What Is SwiftSearch API?

SwiftSearch is SearchHive's real-time web search API that returns structured free JSON formatter results from across the web. Instead of scraping Google, parsing HTML, and dealing with rate limits, you make a single API call and get back titles, URLs, snippets, and metadata — ready to use in your application.

Whether you're building an AI agent that needs current information, a price monitoring dashboard, or a content research pipeline, SwiftSearch gives you programmatic web search without the infrastructure headache.

Key Takeaways

• SwiftSearch is SearchHive's web search API — it returns structured JSON search results via a REST endpoint
• No HTML parsing required — results include titles, URLs, snippets, and metadata out of the box
• Sub-second latency on most queries, designed for production workloads
• Free tier available — get started without a credit card at searchhive.dev
• Works alongside ScrapeForge (scraping) and DeepDive (AI-powered content extraction) for a complete search-to-insight pipeline

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What does SwiftSearch actually do?

SwiftSearch queries the web and returns search results as clean, structured JSON. You send a search query, and you get back an array of results — each with a title, URL, description snippet, and optional metadata like publication date, site name, and relevance score.

Think of it as Google SERPs, but as an API response that doesn't break when Google changes its layout.

from searchhive import SwiftSearch

client = SwiftSearch(api_key='your-api-key')
results = client.query('best Python web frameworks 2026')

for result in results:
    print(f"Title: {result['title']}")
    print(f"URL: {result['url']}")
    print(f"Snippet: {result['snippet']}")
    print('---')

The response format is consistent and predictable — unlike scraping search engines directly, where HTML structure changes constantly.

How is SwiftSearch different from scraping Google?

Scraping Google directly is fragile, slow, and legally risky. Here's how SwiftSearch compares:

Google actively blocks scrapers with CAPTCHAs, IP bans, and dynamic page elements that require JavaScript rendering. SwiftSearch bypasses all of this because it's a legitimate API — not a scraper pretending to be a browser.

What programming languages does SwiftSearch support?

SwiftSearch is a REST API, so it works with any language that can make HTTP requests. SearchHive provides official SDKs for Python and JavaScript, with community libraries available for other languages.

Python:

pip install searchhive

JavaScript/Node.js:

npm install @searchhive/swiftsearch

Direct REST (any language):

import requests

response = requests.get(
    'https://api.searchhive.dev/v1/search',
    params={'q': 'retrieval augmented generation', 'count': 10},
    headers={'Authorization': 'Bearer your-api-key'}
)
data = response.json()
print(data['results'])

What parameters does SwiftSearch accept?

The main query parameters you'll use:

• q (required) — your search query string
• count — number of results to return (default: 10, max varies by plan)
• country — geographic market for localized results (e.g., us, gb, de)
• language — preferred language for results (e.g., en, es, fr)
• safe_search — filter explicit content (true/false)
• date_range — restrict results to a time period (e.g., day, week, month, year)

from searchhive import SwiftSearch

client = SwiftSearch(api_key='your-api-key')

# Time-filtered search for recent AI news
results = client.query(
    query='AI regulation',
    count=20,
    date_range='week',
    country='us',
    language='en'
)

Can I use SwiftSearch with AI agents?

Yes — this is one of SwiftSearch's strongest use cases. AI agents often need current information that isn't in their training data. SwiftSearch gives them real-time web search capability through a clean API interface.

Here's an example of wiring SwiftSearch into an agent as a tool:

import json
from searchhive import SwiftSearch

client = SwiftSearch(api_key='your-api-key')

def web_search_tool(query: str, num_results: int = 5) -> str:
    """Search the web and return formatted results for the LLM."""
    results = client.query(query, count=num_results)
    formatted = []
    for r in results:
        formatted.append(f"- {r['title']}: {r['url']}\n  {r['snippet']}")
    return '\n'.join(formatted)

# Register as a function-calling tool
tools = [{
    'name': 'web_search',
    'description': 'Search the web for current information. Use when you need up-to-date facts.',
    'parameters': {
        'type': 'object',
        'properties': {
            'query': {'type': 'string', 'description': 'Search query'},
            'num_results': {'type': 'integer', 'default': 5}
        },
        'required': ['query']
    }
}]

This pattern works with OpenAI function calling, Anthropic tool use, LangChain, LlamaIndex, and any framework that supports tool definitions. For a deeper walkthrough, see How to Build an AI Agent with Web Access.

How much does SwiftSearch cost?

SwiftSearch offers a free tier that lets you test the API without a credit card. Paid plans scale with your usage:

• Free tier — limited monthly requests, perfect for prototyping
• Pro plans — higher volume with lower per-request costs
• Enterprise — custom volume, SLA, and dedicated support

Compared to scraping Google with proxy services (Bright Data SERP API at $3–5/1000 requests, or SerpAPI at similar pricing), SwiftSearch is competitive on price while being significantly simpler to integrate.

See the full pricing details and compare against alternatives.

How does SwiftSearch fit into the SearchHive ecosystem?

SwiftSearch is part of a three-product suite:

1. SwiftSearch — web search API (find URLs and content summaries)
2. ScrapeForge — web scraping API (extract full content from specific pages)
3. DeepDive — AI-powered content extraction (structured data from unstructured pages)

The typical workflow:

from searchhive import SwiftSearch, ScrapeForge, DeepDive

search = SwiftSearch(api_key='key')
scraper = ScrapeForge(api_key='key')
deeper = DeepDive(api_key='key')

# Step 1: Find relevant pages
results = search.query('RAG best practices 2026')

# Step 2: Scrape full content from top results
articles = []
for r in results[:5]:
    page = scraper.scrape(r['url'])
    articles.append(page['markdown'])

# Step 3: Extract structured data with AI
for article in articles:
    structured = deeper.extract(article, schema={
        'title': 'str',
        'key_points': 'list[str]',
        'tools_mentioned': 'list[str]'
    })
    print(structured)

This search → scrape → extract pipeline is what makes SearchHive more than just a search API. It's a complete web data platform. Learn more in How to Use SearchHive with Python.

Is SwiftSearch reliable for production use?

SwiftSearch is designed for production workloads with:

• Consistent response format — no HTML parsing that breaks on layout changes
• Built-in rate limiting — the API manages upstream limits so you don't have to
• Error handling — structured error responses with retry guidance
• Latency SLA — sub-second typical response times on standard queries

Unlike DIY Google scraping, where you're one CAPTCHA update away from broken pipelines, SwiftSearch's API contract guarantees stability.

Summary

SwiftSearch is SearchHive's web search API that returns structured JSON results without the fragility of search engine scraping. It's fast, reliable, and easy to integrate with any programming language. Combined with ScrapeForge and DeepDive, it provides a complete web data pipeline from search to structured extraction.

Start building with the free tier — no credit card required.

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Related reading: How to Use SearchHive with Python | How to Handle Rate Limiting in Web Scraping | Compare SearchHive vs SerpAPI