Build Python LLM apps in minutes ⚡️
Chainlit lets you create ChatGPT-like UIs on top of any Python code in minutes! Some of the key features include intermediary steps visualisation, element management & display (images, text, carousel, etc.) as well as cloud deployment.
chainlit-intro.mp4
Open a terminal and run:
$ pip install chainlit
$ chainlit helloIf this opens the hello app in your browser, you're all set!
Please see here for full documentation on:
- Getting started (installation, simple examples)
- Examples
- Reference (full API docs)
Create a new file demo.py with the following code:
import chainlit as cl
@cl.on_message # this function will be called every time a user inputs a message in the UI
async def main(message: str):
# this is an intermediate step
await cl.Message(author="Tool 1", content=f"Response from tool1", indent=1).send()
# send back the final answer
await cl.Message(content=f"This is the final answer").send()Now run it!
$ chainlit run demo.py -w
Check out our plug-and-play integration with LangChain!
You can find various examples of Chainlit apps here that leverage tools and services such as OpenAI, Anthropiс, LangChain, LlamaIndex, ChromaDB, Pinecone and more.
- New UI elements (spreadsheet, video, carousel...)
- Create your own UI elements via component framework
- DAG-based chain-of-thought interface
- Support more LLMs in the prompt playground
- App deployment
Tell us what you would like to see added in Chainlit using the Github issues or on Discord.
As an open-source initiative in a rapidly evolving domain, we welcome contributions, be it through the addition of new features or the improvement of documentation.
For detailed information on how to contribute, see here.
Chainlit is open-source and licensed under the Apache 2.0 license.