exa-entities

Exa Entity Search

Quick Reference

Topic	When to Use	Reference
Company Search	Finding companies, competitive research	company-search.md
People Search	Finding profiles, recruiting	people-search.md
Websets	Data collection at scale, monitoring	websets.md

Essential Patterns

Company Search

from exa_py import Exa

exa = Exa()

results = exa.search_and_contents(
    "AI startups in healthcare series A funding",
    category="company",
    num_results=20,
    text=True
)

for company in results.results:
    print(f"{company.title}: {company.url}")

People Search

results = exa.search_and_contents(
    "machine learning engineers San Francisco",
    category="linkedin_profile",
    num_results=20,
    text=True
)

for profile in results.results:
    print(f"{profile.title}: {profile.url}")

Websets for Lead Generation

# Create a webset for company collection
webset = exa.websets.create(
    name="AI Healthcare Companies",
    search_query="AI healthcare startups",
    category="company",
    max_results=100
)

# Monitor for new matches
exa.websets.add_monitor(
    webset_id=webset.id,
    schedule="daily"
)

Category Reference

Category	Use Case	Index Size
company	Company websites, about pages	Millions
linkedin_profile	Professional profiles	1B+ profiles
personal_site	Individual blogs, portfolios	Millions
github	Repositories, developer profiles	Millions

Common Mistakes

1. Not using category filter - Always set category="company" or category="linkedin_profile" for entity search
2. Expecting structured data - Exa returns web pages; parse text for structured fields
3. Over-broad queries - Add location, industry, or role specifics for better results
4. Ignoring rate limits - Batch requests and implement backoff for large-scale collection
5. Missing domain filters - Use include_domains=["linkedin.com"] for profile-only results