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