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buffer121328/deparchitect

13PythonAudience · developerComplexity · 2/5Setup · easy

TLDR

A Python command-line tool that analyzes your project's dependency health, detecting circular imports, duplicate libraries, and unnecessary packages, and gives an overall architectural quality score.

Mindmap

mindmap
  root((deparchitect))
    Commands
      scan
      cycles
      review-add
      review-upgrade
    Analysis
      Circular imports
      Unused deps
      Duplicate libs
      Health score
    Output
      Terminal report
      JSON export
      HTML with charts
    Optional AI layer
      Explains findings
      Rule-based scores
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Code map

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Things people build with this

USE CASE 1

Scan a Python project to find unused or duplicate dependencies before a production deployment

USE CASE 2

Detect circular import loops between your own modules and visualize them in an interactive HTML report

USE CASE 3

Check whether a new library you want to add is actually necessary given what your project already uses

USE CASE 4

Assess the risk of upgrading a package that may have breaking changes before you run the upgrade

Tech stack

PythonASTNetworkXHTML

Getting it running

Difficulty · easy Time to first run · 30min

The optional AI explanation layer requires an API key, the core static analysis works entirely without one.

In plain English

DepArchitect is a command-line tool for Python developers who want to understand and manage the health of their project's dependencies at an architectural level, not just check for outdated packages or security holes. The project was built as a practical learning case for a development style called vibe coding, using a workflow that combines open tools for planning, specification, and code generation. Most dependency tools tell you which packages are out of date or have known vulnerabilities. DepArchitect goes further by looking at the shape and structure of your dependencies: how deep the dependency chain runs, whether multiple packages are solving the same problem, whether your own internal modules create circular import loops, and whether adding a new library is actually necessary given what you already have. It outputs results in the terminal, as JSON, or as interactive HTML reports with visualizations. The tool has four main commands. The scan command checks a project for things like unused dependencies, duplicate capabilities, overly heavy packages, and a calculated health score. The cycles command maps how your own Python files import each other, highlighting circular imports and modules that are too tightly connected to everything else. The review-add command takes a proposed new dependency and a feature description, then checks whether your existing packages could already handle it and what the cost of adding the new one would be. The review-upgrade command assesses the risk of upgrading a specific package, such as moving from an older version of a popular data-validation library to a newer one that has breaking changes. Under the hood, the static analysis runs first using Python's abstract syntax tree parser, a dependency graph library, and metadata inspection tools. An optional AI layer sits on top and can explain findings or make suggestions, but it does not change the scores or invent new issues. All judgments come from the rule-based analysis engine. The project is actively being built. Dependency scanning and circular import detection are complete, and unused dependency detection, duplicate capability detection, health scoring, and the review commands are scheduled for delivery by the end of June 2026.

Copy-paste prompts

Prompt 1
Run deparchitect's scan command on my FastAPI project and explain each issue it flags, including what the health score means
Prompt 2
Help me fix the circular import that deparchitect's cycles command found between my auth and user modules
Prompt 3
I want to add the httpx library to my project, use deparchitect's review-add command to check if my existing dependencies already cover what I need
Prompt 4
Show me how to generate deparchitect's HTML report and share it with my team before a code review
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