exa-search

Exa Search Tool - Intelligent Retrieval Solution for Scientific Research and Technology

Skill Overview

exa-search is a web retrieval toolkit based on the Exa engine, optimized specifically for scientific and technical content. It provides semantic search and URL content extraction capabilities, helping researchers quickly discover high-quality academic resources.

Use Cases

1. Academic Research and Paper Retrieval

When you need to find peer-reviewed journal articles, conference papers, or preprints (such as arXiv and bioRxiv), exa-search can use research paper category filters and academic domain restrictions to prioritize reliable academic sources over blogs or news reports.

2. Technical Documentation and Resource Research

When exploring new technology frameworks, searching for technical documentation, or collecting case-study materials, the tool’s semantic retrieval capabilities can understand conceptual queries rather than relying on exact keyword matching, helping you locate relevant technical content more quickly.

3. Batch Webpage and PDF Content Extraction

When you already have a list of URLs (such as reference links or resource lists) and need to retrieve their complete content, exa-search supports batch extraction of web articles and academic PDFs, eliminating the need to manually download and copy each one individually.

Core Features

1. Semantic Web Search

Going beyond traditional keyword matching, Exa combines high-quality keywords with semantic retrieval to understand the intent behind scientific and technical queries. The --category "research paper" option can be used to favor academic sources, while --include-domains can be used to specify authoritative domains (such as nature.com and arxiv.org) to further filter the results.

2. Batch URL Content Extraction

Based on the exa-py Python SDK, the tool supports extracting content from multiple URLs at once, including ordinary webpages and academic PDFs. It is suitable for processing reference lists, resource collections, and similar scenarios, and is more efficient than visiting each URL individually.

3. Academic Source Prioritization Strategy

The built-in academic source priority is: peer-reviewed articles > preprints > institutional and government sources (NIH, WHO, NIST) > commercial websites. This strategy ensures that scientific research queries return more authoritative resources. When citing sources, it is recommended to include the author and year (for example, [Smith et al., 2025]).

Frequently Asked Questions

What is the Exa Search Tool, and how does it differ from Google Search?

Exa is a search engine indexing service focused on semantic retrieval. Compared with traditional search engines, it performs better at understanding conceptual queries in science and technology and provides dedicated academic paper filtering capabilities. The exa-search skill wraps Exa’s Python interface, allowing users to quickly perform web searches and extract content through the command line.

How do I obtain and use an EXA_API_KEY?

You need to register and obtain an API key at dashboard.exa.ai/api-keys. The recommended approach is to create a .env file in the project root directory and add EXA_API_KEY=your-key, then use dotenv -f .env run -- to load the environment variable before running the script. You can also set it directly as a session environment variable with export EXA_API_KEY="your-key".

How do I install the exa-py SDK?

Because the scripts use PEP 723 inline dependency declarations, you can run them directly with uv run --with exa-py python without pre-installation. If you prefer a persistent installation, run uv pip install "exa-py>=1.14.0". All scripts set the x-exa-integration request header for usage statistics.