能力标签
AI支出追踪
🛠
AI工具

AI支出追踪

基于 TypeScript · 开源免费,本地部署,数据完全自主可控
英文名:codeburn
⭐ 8.1k Stars 🍴 635 Forks 💻 TypeScript 📄 MIT 🏷 AI 8.0分
8.0AI 综合评分
ai-codingcost-trackingtypescript
✦ AI Skill Hub 推荐

AI支出追踪 是 AI Skill Hub 本期精选AI工具之一。已获得 8.1k 颗 GitHub Star,综合评分 8.0 分,整体质量较高。我们强烈推荐将其纳入你的 AI 工具库,帮助提升工作效率。

📚 深度解析

AI支出追踪 是一款基于 TypeScript 的开源工具,在 GitHub 上收获 8k+ Star,是ai-coding、cost-tracking、typescript领域中的优质开源项目。开源工具的最大优势在于代码完全透明,你可以审计每一行代码的安全性,也可以根据自身需求进行二次开发和定制。

**为什么要使用开源工具而非商业 SaaS?**
对于个人开发者和有隐私需求的用户,本地部署的开源工具意味着数据不离本机,不受第三方服务商的数据政策约束。同时,开源工具通常没有使用次数限制和月度费用,一次安装即可长期使用,对于高频使用场景的总拥有成本(TCO)远低于订阅制商业工具。

**安装与环境准备**
AI支出追踪 依赖 TypeScript 运行环境。建议通过 pyenv(Python)或 nvm(Node.js)管理 TypeScript 版本,避免全局环境污染。对于新手用户,推荐先创建虚拟环境(python -m venv venv && source venv/bin/activate),再安装依赖,这样即使出现问题也可以随时删除虚拟环境重新开始,不影响系统稳定性。

**社区与维护**
GitHub Issue 和 Discussion 是获取帮助的最快渠道。在提问前建议先检查 Closed Issues(已关闭的问题),大多数常见问题都已有解答。遇到 Bug 时,提供 pip list 的输出、完整错误堆栈和最小可复现示例,能显著提高开发者响应速度。AI Skill Hub 将持续追踪 AI支出追踪 的版本更新,及时通知重要功能变化。

📋 工具概览

可视化AI支出,实时跟踪Token使用和成本

AI支出追踪 是一款基于 TypeScript 开发的开源工具,专注于 ai-coding、cost-tracking、typescript 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。

GitHub Stars
⭐ 8.1k
开发语言
TypeScript
支持平台
Windows / macOS / Linux
维护状态
持续维护,定期更新
开源协议
MIT
AI 综合评分
8.0 分
工具类型
AI工具
Forks
635

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理,如需查看完整原始文档请访问底部「原始来源」。

可视化AI支出,实时跟踪Token使用和成本

AI支出追踪 是一款基于 TypeScript 开发的开源工具,专注于 ai-coding、cost-tracking、typescript 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。

📌 核心特色
  • 开源免费,支持本地部署,数据完全自主可控
  • 活跃的 GitHub 开源社区,持续迭代更新
  • 提供详细文档和使用示例,新手友好
  • 支持自定义配置,灵活适配不同使用环境
  • 可作为基础组件集成进现有技术栈或进行二次开发
🎯 主要使用场景
  • 本地部署运行,保护数据隐私,满足合规要求
  • 自定义集成到现有系统,扩展技术栈能力
  • 作为开源基础组件进行商业化二次开发
以下安装命令基于项目开发语言和类型自动生成,实际以官方 README 为准。
安装命令
# 方式一:npm 全局安装
npm install -g codeburn

# 方式二:npx 直接运行(无需安装)
npx codeburn --help

# 方式三:项目依赖安装
npm install codeburn

# 方式四:从源码运行
git clone https://github.com/getagentseal/codeburn
cd codeburn
npm install
npm start
📋 安装步骤说明
  1. 访问 GitHub 仓库页面
  2. 按照 README 文档完成依赖安装
  3. 根据系统环境完成初始化配置
  4. 参考官方示例或文档开始使用
  5. 遇到问题可在 GitHub Issues 中查找解答
以下用法示例由 AI Skill Hub 整理,涵盖最常见的使用场景。
常用命令 / 代码示例
# 命令行使用
codeburn --help

