OpenDeepResearcher - an open-source AI research tool that automates search, evaluation, extraction, and report generation.

What is OpenDeepResearcher?

OpenDeepResearcher is an open-source AI research tool that helps users efficiently complete complex information retrieval and analysis tasks. Based on iterative search and inference, it automatically retrieves relevant data from the web and generates comprehensive reports. The system supports asynchronous processing to improve efficiency and features deduplication to avoid processing the same content repeatedly. Users only need to input their research topic, and the system automatically completes the entire process of searching, evaluating, extracting, and generating reports. OpenDeepResearcher is suitable for fields such as finance, science, policy, and engineering, providing researchers with a low-cost and efficient solution.

Main functions of OpenDeepResearcher

• Iterative research cycleBased on the research topic input by the user, the system generates an initial search query. In each iteration, the system generates new search queries based on the information already acquired, gradually refining the research scope until the research objectives are met or the set number of iterations is reached.
• Asynchronous processingOperations such as searching, web page crawling, page evaluation, and context extraction are performed in parallel, significantly improving processing speed and efficiency.
• Deduplication functionThe system automatically aggregates and deduplicates links, avoiding the repeated processing of the same web page content and saving time and resources.
• LLM-driven decision makingThe system uses a large language model (LLM) to generate search queries, evaluate page relevance, extract key information, and generate a final comprehensive report.

OpenDeepResearcher's technical principles

• Search and Data AcquisitionThis tool uses the SERPAPI to perform Google searches and quickly retrieve webpage links relevant to user queries. It also uses Jina to extract webpage content and convert HTML pages into plain text suitable for LLM processing.
• LLM-driven inferenceNatural language processing and reasoning are performed using LLMs provided by OpenRouter (such as anthropic/claude-3.5-haiku). The LLM is responsible for generating search queries, evaluating the relevance of web pages, extracting key information, and determining whether further searching is needed.
• Iterative optimizationThe system feeds back the information obtained in each iteration to the LLM, generates new search queries based on the LLM's evaluation results, and gradually optimizes the research results.
• Asynchronous processing and deduplicationThis approach utilizes asynchronous programming techniques such as nest_asyncio to achieve parallel processing of searching, fetching, evaluating, and extracting data. A deduplication mechanism is used to avoid processing the same links repeatedly, thus improving efficiency.

OpenDeepResearcher project address

• GitHub repository:https://github.com/mshumer/OpenDeepResearcher

Application scenarios of OpenDeepResearcher

• literature reviewIt helps researchers quickly collect and organize literature in relevant fields and generate literature review reports.
• Industry AnalysisEnterprises collect market dynamics, competitive landscape and trend information of specific industries and generate industry analysis reports.
• Company ResearchAnalysts quickly collect financial data, news reports, and market evaluations of target companies to generate company research reports.
• Policy assessmentResearchers collect data and cases related to specific policies to analyze the effectiveness and impact of policy implementation.
• Technical Trend AnalysisEngineers collect and analyze the latest advancements and trends in specific technological fields to provide direction for technological research and development.