Analysis updated 2026-05-18
See a unified cost breakdown across every AI coding assistant used on a project.
Find sessions that used an expensive model when a cheaper one would have worked.
Detect wasted spend from an assistant re-reading files that have not changed.
Let AI tools query their own past activity and file history through an MCP server.
| marmutapp/superbased-observer | alexremn/finalizer-doctor | azer/diskwhere | |
|---|---|---|---|
| Stars | 3 | 3 | 3 |
| Language | Go | Go | Go |
| Setup difficulty | easy | easy | easy |
| Complexity | 3/5 | 3/5 | 1/5 |
| Audience | developer | ops devops | developer |
Figures from each repo's GitHub metadata at analysis time.
Single Go binary install via npm or go install, then a first-run command registers hooks with detected AI tools.
SuperBased Observer is a single Go program that watches how AI coding assistants like Claude Code, Codex, Cursor, Cline, GitHub Copilot, and several others are actually used on your machine, and turns that raw activity into a clear picture of what is happening and what it costs. Rather than working with just one tool, it organizes everything it sees by the git repository being worked on, so knowledge picked up from one assistant is shared with all the others touching that same project. The problem it solves is that billing dashboards from AI providers only show a total dollar figure, not which project, model, or session actually generated the spend. Observer answers more specific questions: which sessions are burning through an expensive model when a cheaper one would have done the job, whether cached prompts are actually getting reused, and how much money gets wasted when an assistant re-reads a file that has not changed since the last time it looked at it. It works passively, reading each tool's own session logs and optionally sitting between the tool and the AI provider's API to get exact token counts. Everything runs locally and nothing is sent anywhere except the normal upstream calls your tools already make. Results show up three ways: a local web dashboard with tabs for cost breakdown, per-session detail, and a waste detector called Discovery, a command line interface, and an MCP server exposing more than a dozen tools so the AI assistants themselves can query their own history, check whether a file has already been read, or recall what a past session did. To keep costs and context size down, it also compresses and indexes large tool outputs before they reach the model, with each compression layer able to be turned on or off independently. Installation is a single Go binary, available through npm or go install, backed by a pure Go SQLite database with no external dependencies.
A Go tool that tracks how AI coding assistants like Claude Code, Cursor, and Copilot are used across a project, showing exactly what each session cost and where money is wasted.
Mainly Go. The stack also includes Go, SQLite, MCP.
The README does not state a license, so terms of use are unclear.
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.