开源AI开发生态大洗牌：低代码平台逆袭，传统LLM框架日渐式微

Core Insights - The report and the comprehensive panorama released by Ant Group provide a detailed analysis of the current open-source ecosystem for large models, highlighting its evolution and trends [1][4][40] Group 1: Overview of the Open-Source Ecosystem - The open-source ecosystem for large models is described as a "real-world hackathon," emphasizing the collaborative nature of development [2][3] - Ant Group's report includes a panorama covering 19 technical fields and 135 projects, from model infrastructure to intelligent applications [5][10] - The analysis identifies three dominant technical tracks in the current open-source ecosystem: model training frameworks, efficient inference engines, and low-code application development frameworks [10][11] Group 2: Key Projects and Trends - The report lists the top 20 projects for 2025, highlighting significant growth and decline among various projects [7] - PyTorch ranks first in influence among all projects in the panorama, while vLLM and SGlang are noted for rapid iteration in the inference category [14][31] - Dify and RAGFlow are emerging as leading platforms in application development, driven by their ability to meet enterprise user needs through low-code workflows [18][35] Group 3: Development Paradigms and Standards - The shift towards low-code development is becoming mainstream, with traditional agent frameworks declining in popularity [20][17] - New communication standards for models and applications are being established, such as the MCP protocol and A2A protocol, which facilitate interaction between different agents [22][25] - The report emphasizes the importance of standardization in the evolving landscape of large model services, suggesting that the standard protocol layer will become a strategic battleground for leading players [24][26] Group 4: Implications for Developers - Developers are encouraged to focus on enhancing user experience and deepening their understanding of specific application scenarios to gain competitive advantages [34][35] - The report highlights the need for developers to adapt to rapid changes in project cycles and to embrace a trial-and-error approach in development [37][38] - Overall, the report serves as a valuable resource for understanding the underlying mechanisms of the large model open-source ecosystem and its future direction [41][42]