Open Deep Research - AI-Powered Autonomous Research Workflow

Open Deep Research automates the research process by generating precise search queries and extracting relevant information from multiple sources. This AI-powered workflow efficiently compiles comprehensive research reports, saving time and enhancing the quality of insights. With 17 integrated nodes, it seamlessly connects various tools for a streamlined research experience.

7/8/2025
17 nodes
Complex
manualcomplexlangchainsplitinbatchessticky noteadvancedapiintegration
Categories:
Complex WorkflowManual Triggered
Integrations:
LangChainSplitInBatchesSticky Note

Target Audience

This workflow is ideal for:
- Researchers: Individuals conducting thorough investigations on various topics.
- Academics: Professors and students who require comprehensive data analysis and reporting.
- Content Creators: Writers and marketers needing well-structured research reports to support their content.
- Business Analysts: Professionals seeking detailed insights and summaries for decision-making processes.
- Developers: Those looking to integrate AI-driven research capabilities into their applications.

Problem Solved

This workflow addresses the challenge of efficiently gathering, analyzing, and reporting information from diverse sources. It automates the research process, allowing users to:
- Generate precise search queries based on user input.
- Retrieve and format search results from APIs like SerpAPI and Jina AI.
- Extract relevant information from web content using AI models.
- Compile comprehensive reports that present findings in an organized manner, thus saving time and enhancing productivity.

Workflow Steps

  • Trigger: The workflow begins with a manual trigger via a chat message, prompting user input.
    2. Query Generation: An AI model generates up to four distinct search queries based on the user's input.
    3. Data Retrieval: The workflow sends requests to the SerpAPI to fetch search results and formats the results for further processing.
    4. Data Chunking: The retrieved data is parsed and split into manageable chunks for analysis.
    5. Context Extraction: Relevant information is extracted from the fetched content using another AI model.
    6. Comprehensive Reporting: The extracted contexts are compiled into a well-structured research report, formatted in Markdown for clarity.
    7. Memory Buffers: Memory buffers store input and report context for enhanced AI responses during the workflow execution.

    • Customization Guide

    Users can customize the workflow by:
    - Modifying Search Queries: Adjust the prompts used for generating search queries to fit specific research needs.
    - Changing API Credentials: Update the API keys for SerpAPI, Jina AI, and OpenRouter as per the user's account settings.
    - Altering Data Processing Logic: Edit the JavaScript code in nodes like 'Parse and Chunk JSON Data' and 'Format SerpAPI Organic Results' to refine how data is handled.
    - Personalizing Report Structure: Customize the system message in the 'Generate Comprehensive Research Report' node to change how the report is formatted and what information is included.
    - Adding New Nodes: Integrate additional nodes to expand functionality, such as incorporating other data sources or analysis tools.