Extensible design of the framework
DeerFlow adopts a modular decoupled design, through the conf.yaml configuration file to achieve a flexible combination of functional units. Its technology stack contains three key layers:
- Integration of third-party APIs such as Tavily/Volcengine at the interface layer
- Logic layer manages intelligent body collaboration via LangGraph
- The presentation layer supports both CLI and cloud platform interactions.
This architecture allows developers to quickly replace search engine or language model components without changing the core logic. For example, users can switch the default GPT model to Claude by simply modifying the llm_provider parameter in the configuration file. The cloud deployment solution encapsulates the dependency environment through Docker containers and realizes minute-level service on-line in Volcengine platform, which shortens the deployment cycle by 90% compared to traditional research tools.
This answer comes from the articleDeerFlow: an open source automated framework for deep researchThe