# 基本用法
codeburn [options] <input>

# Node.js 代码中使用
const codeburn = require('codeburn');

const result = await codeburn.run(options);
console.log(result);
以下配置示例基于典型使用场景生成,具体参数请参照官方文档调整。
配置示例
# codeburn 配置说明
# 查看配置选项
codeburn --config-example > config.yml

# 常见配置项
# output_dir: ./output
# log_level: info
# workers: 4

# 环境变量(覆盖配置文件)
export CODEBURN_CONFIG="/path/to/config.yml"
📑 README 深度解析 真实文档 完整度 16/100 查看 GitHub 原文 →
以下内容由系统直接从 GitHub README 解析整理,保留代码块、表格与列表结构。

简介

Codex and Claude for Open Source Recipient CodeBurn - See where your AI coding spend actually goes | Product Hunt

<p align="center"> <img src="https://raw.githubusercontent.com/getagentseal/codeburn/main/assets/providers.png" alt="CodeBurn" width="420" /> </p>

<p align="center"><strong>See where your AI spend goes.</strong></p>

<p align="center"> <a href="https://www.npmjs.com/package/codeburn"><img src="https://img.shields.io/npm/v/codeburn.svg?color=F97316" alt="npm version" /></a> <a href="https://www.npmjs.com/package/codeburn"><img src="https://img.shields.io/npm/dt/codeburn.svg?color=F97316" alt="total downloads" /></a> <a href="https://github.com/getagentseal/codeburn/blob/main/LICENSE"><img src="https://img.shields.io/npm/l/codeburn.svg?color=F97316" alt="license" /></a> <a href="https://github.com/getagentseal/codeburn"><img src="https://img.shields.io/badge/node-%3E%3D22-F97316.svg" alt="node version" /></a> <a href="https://discord.gg/w2sw8mCqep"><img src="https://img.shields.io/badge/discord-join-F97316?logo=discord&logoColor=white" alt="Discord" /></a> <a href="https://x.com/_codeburn"><img src="https://img.shields.io/badge/%40__codeburn-F97316?logo=x&logoColor=white" alt="Follow @_codeburn on X" /></a> <a href="https://github.com/sponsors/iamtoruk"><img src="https://img.shields.io/badge/sponsor-♥-F97316?logo=github" alt="Sponsor" /></a> </p>

<p align="center">If CodeBurn shows you something your bill never did, <a href="https://github.com/getagentseal/codeburn/stargazers">star the repo</a> so other developers find it, and consider <a href="https://github.com/sponsors/iamtoruk">sponsoring</a> to keep 42 integrations honest.</p>

<p align="center"><code>npx codeburn</code></p>

<p align="center"><sub>To keep it: <code>npm install -g codeburn</code> or <code>brew install codeburn</code>. Needs Node.js 22.13+.</sub></p>

<p align="center"> <a href="https://codeburn.app/download/mac-arm64"><img src="https://img.shields.io/badge/macOS-Apple_Silicon-F97316?logo=apple&logoColor=white" alt="Download CodeBurn for macOS (Apple Silicon)" /></a> <a href="https://codeburn.app/download/mac-x64"><img src="https://img.shields.io/badge/macOS-Intel-F97316?logo=apple&logoColor=white" alt="Download CodeBurn for macOS (Intel)" /></a> <a href="https://apps.microsoft.com/detail/9P0R4ZL5XMB8"><img src="https://img.shields.io/badge/Windows-Microsoft_Store-F97316?logo=microsoft&logoColor=white" alt="Get CodeBurn from the Microsoft Store" /></a> <a href="https://codeburn.app/download/windows-exe"><img src="https://img.shields.io/badge/Windows-.exe-F97316?logo=windows&logoColor=white" alt="Download the CodeBurn Windows installer (.exe)" /></a> <a href="https://codeburn.app/download/linux-deb"><img src="https://img.shields.io/badge/Linux-.deb-F97316?logo=debian&logoColor=white" alt="Download CodeBurn for Linux (.deb)" /></a> <a href="https://codeburn.app/download/linux-rpm"><img src="https://img.shields.io/badge/Linux-.rpm-F97316?logo=redhat&logoColor=white" alt="Download CodeBurn for Linux (.rpm)" /></a> <a href="https://codeburn.app/download/linux-appimage"><img src="https://img.shields.io/badge/Linux-AppImage-F97316?logo=linux&logoColor=white" alt="Download CodeBurn for Linux (AppImage)" /></a> </p>

