python-uv

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Manages Python projects with uv by Astral — ultra-fast dependency resolution, virtual environments, workspace/multirepo setup, build tools, and pip compatibility for modern Python development workflows.

paulpas By paulpas schedule Updated 6/4/2026
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name: python-uv description: Manages Python projects with uv by Astral — ultra-fast dependency resolution, virtual environments, workspace/multirepo setup, build tools, and pip compatibility for modern Python development workflows. license: MIT compatibility: opencode metadata: version: "1.0.0" domain: coding triggers: uv, astral uv, python package manager, uv sync, uv lock, python project management, ultra-fast dependency resolution, pip compatibility management archetypes:

  • tactical
  • generation anti_triggers:
  • brainstorming
  • vague ideation
  • code golf
  • over-engineering response_profile: verbosity: low directive_strength: high abstraction_level: operational role: implementation scope: implementation output-format: code content-types:
  • code
  • guidance
  • examples
  • do-dont
  • config related-skills: coding-poetry, coding-package-ecosystem-navigator, coding-dependency-supply-chain-security

uv — Ultra-Fast Python Package Manager

Manages Python projects with uv by Astral — the ultra-fast Python package and project manager written in Rust. Covers dependency resolution, virtual environment management, workspace/multirepo setup, build tool integration, CI/CD optimization, and pip compatibility layer for modern Python development workflows.

TL;DR Checklist

  • Use uv init <project> to scaffold a new project — generates pyproject.toml with src layout
  • Always commit uv.lock for reproducible builds across environments and CI runners
  • Use uv add <pkg> to install dependencies (never pip install in uv-managed projects)
  • Pin Python version with requires-python = ">=3.12" in pyproject.toml
  • Use uv sync instead of pip install — resolves and installs from lockfile atomically
  • Use uv run <command> to execute scripts within the project's environment
  • Configure workspaces with [tool.uv.workspace] for multi-package monorepo setups

When to Use

Use this skill when:

  • Setting up a new Python project and wanting maximum dependency resolution speed (10-100x faster than pip/pip-tools/poetry)
  • Managing dependency trees across multiple environments (local dev, CI/CD, production) with deterministic lockfiles
  • Configuring uv workspaces for monorepo or multi-package Python projects
  • Optimizing CI/CD pipelines where dependency installation time is a bottleneck
  • Migrating from pip/requirements.txt, pip-tools, Poetry, or pdm to a faster alternative
  • Integrating Python tooling (ruff, mypy, pytest) as uv-managed tools rather than project dependencies

When NOT to Use

Avoid this skill for:

  • Projects that already have an established Poetry or pdm workflow with no speed pressure — the migration effort may not justify the benefit
  • Purely system-level Python management (e.g., OS package managers like apt/yum) — uv manages user-space environments only
  • Non-standard build backends requiring custom hooks — uv uses standard PEP 517/621 tooling and may not support exotic build configurations

Core Workflow

Phase 1: Project Initialization

  1. Scaffold a new project — Run uv init <project-name> to create a fully configured project with pyproject.toml, src layout, and a uv.lock file pre-generated.

    # Create a new project (auto-generates pyproject.toml + src/ layout)
uv init my-service
cd my-service

# Project structure:
# my-service/
# ├── README.md
# ├── pyproject.toml
# └── src/my_service/__init__.py

# For a script-style project (no package, just executable):
uv init --script my-script.py

  2. Verify the generated pyproject.toml — The scaffold creates a minimal but complete PEP 621 compliant configuration:

    [project]
name = "my-service"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.12"
dependencies = []

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

Phase 2: Dependency Management

  1. Add production and development dependencies — Use uv add for all dependency operations. Dependencies are resolved atomically against PyPI, written to pyproject.toml, and the lockfile is updated in a single pass.

    # Add production dependency (latest compatible version)
uv add httpx

# Pin to specific version range
uv add "pydantic>=2.5,<3"

# Add development-only dependency
uv add --dev pytest ruff mypy

# Add with extras
uv add "psycopg2-binary"

# Add from git repository
uv add "my-lib @ git+https://github.com/user/my-lib.git@main"

# Add local path dependency (for workspace members)
uv add ../shared-utils

# Add optional/extra dependency
uv add --optional async "httpx[socks]"

  2. Configure the complete project manifest — Structure pyproject.toml with production dependencies, dev groups, optional features, and tool configurations:

