Automated Company Research Python: AI Guide
Build robust AI agents for company research in Python. SearchCans delivers LLM-ready Markdown, cutting token costs 40%.
This Python example demonstrates the complete workflow of using SearchCans API services. Both APIs use POST requests with JSON payloads. First, use the SERP API to search Google for "AI Agents" with parameters: 's' for search query, 't' for engine type (google), and 'd' for timeout. Then, extract the first URL from the results and use the Reader API with parameters: 's' for source URL, 't' for type (url), and 'b' for browser rendering to convert that webpage into clean, LLM-ready Markdown format. This is the typical workflow for AI agents, RAG systems, and LLM applications that need to search and process web content programmatically.
The code shows proper authentication using Bearer tokens with API_KEY variable, HEADERS constant for reusability, JSON request body configuration, and JSON response handling. SearchCans provides both APIs at an affordable price of $0.56 per 1,000 requests, making it 10x cheaper than alternatives like SerpApi and Firecrawl.
Our dual-engine platform offers a complete solution: a powerful SERP API for Google and Bing Search API access, plus a Reader API to convert a URL into clean, LLM-ready Markdown format. Parallel Search Lanes allow simultaneous in-flight requests without hourly caps, making it perfect for AI agents and RAG systems that need to search the web and process web content.
New users get 100 free credits upon registration.
import requests
# 1. Search Google (SERP API)
search_resp = requests.post(
"https://www.searchcans.com/api/search",
headers="Authorization": "Bearer YOUR_KEY",
json=
"s": "AI Agents", # Search query
"t": "google", # Engine type
"d": 20000 # 20s server timeout
,
timeout=30 # 30s client timeout
)
# Get the first URL result
first_url = search_resp.json()['data'][0]['url']
# 2. Extract Content (Reader API)
reader_resp = requests.post(
"https://www.searchcans.com/api/url",
headers="Authorization": "Bearer YOUR_KEY",
json=
"s": first_url, # Source URL
"t": "url",
"b": True, # Use browser rendering
"w": 3000 # Wait 3s for JS (use 5000ms+ for heavy sites)
,
timeout=30 # 30s client timeout
)
# Output clean LLM-ready Markdown
print(reader_resp.json()['data']['markdown']) SERP API + Reader API: The complete data infrastructure for AI applications
Industry-lowest pricing at just $0.56 per 1,000 searches. Significantly cheaper than Serper and SerpAPI.
Our Reader API is a powerful URL to Markdown API that converts messy web pages into clean structured markdown. The perfect markdown output for RAG.
Latency as low as 1.5 seconds for standard requests. Optimized for AI applications.
Enterprise-grade reliability with redundant infrastructure. Your AI applications run without interruption.
All our search APIs provide structured JSON for SERP data. Easily integrate with LangChain and LlamaIndex to give your AI access to real-time web content.
Ready to integrate? Check out our comprehensive documentation
10x cheaper than Serper, SerpAPI, Jina Reader, and Firecrawl
All plans include full access to SERP API and Reader API
Credits valid for 6 months
Credits valid for 6 months
Credits valid for 6 months
Credits valid for 6 months
Start with our shared high-performance cluster for testing and side projects. When you're ready to scale, upgrade to a Dedicated Cluster Node to ensure zero-queue latency for your production AI Agents.
For testing & side projects
Get StartedFor high-volume & compliance
Contact Sales† Parallel Search Lanes: Refers to simultaneous in-flight requests. Zero hourly throughput limits. Perfect for bursty AI workloads.
SERP API and Reader API best practices, AI development guides, and industry insights
Build robust AI agents for company research in Python. SearchCans delivers LLM-ready Markdown, cutting token costs 40%.
Unlock Ollama's potential with real-time internet access. SearchCans delivers LLM-ready Markdown, cutting token costs 40%.
Raw HTML kills LLM performance. Convert HTML to Markdown for 40% token savings and better RAG accuracy with SearchCans.
Join thousands of developers using SearchCans for their AI applications. Check our documentation or try the API playground first.