smithery.ai

exa-entities

Exa.ai company and people search for lead generation, competitive intelligence, and data enrichment. Use when searching for companies, finding people profiles, building lead gen tools, or implementing Websets for data collection at scale. Triggers on: Exa company search, Exa people search, category company, lead generation, company research, profile search, LinkedIn profiles, Websets API, data enrichment, company lookup, find companies, competitive intelligence, recruiting, talent search, 1B pr…

First seen Apr 11, 2026

Installation

$ npx skills add https://smithery.ai

Summary

  • Exa.ai company and people search for lead generation, competitive intelligence, and data enrichment.
  • Use when searching for companies, finding people profiles, building lead gen tools, or implementing Websets for data collection at scale.
  • Triggers on: Exa company search, Exa people search, category company, lead generation, company research, profile search, LinkedIn profiles, Websets API, data enrichment, company lookup, find companies, competitive intelligence, recruiting, talent search, 1B profiles.

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More details

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Skill metadata

Parsed from SKILL.md frontmatter.

Version1.0.0
LicenseMIT
More metadata
author
ejirocodes
version
1.0.0

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,811 B
  • docs SUMMARY.md 526 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1 installs

SKILL.md

Exa Entity Search

Quick Reference

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