Python Dependency Research with Claude Code and PyPI Stats

Claude Code is great at writing Python, but when it comes to picking which packages to use, it’s limited to what it learned during training. Packages release new versions, download patterns shift, and Python version support changes. AgentPatch’s pypi-package-stats tool gives Claude Code access to live PyPI data so it can make recommendations based on what’s current.

Why This Matters

Adding a dependency to a Python project is a commitment. You want to know the package is maintained, supports your Python version, and has real adoption. Checking pypi.org manually works, but when you’re already in a Claude Code session making architecture decisions, context-switching to a browser breaks your flow.

With pypi-package-stats connected, Claude Code can pull download counts, version info, release dates, classifiers, and supported Python versions for any package on PyPI. It answers your questions with real data instead of hedging with “as of my last training data.”

Setup

The AgentPatch CLI is designed for AI agents to use via shell access. Install it, and your agent can discover and invoke any tool on the marketplace.

Install (zero dependencies, Python 3.10+):

pip install agentpatch

Set your API key:

export AGENTPATCH_API_KEY=your_api_key

Example commands your agent will use:

ap search "web search"
ap run google-search --input '{"query": "test"}'

Get your API key from the AgentPatch dashboard.

Install the AgentPatch skill — it teaches Claude Code when to use AgentPatch and how to use the CLI:

/plugin marketplace add fullthom/agentpatch-claude-skill
/plugin install agentpatch@agentpatch

MCP Server (Alternative)

If you prefer raw MCP tool access instead of the skill:

claude mcp add -s user --transport http agentpatch https://agentpatch.ai/mcp \
  --header "Authorization: Bearer YOUR_API_KEY"

Replace YOUR_API_KEY with your actual key from the AgentPatch dashboard.

Example

You’re setting up a new FastAPI project and need to choose a database toolkit. You’re considering SQLAlchemy and the newer SQLModel:

“Get the PyPI stats for sqlalchemy and sqlmodel. Is sqlmodel mature enough for production use?”

Claude Code checks both:

Fetching PyPI stats for sqlalchemy...
Fetching PyPI stats for sqlmodel...

sqlalchemy:
- Monthly downloads: 95.2M
- Latest version: 2.0.37 (published 1 week ago)
- Python versions: 3.7, 3.8, 3.9, 3.10, 3.11, 3.12, 3.13
- License: MIT
- Classifiers: Database, SQL

sqlmodel:
- Monthly downloads: 4.1M
- Latest version: 0.0.24 (published 3 weeks ago)
- Python versions: 3.8, 3.9, 3.10, 3.11, 3.12, 3.13
- License: MIT

SQLAlchemy is the foundation — 95M monthly downloads, version 2.0, extremely mature.
SQLModel is built on top of SQLAlchemy (it's a wrapper by the FastAPI author) with
4M monthly downloads. The 0.0.x version number looks concerning, but the download
count shows real production adoption. It integrates cleanly with FastAPI and Pydantic.

For a FastAPI project specifically, SQLModel is a reasonable choice — it reduces
boilerplate and plays well with your existing Pydantic models. For anything else,
SQLAlchemy directly gives you more control and a much larger ecosystem.

Claude Code combined live PyPI data with its understanding of the Python ecosystem to give you a nuanced recommendation. You stayed in the terminal the entire time.

Wrapping Up

Connecting AgentPatch to Claude Code gives it real-time access to PyPI package data during any session. It’s one of many tools on the marketplace — the same connection also exposes npm stats, GitHub stats, and more. Explore what’s available at agentpatch.ai.

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