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Open Deep Research

AI assistants capable of in-depth, autonomous ("deep") research on user-supplied topics has become a major area of interest. Here, we discus a few of the common architectures across various closed / open deep research tools and provide an overview of our implementation. We show how to run it locally using LangGraph studio. We also review many of the configurations, allowing users to customize the input, the models, the search API, the report structure, and the depth of research. Repo: https://github.com/langchain-ai/open_... Video notes: https://mirror-feeling-d80.notion.sit... Chapters: 00:00 - Introduction to Deep Research 00:45 - Live Demo in LangGraph Studio 01:30 - Human Feedback Loop for Report Planning 02:15 - Parallel Deep Research Process 03:00 - Core Components of Deep Research Systems 04:00 - Comparing Human-in-Loop Approaches 05:00 - Tool-Calling Agents vs Workflows 06:15 - Popular Open Source Implementations 07:30 - Architectural Tradeoffs 08:00 - Evaluations (Gaia and Humanities Last Exam) 09:00 - Configurability Advantages 10:00 - Setting Up Custom Configurations 10:45 - Example Report Comparison 12:00 - Cost Analysis (vs $200/month subscriptions) 12:45 - Benefits of Open Source Research Tools