angreal.integrations.venv

Virtual environment and package management powered by UV.

Overview

Angreal’s virtual environment integration uses UV to create virtual environments, install packages, and manage Python versions. UV is automatically installed when first used, requiring no additional setup.

Installation Requirements

UV is automatically installed when first used. No manual installation required.

Supported Platforms:

• macOS (via curl)
• Linux (via curl)
• Windows (via PowerShell)

Functions

venv_required

Decorator that wraps a function in a virtual environment before execution. The virtual environment is activated before the function runs and deactivated afterward, ensuring packages installed in the venv are available for import.

@venv_required(path, requirements=None)

Parameters:

• path (str): The path to the virtual environment (or where it should be created if it doesn’t exist)
• requirements (str | List[str], optional): A string containing a single module, a list of module names, or a string containing a requirements file path. Defaults to None.

Example:

from angreal.integrations.venv import venv_required

@venv_required("/path/to/venv", requirements=["requests", "pandas"])
def process_data():
    # These imports work because the venv is activated
    import requests
    import pandas as pd

    # Your code here - runs in the activated virtual environment
    df = pd.DataFrame({'data': [1, 2, 3]})
    response = requests.get('https://api.example.com')

# After the function completes, the original environment is restored

Classes

VirtualEnv

Manage virtual environments using UV’s ultra-fast operations.

from angreal.integrations.venv import VirtualEnv

venv = VirtualEnv(path="/path/to/venv")

Constructor

VirtualEnv(path, python=None, requirements=None, now=True)

Parameters:

• path (str | Path): The path to the virtual environment. Relative paths are resolved to absolute paths based on the current working directory.
• python (str, optional): Python version to use (e.g., “3.11”, “3.12”)
• requirements (str | List[str], optional): Requirements to install
• now (bool, optional): Create environment immediately. Defaults to True.

Properties

exists

Check if the virtual environment exists.

@property
def exists(self) -> bool

Returns:

• bool: True if the virtual environment exists, False otherwise

python_executable

Get the Python executable path for this virtual environment.

@property
def python_executable(self) -> Path

Returns:

• Path: Path to the Python executable

Instance Methods

install_requirements

Install the requirements set during initialization.

def install_requirements(self) -> None

Raises:

• TypeError: If requirements is not a file path, list, or string
• RuntimeError: If UV fails to install the requirements

install

Install packages or requirements file.

def install(self, packages: Union[str, List[str], Path]) -> None

Parameters:

• packages: Package names, requirements file path, or list of packages

Example:

# Install single package
venv.install("requests")

# Install multiple packages
venv.install(["pandas", "numpy", "matplotlib"])

# Install from requirements file
venv.install("requirements.txt")

activate

Activate the virtual environment in the current Python process.

def activate(self) -> None

This method modifies the current Python process’s sys.prefix, sys.exec_prefix, and sys.path so the virtual environment’s packages are importable. It also updates os.environ, setting VIRTUAL_ENV to the environment’s path and prepending the environment’s bin (Scripts on Windows) directory to PATH. Because environment variables are inherited by child processes, subprocesses spawned after activation run with the virtual environment on PATH.

Example:

venv = VirtualEnv("myenv", now=True)
venv.install("requests")

# Before activation, requests might not be importable
venv.activate()

# Now requests can be imported
import requests
response = requests.get("https://api.github.com")

# Remember to deactivate when done
venv.deactivate()

Raises:

• RuntimeError: If the virtual environment does not exist

deactivate

Restore the original Python environment.

def deactivate(self) -> None

This method restores the sys state (sys.prefix, sys.exec_prefix, sys.path) and the os.environ state (PATH, and VIRTUAL_ENV — removed if it was unset before activation) to their values prior to activation. Safe to call multiple times.

Example:

venv = VirtualEnv("myenv", now=True)
venv.activate()
# Use the virtual environment
venv.deactivate()  # Restore original environment

Context Manager Support

VirtualEnv supports the context manager protocol for automatic activation/deactivation:

with VirtualEnv("myenv", now=True) as venv:
    venv.install("numpy")

    # Virtual environment is activated here
    import numpy as np
    array = np.array([1, 2, 3])

# Virtual environment is automatically deactivated here

Class Methods

discover_available_pythons

Discover all Python installations on the system using UV.

@classmethod
def discover_available_pythons(cls) -> List[tuple[str, str]]

Returns:

• List[tuple[str, str]]: List of (version, path) tuples for available Python installations

Example:

pythons = VirtualEnv.discover_available_pythons()
for version, path in pythons:
    print(f"Python {version}: {path}")

ensure_python

Ensure a specific Python version is available, installing if needed.