    [project]
name = "api-service"
version = "1.0.0"
description = "High-performance async API service"
readme = "README.md"
requires-python = ">=3.12"
dependencies = [
    "httpx>=0.27",
    "pydantic>=2.5,<3",
    "sqlalchemy>=2.0,<3",
    "structlog>=24.1",
    "prometheus-client>=0.20",
]

[project.optional-dependencies]
postgres = ["asyncpg>=0.29"]
cache = ["redis>=5.0"]

[project.scripts]
api-server = "api_service.cli:main"
migrate-db = "api_service.db:migrate"

[tool.uv]
dev-dependencies = [
    "pytest>=8.0",
    "pytest-asyncio>=0.23",
    "pytest-cov>=5.0",
    "hypothesis>=6.90",
    "ruff>=0.4",
    "mypy>=1.8",
    "pre-commit>=3.5",
]

[tool.uv.workspace]
members = ["packages/*"]

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

  3. Install/sync all dependencies — uv sync resolves the lockfile and installs all dependencies into the project's virtual environment. This is the primary installation command, replacing pip install entirely.

    # Install ALL dependencies (production + dev) from lockfile
uv sync

# Production only (skip dev dependencies)
uv sync --frozen  # Use exact versions from lockfile, no resolution

# After adding/updating pyproject.toml without updating lock:
uv lock              # Resolve and update lockfile
uv sync              # Install resolved dependencies

# Update a specific package to its latest version
uv add --upgrade httpx
uv add --upgrade-package "pydantic"

# Remove a dependency entirely
uv remove structlog

Phase 3: Environment and Tool Management

  1. Manage the virtual environment — uv creates and manages a .venv directory automatically. No manual venv creation or activation needed. Use uv run to execute any command within the managed environment.

    # Run any command in the project's virtual environment
uv run pytest tests/ --cov=src --cov-report=term-missing
uv run ruff check .
uv run mypy src/api_service/
uv run api-server --host 0.0.0.0 --port 8000

# Run Python with the correct interpreter and environment
uv run python -c "import sys; print(sys.version)"

# Sync ensures the venv matches pyproject.toml exactly
uv sync

# Remove the venv entirely (will be recreated on next sync)
rm -rf .venv
uv sync            # Fresh environment from scratch

# Use a specific Python version (uv installs it automatically if needed)
uv venv --python 3.12
uv sync

# List all installed packages in the current environment
uv pip list

  2. Configure uv as a tool manager — Separate tool installations from project dependencies using uv tool install. This keeps your virtual environment lean and allows running tools system-wide:

    # Install tools globally (separate from project dependencies)
uv tool install ruff
uv tool install mypy
uv tool install pytest
uv tool install pre-commit

# Run a tool — available in PATH after install
ruff check .
mypy src/

# Manage installed tools
uv tool list          # Show all installed tools
uv tool upgrade --all # Upgrade all tools to latest versions

# Tools can also be run via `uvx` (cross-platform)
uvx ruff check .
uvx mypy src/

# Install a specific version of a tool
uv tool install pre-commit==3.5.0

# Create a script that bundles a tool as a dependency
cat > run-tests <<'EOF'
#!/usr/bin/env -S uv run --with pytest --with pytest-cov
pytest tests/ --cov=src
EOF
chmod +x run-tests
./run-tests           # Automatically installs pytest + pytest-cov into ephemeral venv

Phase 4: Workspace (Monorepo) Setup

  1. Configure uv workspaces for multi-package projects — Define a root pyproject.toml that declares workspace members, each with its own dependencies:

    # Root pyproject.toml
[project]
name = "monorepo"
version = "0.1.0"
requires-python = ">=3.12"
dependencies = []

[tool.uv.workspace]
members = ["packages/*"]

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

    Each workspace member has its own pyproject.toml:

    # packages/core/pyproject.toml
[project]
name = "api-core"
version = "0.1.0"
requires-python = ">=3.12"
dependencies = [
    "pydantic>=2.5",
    "structlog>=24.1",
]

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

# packages/api/pyproject.toml
[project]
name = "api-server"
version = "0.1.0"
requires-python = ">=3.12"
dependencies = [
    "httpx>=0.27",
    "sqlalchemy>=2.0",
    { include-group = "core", name = "api-core" },  # workspace reference
]

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

# packages/cli/pyproject.toml
[project]
name = "api-cli"
version = "0.1.0"
requires-python = ">=3.12"
dependencies = [
    { include-group = "core", name = "api-core" },
    { include-group = "api", name = "api-server" },
]

[project.scripts]
api-cli = "api_cli.cli:main"

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

    Checkpoint: Run uv sync from the workspace root — uv resolves all workspace members, creates a single unified lockfile with no inter-package version conflicts, and installs everything into one virtual environment.

