ExperQuick is an initiative enabling transparent, scalable, and collaborative scientific experimentation. Build, manage, and reproduce research workflows with ease.
To provide easy interface to research and/or experimental mess.
To make researchers and R&D section focus more on decision making rather than managing codes/ experimental-configs their atifacts etc all.
Research often feels like a maze of scattered experiments, disconnected code versions, and inconsistent results.
ExperQuick brings clarity with integrated workflows, ensuring reproducibility and making collaboration effortless.
- 🧩 Streamline Experiment Workflows — manage code, data, and results in harmony.
- 🧠 Guarantee Reproducibility — every run is traceable and regenerable.
- 🤝 Empower Collaboration — share results and configurations seamlessly.
- ⚙️ Support Any Domain — from AI and data science to computational chemistry and quantitative analysis.
- PyLabFlow: A lightweight, domain-agnostic framework for managing experiments and workflows.
- PyTorchLabFlow: PyLabFlow with Deep learning plug-in. It is under development but you can access a limited version at BBEK-Anand/PyTorchLabFlow
ExperQuick is open to everyone — from students to research labs.
You can contribute by:
- Improving documentation
- Building plugins or integrations
- Adding examples and reproducible workflows
- Participating in discussions and proposals
Check out our Contributing Guide for details.
We believe the future of science is open, modular, and reproducible. By building the infrastructure once — and sharing it widely — we amplify discovery for everyone.
© 2025 ExperQuick — Open, transparent, and collaborative research infrastructure for the next era of science.
