Open Deep Research - AI-Powered Autonomous Research Workflow
This workflow utilizes AI language models and various data sources to achieve automated deep information retrieval and research report generation. After the user inputs a query, the system generates precise search keywords, conducts web searches using SerpAPI, and combines content analysis with Jina AI, ultimately integrating the results into a structured research report. This process enhances research efficiency, ensures the coherence and accuracy of information extraction, and is applicable in scenarios such as academic research, market research, content creation, and corporate decision-making, helping users quickly obtain high-quality materials.
Workflow Name
Open Deep Research - AI-Powered Autonomous Research Workflow
Key Features and Highlights
This workflow leverages advanced AI language models and multiple data sources to enable automated, intelligent deep information retrieval and research report generation. Based on user input queries, it automatically generates multiple precise search keywords, performs web searches via SerpAPI, integrates content analysis through Jina AI, and ultimately compiles a structured, detailed research report using AI. A memory buffer is incorporated to maintain contextual relevance, ensuring coherence and accuracy throughout information extraction and report generation.
Core Problems Addressed
Traditional deep research often requires manual construction of search terms, multi-platform searching, and information filtering, which is time-consuming, labor-intensive, and difficult to ensure comprehensiveness and professionalism. This workflow automates query generation, acquires information from multiple channels, and intelligently distills data, significantly improving research efficiency and report quality. It effectively solves issues related to fragmented information sources, filtering difficulties, and low efficiency of manual compilation.
Application Scenarios
- Academic researchers needing rapid access to multidimensional domain materials
- Market researchers conducting competitive intelligence gathering and analysis
- Content creators requiring high-quality background information support
- Corporate decision-makers performing information-driven strategic analysis
- Consulting professionals preparing professional and detailed client research reports
Main Process Steps
- User inputs a query request via chat trigger
- AI language model generates multiple precise search keywords
- Keywords are split into batches and SerpAPI is called to perform Google searches
- Search results are parsed and formatted, then split into batches for content analysis by Jina AI
- Relevant contextual information is extracted from web pages using LLM
- All extracted information is integrated to produce a structured, comprehensive research report (in Markdown format)
- A memory buffer node manages context to ensure coherence and richness in information processing
Involved Systems or Services
- Langchain AI nodes (for query generation, information extraction, report generation)
- OpenRouter (providing Google Gemini 2.0 model support)
- SerpAPI (Google Search API for retrieving web search results)
- Jina AI (content analysis and information extraction)
- Wikipedia tool node (supplementing authoritative knowledge)
- n8n built-in features (webhook triggers, data splitting, code node processing, etc.)
Target Users and Value
This workflow is ideal for professionals requiring efficient, systematic data research, including researchers, market analysts, content creators, corporate decision-makers, and consultants. It significantly reduces time spent on information gathering and organization, enhances research depth and report quality, and enables AI-assisted autonomous deep research, empowering users to make more informed decisions and create with greater insight.