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# Introduction
AI brokers are solely as efficient as their entry to recent, dependable info. Behind the scenes, many brokers use net search instruments to drag the newest context and guarantee their outputs stay related. Nevertheless, not all search APIs are created equal, and never each possibility will match seamlessly into your stack or workflow.
On this article, we overview the highest 7 net search APIs which you can combine into your agent workflows. For every API, one can find instance Python code that can assist you get began shortly. Better of all, each API we cowl presents a free (although restricted) tier, permitting you to experiment while not having to enter a bank card or encounter further hurdles.
1. Firecrawl
Firecrawl gives a devoted Search API constructed “for AI,” alongside its crawl/scrape stack. You possibly can select your output format: clear Markdown, uncooked HTML, hyperlink lists, or screenshots, so the information suits your downstream workflow. It additionally helps customizable search parameters (e.g. language and nation) to focus on outcomes by locale, and is constructed for AI brokers that want net knowledge at scale.
Set up: pip set up firecrawl-py
from firecrawl import Firecrawl
firecrawl = Firecrawl(api_key="fc-YOUR-API-KEY")
outcomes = firecrawl.search(
question="KDnuggets",
restrict=3,
)
print(outcomes)
2. Tavily
Tavily is a search engine for AI brokers and LLMs that turns queries into vetted, LLM-ready insights in a single API name. As an alternative of returning uncooked hyperlinks and noisy snippets, Tavily aggregates as much as 20 sources, then makes use of proprietary AI to attain, filter, and rank essentially the most related content material to your activity, decreasing the necessity for customized scraping and post-processing.
Set up: pip set up tavily-python
from tavily import TavilyClient
tavily_client = TavilyClient(api_key="tvly-YOUR_API_KEY")
response = tavily_client.search("Who's MLK?")
print(response)
3. Exa
Exa is an progressive, AI-native search engine that provides 4 modes: Auto, Quick, Key phrase, and Neural. These modes successfully steadiness precision, velocity, and semantic understanding. Constructed by itself high-quality net index, Exa makes use of embeddings-powered “next-link prediction” in its Neural search. This function surfaces hyperlinks based mostly on which means quite than precise phrases, making it notably efficient for exploratory queries and sophisticated, layered filters.
Set up: pip set up exa_py
from exa_py import Exa
import os
exa = Exa(os.getenv('EXA_API_KEY'))
outcome = exa.search(
"hottest AI medical startups",
num_results=2
)
4. Serper.dev
Serper is a quick and cost-effective Google SERP (Search Engine Outcomes Web page) API that delivers leads to simply 1 to 2 seconds. It helps all main Google verticals in a single API, together with Search, Photographs, Information, Maps, Locations, Movies, Buying, Scholar, Patents, and Autocomplete. It gives structured SERP knowledge, enabling you to construct real-time search options with out the necessity for scraping. Serper helps you to get began immediately with 2,500 free search queries, no bank card required.
Set up: pip set up --upgrade --quiet langchain-community langchain-openai
import os
import pprint
os.environ["SERPER_API_KEY"] = "your-serper-api-key"
from langchain_community.utilities import GoogleSerperAPIWrapper
search = GoogleSerperAPIWrapper()
search.run("High 5 programming languages in 2025")
5. SerpAPI
SerpApi presents a robust Google Search API, together with help for added search engines like google and yahoo, delivering structured Search Engine Outcomes Web page knowledge. It options strong infrastructure, together with international IPs, an entire browser cluster, and CAPTCHA fixing to make sure dependable and correct outcomes. Moreover, SerpApi gives superior parameters, equivalent to exact location controls via the placement parameter and a /places.json helper.
Set up: pip set up google-search-results
from serpapi import GoogleSearch
params = {
"engine": "google_news", # use Google Information engine
"q": "Synthetic Intelligence", # search question
"hl": "en", # language
"gl": "us", # nation
"api_key": "secret_api_key" # substitute together with your SerpAPI key
}
search = GoogleSearch(params)
outcomes = search.get_dict()
# Print prime 5 information outcomes with title + hyperlink
for idx, article in enumerate(outcomes.get("news_results", []), begin=1):
print(f"{idx}. {article['title']} - {article['link']}")
6. SearchApi
SearchApi presents real-time SERP scraping throughout many engines and verticals, exposing Google Internet together with specialised endpoints equivalent to Google Information, Scholar, Autocomplete, Lens, Finance, Patents, Jobs, and Occasions, plus non-Google sources like Amazon, Bing, Baidu, and Google Play; this breadth lets brokers goal the proper vertical whereas retaining a single JSON schema and constant integration path.
import requests
url = "https://www.searchapi.io/api/v1/search"
params = {
"engine": "google_maps",
"q": "finest sushi eating places in New York"
}
response = requests.get(url, params=params)
print(response.textual content)
7. Courageous Search
Courageous Search presents a privacy-first API on an impartial net index, with endpoints for net, information, and pictures that work nicely for grounding LLMs with out person monitoring. It’s developer-friendly, performant, and features a free utilization plan.
import requests
url = "https://api.search.courageous.com/res/v1/net/search"
headers = {
"Settle for": "software/json",
"Settle for-Encoding": "gzip",
"X-Subscription-Token": ""
}
params = {
"q": "greek eating places in san francisco"
}
response = requests.get(url, headers=headers, params=params)
if response.status_code == 200:
knowledge = response.json()
print(knowledge)
else:
print(f"Error {response.status_code}: {response.textual content}")
Wrapping Up
I pair search APIs with Cursor IDE via MCP Search to drag recent documentation proper inside my editor, which accelerates debugging and improves my programming move. These instruments energy real-time net purposes, agentic RAG workflows, and extra, whereas retaining outputs grounded and decreasing hallucinations in delicate eventualities.
Key benefits:
- Customization for exact queries, together with filters, freshness home windows, area, and language
- Versatile output codecs like JSON, Markdown, or plaintext for seamless agent handoffs
- The choice to go looking and scrape the net to complement context to your AI brokers
- Free tiers and reasonably priced usage-based pricing so you may experiment and scale with out fear
Choose the API that matches your stack, latency wants, content material protection, and price range. When you want a spot to begin, I extremely suggest Firecrawl and Tavily. I take advantage of each nearly day by day.
Abid Ali Awan (@1abidaliawan) is a licensed knowledge scientist skilled who loves constructing machine studying fashions. At present, he’s specializing in content material creation and writing technical blogs on machine studying and knowledge science applied sciences. Abid holds a Grasp’s diploma in expertise administration and a bachelor’s diploma in telecommunication engineering. His imaginative and prescient is to construct an AI product utilizing a graph neural community for college kids fighting psychological sickness.