Phase 5: Build and Distribution

  1. Build distribution packages — Generate wheel (.whl) and source distribution (sdist) artifacts using standard PEP 517/621 tooling:

    # Build both wheel and sdist into dist/ directory
uv build

# Output files:
# dist/api_service-0.1.0-py3-none-any.whl
# dist/api_service-0.1.0.tar.gz

# Build only wheel (faster, preferred for PyPI upload)
uv build --wheel

# Verify the distribution before publishing
twine check dist/*

# Publish to PyPI
uv publish --token $PYPI_TOKEN

# Or use twine directly (works with any build backend)
poetry build && twine check dist/* && twine upload dist/*

  2. Integrate into CI/CD pipelines — Optimize for speed by using frozen lockfile installs and uv's cache:

    # .github/workflows/ci.yml
name: CI
on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Install uv
        uses: astral-sh/setup-uv@v3
        with:
          python-version: "3.12"

      # Fast dependency resolution from cached lockfile
      - name: Sync dependencies
        run: uv sync --frozen

      # Run tests with correct environment
      - name: Run tests
        run: uv run pytest tests/ --cov=src --cov-report=xml

      - name: Lint
        run: uv run ruff check .

      - name: Type check
        run: uv run mypy src/

Implementation Patterns

Pattern 1: Complete pyproject.toml for a Production Microservice

[project]
name = "data-pipeline"
version = "2.0.0"
description = "High-throughput async data processing pipeline with configurable connectors"
readme = "README.md"
requires-python = ">=3.12"
dependencies = [
    "httpx>=0.28,<1.0",
    "pydantic>=2.10,<3",
    "structlog>=24.3",
    "click>=8.1",
    "pyyaml>=6.0",
]

[project.optional-dependencies]
s3 = ["boto3>=1.35"]
redis = ["redis>=5.0,<6"]
postgres = ["asyncpg>=0.30"]

[project.scripts]
pipeline = "data_pipeline.cli:main"
migrate = "data_pipeline.db:migrate"

[tool.uv]
dev-dependencies = [
    "pytest>=8.3",
    "pytest-asyncio>=0.24",
    "pytest-cov>=5.0",
    "hypothesis>=6.110",
    "ruff>=0.8",
    "mypy>=1.11",
    "pre-commit>=4.0",
]

[tool.uv.sources]
# Override a dependency to use a local path (for development)
pydantic = { path = "../pydantic", editable = true }

# Use a specific index URL for private packages
boto3 = { index = "private-pypi" }

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[tool.ruff]
target-version = "py312"
line-length = 100

[tool.ruff.lint]
select = ["E", "F", "I", "UP", "B", "SIM", "RUF"]

[tool.ruff.format]
quote-style = "double"
indent-style = "space"

[tool.pytest.ini_options]
asyncio_mode = "auto"
testpaths = ["tests"]

[tool.mypy]
python_version = "3.12"
strict = true
warn_return_any = true

[[tool.mypy.overrides]]
module = "tests.*"
disallow_untyped_defs = false

Pattern 2: Dependency Resolution — BAD vs. GOOD Version Constraints

# ❌ BAD — Overly loose constraints cause non-deterministic builds across environments
[project]
dependencies = [
    "requests",              # No version pin at all!
    "httpx>=0.20",           # Too wide a range — may pull breaking changes in 0.x
    "pydantic==1.0.0",       # Exact pin without upper bound — blocks any update ever
]

# ❌ BAD — Using pip-style version specifiers (no uv awareness of resolution strategy)
dependencies = [
    "httpx~=0.27",           # PEP 440 compatible release is fine, but ~ syntax can be confusing
    "pydantic^2.5",          # ^ syntax is not valid in PEP 440 — will fail resolution!
]

# ✅ GOOD — PEP 440 compliant constraints with sensible version ranges
[project]
dependencies = [
    "httpx>=0.28,<1.0",     # Allow minor/patch updates within major version
    "pydantic>=2.10,<3",     # Major version boundary for API stability
    "structlog>=24.1.0",     # Specific minimum with no upper bound (safe for well-maintained libs)
]

# ❌ BAD — Conflicting version constraints across the workspace
[project]
name = "service-a"
dependencies = ["httpx>=0.28"]

[project]
name = "service-b"  
dependencies = ["httpx==0.27.0"]  # Hard conflict with service-a!

