Template description
Open Deep Research - AI-Powered Autonomous Research Workflow
Description
This workflow automates deep research by leveraging AI-driven search queries, web scraping, content analysis, and structured reporting. It enables autonomous research with iterative refinement, allowing users to collect, analyze, and summarize high-quality information efficiently.
How it works
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πΉ User Input
- The user submits a research topic via a chat message.
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π§ AI Query Generation
- A Basic LLM generates up to four refined search queries to retrieve relevant information.
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π SERPAPI Google Search
- The workflow loops through each generated query and retrieves top search results using the SerpAPI API.
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π Jina AI Web Scraping
- Extracts and summarizes webpage content from the URLs obtained via SerpAPI.
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π AI-Powered Content Evaluation
- An AI Agent evaluates the relevance and credibility of the extracted content.
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π Iterative Search Refinement
- If the AI finds insufficient or low-quality information, it generates new search queries to improve results.
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π Final Report Generation
- The AI compiles a structured markdown report, including sources with citations.
Set Up Instructions
π Estimated setup time: ~10-15 minutes
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β Required API Keys:
- SerpAPI β For Google Search results
- Jina AI β For text extraction
- OpenRouter β For AI-driven query generation and summarization
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βοΈ n8n Components Used:
- AI Agents with memory buffering for iterative research
- Loops to process multiple search queries efficiently
- HTTP Requests for direct API interactions with SerpAPI and Jina AI
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π Recommended Enhancements:
- Add sticky notes in n8n to explain each step for new users
- Implement Google Drive or Notion Integration to save reports automatically
π― Ideal for:
βοΈ Researchers & Analysts - Automate background research
βοΈ Journalists - Quickly gather reliable sources
βοΈ Developers - Learn how to integrate multiple AI APIs into n8n
βοΈ Students - Speed up literature reviews
π Completely free and open-source! π
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