# Conda Environments > Create, import, and manage conda environments and their packages from Kilo Desktop. - 网址：https://funcoding.ai/agents/kilo-code/desktop/features/environments/ - 来源：Kilo Code 官方文档原文（英文），MIT 许可，同步于 2026-10-11 - 官方原文：https://kilo.ai/docs/desktop/features/environments --- Manage conda environments and their packages from within the app, so you can set up the Python environment your work needs without dropping to a separate terminal. You'll find **Conda Environments** in [Settings](https://funcoding.ai/agents/kilo-code/desktop/settings/general/#conda-environments), under **General**.
Conda environments require a [Kilo account](https://funcoding.ai/agents/kilo-code/desktop/settings/ai/#ai-providers). Sign in to create and manage them.
## Why conda? Scientific computing, machine learning, and AI work depends on more than Python packages. Libraries like NumPy, PyTorch, and TensorFlow rely on compiled code and native libraries — such as CUDA for GPUs, BLAS or MKL for linear algebra, and C, C++, and Fortran runtimes — that Python-only tools aren't designed to manage. Conda manages the full stack: - **Beyond Python** — install native libraries, GPU toolkits, compilers, and packages for other languages such as R, alongside your Python packages. Conda manages the Python version itself, too. - **Prebuilt binaries** — packages arrive precompiled for Windows, macOS, and Linux, so you don't need to build libraries from source or install developer tools. - **Compatible dependencies** — conda's solver resolves the entire environment at once, so every package and native library it installs works together. - **Isolation** — each conda environment is self-contained, so projects that need different versions of Python, PyTorch, or CUDA don't interfere with each other. - **Reproducibility** — export an environment to an `environment.yml` file to recreate it on another machine or share it with a teammate. To learn more, see [12 reasons to choose conda](https://www.anaconda.com/blog/12-reasons-to-choose-conda). ## Conda concepts New to conda? Learn more about fundamental conda concepts in the official Anaconda documentation: - **[Environment](https://www.anaconda.com/docs/getting-started/concepts/what-is-an-environment)** — an isolated set of packages (and a Python version) for a project. - **[Package](https://www.anaconda.com/docs/getting-started/concepts/what-is-a-package)** — an installable library or tool. - **[Channel](https://www.anaconda.com/docs/getting-started/concepts/what-is-a-channel)** — where packages are downloaded from. - **[environment.yml](https://www.anaconda.com/docs/getting-started/working-with-conda/environments#sharing-an-environment)** — a file that describes an environment so it can be recreated or shared. ## Use a conda environment in your work Managing a conda environment here doesn't automatically "activate" it — there's no activate switch in the app. Instead, the agent connects it to your work. Ask an agent to find an appropriate conda environment for a notebook and point the notebook at it, then restart the notebook so it picks up the change. The most durable approach is to have the agent write an `environment.yml` into the workspace. The notebooks you already have, and any new ones you create there, then use that environment automatically. See [Notebooks](https://funcoding.ai/agents/kilo-code/desktop/features/notebooks/) for working with conda environments from a chat. ## Browse your conda environments The page lists your conda environments by **name** and **prefix** (the full path on disk). Search by name or path, sort the list, and page through it. Click the actions menu on an environment's row to **Clone** or **Delete** an environment. Select a row to open the environment and work with its packages. ## Create a conda environment Click **New environment**, then select **Create New** to open the Create environment dialog. On the Create environment page, configure your environment: - **Location** — where the environment is created. - **Name** — the environment's name. - **Python version** — a specific version, or **Latest**. Under **Select packages**, search the list and check the packages to include. Each package has a version dropdown (defaulting to **latest**), and your choices collect in the **Selected Packages** panel. Use the **Channels** control to choose where packages come from (see [Choose a channel](#choose-a-channel)). Select **Create Environment** to build it. ## Import from a file Click **New environment**, then select **Import file** to open the Import from file dialog. Navigate to or drop a conda environment file (`.yml` or `.yaml`). Optionally set a **name override** to use instead of the name declared in the file. ## Work with a conda environment's packages Selecting an environment opens its detail view. The header shows the environment name, its **Python** version, and the full prefix path. Below it, a table lists every installed package with its **Version**, **Source** (conda or pip), and **Channel**. Search by name and sort by any column. ### Update packages When newer versions are available, an **Update** button appears. Click the **Update** button to open the Update dialog. From there, you can update all packages at once, or filter the table down to just the packages that you want to update. A **Dependency changes** panel previews what the update pulls in and flags any **dependency conflict** it can't resolve, so you can adjust your selection and try again. Turn on **Back up before updating** to clone the environment first, so you can roll back if needed. Alternatively, on the packages table on the environment's details page, you can click the actions menu on a package's row to **Update** or **Remove** just that one. **Update** is unavailable when a package is already at the latest available version. ### Add packages From an open environment, select **+ Add Packages** to open the Add Packages dialog. Search for a package, and for each result pick a version (defaulting to **latest**). Your selections and their dependencies are added to the **Selected packages** list, and the **Channels** control sets where packages come from (see [Choose a channel](#choose-a-channel)). Before anything is applied, the **Dependency changes** panel previews the dependency changes the solver will make — packages that will be upgraded, downgraded, added, removed, or rebuilt — so there are no surprises. Select **Install** to apply the changes. A long-running install can be sent to **Run in background** so you can keep working while it finishes. ## Choose a channel When you select packages, you choose which channels they install from with the **Channels** control: - **Anaconda `Main`** — secure, vetted packages; requires accepting Anaconda's terms of service. - **conda-forge** — community-maintained and open-source. Any channels configured in your conda settings appear here too.