<p align="center"><sub>The macOS builds are signed with a Developer ID and notarized by Apple.</sub></p>

Questions

My spend

<details> <summary><strong>I know what the month cost. How much of it was wasted, and how would I know?</strong></summary>

Run codeburn optimize. It reads the last 30 days of sessions and your ~/.claude/ config, lists what cost tokens without earning them, and prints a Potential savings line in tokens and dollars for the period it scanned. Findings come in three groups: Fix now, which CodeBurn can apply for you, Habits, which is how you drive the next session, and FYI, which may well be money well spent. Each finding says whether its number is measured from your own token counts or modelled from an average. The setup gets a grade from A to F, and that grade rates the config, not the spending, so an expensive month with a clean setup still scores an A. See Optimize.

</details>

<details> <summary><strong>Which sessions burn the most, and why?</strong></summary>

The desktop Overview and the terminal dashboard both list the five most expensive sessions. Click one in the app and you land on Sessions with the drawer open on it: project, models, tokens, and the per-model, per-category, per-branch and per-day split of that one session. codeburn sessions is the same list in the terminal. codeburn optimize flags sessions that cost more than twice their own project's average. The drawer usually shows the cause, such as one model doing most of the work or one category taking most of the turns. See Drill-through.

</details>

<details> <summary><strong>My agent spent an hour on a five-minute task. Where did the money go?</strong></summary>

Open that session in the Sessions drawer for the per-turn cost, then run codeburn context and pick it from the list. codeburn context prints what filled the context window: assistant split into text, reasoning and tool calls with a row per tool, user split into text, images and compaction summaries, and tool with the tool-result total, which is usually the biggest line. It also counts how many compactions happened, which is the usual sign of an hour spent re-reading. The block sizes are estimated from character counts. The exact context figure comes from the last call's reported usage, and the output labels it as such.

</details>

<details> <summary><strong>Is Opus worth it, or would Sonnet have done the same job?</strong></summary>

Run codeburn compare, or press c in the dashboard, to put two models side by side on your own history: one-shot rate, retry rate, self-correction, cost per call, cost per edit, cache hit rate. Cohorts mode narrows that to the same kind of work, one row per edit turn, with median and P90 cost per edit turn and the sample list you can read yourself. Turns that mixed two models are excluded and counted, never assigned to one. codeburn optimize goes further and prints a Model defaults recommendation when a project has enough edit turns to judge: a cheaper model you already used there whose one-shot rate held up, applied with codeburn act apply-model <project>. See Cohort comparison.

</details>

<details> <summary><strong>Which project costs the most, and which branch inside it?</strong></summary>

Open the Spend page and pick a project in the By branch panel. Each branch row shows its cost, calls, tokens, distinct sessions and activity window, attributed turn by turn, so a session that switched branches lands on both rows with its own slice instead of counting twice. From the terminal the same report is codeburn spend --format branch-json. Branch is recorded per turn by Claude Code and by almost nothing else, so the coverage note under the rows splits the project three ways: spend on named branches, spend before a branch was recorded, and spend from tools that carry no branch at all. See Spend by branch.

</details>

<details> <summary><strong>Do my subagents cost more than they save?</strong></summary>

codeburn sessions --by-work-unit gives one row per orchestration root with its delegated children folded underneath, so the parent and its fan-out read as one number. codeburn models --by-agent splits each model's spend by the Claude subagent that drove it, with main sessions and other tools bucketed under main. The dashboard has a Claude Agent Types panel with calls and cost per agent type, and Delegation is one of the task categories. CodeBurn tells you what the fan-out cost. Whether the same work would have been cheaper in one long session is not something it can measure.

</details>

<details> <summary><strong>What did that pull request cost me?</strong></summary>

The app has a Pull requests page, and codeburn sessions --by-pr is the same report in the terminal. It groups spend by the PR links your sessions recorded, and expanding a row takes you to the sessions behind it. Turns that touched more than one PR contribute their share to each, so the rows are not an exclusive split, and spend tied to no PR is labeled rather than hidden. The links come from what the session wrote down, so CodeBurn never calls GitHub and never checks whether the PR merged. For that part, codeburn yield correlates sessions with commits and sorts the spend into productive, reverted and abandoned, and the Overview's Cost per outcome panel shows cost per commit and cost per productive session. See Yield.

