exa-search

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Translated

Exa.ai search API integration for neural and keyword web search with content retrieval. Use when implementing web search features, integrating Exa SDK (exa_py, exa-js), or retrieving web content. Triggers on: Exa, exa_py, exa-js, neural search, web search API, search_and_contents, searchAndContents, find_similar, findSimilar, domain filtering, date filtering, text extraction, page summaries, highlights, search auto mode, fast search, search categories, livecrawl, excluding domains, include text, exclude text, EXA_API_KEY.

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Sourceejirocodes/agent-skills
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NPX Install

npx skill4agent add ejirocodes/agent-skills exa-search

Tags

Translated version includes tags in frontmatter
exa-api-integrationneural-searchweb-searchcontent-retrievalsdk-usage

SKILL.md Content

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Exa Search Integration

Quick Reference

TopicWhen to UseReference
Search ModesChoosing between auto, neural, and keyword searchsearch-modes.md
FiltersDomain, date, text, and category filteringfilters.md
ContentsText extraction, highlights, summaries, livecrawlcontents.md
SDK PatternsPython (exa_py) and TypeScript (exa-js) usagesdk-patterns.md

Essential Patterns

Basic Search (Python)

python
from exa_py import Exa

exa = Exa(api_key="your-api-key")  # or set EXA_API_KEY env var

results = exa.search_and_contents(
    "latest developments in quantum computing",
    type="auto",
    num_results=10,
    text=True,
    highlights=True
)

for result in results.results:
    print(f"{result.title}: {result.url}")
    print(result.text[:500])

Basic Search (TypeScript)

typescript
import Exa from "exa-js";

const exa = new Exa(process.env.EXA_API_KEY);

const results = await exa.searchAndContents(
  "latest developments in quantum computing",
  {
    type: "auto",
    numResults: 10,
    text: true,
    highlights: true,
  }
);

results.results.forEach((result) => {
  console.log(`${result.title}: ${result.url}`);
});

Search with Filters

python
results = exa.search_and_contents(
    "AI startup funding rounds",
    type="neural",
    num_results=10,
    include_domains=["techcrunch.com", "venturebeat.com"],
    start_published_date="2024-01-01",
    text={"max_characters": 2000},
    summary=True
)

Find Similar Links

python
similar = exa.find_similar_and_contents(
    "https://example.com/interesting-article",
    num_results=10,
    exclude_source_domain=True,
    text=True
)

Search Mode Selection

ModeWhen to UseNotes
auto
Default for most queriesExa optimizes between neural/keyword automatically
neural
Natural language, conceptual queriesBest for "what is...", "how to...", topic exploration
keyword
Exact matches, technical terms, namesBest for specific product names, error codes, proper nouns

Common Mistakes

  1. Using 
    keyword
    for conceptual queries     - Neural search understands intent better; use 
    auto
    or 
    neural
    for natural language questions
  2. Not setting 
    text=True
     - Search returns URLs only by default; explicitly request content with 
    text=True
  3. Ignoring 
    highlights
     - Use 
    highlights=True
    for relevant snippets without downloading full page text
  4. Missing API key - Set 
    EXA_API_KEY
    environment variable or pass explicitly to constructor
  5. Over-filtering initially - Start with broad searches, then add domain/date filters to refine
  6. Not using 
    summary
     - For RAG applications, 
    summary=True
    provides concise context without full page text
  7. Expecting scores in auto mode - Relevance scores are only returned with 
    type="neural"
    ; auto mode doesn't include them