原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

Llmkit

作者:smigolsmigol版本 0.4.10i★ –i在你的电脑上运行
注册名称io.github.smigolsmigol/llmkit

安装

概览

作者提供的说明(英文原文)

AI cost tracking: 11 tools for spend, budgets, and Claude Code + Cursor + Cline costs

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 开发者工具
  • 你需要填写:LLMKIT_API_KEY

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 需要你自己填写: LLMKIT_API_KEY, LLMKIT_PROXY_URL

标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。

你需要

  • npm已安装 Node.js(用于 npx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "llmkit": {
      "command": "npx",
      "args": [
        "-y",
        "@f3d1/llmkit-mcp-server"
      ],
      "env": {
        "LLMKIT_API_KEY": "your-value",
        "LLMKIT_PROXY_URL": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add llmkit --env LLMKIT_API_KEY=your-value --env LLMKIT_PROXY_URL=your-value -- npx -y @f3d1/llmkit-mcp-server

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "llmkit": {
      "command": "npx",
      "args": [
        "-y",
        "@f3d1/llmkit-mcp-server"
      ],
      "env": {
        "LLMKIT_API_KEY": "your-value",
        "LLMKIT_PROXY_URL": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "llmkit": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@f3d1/llmkit-mcp-server"
      ],
      "env": {
        "LLMKIT_API_KEY": "your-value",
        "LLMKIT_PROXY_URL": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

需要你填写的值

  • LLMKIT_API_KEY 机密 env
    LLMKit API key. Optional: local tools work without it.
  • LLMKIT_PROXY_URL env
    LLMKit proxy URL (defaults to hosted service)

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月8日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 smigolsmigol 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“开发者工具”中的其他服务器比较i

  • 星标数多于此分类中其他 5549 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 5549 个服务器的 33% 更新
  • 基础检查结果等于或优于此分类中其他 5549 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

Llmkit 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

你需要填写:LLMKIT_API_KEY

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.4.10)于2个月前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。

有什么可以替代它?

同一分类中有:GitHub, Desktop Commander, Cloudflare MCP

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:↓ 每周下载量i★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

GitHub

Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.

作者:github广泛使用★ 3.4万5天前更新

电脑或云端

Desktop Commander

MCP server for terminal commands, file operations, and process management

作者:wonderwhy-er广泛使用↓ 71万/周★ 1万17天前更新

在你的电脑上运行

Cloudflare MCP

Cloudflare MCP servers

作者:cloudflare广泛使用★ 444614个月前更新

在云端运行✓ mcp.cloudflare.com

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

Measure what your AI agents cost. Stop requests before they exceed a budget.

Website | Docs | Pricing API | Architecture | Security | Assurance case

LLMKit is an open-source AI gateway and SDK suite for cost attribution, budget admission, and request evidence. The gateway reserves estimated spend before provider dispatch. It rejects requests that cannot fit the active budget, then settles admitted reservations to actual usage when the response completes.

The repository also ships local tracking surfaces that do not require an LLMKit account or proxy.

Choose a surface

SurfaceUse it whenPackage
Python transportYou want local cost estimates around existing SDK callsllmkit-sdk
CLI wrapperYour OpenAI or Anthropic client honors its standard base-URL environment variable@f3d1/llmkit-cli
TypeScript SDKYou have an existing key and want sessions, streaming, and gateway access from TypeScript@f3d1/llmkit-sdk
MCP serverYou want spend, budget, and local coding-session tools inside an MCP client@f3d1/llmkit-mcp-server
AI SDK providerYou use Vercel AI SDK 6@f3d1/llmkit-ai-sdk-provider
Gateway and dashboardYou need shared budgets, provider routing, receipts, and analyticspackages/proxy, packages/dashboard

Quick start

Local Python tracking
pip install llmkit-sdk
from llmkit import tracked

from openai import OpenAI

costs = [] client = OpenAI(http_client=tracked(on_cost=costs.append))

client.chat.completions.create( model="gpt-4.1", messages=[{"role": "user", "content": "Summarize this incident."}], )

print(f"${sum(item.total_cost or 0 for item in costs):.6f}")

The transport reads provider usage metadata and estimates cost from the bundled pricing catalog. It does not send tracking data to LLMKit.

Zero-code CLI tracking
npx @f3d1/llmkit-cli -- python my_agent.py

Use -v for per-request output or --json for machine-readable results.

