Chainlit is an open-source Python framework for building production-ready conversational AI applications¹⁾. With over 12,000 stars on GitHub, it lets developers go from a Python script to a polished chat interface in minutes — with real-time streaming, step-by-step observability, file uploads, authentication, and integrations with LangChain, LlamaIndex, and OpenAI.

Chainlit focuses on the gap between AI backend logic and user-facing interfaces, providing an event-driven architecture with Python decorators that eliminates the need for frontend development expertise while delivering a professional chat experience²⁾.

Chainlit uses an event-driven architecture with Python decorators for chat lifecycle hooks. The core decorators — @cl.on_chat_start and @cl.on_message — handle session initialization and message processing. The framework automatically renders a web-based chat UI with streaming support, message history, and step visualization.

A key feature is Chain of Thought observability: Chainlit visualizes intermediate AI steps (retrieval, reasoning, tool calls) in the UI, giving users transparency into how the AI reached its answer. This builds trust and aids debugging.

• Real-time streaming — Stream LLM responses token-by-token to the UI
• Chain of Thought — Visualize intermediate AI reasoning steps
• Customizable UI — Theming, welcome messages, buttons, images, file uploads
• Session management — Persistent chat history via cl.user_session
• MCP support — Model Context Protocol for dynamic tool integration
• Authentication — Built-in user auth and access control
• Feedback collection — Thumbs up/down for model improvement
• Multi-modal — Support for images, files, and data visualization

# Install Chainlit
# pip install chainlit
 
import chainlit as cl
from openai import AsyncOpenAI
 
client = AsyncOpenAI()
 
@cl.on_chat_start
async def start():
    cl.user_session.set("history", [])
    await cl.Message(content="Hello! How can I help you today?").send()
 
@cl.on_message
async def main(message: cl.Message):
    history = cl.user_session.get("history")
    history.append({"role": "user", "content": message.content})
 
    msg = cl.Message(content="")
    await msg.send()
 
    # Stream response from OpenAI
    stream = await client.chat.completions.create(
        model="gpt-4o",
        messages=history,
        stream=True
    )
 
    async for chunk in stream:
        if chunk.choices[0].delta.content:
            await msg.stream_token(chunk.choices[0].delta.content)
 
    history.append({"role": "assistant", "content": msg.content})
    cl.user_session.set("history", history)
    await msg.update()
 
# Run with: chainlit run app.py
 
# LangChain integration example
from langchain_openai import ChatOpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
 
@cl.on_message
async def langchain_handler(message: cl.Message):
    llm = ChatOpenAI(model="gpt-4o", streaming=True)
    prompt = PromptTemplate.from_template("Answer: {question}")
    chain = LLMChain(llm=llm, prompt=prompt)
 
    cb = cl.AsyncLangchainCallbackHandler()
    result = await chain.acall(
        message.content,
        callbacks=[cb]
    )
    await cl.Message(content=result["text"]).send()

%%{init: {'theme': 'dark'}}%%
graph TB
    User([User]) -->|Chat Message| WebUI[Chainlit Web UI]
    WebUI -->|WebSocket| Server[Chainlit Server]
    Server -->|@cl.on_chat_start| Init[Session Init]
    Server -->|@cl.on_message| Handler[Message Handler]
    Handler -->|API Call| LLM[LLM Provider]
    LLM -->|Stream Tokens| Handler
    Handler -->|cl.Message| WebUI
    Init -->|cl.user_session| Session[Session Store]
    Handler -->|Steps| COT[Chain of Thought]
    COT -->|Visualization| WebUI
    Handler -->|Callbacks| LC[LangChain]
    Handler -->|Callbacks| LI[LlamaIndex]
    Handler -->|MCP| Tools[External Tools]
    Config[config.toml] -->|Theme + Auth| Server

• GitHub Repository
• Official Documentation
• Official Website
• DataCamp Tutorial

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