Chainlit

Chainlit hits a sweet spot for Python developers tired of stitching together chat UIs from scratch. You can go from a raw LLM call to a hosted app with user auth, chat history, and streaming in an afternoon. The MCP support and multi-modality updates show the team is keeping pace with the ecosystem. Pick this when you're building a prototype that might go to production—Chainlit's data persistence and auth are production-grade. It's especially strong for teams already using LangChain or Llama Index, as the integrations are tight. The CLI tooling and FastAPI mounting make deployment straightforward. Skip this if you need a no-code builder or a mobile SDK. Chainlit is Python-first and web-only. For non-technical stakeholders, it still requires a developer to operate. Also, while the UI is clean, it's not as customizable as building from scratch with React; you'll hit limits if you need very custom UI components. Where it bites: the documentation, while thorough, can be dense, and some advanced features (like custom CSS/JS) require digging. The community is active but smaller than Streamlit's, so finding niche solutions can take longer. Compared to Streamlit, Chainlit is more opinionated about conversational structure—streaming, steps, session management are first-class. Streamlit is more flexible for general data apps but requires more work to build a chat-specific interface. In practice, we'd reach for Chainlit over alternatives when the core requirement is a chat interface with minimal overhead, especially if LangChain is already in the stack. It's less suited for dashboards or complex multi-page apps that mix chat with traditional UI patterns.