Intelligent Workflow System Based on LangGraph
The technical core of Open Deep Research is built on top of the LangGraph workflow engine. This memory checkpoint system (MemorySaver) realizes the state management and process control of research tasks. The flowchart generated through the builder.compile method can intuitively display the complete report generation logic, and supports visualization through Mermaid charts.
At the execution level, the system uses an asynchronous streaming processing architecture (astream), where each research task is identified and tracked by a unique thread_id. The workflow consists of three main phases: firstly, a report outline is generated using the planning model, followed by a search API to obtain the research material, and finally the full report is integrated and output by the writing model. Each stage enables multiple rounds of iterations until predefined quality criteria are met.
The advantage of this architecture is to decompose the complex intelligent research process into manageable modular components, and developers can directly call the underlying graph object through Jupyter Notebook to achieve more flexible research process customization and effect monitoring.
This answer comes from the articleOpen Deep Research: LangChain's Open Source Intelligent Assistant for Deep ResearchThe
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