LangChain-Streamlit Template

This repo serves as a template for how to deploy a LangGraph agent on Streamlit.

This repo contains an main.py file which has a template for a chatbot implementation.

Adding your chain

To add your chain, you need to change the load_chain function in main.py. Depending on the type of your chain, you may also need to change the inputs/outputs that occur later on.

Run locally

After installing dependencies with e.g. $ pip install -r requirements.txt, you can run this project locally with the following command:

$ streamlit run main.py

Deploy on Streamlit

This is easily deployable on the Streamlit platform. Note that when setting up your Streamlit app you should make sure to add OPENAI_API_KEY as a secret environment variable.

Setting up LangSmith

To quickly spot issues and improve the performance of your LangGraph projects, sign up for LangSmith. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started here.