LangServe is a framework for deploying a chain or an agent easily. It is built with modularity in mind. This modularity enables LLM developers to develop a chain and an agent as a separate package called LangChain Template.

• LangServe Template
  • How this is created?
  • How to develop
    • Codespaces
    • Local Development
  • How to deploy

The project is created with langchain-cli

langchain app new .

The command will generate these files. To install a virtual environment, you run

poetry install

To run the server

langchain serve

This repository is set as a template. You can always create a new repository based on this one easily. Or if you want to fork, feel free to do so.

After you spawn the Dev Container regardless of using Codespaces or Local Dev Container, it will install the extension and perform the poetry install right away. The result of poetry install will create the .venv folder with nesseary Python package to run the project. You can try to run

langchain serve

to see if it works.

The template is created with GitHub Codespaces. We recommend you use Codespaces to develop.

We tested the .devcontainer on Macbook Pro M3 pro with Docker Desktop version 4.27.2. It seems to work just like Codespaces.

We have not yet tested this on Windows and Linux/Ubuntu but they should just work fine too.

To deploy, we create a docker-compose.yml that will build the Dockerfile. What you will need to provide is the .env file.

LANGCHAIN_TRACING_V2="false"
LANGCHAIN_API_KEY="<YOUR-API-KEY>"  # Update to your API key
LANGCHAIN_PROJECT="default"
LANGCHAIN_ENDPOINT="https://api.smith.langchain.com"

If you need more environment variables, you can add the .env file or create another one and append the list to env_files in docker-compose.yml

To build and run Docker.

docker compose up -d --build

This will spawn a Docker container that only runs the project (similar to the Production). If this works fine, you should be safe to deploy.