---
title: "envd"
type: "tool"
slug: "tensorchord-envd"
canonical_url: "https://www.graphcanon.com/tools/tensorchord-envd"
github_url: "https://github.com/tensorchord/envd"
homepage_url: "https://envd.tensorchord.ai/"
stars: 2214
forks: 168
primary_language: "Go"
license: "Apache-2.0"
categories: ["developer-tools"]
tags: ["llmops", "docker", "codex", "buildkit", "code-agent", "development-environment", "agent", "developer-tools"]
updated_at: "2026-07-07T18:45:11.124922+00:00"
---

# envd

> Reproducible development environment for humans and agents

envd is a command-line tool designed to simplify the creation of container-based development environments specifically tailored for AI/ML projects, ensuring consistency and ease in managing dependencies.

## Facts

- Repository: https://github.com/tensorchord/envd
- Homepage: https://envd.tensorchord.ai/
- Stars: 2,214 · Forks: 168 · Open issues: 136 · Watchers: 22
- Primary language: Go
- License: Apache-2.0
- Last pushed: 2026-07-03T15:13:56+00:00

## Categories

- [Developer Tools](/categories/developer-tools.md)

## Tags

llmops, docker, codex, buildkit, code-agent, development-environment, agent, developer-tools

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## README (excerpt)

```text
<div align="center">
<img src="https://user-images.githubusercontent.com/12974685/200007223-cd94fe9a-266d-4bbd-ac23-e71043d0c3dc.svg#gh-light-mode-only" alt="envd cat wink"/>
<img src="https://user-images.githubusercontent.com/12974685/200007265-4e47ff2c-c2a0-4e77-baaa-760ee8728388.svg#gh-dark-mode-only" alt="envd cat wink"/>

<p>Development environment for AI/ML</p>
</div>

<p align=center>
<a href="https://discord.gg/KqswhpVgdU"><img alt="discord invitation link" src="https://img.shields.io/discord/974584200327991326?style=flat&logo=discord&cacheSeconds=60"></a>
<a href="https://twitter.com/TensorChord"><img src="https://img.shields.io/twitter/follow/tensorchord?style=flat&logo=X&cacheSeconds=60" alt="trackgit-views" /></a>
<a href="https://pypi.org/project/envd"><img src="https://img.shields.io/pypi/pyversions/envd" alt="Python Version" /></a>
<a href="https://github.com/tensorchord/envd#contributors-"><img alt="all-contributors" src="https://img.shields.io/github/all-contributors/tensorchord/envd/main"></a>
<a href="https://pypi.org/project/envd/"><img alt="envd package downloads" src="https://static.pepy.tech/personalized-badge/envd?period=month&units=international_system&left_color=grey&right_color=brightgreen&left_text=downloads/month"</a>
<a href="https://github.com/tensorchord/envd/actions/workflows/CI.yml"><img alt="continuous integration" src="https://github.com/tensorchord/envd/actions/workflows/CI.yml/badge.svg"></a>
<a href='https://coveralls.io/github/tensorchord/envd?branch=main'><img src='https://coveralls.io/repos/github/tensorchord/envd/badge.svg?branch=main' alt='Coverage Status' /></a>
</p>

## What is envd?

envd (`ɪnˈvdɪ`) is a command-line tool that helps you create the container-based development environment for AI/ML.

Creating development environments is not easy, especially with today's complex systems and dependencies. With everything from Python to CUDA, BASH scripts, and Dockerfiles constantly breaking, it can feel like a nightmare - until now!

Instantly get your environment running exactly as you need with a simple declaration of the packages you seek in build.envd and just one command: `envd up`!

<p align="center">
  <img src="https://user-images.githubusercontent.com/5100735/207217321-34c30dde-4b55-4871-b6fe-f9fc6ec19986.svg" width="75%"/>
</p>

## Why use `envd`?

Environments built with `envd` provide the following features out-of-the-box:

**Simple CLI and language**

`envd` enables you to quickly and seamlessly integrate powerful CLI tools into your existing Python workflow to provision your programming environment without learning a new language or DSL.

```python
def build():
    base(dev=True)
    install.conda()
    install.python()
    install.python_packages(name = [
        "numpy",
    ])
    shell("fish")
    config.jupyter()
```

**Isolation, compatible with OCI image**

With `envd`, users can create an isolated space to train, fine-tune, or serve. By utilizing sophisticated virtualization technology as well as other features like [buildkit](https://github.com/moby/buildkit), it's an ideal solution for environment setup.

`envd` environment image is compatible with [OCI image specification](https://github.com/opencontainers/image-spec). By leveraging the power of an OCI image, you can make your environment available to anyone and everyone! Make it happen with a container registry like Harbor or Docker Hub.

**Local, and cloud**

`envd` can now be used on a hybrid platform, ranging from local machines to clusters hosted by Kubernetes. Any of these options offers an efficient and versatile way for developers to create their projects!

```sh
$ envd context use local
# Run envd environments locally
$ envd up
...
$ envd context use cluster
# Run envd environments in the cluster with the same experience
$ envd up
```

Check out the [doc](https://envd.tensorchord.ai/teams/kubernetes.html) for more details.

**Build anywhere, faster**

`envd` offers a wealth of advantages, such as remo
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/tensorchord-envd`](/api/graphcanon/tools/tensorchord-envd)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_