@classmethod
def ensure_python(cls, version: str) -> str

Parameters:

• version (str): Python version to ensure (e.g., “3.11”, “3.12”)

Returns:

• str: Path to the Python installation

Example:

python_path = VirtualEnv.ensure_python("3.11")
print(f"Python 3.11 available at: {python_path}")

version

Get UV version information.

@classmethod
def version(cls) -> str

Returns:

• str: UV version string

Module-level Functions

The following functions are exported at the top level of the angreal module. They operate directly on UV and virtual environment paths without constructing a VirtualEnv instance.

ensure_uv_installed

Ensure the UV binary is installed, installing it if not present.

def ensure_uv_installed() -> None

Raises:

• RuntimeError: If UV installation fails

uv_version

Get the installed UV version.

def uv_version() -> str

Returns:

• str: UV version string

Raises:

• RuntimeError: If the UV version cannot be determined

create_virtualenv

Create a virtual environment at the given path.

def create_virtualenv(path: str, python_version: str | None = None) -> None

Parameters:

• path (str): Path at which to create the virtual environment
• python_version (str, optional): Python version to use (e.g., “3.11”)

Raises:

• RuntimeError: If creation fails

install_packages

Install a list of packages into an existing virtual environment.

def install_packages(venv_path: str, packages: list[str]) -> None

Parameters:

• venv_path (str): Path to the virtual environment
• packages (list[str]): Package names to install

Raises:

• RuntimeError: If installation fails

install_requirements

Install packages from a requirements file into an existing virtual environment.

def install_requirements(venv_path: str, requirements_file: str) -> None

Parameters:

• venv_path (str): Path to the virtual environment
• requirements_file (str): Path to the requirements file

Raises:

• RuntimeError: If installation fails

discover_pythons

Discover available Python installations using UV.

def discover_pythons() -> list[tuple[str, str]]

Returns:

• list[tuple[str, str]]: List of (version, path) tuples

Raises:

• RuntimeError: If discovery fails

install_python

Ensure a Python version is installed, installing it if needed.

def install_python(version: str) -> str

Parameters:

• version (str): Python version to ensure (e.g., “3.11”)

Returns:

• str: Path to the Python installation

Raises:

• RuntimeError: If the version cannot be installed

get_venv_activation_info

Get activation details for a virtual environment.

def get_venv_activation_info(venv_path: str) -> ActivationInfo

Parameters:

• venv_path (str): Path to the virtual environment

Returns:

• ActivationInfo: Activation details for the environment

Raises:

• RuntimeError: If the information cannot be determined

ActivationInfo

Activation details for a virtual environment. All attributes are read-only.

Attributes:

• venv_path (str): Path to the virtual environment
• venv_prefix (str): Prefix (sys.prefix) for the virtual environment
• site_packages (str): Path to the environment’s site-packages directory
• python_executable (str): Path to the environment’s Python executable

Activation Behavior

How Activation Works

Activation modifies both in-process Python state and the process environment:

1. Modifying sys.prefix and sys.exec_prefix to point to the virtual environment
2. Updating sys.path to prioritize the virtual environment’s site-packages, so imports resolve against the venv
3. Setting os.environ["VIRTUAL_ENV"] to the environment’s path and prepending the environment’s bin (Scripts on Windows) directory to os.environ["PATH"]
4. Preserving the original sys and os.environ state for restoration during deactivation

Activation Notes

• In-Process State: Activation modifies the running Python process’s sys.prefix, sys.exec_prefix, and sys.path. It does not modify the parent shell or its prompt
• Import Availability: After activation, packages installed in the venv become importable
• Environment Variables: Activation also sets VIRTUAL_ENV and prepends the venv’s bin (Scripts on Windows) directory to PATH in os.environ
• Subprocess Behavior: Because os.environ is inherited by child processes, subprocesses started after activation (e.g. via subprocess.run([...])) run with the venv on PATH. For fully explicit, robust invocation that does not depend on PATH ordering or activation state, pass the venv’s python_executable directly
• Multiple Activations: Only one virtual environment can be active at a time. Activating a second venv will override the first
• Thread Safety: Activation modifies global Python state (sys.prefix, sys.path) and process state (os.environ) and is not thread-safe

Limitations

• No Nested Activation: The current implementation does not support nested virtual environment activation. Deactivating always restores the original environment, not any previously activated environment
• No Shell Integration: This activation does not affect your shell environment or terminal prompt
• Module Caching: Python’s module import cache may retain modules from before activation. Use importlib.reload() if needed

