Open Deep Research - AI-Powered Autonomous Research Workflow
This workflow utilizes advanced artificial intelligence technology to automate the execution of in-depth research tasks. Users only need to input the research topic, and the system can generate precise search queries, conduct multiple rounds of online searches, and integrate information from various authoritative sources through intelligent analysis. Ultimately, the workflow produces a structured research report in Markdown format, significantly enhancing research efficiency and information accuracy. It is suitable for various scenarios such as academic research, market analysis, and product research, helping users quickly obtain comprehensive and valuable research results.
Workflow Name
Open Deep Research - AI-Powered Autonomous Research Workflow
Key Features and Highlights
This workflow leverages advanced Large Language Models (LLMs) to automate deep research tasks. It can automatically generate precise search queries based on user-input research topics, perform multi-batch web searches via SerpAPI, integrate intelligent analysis from Jina AI, and supplement with authoritative resources such as Wikipedia. Ultimately, it consolidates and refines the gathered information to produce structured and comprehensive research reports. Highlights include multi-turn AI collaboration, intelligent contextual memory, batch processing to optimize search efficiency, and professional report output in Markdown format.
Core Problems Addressed
Traditional research processes are time-consuming, with dispersed information sources that are difficult to integrate, often requiring users to manually sift through vast amounts of data. This workflow significantly enhances research efficiency and information accuracy through full automation, multi-channel deep mining, and intelligent summarization, enabling users to quickly obtain comprehensive and valuable research outcomes.
Application Scenarios
- Academic researchers rapidly collecting and organizing literature
- Market analysts conducting competitor and industry trend research
- Product managers or planners performing preliminary research and proposal preparation
- Consulting professionals quickly consolidating client industry information
- Any scenario requiring systematic, in-depth information gathering and analysis
Main Process Steps
- User Input Trigger: Research requests are initiated via chat messages.
- Search Query Generation: The LLM generates multiple precise search keywords based on the user’s question.
- Query Splitting and Batch Processing: Keywords are divided into batches to optimize calls to SerpAPI and Jina AI interfaces.
- Web Search: SerpAPI is invoked to perform Google searches, retrieving rich organic search results.
- Data Formatting and Analysis: Search results are formatted and subjected to deep content analysis by Jina AI.
- Contextual Information Extraction: The LLM extracts content segments most relevant to the user query.
- Wikipedia Assistance: Wikipedia tool nodes are called to supplement authoritative information.
- Comprehensive Report Generation: Based on the extracted information, a structured and well-organized research report is generated in Markdown format.
- Context Memory Management: An LLM memory buffer node maintains research context to support multi-turn interactions.
Involved Systems or Services
- OpenRouter: Efficient LLM service provider supporting multi-node AI language model calls.
- SerpAPI: Professional Google Search API enabling programmatic web search.
- Jina AI: Powerful AI analysis engine for content understanding and information extraction.
- Wikipedia Tool Node: Provides authoritative encyclopedia content to assist research.
- n8n Native Nodes: Including code execution, data splitting and batch processing, HTTP requests, chat triggers, and more.
Target Users and Value Proposition
This workflow is ideal for researchers, data analysts, market researchers, product managers, and any professionals requiring efficient information retrieval and report writing. It not only saves time spent on manual searching and data filtering but also enhances the depth and breadth of research, helping users quickly obtain high-quality, structured research outputs to support decision-making and knowledge accumulation.