Chainlit
Chainlit enables you to build conversational AI apps using Python and Langchain, streamlining chatbot development for interactive experiences.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add chainlit npx -- -y @trustedskills/chainlit
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"chainlit": {
"command": "npx",
"args": [
"-y",
"@trustedskills/chainlit"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The chainlit skill provides a framework to build conversational AI applications with asynchronous callbacks and streaming capabilities. It simplifies creating user interfaces (UIs) for agents, enabling real-time interactions and feedback. This allows for more engaging and responsive agent experiences compared to traditional methods.
When to use it
- Building interactive chatbots: Create bots that respond in real-time and display information dynamically.
- Developing data analysis tools: Visualize data streams or results of complex computations within a conversational interface.
- Creating AI assistants with streaming output: Enable agents to show progress updates or intermediate results as they work on tasks.
- Rapid prototyping of agent UIs: Quickly build and iterate on user interfaces for your AI agents.
Key capabilities
- Asynchronous callbacks
- Streaming capabilities
- UI framework for conversational AI applications
- Real-time interactions
Example prompts
- "Build a chatbot that can answer questions about my company's products."
- "Create an interface to visualize the results of this data analysis process in real time."
- "Show me the progress of this long calculation as it happens."
Tips & gotchas
This skill requires familiarity with Python and potentially FastAPI for backend integration. The asynchronous nature of chainlit might necessitate adjustments to existing codebases that are not designed for async operations.
Tags
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