Today's AI Frameworks & SDKs: Fastest-Growing Projects — May 26, 2026
Today's the AI Frameworks & SDKs space, there's a noticeable trend towards innovative virtualization and deployment strategies for large language models (LLMs) and other AI agents. Developers are increasingly looking to optimize performance and scalability while reducing computational overhead. Strukto-ai's Mirage leads this category with a high growth score of 93.78 and over 2,600 stars, highlighting the community's interest in unified virtual filesystems designed for AI agents.
strukto-ai/mirage: This repository provides a unified virtual filesystem for AI agents, enabling seamless interaction between various components of an AI system. Its impressive growth score of 93.78 and over 2,600 stars indicate that developers are actively seeking solutions to streamline the management and integration of AI agent files.
MoyuFamily/ai-relay: Ai-Relay is a serverless API gateway for AI services, offering multi-provider routing and failover capabilities. With a growth score of 57.83 and 52 stars, it suggests that developers are looking for flexible and scalable ways to deploy and manage AI APIs across different cloud providers.
lightseekorg/tokenspeed: TokenSpeed is an inference engine designed to operate at the speed of light, optimizing large language model performance. Its growth score of 52.83 and 1,168 stars reflect a growing demand for high-speed LLM inference solutions that can handle real-time applications efficiently.
simonlin1212/TradingAgents-astock: This project offers a multi-agent investment research framework tailored specifically to the A-share market in China, featuring seven AI analysts and advanced risk assessment tools. With 632 stars and a growth score of 50.73, it demonstrates the interest among developers in sophisticated financial analysis tools powered by AI.
deeplethe/forkd: Forkd is designed for AI agent microVMs, allowing rapid spawning of child VMs from a parent VM with minimal latency. Its growth score of 50.60 and 759 stars indicate that there is significant interest in efficient, isolated environments for running AI agents.
noonghunna/club-3090: This community-driven project provides recipes and configurations for serving large language models on NVIDIA RTX 3090 GPUs, supporting multiple engines. With a growth score of 34.21 and 1,139 stars, it highlights the demand for optimized performance tuning and model deployment on high-end hardware.
Ontos-AI/knowhere: Knowhere is a tool that extracts and parses unstructured data into structured chunks ready for AI agents or retrieval-augmented generation (RAG). With 540 stars and a growth score of 25.33, it suggests an increasing need for tools that can efficiently process and prepare large datasets for AI consumption.
jhaizhou-ops/pinrule: PinRule offers a universal framework for defining and enforcing behavior rules in long-term AI tasks to prevent drift. Its growth score of 24.38 and 31 stars indicate interest among developers looking to ensure consistent performance and adherence to rules over extended periods without relying on additional resources.
zimingttkx/QuantumFlow: QuantumFlow is a distributed framework for scheduling LLM inferences with support for multiple backends, adaptive strategies, and cluster management. With a growth score of 22.60 and 146 stars, it reflects the growing need for scalable, efficient inference solutions that can handle complex deployment scenarios.
HanGuo97/coda-kernels: CODA is an innovative approach to rewriting transformer blocks as GEMM-epilogue programs, aiming to optimize performance in neural network computations. Its growth score of 19.28 and 152 stars suggest a community interest in pushing the boundaries of computational efficiency in AI models.
Today's data highlights the diverse needs within the AI frameworks and SDKs space, from virtualization and deployment optimization to specialized applications like financial analysis and performance tuning on high-end hardware. The trend towards innovative solutions that tackle real-world challenges continues to drive growth across these projects.
strukto-ai/mirage: This repository provides a unified virtual filesystem for AI agents, enabling seamless interaction between various components of an AI system. Its impressive growth score of 93.78 and over 2,600 stars indicate that developers are actively seeking solutions to streamline the management and integration of AI agent files.
MoyuFamily/ai-relay: Ai-Relay is a serverless API gateway for AI services, offering multi-provider routing and failover capabilities. With a growth score of 57.83 and 52 stars, it suggests that developers are looking for flexible and scalable ways to deploy and manage AI APIs across different cloud providers.
lightseekorg/tokenspeed: TokenSpeed is an inference engine designed to operate at the speed of light, optimizing large language model performance. Its growth score of 52.83 and 1,168 stars reflect a growing demand for high-speed LLM inference solutions that can handle real-time applications efficiently.
simonlin1212/TradingAgents-astock: This project offers a multi-agent investment research framework tailored specifically to the A-share market in China, featuring seven AI analysts and advanced risk assessment tools. With 632 stars and a growth score of 50.73, it demonstrates the interest among developers in sophisticated financial analysis tools powered by AI.
deeplethe/forkd: Forkd is designed for AI agent microVMs, allowing rapid spawning of child VMs from a parent VM with minimal latency. Its growth score of 50.60 and 759 stars indicate that there is significant interest in efficient, isolated environments for running AI agents.
noonghunna/club-3090: This community-driven project provides recipes and configurations for serving large language models on NVIDIA RTX 3090 GPUs, supporting multiple engines. With a growth score of 34.21 and 1,139 stars, it highlights the demand for optimized performance tuning and model deployment on high-end hardware.
Ontos-AI/knowhere: Knowhere is a tool that extracts and parses unstructured data into structured chunks ready for AI agents or retrieval-augmented generation (RAG). With 540 stars and a growth score of 25.33, it suggests an increasing need for tools that can efficiently process and prepare large datasets for AI consumption.
jhaizhou-ops/pinrule: PinRule offers a universal framework for defining and enforcing behavior rules in long-term AI tasks to prevent drift. Its growth score of 24.38 and 31 stars indicate interest among developers looking to ensure consistent performance and adherence to rules over extended periods without relying on additional resources.
zimingttkx/QuantumFlow: QuantumFlow is a distributed framework for scheduling LLM inferences with support for multiple backends, adaptive strategies, and cluster management. With a growth score of 22.60 and 146 stars, it reflects the growing need for scalable, efficient inference solutions that can handle complex deployment scenarios.
HanGuo97/coda-kernels: CODA is an innovative approach to rewriting transformer blocks as GEMM-epilogue programs, aiming to optimize performance in neural network computations. Its growth score of 19.28 and 152 stars suggest a community interest in pushing the boundaries of computational efficiency in AI models.
Today's data highlights the diverse needs within the AI frameworks and SDKs space, from virtualization and deployment optimization to specialized applications like financial analysis and performance tuning on high-end hardware. The trend towards innovative solutions that tackle real-world challenges continues to drive growth across these projects.