aicodestat 0.0.1

CodeStat · AI Code Metrics

Quantify how much AI actually contributes to your codebase.

CodeStat is a local metrics tool that analyzes how you use AI coding assistants: how many lines are generated by AI, how many are kept, and how this evolves over time.

中文文档见：README.zh-CN.md

Features

• Global dashboard for all data

  • AI generated lines, adopted lines, adoption & generation rates
  • File count, session count, quick bar chart overview

• Multi‑dimension queries

  • By file: see how much of a file comes from AI and how much you kept
  • By session: analyze one coding session with detailed diff lines
  • By project: aggregate metrics for an entire repository

• Agent / model comparison

  • Compare multiple sessions (agents / models / settings) side‑by‑side
  • See which one actually produces more adopted code instead of just more tokens

• Local‑first & privacy‑friendly

  • All metrics are computed locally from your own diffs
  • No source code or prompts are sent to any remote service

• Nice CLI UX

  • Rich‑based tables & colors, arrow‑key navigation
  • Minimal but informative header (MCP status + repo info)

Demo

TODO: add real screenshots / GIFs from your terminal

• Global dashboard

  (insert GIF or screenshot here)

• Session metrics with diff lines

  (insert GIF or screenshot here)

Quickstart

Install

git clone https://github.com/2hangchen/CodeStat.git
cd CodeStat
pip install -r requirements.txt

Once published to PyPI you can alternatively run:
pip install codestat-ai

Start the CLI

python .\cli\main.py

Use ↑/↓ to move, Enter to confirm.
Choose “📈 Global Dashboard (All Data)” to see an overview of your local metrics.

Typical Workflows

• Measure your own AI usage

  • Record one or more coding sessions with your IDE + MCP server
  • Run CodeStat and inspect:
    • AI generated vs adopted lines
    • Which files receive the most AI help

• Compare agents / models / prompts

  • Map different sessions to different agents / models
  • Use Compare Agents to get a per‑session comparison table

• Project‑level health check

  • For a given repo, run project metrics to see:
    • Where AI contributes the most
    • Whether AI‑generated code is actually being kept