Exa Search

Overrides (append to arguments):

• /exa-search "RAG pipelines" — max: 5 — top 5 results
• /exa-search "diffusion models" — category: research paper — research papers only
• /exa-search "startup funding" — category: news, start date: 2025-01-01 — recent news
• /exa-search "transformer" — content: text, max chars: 8000 — full text mode
• /exa-search "transformer" — content: summary — LLM-generated summaries
• /exa-search "transformer" — domains: arxiv.org,huggingface.co — domain filter
• /exa-search "https://arxiv.org/abs/2301.07041" — similar — find similar pages

Setup

Exa requires the exa-py SDK and an API key:

pip install exa-py

Set your API key:

export EXA_API_KEY=your-key-here

Get a key from exa.ai.

Workflow

Step 1: Parse Arguments

Parse $ARGUMENTS for:

• query: The search query (required) or a URL (for find-similar mode)
• similar: If present, use find-similar mode instead of search
• max: Override MAX_RESULTS
• category: research paper, news, company, personal site, financial report, people
• content: highlights (default), text, summary, none
• max chars: Max characters for content extraction
• type: Search type — auto (default), neural, fast, instant
• domains: Comma-separated include domains
• exclude domains: Comma-separated exclude domains
• include text: Phrase that must appear in results
• exclude text: Phrase to exclude from results
• start date: ISO 8601 date — only results after this
• end date: ISO 8601 date — only results before this
• location: Two-letter ISO country code

Step 2: Locate Script

SCRIPT=$(find tools/ -name "exa_search.py" 2>/dev/null | head -1)

If not found, tell the user:

exa_search.py not found. Make sure tools/exa_search.py exists and exa-py is installed:
pip install exa-py

Step 3: Execute Search

Standard search:

python3 "$SCRIPT" search "QUERY" --max 10 --content highlights

With filters:

python3 "$SCRIPT" search "QUERY" --max 10 \
  --category "research paper" \
  --start-date 2025-01-01 \
  --content text --max-chars 8000

Find similar pages:

python3 "$SCRIPT" find-similar "URL" --max 5 --content highlights

Get content for known URLs:

python3 "$SCRIPT" get-contents "URL1" "URL2" --content text

Step 4: Present Results

Format results as a structured table:

| # | Title | URL | Date | Key Content |
|---|-------|-----|------|-------------|

For each result:

• Show title and URL
• Show published date if available
• Show highlights, text excerpt, or summary depending on content mode
• Flag particularly relevant results

Step 5: Offer Follow-up

After presenting results, suggest:

• Deepen: "I can fetch full text for any of these results"
• Find similar: "I can find pages similar to any result"
• Narrow: "I can re-search with domain/date/text filters"

Key Rules

• Always check that EXA_API_KEY is set before searching
• Default to highlights content mode for a good balance of speed and context
• Use category: "research paper" when the user is clearly looking for academic content
• Use text content mode when the user needs full page content
• Combine with /arxiv or /semantic-scholar for comprehensive literature coverage