Hi,
I just built Chainything, a Rust framework and UI designed to easily generate and execute DAG (Directed Acyclic Graph) workflows using reusable components.
The project is designed to be highly modular and is composed of a few key elements:
The Core Library: A processor-based library that allows you to define and run a pipeline programmatically (e.g., Load Image -> Apply Resize -> Run Convolutional Network Inference -> Send the result to a database). It handles topological sorting via Kahn's algorithm and dynamic data transfer between nodes.
The UI (egui): A visual, no-code node editor interface built to create, configure, and run your pipelines graphically.
AI Agent Integration: You can use a natural language prompt to describe your needs, and the agent will automatically generate and connect the pipeline for you in the UI.
I have implemented some basic components (processors) to get started, and I plan to add more in the coming days/weeks. However, I believe that what will make this tool truly powerful is community contributions.
I would love to get your feedback! Since the architecture is designed to make it easy to create new custom processors, I am very interested in your thoughts on:
The overall architecture (especially how the Processor trait and data passing via type erasure are designed)
Code quality and whether the patterns used are idiomatic Rust
Any features or specific processors you'd like to see
Repository & Demo: GitHub - Bessouat40/chainything: Chain operations on images, text, data, and more · GitHub
Thank you in advance for your time and feedback!
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