Llm Usage & Cost Tracker

Name: Llm Usage & Cost Tracker
Author: zhaoyue722

@zhaoyue722

关于 Llm Usage & Cost Tracker

A local-first, multi-provider cost meter for LLM usage, exposed as MCP tools. Captures every call into a local SQLite ledger and lets any coding agent query spend, compare providers, and get recommendations — no cloud, no account. First-class support for Chinese providers (Qwen,

#cost-tracking #llm #observability #pricing #local-first #anthropic #openai #deepseek #qwen

基本信息

许可证

MIT

运行时

python

传输方式

stdio

发布者

zhaoyue722

提交者

越越的糖

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "llm-usage": {
      "command": "uvx",
      "args": [
        "llm-usage-mcp"
      ]
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Llm Usage & Cost Tracker?

Llm Usage & Cost Tracker is a local-first MCP server and CLI that captures LLM API calls across multiple providers (Anthropic, OpenAI, DeepSeek, Qwen), calculates costs, and answers spend queries through an agent or terminal. It is a cost meter, not a router.

How to use Llm Usage & Cost Tracker?

Install via uv tool install llm-usage-mcp (or clone from source). Set at least one provider API key as an environment variable. Run the capture proxy (llm-usage-proxy), which listens on 127.0.0.1:5525. Point your LLM client’s base URL to the proxy. Use the MCP server (e.g., claude mcp add llm-usage -- uv run llm-usage-mcp) to ask agent questions, or run CLI commands like llm-usage spend, llm-usage compare, and llm-usage recommend.

Key features of Llm Usage & Cost Tracker

Local-first SQLite storage, no telemetry.
Supports Anthropic, OpenAI, DeepSeek, and Qwen.
MCP server with seven query tools.
CLI with subcommands for spend, compare, recommend.
Capture proxy logs all API calls idempotently.
Cross-provider cost comparison and recommendation.
Works with streaming and non-streaming calls.
No SaaS signup or account needed.

Use cases of Llm Usage & Cost Tracker

Monitor daily, weekly, or monthly spend per provider or model.
Compare hypothetical workload costs across all priced models.
Get the cheapest model recommendation for a budget and workload.
Integrate with Claude Code, Cursor, or any MCP client.
Manually log usage when the capture proxy is not in use.

FAQ from Llm Usage & Cost Tracker

What does it do vs a router?

It is a cost meter, not a router. It tells you what you spent and which provider fits a workload, but never changes your calls. It pairs happily alongside a router or a model-leaderboard tool.

What are the runtime requirements?

Python 3.13+, uv or pipx for installation. SQLite is built into Python, no extra database setup.

Where does my usage data live?

All data is stored locally in a SQLite file at ~/.llm-usage/usage.db.

Which providers are supported?

Anthropic, OpenAI, DeepSeek, and Qwen are supported for both streaming and non-streaming. More providers (Gemini, Bedrock, Moonshot) are planned.

How do I authenticate and connect?

API keys are set as environment variables (e.g., ANTHROPIC_API_KEY). The proxy holds keys server-side; clients point their base URL at http://127.0.0.1:5525 and never need keys directly.