Core Insights - Ant Group's open-source team unveiled the 2.0 version of the "2025 Large Model Open Source Development Ecosystem Panorama" at the Shanghai Bund Conference, showcasing significant changes in the open-source landscape [2][4][10] Group 1: Ecosystem Changes - The updated panorama includes 114 projects, a decrease of 21 from the previous version, with 39 new projects and 60 projects that have exited the stage, including notable ones like TensorFlow, which has been overtaken by PyTorch [4][5] - The overall trend indicates a significant reshuffling within the ecosystem, with a median age of only 30 months for projects, highlighting a youthful and rapidly evolving environment [5][10] - Since the "GPT moment" in October 2022, 62% of the projects have emerged, indicating a dynamic influx of new entrants and exits [5][10] Group 2: Project Performance - The top ten most active open-source projects reflect a focus on AI, LLM, Agent, and Data, indicating the primary areas of interest within the ecosystem [7][9] - The classification framework has evolved from broad categories to more specific segments, including AI Agent, AI Infra, and AI Data, emphasizing the shift towards an "agent-centric" era [10][19] Group 3: Contributions by Region - Among 366,521 developers, the US and China contribute over 55%, with the US leading at 37.41% [10][12] - In specific areas, the US shows a significant advantage in AI Infra and AI Data, with contributions of 43.39% and 35.76% respectively, compared to China's 22.03% and 21.5% [12][14] Group 4: Methodological Evolution - The methodology for selecting projects has shifted from a known starting point to a broader approach that captures high-activity projects, increasing the threshold for inclusion [15][18] - The new methodology aligns with Ant Group's goal of providing insights for internal decision-making and guidance for the open-source community [15][18] Group 5: AI Agent Developments - The AI Agent category has evolved into a structured system with various specialized tools, indicating a transition from chaotic growth to systematic differentiation [19][21] - AI Coding has expanded its capabilities, covering the entire development lifecycle and supporting multimodal and context-aware functionalities [23][27] Group 6: Market Trends - The report predicts significant commercial potential in AI Coding, with new revenue models emerging from subscription services and value-added features [24][27] - Chatbot applications have seen a peak but are now stabilizing, with a shift towards integrating knowledge management for long-term productivity [28][30] Group 7: Infrastructure and Operations - The Model Serving segment remains a key battleground, with high-performance cloud inference solutions like vLLM and SGLang leading the way [42][45] - LLMOps is rapidly growing, focusing on the full lifecycle management of models, emphasizing stability and observability [50][52] Group 8: Data Ecosystem - The AI Data sector appears stable, with many projects originating from the AI 1.0 era, but is facing challenges in innovation and engagement [58][60] - The evolution of data infrastructure is anticipated, moving from static repositories to dynamic systems that provide real-time insights for models [60][61] Group 9: Open Source Dynamics - A trend towards customized open-source licenses is emerging, allowing for more control and flexibility in commercial negotiations [62][63] - The landscape of open-source projects is being challenged, with some projects operating under restrictive licenses, raising questions about the definition of "open source" [62][63] Group 10: Competitive Landscape - The competitive landscape is marked by a divergence between open-source and closed-source models, with Chinese projects flourishing while Western firms tighten their open-source strategies [67][68] - The introduction of MoE architectures and advancements in reasoning capabilities are becoming standard features in new models, indicating a shift in focus from scale to reasoning [69][70]

Core Viewpoint - The forum titled "AI Open Source Era: Building Global Ecosystem and Sustainable Growth" will explore the core logic of the AI open-source ecosystem through various perspectives, highlighting the trends and practices in the field [1][5]. Group 1: Forum Overview - The forum will feature three keynote speeches that will analyze the global large model open-source ecosystem, community practices, and the competitive landscape of open-source models [1][2]. - Keynote speakers include Wang Xu from Ant Group, Chen Yingda from Modao Community, and Yang Pan from Silicon-based Flow, each providing insights into different aspects of the AI open-source landscape [1][6][10]. Group 2: Keynote Topics - Wang Xu will discuss the panoramic view and trends of the global large model open-source ecosystem, using community data as a reference for technical decision-making [1][6]. - Chen Yingda will share the construction experience behind over 90,000 quality models and how the "Model as a Service" (MaaS) concept drives the evolution of the open-source ecosystem [1][8]. - Yang Pan will analyze the competitive and collaborative dynamics of the global open-source model ecosystem, focusing on the transition from belief to confidence in technology [1][9]. Group 3: Roundtable Discussions - Following the keynotes, two roundtable discussions will focus on Vibe Coding and AI Agents, addressing real-world applications, potential issues, and future possibilities in human-machine collaboration [2][11]. - The discussions will feature practitioners and entrepreneurs from various organizations, including Ant Group and ByteDance, who will provide multi-dimensional insights into the evolution of AI coding products and the path towards AGI [2][13][15]. Group 4: Event Logistics - The forum will take place at the C2 Hall of the Expo Garden in Huangpu District, Shanghai, with a limited capacity of 350 professional audience seats [2][5]. - Registration for professional attendees is now open, inviting participants to engage in discussions and capture technological opportunities [2].

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]