# ✅ GOOD — Aligned constraints via workspace-level minimum
[project]
name = "service-a"
dependencies = ["httpx>=0.28,<1.0"]

[project]
name = "service-b"
dependencies = ["httpx>=0.28,<1.0"]  # Same constraint — uv resolves to highest compatible

Pattern 3: pip Compatibility Layer (Drop-in Replacement)

uv provides a full pip install compatibility interface, making it a drop-in replacement for any script using pip:

# uv acts as a direct pip replacement — same commands, faster resolution
uv pip install httpx pydantic
uv pip install -r requirements.txt
uv pip freeze > current-deps.txt

# Sync a requirements file exactly (replaces pip-sync from pip-tools)
uv pip sync requirements.txt

# Install from requirements.txt with constraints file
uv pip install -r requirements.txt -c constraints.txt

# Use uv as pip in Docker builds (direct replacement, no venv needed):
# FROM python:3.12-slim
# RUN curl -LsSf https://astral.sh/uv/install.sh | sh
# COPY requirements.txt .
# RUN uv pip install --system -r requirements.txt

# For virtual environments with pip compatibility:
uv venv .venv
source .venv/bin/activate  # Or use `uv run` to skip activation entirely
uv pip install httpx pydantic

Pattern 4: CI/CD Optimization — From 4 Minutes to 30 Seconds

# ❌ SLOW — Standard pip + requirements.txt flow (typical CI time)
pip install --upgrade pip          # ~5 seconds
pip install -r requirements.txt    # ~2-3 minutes (slow resolution)
pip install pytest ruff mypy       # Additional packages: ~1 minute
pytest tests/                      # Run tests

# ✅ FAST — uv flow (typical CI time)
curl -LsSf https://astral.sh/uv/install.sh | sh  # ~2 seconds (cached by setup-uv action)
uv sync --frozen                                 # ~5-10 seconds (uses pre-cached resolution)
uv run pytest tests/ --cov=src                   # Execute in managed venv

# Even faster: cache the uv cache directory between CI runs
# In GitHub Actions:
# - uses: actions/cache@v4
#   with:
#     path: ~/.cache/uv
#     key: ${{ runner.os }}-uv-${{ hashFiles('uv.lock') }}

Constraints

MUST DO

  • Commit uv.lock to version control — this is the single source of truth for reproducible builds across all environments and CI runners
  • Use requires-python = ">=X.Y" to pin the minimum Python version in pyproject.toml (e.g., ">=3.12")
  • Use PEP 440 version specifiers (>=, <, ~=) — never use Poetry's ^ or ~ syntax (those are not valid PEP 440)
  • Run uv sync --frozen in CI to enforce exact lockfile versions without network resolution
  • Use uv run <command> for all project commands — never manually activate .venv or rely on system Python
  • Separate tool installations (uv tool install) from project dependencies to keep environments lean
  • Validate distributions with twine check dist/* before publishing to any registry
  • Pin requires-python consistently across all workspace member packages

MUST NOT DO

  • Edit uv.lock manually — always regenerate with uv lock, even after single dependency changes
  • Mix pip install directly with uv-managed environments — this bypasses the lockfile and corrupts the dependency tree
  • Commit pyproject.toml without a matching uv.lock — future builds will be non-deterministic
  • Use Poetry's ^ version constraint syntax in pyproject.toml — uv follows PEP 440, not Poetry's resolver
  • Run uv pip install inside an active virtual environment — use uv sync at the project root instead
  • Remove uv.lock to "fix" dependency conflicts — adjust version constraints in pyproject.toml and re-resolve with uv lock

Output Template

When configuring or auditing a uv-managed project, produce:

  1. Dependency Audit — List all production and dev dependencies with resolved versions from uv.lock, flagging any overly loose version constraints or potential conflicts
  2. pyproject.toml Review — Validate structure: presence of [project], requires-python, dependency lists, tool configurations, build-system specification
  3. Lockfile Consistency Check — Verify uv.lock matches pyproject.toml; report if manual edits may have drifted the lockfile from the manifest
  4. Workspace Analysis — For monorepo setups: confirm all workspace members declared in [tool.uv.workspace], cross-references use proper dependency specifications, no inter-package conflicts
  5. CI/CD Pipeline Audit — Confirm uv sync --frozen is used, cache configuration for .cache/uv exists, and tool installations are separated from project dependencies

Related Skills

Skill Purpose
poetry Poetry alternative — different resolver and plugin ecosystem but similar workflow patterns
package-ecosystem-navigator General package manager ecosystem comparison (npm, pypi, cargo, pip-tools)
dependency-supply-chain-security Dependency security auditing, CVE scanning, and supply chain protections

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

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