Today's AI Frameworks & SDKs: Fastest-Growing Projects — May 06, 2026
Today's AI Frameworks & SDKs, we see a surge in tools focused on optimizing and accelerating AI model performance, particularly for large language models (LLMs). Several projects are leveraging innovative techniques such as caching, lookahead prediction, and async DMA prefetching to reduce latency and improve efficiency. Meanwhile, other frameworks are gaining traction by providing seamless integration with popular AI engines and libraries.
HKUDS-AI's polymarket-ai-trading repository has seen significant growth, with a Growth Score of 88.28 and 140 stars. This project provides an AI-assisted paper trading platform for Polymarket, utilizing OpenAI market insights, vector search, and Kelly sizing to inform trading decisions. Its popularity can be attributed to the increasing interest in using AI to optimize trading strategies.
MaaNTE, developed by 1bananachicken, boasts a staggering 797 stars and a Growth Score of 81.25. This MAA异环小助手 (MAA Framework-powered assistant) is designed for QQ exchanges and has likely gained popularity due to its seamless integration with the MAAF framework. The project's high commit activity, with 100 updates in the past 30 days, suggests an active community driving its growth.
The noonghunna/club-3090 repository, with a Growth Score of 77.81 and 555 stars, provides community recipes for serving LLMs on RTX 3090 GPUs. Its popularity can be attributed to the growing demand for efficient and scalable solutions for deploying large language models. The project's focus on multi-engine support (vLLM, llama.cpp, SGLang) and model-agnostic configurations has likely contributed to its appeal.
Stash, developed by alash3al, is a persistent memory layer for AI agents that stores episodes, facts, and working context in Postgres. With 651 stars and a Growth Score of 54.88, Stash's popularity can be attributed to its unique approach to providing a self-hosted, single-binary solution that eliminates the need for cloud services.
Reasonix, created by esengine, is a DeepSeek-native agent framework with features like Cache-First Loop, R1 Thought Harvesting, and Tool-Call Repair. Boasting 372 stars and a Growth Score of 34.50, Reasonix's growth can be attributed to its innovative approach to improving AI model performance.
Other notable projects in this space include vLLM Metal plugin (TheTom/vllm-swift), Cairn state-space search engine (oritera/Cairn), private on-device AI suite for Android (jegly/Box), and Project Chronos zero-stall MoE inference (FonaTech/Project_Chronos). These projects demonstrate the diversity of innovative solutions being developed to optimize and accelerate AI performance.
While some projects, like Swarm Forge (unclebob/swarm-forge) and MaaNTE, have garnered significant attention with their high star counts, others, such as polymarket-ai-trading and noonghunna/club-3090, are making waves with their impressive growth scores.
HKUDS-AI's polymarket-ai-trading repository has seen significant growth, with a Growth Score of 88.28 and 140 stars. This project provides an AI-assisted paper trading platform for Polymarket, utilizing OpenAI market insights, vector search, and Kelly sizing to inform trading decisions. Its popularity can be attributed to the increasing interest in using AI to optimize trading strategies.
MaaNTE, developed by 1bananachicken, boasts a staggering 797 stars and a Growth Score of 81.25. This MAA异环小助手 (MAA Framework-powered assistant) is designed for QQ exchanges and has likely gained popularity due to its seamless integration with the MAAF framework. The project's high commit activity, with 100 updates in the past 30 days, suggests an active community driving its growth.
The noonghunna/club-3090 repository, with a Growth Score of 77.81 and 555 stars, provides community recipes for serving LLMs on RTX 3090 GPUs. Its popularity can be attributed to the growing demand for efficient and scalable solutions for deploying large language models. The project's focus on multi-engine support (vLLM, llama.cpp, SGLang) and model-agnostic configurations has likely contributed to its appeal.
Stash, developed by alash3al, is a persistent memory layer for AI agents that stores episodes, facts, and working context in Postgres. With 651 stars and a Growth Score of 54.88, Stash's popularity can be attributed to its unique approach to providing a self-hosted, single-binary solution that eliminates the need for cloud services.
Reasonix, created by esengine, is a DeepSeek-native agent framework with features like Cache-First Loop, R1 Thought Harvesting, and Tool-Call Repair. Boasting 372 stars and a Growth Score of 34.50, Reasonix's growth can be attributed to its innovative approach to improving AI model performance.
Other notable projects in this space include vLLM Metal plugin (TheTom/vllm-swift), Cairn state-space search engine (oritera/Cairn), private on-device AI suite for Android (jegly/Box), and Project Chronos zero-stall MoE inference (FonaTech/Project_Chronos). These projects demonstrate the diversity of innovative solutions being developed to optimize and accelerate AI performance.
While some projects, like Swarm Forge (unclebob/swarm-forge) and MaaNTE, have garnered significant attention with their high star counts, others, such as polymarket-ai-trading and noonghunna/club-3090, are making waves with their impressive growth scores.