Skip to content

⚙️ fastenv 🚀

Unified environment variable and settings management for FastAPI and beyond

PyPI coverage ci Ruff


Environment variables are key-value pairs provided to the operating system with syntax like VARIABLE_NAME=value. Collections of environment variables are stored in files commonly named .env and called "dotenv" files. The Python standard library os module provides tools for reading environment variables, such as os.getenv("VARIABLE_NAME"), but only handles strings, and doesn't include tools for file I/O. Additional logic is therefore needed to load environment variables from files before they can be read by Python, and to convert variables from strings to other Python types.

This project aims to:

  • Replace the aging python-dotenv project with a similar, but more intuitive API, and modern syntax and tooling.
  • Implement asynchronous file I/O. Reading and writing files can be done asynchronously with packages like AnyIO.
  • Implement asynchronous object storage integration. Dotenv files are commonly kept in cloud object storage, but environment variable management packages typically don't integrate with object storage clients. Additional logic is therefore required to download .env files from object storage prior to loading environment variables. This project aims to integrate with S3-compatible object storage, with a focus on downloading and uploading file objects.
  • Read settings from TOML. It's all about pyproject.toml now. The Python community has pushed PEP 517 build tooling and PEP 518 build requirements forward, and even setuptools has come around. PEP 621 defined how to store package metadata and dependencies in pyproject.toml. Why don't we use the metadata from our pyproject.toml files in our Python applications?
  • Unify settings management for FastAPI. Uvicorn, Starlette, and pydantic each have their own ways of loading environment variables and configuring application settings. This means that, when configuring a FastAPI application, there are at least three different settings management tools available, each with their own pros and cons. It would be helpful to address the limitations of each of these options, potentially providing a similar, improved API for each one.

The source code is 100% type-annotated and unit-tested.


Install fastenv into a virtual environment:

python3 -m venv .venv
. .venv/bin/activate
python -m pip install fastenv

Then start a REPL session and try it out:

.venv  python
# instantiate a DotEnv with a variable
import fastenv
dotenv = fastenv.DotEnv("EXAMPLE_VARIABLE=example_value")
# add a variable with dictionary syntax
dotenv["ANOTHER_VARIABLE"] = "another_value"
# delete a variable
del dotenv["ANOTHER_VARIABLE"]
# add a variable by calling the instance
# {'I_THINK_FASTENV_IS': 'awesome'}
# return a dict of the variables in the DotEnv instance
# {'EXAMPLE_VARIABLE': 'example_value', 'I_THINK_FASTENV_IS': 'awesome'}
# save the DotEnv instance to a file
import anyio, dotenv)
# Path('/path/to/this/dir/.env')

Use fastenv in your FastAPI app:

from contextlib import asynccontextmanager
from typing import AsyncIterator, TypedDict

import fastenv
from fastapi import FastAPI, Request

class LifespanState(TypedDict):
    settings: fastenv.DotEnv

async def lifespan(_: FastAPI) -> AsyncIterator[LifespanState]:
    """Configure app lifespan.
    settings = await fastenv.load_dotenv(".env")
    lifespan_state: LifespanState = {"settings": settings}
    yield lifespan_state

app = FastAPI(lifespan=lifespan)

async def get_settings(request: Request) -> dict[str, str]:
    settings = request.state.settings
    return dict(settings)