Streamlit vs Chainlit: Choosing the Right Tool for Your AI Applications

Two tools that have gained too much popularity among developers that are Streamlit and Chainlit. Both frameworks simplify the process of building interactive applications, but they serve a little different purposes. In this article, we’ll compare Streamlit and Chainlit, their key features, benefits, and use cases to help you make an informed decision in selection.

What is Streamlit?

Streamlit is an open source Python framework that allows developers to quickly build web apps for data science and machine learning. It is designed with simplicity in mind, letting users turn Python scripts into interactive applications without extensive knowledge of front end technologies like HTML, CSS, or JavaScript. Streamlit has become a go to tool for AI developers, data analysts, and researchers who want to showcase their models in a visually appealing way.

Key Features of Streamlit

1. Ease of Use: Streamlit apps can be created with minimal lines of code. Developers simply write Python scripts and Streamlit handles the web interface automatically.
2. Interactivity: Built in widgets like sliders, buttons, and text input allow users to interact with applications without extra coding.
3. Data Visualization: Streamlit integrates seamlessly with popular Python libraries such as Matplotlib, Plotly, and Altair, enabling dynamic visualizations.
4. Real-Time Updates: Any change in the Python script or user input updates the app in real-time, making prototyping fast and efficient.
5. Deployment: Streamlit apps can be easily deployed using platforms like Streamlit Cloud, Heroku, or AWS.

What is Chainlit?

Chainlit is a newer framework focused specifically on building AI chat applications and conversational interfaces. Unlike Streamlit, which is more general-purpose, Chainlit targets developers creating chatbots, AI agents, or large language model (LLM) applications. Its architecture allows developers to build sophisticated conversational AI apps without the complexity of traditional front-end development.

Key Features of Chainlit

1. Conversational AI Focus: Chainlit is designed for building LLM-based chatbots and AI assistants.
2. Integration with AI Models: It can connect seamlessly with OpenAI, Hugging Face, or custom AI models, simplifying API management.
3. Rich UI Components: Chainlit provides interactive chat widgets, buttons, and text inputs tailored for conversational interfaces.
4. Customizable Workflows: Developers can design multi-turn conversations, conditional logic, and context-aware responses.
5. Developer-Friendly: Chainlit supports Python, allowing AI developers to focus on model logic rather than front-end complexity.

Streamlit vs Chainlit: Feature Comparison

When to Use Streamlit

Streamlit is ideal if you want to:

• Build dashboards for machine learning models.
• Visualize datasets interactively.
• Quickly prototype data-driven apps.
• Share AI results with non-technical stakeholders.

When to Use Chainlit

Chainlit shines in scenarios where conversational AI is central. Use Chainlit if you:

• Are developing a chatbot or AI assistant.
• Need multi-turn conversation flows.
• Want to integrate directly with LLMs like GPT or Hugging Face models.
• Aim for a rich interactive chat interface without manual front-end coding.