Gateway mode (existing key)

Gateway examples require an existing LLMKit API key. Account creation and key management are temporarily unavailable while the authenticated service is restored. If you do not already have a key, use one of the local tracking paths above.

from openai import OpenAI

client = OpenAI( base_url="https://api.llmkit.sh/v1", api_key="llmk_your_key_here", )

response = client.chat.completions.create( model="gpt-4.1", messages=[{"role": "user", "content": "Draft a release note."}], )

The budget path

The control path is built around three boundaries:

  • Atomic admission: a Durable Object owns reservation state for each budget scope. Concurrent requests cannot spend the same remaining balance.
  • Dispatch-aware idempotency: deterministic failures before dispatch release the key. After provider dispatch may have occurred, failures remain terminal to avoid duplicate spend.
  • Bounded responses: non-streaming bodies and individual SSE frames have explicit byte limits. LLMKit cancels upstream reads when a limit is exceeded.

Request receipts bind the admission decision, provider attempt, settlement, and analytics handoff with stable identifiers. Database writes use an outbox, so an analytics outage does not silently erase budget evidence.

MCP server

{

"mcpServers": { "llmkit": { "command": "npx", "args": ["-y", "@f3d1/llmkit-mcp-server"] } } }

Five local tools inspect supported Claude Code sessions and Cline task data without an LLMKit key. Six gateway tools query spend, budgets, keys, sessions, and service health when LLMKIT_API_KEY contains an existing key. Together they expose 11 tools.

Pricing data

The pinned catalog is a bundled reference snapshot, not a live quote. One source file, packages/shared/pricing.json, records the snapshot date and generates the TypeScript, Python, and MCP tables. CI rejects drift between the source and generated files. The public site renders only populated provider tables and displays the source date.

The public comparison endpoint requires no account:

https://api.llmkit.sh/v1/pricing/compare?mode=text-token&models=anthropic%2Fclaude-sonnet-4-6%2Copenai%2Fgpt-4o&input=1000&output=1000&cacheRead=0&cacheWrite=0

The endpoint prices only the exact model keys supplied by the caller. It does not search for or recommend the cheapest model. Pricing is an estimate, not a provider invoice. Provider billing rules, model modality, and catalog freshness remain part of the error boundary.

Evidence and current boundary

ClaimEvidence in this repositoryBoundary
Concurrent budget admission is serializedWorker fixtures exercise competing reservations, retries, settlement, and recoveryDeterministic local Worker and database proof
Retry behavior avoids duplicate dispatchIdempotency tests cover payload mismatch, pre-dispatch release, and post-dispatch indeterminate stateProvider behavior is simulated in CI
Large provider responses are boundedSuccess, error, and unterminated SSE fixtures verify rejection and stream cancellationBound is per buffered response or SSE frame
Pricing artifacts are reproducibleOne generator and CI --check path cover all published language tablesCatalog values still require source updates
Hosted recovery can be evaluated safelyGuarded staging deploy and proof runners bind an isolated Worker, database, revision, and cleanup journalA completed hosted concurrency and outage-recovery receipt is not claimed here

See STAGING_PROOF.md for the isolated hosted proof contract. It deliberately refuses production targets and dirty worktrees.

Project policy and design

DocumentWhat it owns
GovernanceDecision authority, roles, disputes, and the current continuity gap
RoadmapIntended and excluded work through August 2027
ArchitectureComponents, request flows, identity, storage, deployment, and failure boundaries
SecuritySecurity requirements, excluded guarantees, reporting, and supported versions
Security assuranceThreat model, trust boundaries, executable evidence, residual risks, and runtime HOLDs
AccessibilityPublic-site controls, verification method, known gaps, and language scope
ContributingSetup, quality gates, review expectations, and DCO sign-off

Development

git clone https://github.com/smigolsmigol/llmkit

cd llmkit corepack pnpm@9.15.4 install --frozen-lockfile corepack pnpm@9.15.4 build corepack pnpm@9.15.4 quality:pr

Run the Worker locally with development-only bindings:

corepack pnpm@9.15.4 --filter @f3d1/llmkit-proxy dev

Generic deploy commands are intentionally omitted. Staging and production use separate guarded scripts with explicit target confirmation.

Security

Provider credentials are encrypted with AES-256-GCM using a random IV and owner/provider-bound additional authenticated data. LLMKit API keys are hashed before storage. CI includes secret scanning, static analysis, dependency review, CodeQL, and package provenance checks.

Read the security policy and architecture and the machine-readable Security Insights snapshot. Please report vulnerabilities through GitHub private vulnerability reporting or email security@llmkit.sh.

License

MIT

问 PopNx