Poetry - Python Dependency Manager That Doesn't Suck

Anyone who's dealt with Python packaging hell knows exactly why Poetry exists. Before Poetry, managing Python dependencies was a clusterfuck of setup.py, requirements.txt, setup.cfg, MANIFEST.in, and whatever other random config files your project accumulated. Poetry fixes this by putting everything in one file: pyproject.toml.

The Problem: pip Doesn't Resolve Dependencies

pip's approach to dependency resolution is basically "install everything and hope for the best." It doesn't check if package A version 2.0 is compatible with package B version 1.5 - it just installs both and lets your runtime crashes tell you there's a problem. I've wasted entire days tracking down version conflicts that pip silently created.

Poetry's dependency resolver actually works. It figures out which versions are compatible before installing anything. When there are conflicts, it tells you exactly what's wrong instead of just dying silently like pip. No more "works on my machine" bullshit.

Performance Reality Check

Poetry can be painfully slow on large projects with complex dependency trees. The resolver downloads entire packages to read metadata, which sucks on slow connections. But at least it works correctly.

Benchmarks show Poetry beats pipenv for most operations (package removal: 3.5s vs pipenv's 6.2s), but pipenv is faster for full installs. pipenv also has cache corruption issues that'll make you want to throw your laptop out the window.

Virtual Environment Sanity

Poetry automatically creates virtual environments so you don't have to remember to activate them. No more accidentally installing packages globally and breaking your system Python. It handles this automatically and puts environments in sensible locations (unlike conda which scatters them across your drive).

Pro tip: Never install Poetry inside your project's virtual environment. It'll conflict with itself and you'll spend 2 hours debugging it. Use pipx or your system package manager.

The pyproject.toml Revolution

Everything goes in pyproject.toml now - dependencies, build settings, tool configurations. No more juggling 5 different config files. Poetry adopted this standard early and it's become the norm. Even setuptools supports it now, though their implementation is still clunky.

Poetry requires Python 3.9+ and works on Linux, macOS, and Windows, though Windows users occasionally hit path length limits that'll completely fuck your day.

Dependency Groups (Finally, Sane Dev Dependencies)

Poetry's dependency groups are one feature that actually makes sense. You can separate dev tools from production deps:

[tool.poetry.group.dev.dependencies]
pytest = "^7.0"
black = "^23.0"
mypy = "^1.0"

[tool.poetry.group.docs]
optional = true

[tool.poetry.group.docs.dependencies] 
sphinx = "^5.0"

Install just what you need: poetry install --without docs skips documentation tools. Production builds use poetry install --only main for lean deployments.

Gotcha: Don't mix dependency groups with the old [tool.poetry.dev-dependencies] format. It's deprecated and will cause conflicts. Poetry 2.x will error out with cryptic messages about "inconsistent dependency declarations" - I spent like 2 hours debugging this on a legacy project. Turned out to be a single line in an old pyproject.toml file.

Enterprise Hell: Private Repositories

Setting up private repositories with Poetry is straightforward until your corporate network gets involved:

poetry config repositories.internal your-company-pypi.internal/simple/
poetry config http-basic.internal username password

Reality check: Your IT department's proxy will probably break this. You'll spend a day figuring out certificate issues and authentication tokens. Use environment variables for credentials - never hardcode them. Corporate firewalls love to block Poetry's package downloads with SSL errors that blame your "untrusted certificate."

WSL2 users: Poetry sometimes gets confused about file permissions and you'll get cryptic "operation not permitted" errors. The lockfile gets corrupted and I have no idea why this happens. Restarting WSL2 usually fixes it, but sometimes you have to delete poetry.lock and start over.

Check the Poetry networking docs for troubleshooting proxy issues.

Monorepos: Poetry Kinda Handles Them

Poetry supports path dependencies for monorepos:

[tool.poetry.dependencies]
my-shared-lib = {path = "../shared", develop = true}

The develop = true flag installs in editable mode. But Poetry isn't great at managing complex monorepos - pants or bazel might be better for large codebases.

CI/CD Integration (Docker Pain Points)

Poetry in Docker requires some thought. The obvious approach is garbage:

## DON'T DO THIS
FROM python:3.11
RUN pip install poetry
COPY . .
RUN poetry install

Why it sucks: Poetry creates virtual environments you don't need in containers, and builds can be non-reproducible.

Better approach using the official installer:

FROM python:3.11

## Install Poetry
RUN curl -sSL https://install.python-poetry.org | python3 -
ENV PATH="/root/.local/bin:$PATH"

## Configure Poetry: Don't create venv in containers
RUN poetry config virtualenvs.create false

## Install dependencies
COPY pyproject.toml poetry.lock ./
RUN poetry install --only=main --no-interaction --no-ansi

COPY . .

Performance Tuning (When Poetry Is Slow)

Large dependency trees make Poetry crawl. Some tricks that help:

• Pin problematic packages: If a package has 50 versions, pin it: requests = "2.31.0"
• Use constraints: Add a constraints file for transitive dependencies
• Configure resolver: poetry config installer.parallel false sometimes helps with flaky networks
• Clear cache: When in doubt, poetry cache clear pypi --all

Poetry's cache lives in weird places (different per OS). On macOS it's ~/Library/Caches/pypoetry, on Linux ~/.cache/pypoetry. Clear it when builds get weird. Poetry 2.1.x sometimes has cache corruption issues that show up as "failed to download" errors even when packages exist - clearing cache fixes like 90% of install failures. I have no idea why this happens.

Publishing Packages (The Easy Part)

Unlike the rest of Python packaging, Poetry's publishing actually works:

poetry build  # Creates dist/
poetry publish  # Uploads to PyPI

For CI/CD, use PyPI tokens:

poetry config pypi-token.pypi your-token-here
poetry publish

Pro tip: Test on TestPyPI first - poetry publish -r testpypi after configuring the repository. I've accidentally published broken packages to production PyPI too many times. One time I published a package with hardcoded database credentials - had to yank the release and rotate all our keys.

Migration from requirements.txt (Not as Simple as It Looks)

poetry import requirements.txt works for basic cases but misses dependency relationships. You'll need to manually organize deps into main/dev groups and add proper version constraints.

Time estimate: Maybe 30 minutes for simple projects, could be 2 days for complex ones with lots of dependencies. The resolver will find conflicts you didn't know existed.