Today's AI Frameworks & SDKs: Fastest-Growing Projects — May 04, 2026
Today's trends in AI Frameworks & SDKs are dominated by tools that enable efficient deployment and scaling of large language models (LLMs) on various hardware configurations. Another notable trend is the emergence of frameworks focused on state-space search, autonomous testing, and vulnerability research.
The fastest-growing repository this week is noonghunna/club-3090, with a Growth Score of 94.83 and 463 stars. This community-driven project provides recipes for serving LLMs on RTX 3090 cards, supporting multiple engines and models. Its popularity stems from the growing demand for efficient deployment of LLMs on powerful GPUs.
1bananachicken/MaaNTE has a Growth Score of 87.81 and 625 stars, making it another rapidly growing repository. This project appears to be an AI-powered assistant built using the MAA Framework, with its growth driven by the increasing adoption of conversational AI applications in various industries.
alash3al/stash boasts a Growth Score of 65.00 and 634 stars, showcasing its popularity among developers seeking efficient memory management solutions for AI agents. Stash provides a persistent memory layer that stores episodes, facts, and working context in Postgres, making it an attractive choice for those building complex AI systems.
esengine/reasonix has a Growth Score of 37.31 and 310 stars, with its growth driven by the increasing interest in DeepSeek-native agent frameworks. This project offers a Cache-First Loop, R1 Thought Harvesting, and Tool-Call Repair, making it an appealing choice for developers building intelligent agents.
oritera/Cairn has a Growth Score of 33.80 and 648 stars, indicating its growing popularity among researchers and developers working on state-space search problems. This general-purpose engine has been validated in autonomous penetration testing and is likely to attract more users seeking efficient solutions for complex problem-solving tasks.
jegly/Box boasts a Growth Score of 21.24 and 278 stars, with its growth driven by the increasing demand for private on-device AI suites. This project provides a fork of Google's AI Edge Gallery with llama.cpp, whisper.cpp, and other features, making it an attractive choice for developers seeking to deploy AI models on Android devices.
unclebob/swarm-forge has a Growth Score of 20.74 and 441 stars, indicating its growing popularity among developers working on multi-agent systems. This simple tool coordinates several AI agents, making it an appealing choice for those building complex AI-powered applications.
FonaTech/Project_Chronos boasts a Growth Score of 20.67 and 173 stars, with its growth driven by the increasing interest in optimizing inference performance for large language models. This project provides zero-stall MoE inference via lookahead prediction and async DMA prefetching, optimized for SSD I/O with hybrid MLA+sliding window attention.
baidu-baige/LoongForge has a Growth Score of 19.59 and 152 stars, showcasing its growing popularity among researchers and developers working on large-scale language and multimodal models. This modular training framework offers scalability and efficiency, making it an attractive choice for those building complex AI systems.
416rehman/DeepZero boasts a Growth Score of 18.67 and 387 stars, with its growth driven by the increasing demand for automated vulnerability research tools. This framework parses, decompiles, and analyzes thousands of Windows kernel drivers using AI agents, making it an appealing choice for security researchers seeking to identify zero-days.
We skipped MaaNTE's description as it was not meaningful enough to provide a clear understanding of its purpose or functionality.
The fastest-growing repository this week is noonghunna/club-3090, with a Growth Score of 94.83 and 463 stars. This community-driven project provides recipes for serving LLMs on RTX 3090 cards, supporting multiple engines and models. Its popularity stems from the growing demand for efficient deployment of LLMs on powerful GPUs.
1bananachicken/MaaNTE has a Growth Score of 87.81 and 625 stars, making it another rapidly growing repository. This project appears to be an AI-powered assistant built using the MAA Framework, with its growth driven by the increasing adoption of conversational AI applications in various industries.
alash3al/stash boasts a Growth Score of 65.00 and 634 stars, showcasing its popularity among developers seeking efficient memory management solutions for AI agents. Stash provides a persistent memory layer that stores episodes, facts, and working context in Postgres, making it an attractive choice for those building complex AI systems.
esengine/reasonix has a Growth Score of 37.31 and 310 stars, with its growth driven by the increasing interest in DeepSeek-native agent frameworks. This project offers a Cache-First Loop, R1 Thought Harvesting, and Tool-Call Repair, making it an appealing choice for developers building intelligent agents.
oritera/Cairn has a Growth Score of 33.80 and 648 stars, indicating its growing popularity among researchers and developers working on state-space search problems. This general-purpose engine has been validated in autonomous penetration testing and is likely to attract more users seeking efficient solutions for complex problem-solving tasks.
jegly/Box boasts a Growth Score of 21.24 and 278 stars, with its growth driven by the increasing demand for private on-device AI suites. This project provides a fork of Google's AI Edge Gallery with llama.cpp, whisper.cpp, and other features, making it an attractive choice for developers seeking to deploy AI models on Android devices.
unclebob/swarm-forge has a Growth Score of 20.74 and 441 stars, indicating its growing popularity among developers working on multi-agent systems. This simple tool coordinates several AI agents, making it an appealing choice for those building complex AI-powered applications.
FonaTech/Project_Chronos boasts a Growth Score of 20.67 and 173 stars, with its growth driven by the increasing interest in optimizing inference performance for large language models. This project provides zero-stall MoE inference via lookahead prediction and async DMA prefetching, optimized for SSD I/O with hybrid MLA+sliding window attention.
baidu-baige/LoongForge has a Growth Score of 19.59 and 152 stars, showcasing its growing popularity among researchers and developers working on large-scale language and multimodal models. This modular training framework offers scalability and efficiency, making it an attractive choice for those building complex AI systems.
416rehman/DeepZero boasts a Growth Score of 18.67 and 387 stars, with its growth driven by the increasing demand for automated vulnerability research tools. This framework parses, decompiles, and analyzes thousands of Windows kernel drivers using AI agents, making it an appealing choice for security researchers seeking to identify zero-days.
We skipped MaaNTE's description as it was not meaningful enough to provide a clear understanding of its purpose or functionality.