a search query and retrieves a list of papers from arXiv.
• Allows configuration of the maximum number of results to fetch.
• Optionally downloads the PDFs of the matched papers.
• Lets you specify whether to name PDF files using the arXiv ID or paper title.
• Saves downloaded files into a custom or default directory.
• Returns structured summaries of all fetched papers including metadata.

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Arguments

Argument	Type	Required	Description
search_query	str	✅	Search query string (e.g., "transformer neural network").
max_results	int	✅	Number of results to fetch (between 1 and 100).
download_pdfs	bool	❌	Whether to download the corresponding PDFs. Defaults to False.
save_dir	str	❌	Directory to save PDFs (created if it doesn’t exist). Defaults to ./arxiv_pdfs.
use_title_as_filename	bool	❌	Use the paper title as the filename (sanitized). Defaults to False.

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📄 ArxivPaperTool Usage Examples

This document shows how to use the ArxivPaperTool to fetch research paper metadata from arXiv and optionally download PDFs.

🔧 Tool Initialization

from crewai_tools import ArxivPaperTool 

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Example 1: Fetch Metadata Only (No Downloads)

tool = ArxivPaperTool()
result = tool._run(
    search_query="deep learning",
    max_results=1
)
print(result)

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Example 2: Fetch and Download PDFs (arXiv ID as Filename)

tool = ArxivPaperTool(download_pdfs=True)
result = tool._run(
    search_query="transformer models",
    max_results=2
)
print(result)

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Example 3: Download PDFs into a Custom Directory

tool = ArxivPaperTool(
    download_pdfs=True,
    save_dir="./my_papers"
)
result = tool._run(
    search_query="graph neural networks",
    max_results=2
)
print(result)

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Example 4: Use Paper Titles as Filenames

tool = ArxivPaperTool(
    download_pdfs=True,
    use_title_as_filename=True
)
result = tool._run(
    search_query="vision transformers",
    max_results=1
)
print(result)

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Example 5: All Options Combined

tool = ArxivPaperTool(
    download_pdfs=True,
    save_dir="./downloads",
    use_title_as_filename=True
)
result = tool._run(
    search_query="stable diffusion",
    max_results=3
)
print(result)

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Run via __main__

Your file can also include:

if __name__ == "__main__":
    tool = ArxivPaperTool(
        download_pdfs=True,
        save_dir="./downloads2",
        use_title_as_filename=False
    )
    result = tool._run(
        search_query="deep learning",
        max_results=1
    )
    print(result)

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