Gemini Fullstack LangGraph's front-end and back-end separation full-stack architecture not only realizes technical decoupling, but also creates an excellent research experience.
The interactive interface built by the front-end React framework shows the research progress in real time, including: query generation records, webpage screening process, information analysis results, etc.; the back-end FastAPI service guarantees the high efficiency of data processing, especially the immediate processing of large-scale text content; and the process visualization by LangGraph allows the user to intuitively understand the decision-making path of the intelligent agent.
- Real-time feedback: Users can observe the execution details of each step of the research operation
- Historical retrospective: a complete record of all intermediate states in the research process
- Chain-of-evidence presentation: clearly labeled data sources and rationale for each conclusion
This design is particularly suitable for research scenarios that require validation of information sources, such as academic paper writing or business decision support. Researchers not only get the results, but also can track the whole analysis process to ensure the credibility and reproducibility of the conclusions. The full-stack architecture enables AI research to move from 'black box' to 'transparency', dramatically increasing the professional value of the tool.
This answer comes from the articleGemini Fullstack LangGraph: a full-stack application for intelligent research based on Gemini and LangGraphThe
