Deep Research,
Grounded in Live Web Data
Research agents are only as good as the data they read. DataBlue searches the live web and pulls full pages into clean, LLM-ready markdown and JSON — with the source URLs intact — so your model reasons over real, current information instead of stale training data.
Built for Research Pipelines.
Live Web Search
One Search API call returns ranked, real-time results from across the web — the discovery layer your agent needs before it reads anything. No stale index, no rate-limit roulette.
Full-Page Extraction
Hand any URL to the Scrape API and get back clean markdown plus structured JSON. JavaScript-rendered pages, awkward layouts, and messy HTML all come out ready to feed an LLM.
Citations You Can Trust
Every result carries its source URL and metadata, so your research output stays attributable. Build answer engines that cite their sources instead of hallucinating them.
Scale Without Babysitting
High, predictable concurrency and credits that never expire mean a 200-query research run costs the same whether you do it today or next quarter. No proxy pools to manage.
The Whole Loop in a Few Lines.
Search the web, scrape the top results into markdown, and pass them to your model. Two endpoints, one API key — no proxies, no HTML parsing.
import requests
KEY = {"Authorization": "Bearer YOUR_API_KEY"}
# 1. Discover sources with live web search
hits = requests.get(
"https://api.datablue.dev/v1/search",
headers=KEY,
params={"q": "latest breakthroughs in solid-state batteries"},
).json()["results"]
# 2. Scrape the top results into LLM-ready markdown
sources = []
for hit in hits[:5]:
page = requests.post(
"https://api.datablue.dev/v1/scrape",
headers=KEY,
json={"url": hit["url"], "formats": ["markdown"]},
).json()
sources.append({"url": hit["url"], "markdown": page["markdown"]})
# 3. Hand 'sources' to your LLM — grounded, current, and cited.From Question to Cited Answer.
Search the Web
Send the research question to the Search API and get back fresh, ranked results — the candidate sources your agent will read.
Scrape the Sources
Pass each URL to the Scrape API and receive clean markdown and structured JSON, ready to drop into a context window or vector store.
Reason & Cite
Feed the extracted content to your LLM. Because every chunk keeps its source URL, your output can cite exactly where each claim came from.
Deep Research Questions.
What is a deep research API?
A deep research API gives an AI agent live access to the web so it can find and read current sources before answering. DataBlue combines a Search endpoint (to discover sources) and a Scrape endpoint (to extract full-page content as LLM-ready markdown and JSON), so your agent reasons over real, up-to-date information instead of relying on stale training data.
How is this different from a standard search API?
A search API only returns links and snippets. Deep research needs the full content behind those links. DataBlue does both: search to discover, then scrape to extract the complete page — JavaScript rendered, cleaned, and converted to markdown — in the same workflow with one API key.
Does the output include citations?
Yes. Every search result and scraped page carries its source URL and metadata, so you can build answer engines and research reports that attribute every claim to a real, linkable source.
Can it handle JavaScript-heavy pages?
Yes. The Scrape API renders JavaScript by default and returns the fully-loaded content, so single-page apps and dynamically-rendered articles come back complete rather than as empty shells.
How does pricing work for large research runs?
Pricing is credit-based and credits never expire, so a big one-off research run doesn't get penalized. You pay only for the searches and scrapes you actually run, and the free tier includes 1,000 credits a month to prototype with.
Build Research That Reads the Web.
Start with 1,000 free credits a month — no card, no expiry. Wire up search-then-scrape and watch your agent ground its answers in live data.