Today's AI Frameworks & SDKs: Fastest-Growing Projects — April 13, 2026
Today's AI Frameworks & SDKs, we're seeing a surge of interest in tools that enable developers to build and deploy AI models more efficiently. From frameworks for building language models to SDKs for multimodal protein design, the trend is clear: developers are hungry for tools that make it easier to work with AI. With growth scores ranging from 15.50 to 88.79, Today's top tools are certainly delivering.
Rohitg00's ai-engineering-from-scratch repository takes the top spot with a growth score of 88.79 and over 2,463 stars. This comprehensive guide teaches developers how to build and ship AI models from scratch, making it an invaluable resource for those new to the field. Its popularity is no surprise, given the growing demand for AI expertise.
Jxnxts' mcp-brasil repository comes in second with a growth score of 68.53 and over 1,348 stars. This MCP server provides access to 41 public APIs in Brazil, making it an essential tool for developers working on projects that require integration with these services. Its high commit activity (100 commits in the last 30 days) suggests a dedicated community is actively contributing to its development.
Yogthos' chiasmus repository has a growth score of 46.75 and 48 stars, but don't let its relatively low star count fool you - this MCP server gives language models access to formal verification, making it an important tool for developers working on AI safety and reliability. With 84 commits in the last 30 days, chiasmus is clearly gaining traction.
Codefromkarl's ContextAtlas repository boasts a growth score of 30.05 and 22 stars. This context infrastructure for AI coding agents provides hybrid retrieval, project memory, and retrieval observability via CLI, MCP server, or embeddable library. Its unique approach to context management has likely contributed to its growing popularity.
Rcortx's kiwiq repository has a growth score of 25.52 and over 1,021 stars, despite having only 2 commits in the last 30 days. This production-grade multi-agent orchestration platform has been battle-tested on over 200 enterprise AI agents, making it an attractive choice for developers working on large-scale projects.
Rhino-acoustic's NeuronFS repository takes a different approach with its B-tree-based file system designed specifically for AI workloads. With a growth score of 22.44 and 136 stars, NeuronFS is gaining attention for its potential to optimize storage and retrieval of AI data.
Iflytek's iFly-Skills repository has a growth score of 19.71 and over 456 stars, offering a collection of skills for speech, OCR, translation, proofreading, and multimodal AI capabilities. Its moderate commit activity (14 commits in the last 30 days) suggests a dedicated team is actively maintaining the repository.
JiaboLi-GitHub's renderdoc-mcp repository has a growth score of 18.72 and 65 stars. This MCP server for RenderDoc empowers AI assistants to analyze GPU frame captures and debug graphics pipelines, making it an essential tool for developers working on computer vision and graphics projects.
Montimaj's agribound repository boasts a growth score of 18.31 and 53 stars, providing an AI-powered field boundary delineation toolkit that combines satellite foundation models, embeddings, and global training data. Its high commit activity (97 commits in the last 30 days) suggests a dedicated community is actively contributing to its development.
Finally, DISCO-design's DISCO repository has a growth score of 15.50 and 111 stars. This code for the DISCO model enables general multimodal protein design, making it an important tool for researchers working on protein engineering and design.
Rohitg00's ai-engineering-from-scratch repository takes the top spot with a growth score of 88.79 and over 2,463 stars. This comprehensive guide teaches developers how to build and ship AI models from scratch, making it an invaluable resource for those new to the field. Its popularity is no surprise, given the growing demand for AI expertise.
Jxnxts' mcp-brasil repository comes in second with a growth score of 68.53 and over 1,348 stars. This MCP server provides access to 41 public APIs in Brazil, making it an essential tool for developers working on projects that require integration with these services. Its high commit activity (100 commits in the last 30 days) suggests a dedicated community is actively contributing to its development.
Yogthos' chiasmus repository has a growth score of 46.75 and 48 stars, but don't let its relatively low star count fool you - this MCP server gives language models access to formal verification, making it an important tool for developers working on AI safety and reliability. With 84 commits in the last 30 days, chiasmus is clearly gaining traction.
Codefromkarl's ContextAtlas repository boasts a growth score of 30.05 and 22 stars. This context infrastructure for AI coding agents provides hybrid retrieval, project memory, and retrieval observability via CLI, MCP server, or embeddable library. Its unique approach to context management has likely contributed to its growing popularity.
Rcortx's kiwiq repository has a growth score of 25.52 and over 1,021 stars, despite having only 2 commits in the last 30 days. This production-grade multi-agent orchestration platform has been battle-tested on over 200 enterprise AI agents, making it an attractive choice for developers working on large-scale projects.
Rhino-acoustic's NeuronFS repository takes a different approach with its B-tree-based file system designed specifically for AI workloads. With a growth score of 22.44 and 136 stars, NeuronFS is gaining attention for its potential to optimize storage and retrieval of AI data.
Iflytek's iFly-Skills repository has a growth score of 19.71 and over 456 stars, offering a collection of skills for speech, OCR, translation, proofreading, and multimodal AI capabilities. Its moderate commit activity (14 commits in the last 30 days) suggests a dedicated team is actively maintaining the repository.
JiaboLi-GitHub's renderdoc-mcp repository has a growth score of 18.72 and 65 stars. This MCP server for RenderDoc empowers AI assistants to analyze GPU frame captures and debug graphics pipelines, making it an essential tool for developers working on computer vision and graphics projects.
Montimaj's agribound repository boasts a growth score of 18.31 and 53 stars, providing an AI-powered field boundary delineation toolkit that combines satellite foundation models, embeddings, and global training data. Its high commit activity (97 commits in the last 30 days) suggests a dedicated community is actively contributing to its development.
Finally, DISCO-design's DISCO repository has a growth score of 15.50 and 111 stars. This code for the DISCO model enables general multimodal protein design, making it an important tool for researchers working on protein engineering and design.