</details>

Subscriptions and limits

<details> <summary><strong>I pay a flat fee for Claude Max. Why does CodeBurn show me dollars?</strong></summary>

The dollar figure is what your tokens would have cost at API rates. It is not an invoice. The card labels it API-equivalent, not a live provider window. It is still the only per-session, per-project, per-model number you can get on a subscription, because a subscription gives you one price and no breakdown. Run codeburn plan set claude-max and the Plans page then shows what you have spent this cycle as a share of the plan, and whether you are on track or on pace to exceed it. See Plans and quota.

</details>

<details> <summary><strong>I keep hitting my 5-hour or weekly limit. Can I see it coming?</strong></summary>

codeburn quota asks each provider how much of your plan is left, signing the request with the credential that tool already stores on your machine. For Claude that is a 5-hour row and a weekly row, each with a percentage used and a reset time, shown as 42% used · resets in 3h 20m on the Plans page's Live quota panel. The Capacity Dock carries the same reading, and the macOS menu bar can carry a second line with quota remaining and its countdown. CodeBurn does not predict the hour you will run out. What you get is how full the window is and when it clears.

</details>

<details> <summary><strong>Is my plan the right size, or am I paying for capacity I never use?</strong></summary>

Set what you pay for with codeburn plan set claude-max, or claude-pro, cursor-pro, copilot-pro, google-ai-pro, or custom --monthly-usd 200 --provider codex. The Plans page then shows spend this cycle against that budget with a pacing line, either On track or on pace to exceed with the projected figure and the date. After two or three cycles the pattern is clear. Consistently under a quarter of the plan means you are buying capacity you do not use, and consistently over means the cheaper plan is costing you. CodeBurn shows the share and the pace, and leaves the choice of plan to you.

</details>

<details> <summary><strong>Three tools, three subscriptions. What is my real total?</strong></summary>

Plans are stored per provider, so codeburn plan set claude-max, codeburn plan set cursor-pro and a custom Codex plan sit side by side rather than blending into one figure. codeburn plan prints them all, and the app carries one card per active plan with its own spent, percentage and overage. The spend tables stay per tool underneath, so you can see which subscription is carrying the work and which one is idle. Copilot is tracked in AI credits rather than dollars, because credits are what Copilot actually meters.

</details>

How my agent works

<details> <summary><strong>My agent reads the same file twenty times. How do I see that and stop it?</strong></summary>

codeburn optimize has a detector for exactly this, reported as "Claude is re-reading the same files" with the files and what the re-reads cost. Next to it sit reads into node_modules, .git and dist, and sessions that edit far more than they read first. Those two are the ones a written rule can fix: codeburn optimize --apply appends a marker block to the current project's CLAUDE.md, shows you the file before it writes, and backs it up under ~/.config/codeburn/actions/. codeburn act undo --last puts it back. The re-read finding itself has no file to edit, so the fix there is how you open the next session.

</details>

<details> <summary><strong>How much of my context is tool output, and what is it costing me?</strong></summary>

Run codeburn context and pick a session. It works for Claude Code and Codex, and gives you a tree: assistant split into text, reasoning and tool calls with a row per tool, user split into text, images, compaction summaries and meta, then tool with the tool-result line, and system. The headline shows the exact context size from the last call's usage next to the model's window. The tree counts tokens rather than dollars, because that context is re-sent every turn and its price depends on how much of it came from cache. For the dollars over a period, codeburn models prices the same sessions.

</details>

<details> <summary><strong>Which MCP servers and skills am I paying for but never using?</strong></summary>

codeburn optimize compares what is configured against what was actually invoked. It names MCP servers configured but never called, servers with many tools and almost none used, and skills, agents and slash commands that are defined and never invoked. An MCP server's tool schemas ride along in the prompt whether you call it or not, so an unused one costs tokens on every turn. codeburn optimize --apply removes the server entry from ~/.claude.json or the project's .mcp.json, and moves unused skills into ~/.claude/skills/.archived/ rather than deleting them.

