Set Environment Variables — Boundary Documentation

Environment Variables in BAML

Sometimes you’ll see environment variables used in BAML, like in clients:

1 client<llm> GPT4o {
2   provider baml-openai-chat
3   options {
4     model gpt-4o
5     api_key env.OPENAI_API_KEY
6   }
7 }

To set environment variables:

In the VSCode Playground

Once you open a .baml file, in VSCode, you should see a small button over every BAML function: Open Playground.

Then you should be able to set environment variables in the settings tab.

Or type BAML Playground in the VSCode Command Bar (CMD + Shift + P or CTRL + Shift + P) to open the playground.

For your app (default)

BAML will expect these to be set already in your program before you import the baml_client in Python/ TS / etc.

Any of the following strategies for setting env vars are compatible with BAML:

setting them in your shell before running your program
in your Dockerfile
in your next.config.js
in your Kubernetes manifest
from secrets-store.csi.k8s.io
from a secrets provider such as Infisical / Doppler
from a .env file (using dotenv cli)
using account credentials for ephemeral token generation (e.g. Vertex AI Auth Tokens)

$ export MY_SUPER_SECRET_API_KEY="..."
> python my_program_using_baml.py

For your app (manually)

Requires BAML Version 0.57+

If you don’t want BAML to try to auto-load your env vars, you can call manually reset_baml_env_vars with the current environment variables.

1 from baml_client import b
2 from baml_client import reset_baml_env_vars
3 import os
4 import dotenv
5 
6 dotenv.load_dotenv()
7 reset_baml_env_vars(dict(os.environ))

Dynamically setting LLM API Keys

You can set the API key for an LLM dynamically by passing in the key as a header or as a parameter (depending on the provider), using the ClientRegistry.

1	client<llm> GPT4o {
2	provider baml-openai-chat
3	options {
4	model gpt-4o
5	api_key env.OPENAI_API_KEY
6	}
7	}

$	export MY_SUPER_SECRET_API_KEY="..."
>	python my_program_using_baml.py

1	from baml_client import b
2	from baml_client import reset_baml_env_vars
3	import os
4	import dotenv
5
6	dotenv.load_dotenv()
7	reset_baml_env_vars(dict(os.environ))