Examples

Basic Usage

from angreal.integrations.venv import VirtualEnv

# Create virtual environment with Python 3.11
venv = VirtualEnv("/path/to/myenv", python="3.11")

# Check if it exists
if venv.exists:
    print("Virtual environment is ready")
    print(f"Python executable: {venv.python_executable}")

With Requirements

from angreal.integrations.venv import VirtualEnv

# Create venv with a list of packages
venv = VirtualEnv(
    "/path/to/myenv",
    python="3.11",
    requirements=["django", "pillow", "requests"]
)
venv.install_requirements()

# Or with a requirements file
venv = VirtualEnv(
    "/path/to/myenv",
    requirements="requirements.txt"
)
venv.install_requirements()

Using the Decorator

from angreal.integrations.venv import venv_required
import angreal

@angreal.command(name="analyze", about="Analyze data with pandas")
@venv_required(".venv", requirements=["pandas", "numpy"])
def analyze_command():
    """This command runs in an isolated virtual environment."""
    import pandas as pd
    import numpy as np

    # Your analysis code here
    data = pd.DataFrame(np.random.rand(100, 4))
    print(data.describe())

Python Version Management

from angreal.integrations.venv import VirtualEnv

# Discover available Python versions
pythons = VirtualEnv.discover_available_pythons()
print("Available Python installations:")
for version, path in pythons:
    print(f"  {version}: {path}")

# Ensure Python 3.12 is available
python_path = VirtualEnv.ensure_python("3.12")
print(f"Python 3.12 ready at: {python_path}")

# Create environment with specific Python version
venv = VirtualEnv("myproject-venv", python="3.12")

Manual Environment Management

from angreal.integrations.venv import VirtualEnv

# Create but don't activate immediately
venv = VirtualEnv("/path/to/env", now=False)

# Check and create if needed manually
if not venv.exists:
    venv.create()

# Install specific packages
venv.install(["flask", "sqlalchemy", "pytest"])

# Install from requirements file
venv.install("requirements.txt")

Programmatic Activation

from angreal.integrations.venv import VirtualEnv
import sys

# Create and set up environment
venv = VirtualEnv("data-science-env", now=True)
venv.install(["numpy", "pandas", "matplotlib"])

print(f"Before activation - sys.prefix: {sys.prefix}")

# Activate the environment
venv.activate()
print(f"After activation - sys.prefix: {sys.prefix}")

# Now we can import the installed packages
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Do some work with the packages
data = pd.DataFrame(np.random.randn(100, 4), columns=['A', 'B', 'C', 'D'])
data.plot()

# Deactivate when done
venv.deactivate()
print(f"After deactivation - sys.prefix: {sys.prefix}")

# Using context manager for automatic cleanup
with VirtualEnv("analysis-env", now=True) as venv:
    venv.install("scikit-learn")

    # This import works because we're in the activated environment
    from sklearn.datasets import load_iris
    iris = load_iris()
    print(f"Loaded {len(iris.data)} samples")

# Environment is automatically deactivated here

Querying UV Version

from angreal.integrations.venv import VirtualEnv

# Check UV version
print(f"Using UV version: {VirtualEnv.version()}")

Performance Characteristics

UV operations run as separate processes and clean up automatically.

Error Handling

Common Errors

UV Installation Failed:

RuntimeError: UV installation failed

Solution: Check network connectivity or install UV manually

Python Version Not Found:

RuntimeError: Python 3.11 installed but not found

Solution: Specify exact version or use discover_available_pythons() to see available versions

Package Installation Failed:

RuntimeError: Failed to install packages: network timeout

Solution: Check network connectivity and package names

UV Binary Management

Automatic Installation

UV is automatically installed when:

1. Any VirtualEnv operation is performed
2. The venv module is imported
3. UV is not found in the system PATH

Manual UV Operations

While not typically needed, you can check UV status:

# This is handled automatically - shown for reference
from angreal import ensure_uv_installed, uv_version

# Ensure UV is available
ensure_uv_installed()

# Check version
version = uv_version()
print(f"UV version: {version}")

Architecture Notes

• Binary Integration: Uses UV as external binary for maximum performance and stability
• Subprocess Safety: All UV operations use secure subprocess calls with proper argument handling
• Cross-Platform: Supports Windows, macOS, and Linux with platform-specific installation methods
• Backward Compatibility: 100% compatible with existing venv API - drop-in replacement

See Also

• UV Integration Architecture - Architectural decisions and implementation details
• Working with Virtual Environments - How-to guide
• Python Utils - Other Python utilities
• UV Documentation - Official UV documentation