</details>

<details> <summary><strong>How often does my agent actually get it right first try?</strong></summary>

The one-shot rate. It is the One-shot figure on the app's Overview, a column in the dashboard's activity table, and a per-model row in codeburn compare. A retry is counted when the same file is edited again after a shell command ran in between, which is the shape of an edit that did not work. Editing a different file after a shell step is not a retry. Coding at 90% means nine edit turns in ten needed no second pass. File-level tracking works for Claude, Codex and Goose. Other tools fall back to tool names, so their figure is rougher. See How it works.

</details>

<details> <summary><strong>How does CodeBurn know a session was coding rather than debugging or planning?</strong></summary>

From the tools the session used and the words in your own messages, with no model call anywhere. Edit and Write make it Coding. Error and fix words alongside tool use make it Debugging. pytest or vitest in a shell command makes it Testing. Read and Grep with no edits make it Exploration, and the Agent tool makes it Delegation. There are 13 categories and the rules are deterministic, so the same session always lands in the same category. The full table is in How it works.

</details>

Trust the numbers

<details> <summary><strong>On a subscription the dollars are an estimate. What is estimated and what is measured?</strong></summary>

The tokens are measured for most tools. Claude Code, Codex, Gemini, Zed, OpenCode and others write real per-call input, output and cache counts into their own session files, and CodeBurn reads those rather than guessing. The price applied to them is published API pricing, so the dollar figure is arithmetic on measured tokens, not a guess about your bill. A few tools record no counts at all, so Cursor, Kiro and some Copilot sessions are estimated from content length, and those are marked estimated in the tables. Claude Code also bills some calls it does not write to its transcripts: some advisor calls, and subagent replies that keep only part of their usage. For those, CodeBurn can show less than Claude Code's own cost counter. codeburn audit prints a row per provider and model saying where every number came from.

</details>

<details> <summary><strong>Where do the prices come from, and what if a vendor changes them?</strong></summary>

From LiteLLM, fetched and cached for 24 hours under ~/.cache/codeburn/, so a change reaches you within a day of LiteLLM picking it up. The common Claude and GPT models also carry bundled fallback prices, so a lookup miss falls back to a known rate for those. Anything else shows as unpriced rather than guessed. If a model shows $0 its name matched no price row. codeburn models --unpriced lists those ids, codeburn model-alias points a proxy-rewritten name at the real model, and codeburn price-override sets exact rates yourself. A session is priced with today's rates when it is read, not with the rates of the day it ran. See Configuration.

</details>

<details> <summary><strong>Claude Code deletes its sessions after 30 days. Does my history go with them?</strong></summary>

The daily numbers do not. CodeBurn keeps a durable daily history under ~/.cache/codeburn/, holding each day's cost, tokens and per-project and per-model split for ten years, so codeburn report and codeburn status keep answering for days whose transcripts are gone. The per-session detail does go, because it only ever lived in the transcript. So anything that reads sessions, codeburn sessions, models, spend and compare-periods, will show less for an old range than report does for the same range. Compare periods lists those days by name with their unexplained amount instead of quietly folding them into the totals.

</details>

<details> <summary><strong>What actually leaves my computer, and how can I check?</strong></summary>

Model prices are fetched from LiteLLM and cached for 24 hours, so that call happens at most once a day. codeburn quota asks each provider you are signed in to how much of your plan is left, using the credential that tool already stores on your machine. If you set a non-USD currency, exchange rates come from Frankfurter, cached the same way. If you use Cursor, CodeBurn downloads your own usage export from cursor.com with the Cursor app's login, at most about once an hour, so Cursor figures match your bill; nothing is uploaded, and CODEBURN_CURSOR_SYNC=0 or "cursorSync": false in ~/.config/codeburn/config.json turns it off. Commands that install something, such as codeburn menubar, reach GitHub releases, which is the point of them. Your prompts, your code, your file names and your project names are read on disk and never sent anywhere, and nothing else leaves unless you point CodeBurn at a destination yourself. It is not a proxy, so no traffic of yours passes through it. You can watch what does leave with a network monitor.

</details>

Everything else

<details> <summary><strong>I work on a laptop and a desktop. Can I get one number across both?</strong></summary>

Yes, on the same local network. On the second machine run codeburn share --pair, which opens a pairing window and prints a PIN. On your main machine run codeburn devices add to find it and pair with that PIN, then codeburn devices shows combined totals by machine. codeburn devices rm <name> forgets one again. Sharing stops after ten minutes idle unless you pass --always, and pairing never leaves your network. You can also discover and pair from the browser dashboard at codeburn web. See Web dashboard.

