Question: I need a framework that integrates with popular data science libraries like Pandas and Vega-Lite, and lets me build custom components.

If you're looking for a framework that works with widely used data science libraries like Pandas and Vega-Lite, Streamlit is a great option. Streamlit is an open-source Python framework for data scientists and AI/ML engineers to create interactive data apps without needing to know anything about front-end technology. It spans a wide range of categories and works with a lot of tools and libraries, including Pandas and Vega-Lite. Streamlit apps can be deployed instantly, either locally or on Streamlit's Community Cloud, and it also offers the ability to create custom components so you can build your own tools and share them with others.

Another option worth considering is Dash. Dash is a Python library for building interactive data applications that scale. It has a simple installation process and one-click deployment, which makes it easy to work with others and deploy data apps rapidly. Dash supports low-code development, AI and ML integration, and you can build AI and ML-powered apps. It's a good fit for data scientists and engineers who want to take their data analytics and visualization abilities to the next level. Dash Enterprise adds features like an App Manager, Chatbot Builder, and Design Kit that can help you take your data-driven projects to the next level.

If you're looking for a more collaborative environment, Hex is an AI-powered platform for data teams to collaborate on data analysis, exploration, and visualization. Hex features include data exploration on modular canvases with SQL, Python, and R; AI-boosted query generation and visualization; and interactive reporting with drag-and-drop tools. It also integrates with popular data warehouses and databases and includes enterprise-level security controls. Hex is great for teams that need a powerful and secure environment for data analysis and collaboration.