Open Deep Research

LangChain · Beginner ·🤖 AI Agents & Automation ·1y ago
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_deep_research Video notes: https://mirror-feeling-d80.notion.site/Deep-…
Watch on YouTube ↗ (saves to browser)

Chapters (15)

Introduction to Deep Research
0:45 Live Demo in LangGraph Studio
1:30 Human Feedback Loop for Report Planning
2:15 Parallel Deep Research Process
3:00 Core Components of Deep Research Systems
4:00 Comparing Human-in-Loop Approaches
5:00 Tool-Calling Agents vs Workflows
6:15 Popular Open Source Implementations
7:30 Architectural Tradeoffs
8:00 Evaluations (Gaia and Humanities Last Exam)
9:00 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

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 38 Building a Research Assistant from Scratch
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 39 Benchmarking RAG over LangChain Docs
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 40 Skeleton-of-Thought: Building a New Template from Scratch
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 45 LangSmith Highlights: Evaluation
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