Generate a self-contained HTML report of your Pi coding agent usage and costs.
Identify sessions where an expensive AI model was used on a task a cheaper one could handle.
Compare this week's cost and error trends against previous weeks.
Get configuration or skill suggestions based on your actual project setup.
| blazeup-ai/pi-insights | alemtuzlak/kiira | deepelementlab/jupyter-studio | |
|---|---|---|---|
| Stars | 49 | 49 | 49 |
| Language | TypeScript | TypeScript | TypeScript |
| Setup difficulty | easy | moderate | easy |
| Complexity | 2/5 | 2/5 | 2/5 |
| Audience | developer | developer | data |
Figures from each repo's GitHub metadata at analysis time.
Requires Pi version 0.74.0 or later with an AI model already configured.
Pi Insights is an extension for the Pi coding agent that analyzes your session history and generates a report about how you are actually using the tool. If you work with Pi across many sessions over weeks or months, this gives you a structured look at where your time and money are going and where your workflow could improve. The report covers several things. Basic statistics include token usage, cost, lines of code changed, commits made, tool errors, and parallel session counts. A model spend section identifies cases where you are using an expensive model on simple tasks or a cheaper model on complex ones that it handles poorly, and it estimates potential savings from adjusting. A suggestions section recommends Pi features, skills, or configuration changes based on your actual projects and tools, pulling from your existing setup so it does not suggest things you already have. A separate section flags patterns that are costing you time or money and suggests concrete alternatives. What distinguishes this from simpler analytics tools, according to the README, is that it tracks changes over time rather than producing a static snapshot. It compares the current week against previous weeks, weights recent sessions more heavily when computing satisfaction and friction metrics, detects whether costs and errors are trending better or worse, and only surfaces friction points that are still ongoing rather than problems you have already resolved. The pipeline works in five phases: scanning session log files, extracting deterministic stats from each session, using an LLM to classify goals and outcomes per session, aggregating everything with decay weighting and anomaly detection, and finally generating a self-contained HTML report (or Markdown if preferred) using a set of parallel LLM prompts. Results are cached so repeated runs are fast. Installation is one command within Pi. The extension requires Pi version 0.74.0 or later and an active AI model configured in Pi. It is released under the AGPL-3.0 license.
An extension for the Pi coding agent that analyzes your session history and generates a report on token usage, cost, and workflow patterns, comparing recent weeks against past ones to spot trends.
Mainly TypeScript. The stack also includes TypeScript, LLM, HTML.
You can use and modify the code, but if you run a modified version as a network service, you must share your source code changes under the same license.
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.