chainlit
Expert guidance for building conversational AI applications with Chainlit framework in Python. Use when (1) creating chat interfaces for LLM applications, (2) building apps with OpenAI, LangChain, LlamaIndex, or Mistral AI, (3) implementing streaming responses, (4) adding UI elements like images, files, charts, (5) handling user file uploads, (6) implementing authentication (OAuth, password), (7) creating multi-step workflows with visible steps, (8) building RAG applications with document upload, or (9) deploying chat apps to web, Slack, Discord, or Teams.
npx skills add majiayu000/claude-skill-registry --skill chainlit --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Chainlit Build production-ready conversational AI applications in Python with rich UI. ## Installation ```bash pip install chainlit ``` ## Quick Start ```python import chainlit as cl @cl.on_message async def on_message(message: cl.Message): await cl.Message(content=f"You said: {message.content}").send() ``` Run with: ```bash chainlit run app.py -w ``` ## Core Concepts | Concept | Description | |---------|-------------| | **Messages** | Text communication between user and assistant | | **Steps** | Visible processing stages (LLM calls, tool use) | | **Elements** | Rich UI (images, files, charts, dataframes) | | **Actions** | Interactive buttons with callbacks | | **Sessions** | Per-user state management | ## Lifecycle Hooks ```python import chainlit as cl @cl.on_chat_start async def start(): cl.user_session.set("history", []) await cl.Message(content="Hello!").send() @cl.on_message async def on_message(message: cl.Message): await cl.Message(content="Got it!").send() @cl.on_chat_end async def end(): print("Session ended") ``` ## Streaming Responses ```python from openai import AsyncOpenAI import chainlit as cl client = AsyncOpenAI() cl.instrument_openai() @cl.on_message async def on
- Installation
- Quick Start
- Core Concepts
- Lifecycle Hooks
- Streaming Responses
- Steps (Chain of Thought)
- User Session
- Ask User for Input
- UI Elements
- Actions (Buttons)
- Reference Documentation
- Integrations
- Configuration
- Run Commands
pip install chainlit chainlit run app.py -w Development with auto-reload Production chainlit run app.py --host 0.0.0.0 --port 8000 Generate auth secret chainlit create-secret
What does the chainlit skill do?
Expert guidance for building conversational AI applications with Chainlit framework in Python. Use when (1) creating chat interfaces for LLM applications, (2) building apps with OpenAI, LangChain, LlamaIndex, or Mistral AI, (3) implementing streaming responses, (4) adding UI elements like images, files, charts, (5) handling user file uploads, (6) implementing authentication (OAuth, password), (7) creating multi-step workflows with visible steps, (8) building RAG applications with document upload, or (9) deploying chat apps to web, Slack, Discord, or Teams.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill chainlit --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.