Tare

io.github.priberry-data由 GitHub 账号 priberry-data 发布v0.2.0更新于 Nov 8, 2026

See where your coding agent's tokens and money went, with cache-safe fixes. Local.

概览

AI 生成的概览

读取本机编码代理日志,展示 token 与花费去向,并给出每次提交成本与提示缓存状态。

可以做什么
  • 汇总本机编码代理的花费:API 等价成本、套餐额度使用、上下文占用、缓存重建及改进建议
  • 对比修复前后的每次提交成本,给出 90% 区间或数据不足的提示
  • 查看单个会话记录的上下文大小、缓存是否仍热,以及下一条消息热/冷两种成本
  • 三个工具均在本地运行,依据代理留在本机的日志计价
适用场景
想了解或降低本机 Claude Code、Codex CLI、Gemini CLI、Cline、Roo Code、Kilo Code 的花费时适用,也可在发送下一条消息前确认提示缓存是否仍然有效。
运行要求
通过 npx 以本地 stdio 进程运行(需要 Node.js)。未声明账号、API 密钥、环境变量或请求头。读取本机已有的代理日志;仅支持桌面端。
安装前请注意
会读取本机代理日志与会话记录,其中可能含提示词、代码和工具输出;README 称不上传任何内容、不调用模型。MCP 工具为只读,但单独的 CLI 命令会修改代理设置(钩子、状态栏)并在用户主目录下写入备份。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Tare,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

tare

Same work, fewer tokens, with the receipt.

Your coding agent keeps a log of every API call it makes. Tare reads those logs, prices every token, and shows where the money went: Claude Code, Codex CLI, Gemini CLI, and Cline, Roo Code and Kilo Code in VS Code, Cursor, Windsurf and the other VS Code-based editors. For Codex on a ChatGPT plan it also shows how close you came to the plan's usage limits. Then it cuts the waste without breaking the prompt cache, and proves each saving on your own work. It runs locally, uploads nothing and never calls a model.

npx tarelab
  YOUR AGENT, WRAPPED  · last 30 days · 254 sessions · 79,905 API calls
  $6,720 API-equivalent spend  $623 of it was avoidable (9%)
  Where it went  Reading cached context     ██████████████████████ $3,809  Writing to the cache       ████████████ $2,072  Output (thinking incl.)    █████ $838
  The highlights  › ~82k tokens load before you type a word.  › Coffee-break tax: $722 re-caching after 636 pauses.  › Sub-agents spent $2,734 (41%).

A real month of Priberry's own Claude Code use.

Commands

CommandWhat it does
tareWrapped for the last 30 days. --days N, --all, --json, --logs <dir>
tare cardA 1200×630 shareable card (SVG, with a PNG download) and post text. Nothing is uploaded
tare fixInstalls the output trim and the cache status line. --dry-run, --undo, --scope project
tare receiptCost per commit before and after tare fix, with a 90% range and a finish-rate check
tare statusWhat is installed, and how much output the trim has cut so far
tare setupAdds Tare to every AI coding app on this machine. --dry-run, --undo
tare mcpMCP server over stdio: tare_wrapped, tare_receipt, tare_cache

How the numbers work

  • API-equivalent. Every call is priced at Anthropic's API list prices: fresh input, cache writes (1.25× input for the 5-minute cache, 2× for the 1-hour cache), cache reads and output. On a subscription you pay a flat fee; this is what the same work would cost on the API.
  • Carry cost. A tool's output is written to the cache once and read back on every later call until the conversation is compacted. Tare charges each output for the whole time it stays in context.
  • Cache rebuilds. When most of a prompt is written to the cache again instead of read from it, Tare says why: an idle pause past the cache's lifetime, a model switch, a compaction, or a break.
  • Repeats. A tool call that returned byte-for-byte what the agent already had.

The fixes

Tare cuts waste where it enters the context and never rewrites what's already there, because a rewritten history breaks the prompt cache and costs more than it saves.

  • Output trim (a PostToolUse hook on Bash): output over ~2,500 tokens is cut to the first 40 lines, every line that looks like a failure (with context) and the last 60 lines. The full output is saved in ~/.tare/out/ and the agent is told where.
  • Cache countdown (the status line): how long until the cache goes cold, and what the next message costs warm versus cold. Installed only if you don't already have a status line.

Settings are backed up to ~/.tare/backups/ first, and tare fix --undo removes only Tare's entries.

The receipt

tare receipt compares cost per commit in sessions before and after the fixes, with a bootstrap 90% interval, and checks that as many sessions still reach a commit. With fewer than 8 finished sessions on either side it prints "not enough data" and no number.

In your agent

One command adds Tare to every AI coding app it finds: Claude Code, Claude Desktop, Cursor, VS Code, Windsurf, Codex, Gemini CLI, Kiro, opencode, Amazon Q, LM Studio, and Cline, Roo Code and Kilo Code. The entry is pinned to the exact version, each file is backed up first, and --undo removes it.

npx tarelab setup

Or one app at a time:

  • Claude Code plugin: /plugin marketplace add priberry-data/tarelab, then /plugin install tare@priberry.
  • Gemini CLI extension: gemini extensions install https://github.com/priberry-data/tarelab
  • Skill (Claude Code or Codex): copy skills/tare into ~/.claude/skills/ or ~/.codex/skills/.
  • Any MCP client: {"command": "npx", "args": ["-y", "[email protected]", "mcp"]}. Then ask "what did my agent cost this month?"

Limits

Tare reads the logs that agents keep on your machine. Cursor's own agent, Windsurf's Cascade, Copilot and the browser app builders (Lovable, Bolt, v0, Replit) keep usage on their servers, so Tare can't see them yet; opencode is next. Cline-family costs are the ones the extension itself recorded. The fix and receipt commands work on Claude Code, where the hooks exist. Token counts for tool output are estimated from text length (the API reports totals per call, not per block). Prices are as of 2026-10-06 for Anthropic and 2026-11-08 for OpenAI and Google, and live in src/prices.mjs.

Apache-2.0 · Built by Priberry, who also make Crumple.

来源:README.md,提交 ebdc3b6

工具

3

tare_wrapped

Where this machine's coding-agent tokens and money went (Claude Code, Codex CLI, Gemini CLI, Cline, Roo Code, Kilo Code): API-equivalent spend, plan-limit use, what filled the context, cache rebuilds, highlights and the top fixes with estimated savings. Local and free.

输入参数days

tare_receipt

Cost per commit before and after Tare's fixes were installed, with a 90% range, or a plain 'not enough data yet'.

无需输入参数。

tare_cache

For one session transcript: context size, whether the prompt cache is still warm, and what the next message will cost warm versus cold.

输入参数transcript_path必填

版本历史

1
  1. v0.2.0最新Nov 8, 2026

评分

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