</details>

<details> <summary><strong>Can my agent read these numbers itself, without me leaving the conversation?</strong></summary>

Register the local MCP server with claude mcp add codeburn -- npx -y codeburn mcp. It runs over stdio and exposes two tools: get_usage for spend and usage broken down by tool, model, project and task, and get_savings for the waste findings, retry tax and routing waste, which is the slower of the two. Any MCP client works, with command npx and args -y codeburn mcp. It reads the same files on disk the CLI reads, and project names are pseudonymized unless the agent asks for the real ones. See MCP.

</details>

<details> <summary><strong>Why is this free, and what is going to cost money later?</strong></summary>

CodeBurn is MIT licensed and all of it is in this repository: the CLI, the desktop app, the menu bar and tray apps, the GNOME extension. There is no account, no paid tier and no feature held back for one. It is free because it reads files you already have, which costs nothing to run. What it does cost is time, because 42 integrations sit on top of tools that change their config paths and data formats without warning. Sponsorship is what pays for keeping up with them.

</details>

🎯 aiskill88 AI 点评 A 级 2026-06-19

优雅的TUI界面,清晰展示AI支出

📚 实用指南(长尾问题)
适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • 生产部署优先使用 Docker Compose 隔离依赖,并挂载 volume 持久化数据
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效
  • 容器内无法访问宿主机 localhost — 使用 host.docker.internal
部署方案
  • Docker:codeburn 提供官方镜像,docker compose up 一键启动
  • CLI:直接 npm install -g / pip install,命令行调用
  • 云端托管:可放在 Vercel / Railway / Fly.io 等 PaaS 平台
相关搜索
codeburn 中文教程codeburn 安装报错怎么办codeburn MCP 配置codeburn Docker 部署codeburn Agent 工作流codeburn 与同类工具对比codeburn 最佳实践codeburn 适合谁用

⚡ 核心功能

👥 适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
⭐ 最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • 生产部署优先使用 Docker Compose 隔离依赖,并挂载 volume 持久化数据
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
⚠️ 常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效
  • 容器内无法访问宿主机 localhost — 使用 host.docker.internal

👥 适合人群

AI 技术爱好者研究人员和学生开发者和工程师技术创业者

🎯 使用场景

  • 本地部署运行,保护数据隐私,满足合规要求
  • 自定义集成到现有系统,扩展技术栈能力
  • 作为开源基础组件进行商业化二次开发

⚖️ 优点与不足

✅ 优点
  • +GitHub 8.1k Star,社区高度认可
  • +MIT 协议,可免费商用
  • +完全开源免费,无授权费用
  • +本地部署,数据完全自主可控
  • +开发者社区支持,遇问题可查可问
⚠️ 不足
  • −安装和初始配置可能需要一定技术基础
  • −功能完整性通常不如成熟商业产品
  • −技术支持主要依赖开源社区,响应速度不稳定
⚠️ 使用须知

AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。

建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。

📄 License 说明

✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。

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❓ 常见问题 FAQ

codeburn 是一款TypeScript开发的AI辅助工具。开源AI工具:See where your AI spend goes. Interactive TUI dashboard for token usage and cost。⭐8.1k · TypeScript 主要应用场景包括:AI项目成本监控。
💡 AI Skill Hub 点评

经综合评估,AI支出追踪 在AI工具赛道中表现稳健,质量优秀。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。

📚 深入学习 AI支出追踪
查看分步骤安装教程和完整使用指南,快速上手这款工具
🌐 原始信息
原始名称 codeburn
原始描述 开源AI工具:See where your AI spend goes. Interactive TUI dashboard for token usage and cost。⭐8.1k · TypeScript
Topics ai-codingcost-trackingtypescript
GitHub https://github.com/getagentseal/codeburn
License MIT
语言 TypeScript
🔗 原始来源
🐙 GitHub 仓库  https://github.com/getagentseal/codeburn 🌐 官方网站  https://codeburn.app/

收录时间:2026-06-19 · 更新时间:2026-06-20 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。

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