Open Source LLM Development 2025: Landscape, Trends and Insights

Open Source LLM Development 2025: Landscape, Trends and Insights | INCLUSION AI

Skip to main content

Originally published on Medium by Ant Open Source.

「AI Surpasses Cloud Native as the Most Influential Tech Domain」

According to OpenRank data from OpenDigger , AI surpassed Cloud Native in 2023 to become the most influential technology domain in terms of community collaboration on GitHub. AI's total influence score overtook Frontend technologies in 2017, accelerated post-2022, and surpassed the declining Cloud Native in 2023 to claim the top spot.

The LLM Development Ecosystem: A Snapshot ​

https://antoss-landscape.my.canva.site

In February 2025, DeepSeek sparked a surge in the LLM development ecosystem. GitHub's Weekly Trending List reached a peak where 94% of the listed repositories were AI-related . This ecosystem is incredibly new and evolving fast — over the past three months, 60% of LLM-related projects that appeared on GitHub Trending were emerged after 2024, and nearly 21% were created in just the last six months.

We build the landscape by first selecting well-known AI projects (e.g., PyTorch, LangChain, vLLM) as seed nodes. By analyzing developer collaboration relationships across "related" GitHub projects, we explored multiple facets of the ecosystem. We rely on the OpenRank influence metric developed by X-lab at East China Normal University — only projects with an average monthly OpenRank score exceeding 10 in year 2025 are included.

As of May 2025, the Open Source LLM Development Landscape 2025 includes 135 projects across 19 technical domains , spanning both Agent application layers and model infrastructure layers.

Below are the details of projects ranked in the Top 20 of OpenRank:

By stack ranking the year-over-year absolute changes in OpenRank between 2024 and 2025, we converged on 3 key observations:

Model Training Frameworks : PyTorch remains the undisputed leader. Baidu's PaddlePaddle saw a 41% drop in OpenRank compared to the previous year.

Efficient Inference Engines : The high-performance inference engines vLLM and SGLang have undergone rapid iterations, ranking first and third in OpenRank value growth. Their superior GPU inference performance made them the most popular choices for enterprise-level LLM deployment.

Low-Code Application (Agent) Development Frameworks : Agent platforms like Dify and RAGFlow, which integrate RAG-based knowledge retrieval, are experiencing rapid growth as they meet the red-hot demand for quickly building AI applications. Notably, both platforms are strong projects emerging from China's developer community.

After observing over 100 open-source projects, we've reached a pivotal point to make a bold claim: the LLM development ecosystem operates like a real-world Hackathon — developers, empowered by AI, now operate as "super individuals" to rapidly build open-source projects around trending topics, with cycles of rapid creation and dissolution driven by speed and iteration.

Key hackathon observations: ​

1. Developers keep building OSS clones for rapid adoption

When closed-source projects like Devin, Perplexity, and Manus brought shockwaves to the industry, developers quickly replicated open-source versions:

Devin & OpenDevin : In March 2024, Xingyao Wang (PhD candidate at UIUC) launched OpenDevin. Within a month, its OpenRank skyrocketed to 190. The project was rebranded as OpenHands and evolved into All Hands AI.

Perplexity & Perplexica : Independent developer ItzCrazyKns created Perplexica in 2024 as an open-source alternative. It amassed 22K GitHub stars but OpenRank plateaued around 25.

Manus & OpenManus : In March 2025, as Manus went viral, DeepWisdom pulled off a "3-hour replication" with OpenManus, garnering 8K stars on its first day.

2. Ephemeral technical experiments often end up in the AI graveyard

Out of 5,079 AI tools recorded by Dang AI, 1,232 have been archived/abandoned. Dang AI even created an " AI Graveyard ." We've curated an "Open-Source AI Graveyard" for projects that gained massive attention upon launch but became inactive — including BabyAGI (April 2023) and Swarm (OpenAI, formally discontinued March 2025).

3. Model capabilities are reshaping application scenarios

The decline of AI Search projects : The generalization of model capabilities (GPT-4, Gemini 2.0) is squeezing the market for specialized search tools like Morphic.sh and Scira.

The rise of AI Coding projects : Claude 3.7 Sonnet's prowess in coding ushered in "Vibe Coding." IDE plugins like Continue and Cline are thriving open-source options, each with over 3,000 community contributors and steadily rising OpenRank scores.

4. Dynamic competition across ecosystem niches

Divergent trajectories of Agent Frameworks : Application platforms like Dify diverged sharply from development frameworks like LangChain. Special mention: DB-GPT , an open-source project initiated by Ant Group, integrates AI application development into big data application scopes.

The rise of Reinforcement Learning : DeepSeek-R1's "Aha Moment" demonstrated RL's effectiveness as a post-training approach. Frameworks like Verl and OpenRLHF have seen remarkable growth. In February, inclusionAI fully open-sourced their RL framework AReaL , designed to train large inference models that anyone can reproduce.

The blurring of Technical Boundaries : Vector databases, once standalone, now compete with traditional big data systems (e.g., OceanBase adding vector storage support) while maintaining a delicate ecological equilibrium.

Now, the Technical Trends in LLM Open-Source Development Ecosystems ​

We observed and summarized 7 relatively clear technical trends including emerging paradigms such as Agent Frameworks , AI-native communication protocols like MCP , and Coding Agents at the application layer.

1. The Agent Frameworks Boom Diverged in 2025 ​

From 2023 to 2024, "all-in-one" frameworks like LangChain dominated with their pioneering task orchestration capabilities. A huge number of new Agent development frameworks emerged, many focusing on specific features such as tool calling, RAG integration, long-context memory, or ReAct planning.

By the second half of 2024, only a few new frameworks entered the ecosystem. As the initial hype faded, early market leaders like LangChain were gradually declining due to steep learning curves.

Entering 2025, the market showed signs of divergence: platforms like Dify and RAGFlow…