Deep Research
Build agentic research tools with deep web search capabilities
Build automated research agents that search the web, scrape full-page content, and synthesize findings with an LLM. Firecrawl handles source discovery and content extraction so you can focus on analysis, not parsing HTML.
This is the current recommended Deep Research workflow. It is built from Firecrawl Search and Scrape. If you are maintaining the legacy v1 Deep Research API, use the v1 Deep Research endpoint and plan migration to the Search-based workflow.
Start with a Template
Choose from multiple research templates. Clone, configure your API key, and start researching.
How It Works
Build powerful research tools that transform scattered web data into comprehensive insights. The core pattern is a search → scrape → analyze → repeat loop: use Firecrawl’s search API to discover relevant sources, scrape each source for full content, then feed the results into an LLM to synthesize findings and identify follow-up queries.
Search for sources
Use the /search endpoint to find relevant pages for your research topic.
from firecrawl import Firecrawl
firecrawl = Firecrawl(api_key="fc-YOUR-API-KEY")
results = firecrawl.search(
"recent advances in quantum computing",
limit=5,
scrape_options={"formats": ["markdown", "links"]}
)Scrape discovered pages
Extract full content from each result to get detailed information with citations.
for result in results:
doc = firecrawl.scrape(result["url"], formats=["markdown"])
# Feed doc content into your LLM for analysisAnalyze and iterate
Use an LLM to synthesize findings, identify gaps, and generate follow-up queries. Repeat the loop until your research question is fully answered.
Why Researchers Choose Firecrawl
Accelerate Research from Weeks to Hours
Build automated research systems that discover, read, and synthesize information from across the web. Create tools that deliver comprehensive reports with full citations, eliminating manual searching through hundreds of sources.
Ensure Research Completeness
Reduce the risk of missing critical information. Build systems that follow citation chains, discover related sources, and surface insights that traditional search methods miss.
Research Tool Capabilities
- Iterative Exploration: Build tools that automatically discover related topics and sources
- Multi-Source Synthesis: Combine information from hundreds of websites
- Citation Preservation: Maintain full source attribution in your research outputs
- Intelligent Summarization: Extract key findings and insights for analysis
- Trend Detection: Identify patterns across multiple sources
FAQs
Related Use Cases
- AI Platforms - Build AI research assistants
- Content Generation - Research-based content
- Competitive Intelligence - Market research