LLM Usage

[llm-usage-mcp]

llm-usage-mcp

LLM Usage & Cost Tracker — your local-first spend watchdog

[CI] [License: MIT] [Python 3.13+] [Glama score]

English | 中文

Stop treating your LLM API bills like a scary horror movie you only look at through your fingers at the end of the month. Know what your LLM calls actually cost — across every provider, in one place, on your own machine. Ask your coding agent (MCP) or type a command (CLI).

It's a cost meter, not a router: it tells you what you spent and which provider fits a workload — it never changes your calls. Pairs happily alongside a router or a model-leaderboard tool.

[Claude Code answering "how much did I spend?" via llm-usage]

Or straight from the terminal — your week's spend, broken down by provider, and a cross-provider cost comparison before you commit to a model:

[llm-usage CLI: weekly spend by provider and a cross-provider cost comparison]

Why you'd want this

You're calling LLMs from a handful of providers — Claude, GPT, plus Chinese models like Qwen and DeepSeek. Each one bills in its own dashboard, in its own currency, with its own rules for what a "cached token" costs. So the simplest possible question — how much am I spending, and on what? — turns into four browser logins, looking up exchange rates for RMB to USD, and trying to decipher what a "cached context token discount" actually means in midnight math. Most people just cross their fingers and let the bill be a surprise at the end of the month.

llm-usage-mcp captures every call you make into one local store, costs it correctly per provider at the moment it happens, and hands the answer back two ways:

• Ask your coding agent. It's an MCP server, so Claude Code, Cursor, or any MCP client can answer "how much did I spend on Claude this week?" or "which provider is cheapest for a 10k-in / 2k-out call?" in plain English.
• Or type a command. It's also a CLI — llm-usage spend, llm-usage compare, llm-usage recommend — for when you'd rather not round-trip through an agent.

And it stays out of your way:

• Local-first. No SaaS, no signup, no telemetry. Just a SQLite file at ~/.llm-usage/usage.db. Privacy is a feature, not a setting.
• Multi-provider, Chinese models included. Anthropic, OpenAI, DeepSeek, Qwen — streaming and non-streaming for all four. DeepSeek and Qwen run the same capture path as Anthropic and OpenAI, not a bolted-on afterthought. More providers (Gemini, Bedrock, Moonshot, …) are on the way.

Quickstart

Two minutes from git clone to your first captured call. This part is about capture — getting calls recorded. Reading the data back comes next.

1. Install

Install from PyPI with uv (or pipx) — this puts the three console scripts on your PATH:

uv tool install llm-usage-mcp   # or: pipx install llm-usage-mcp

Prefer to hack on it? Clone and sync from source instead:

git clone https://github.com/zhaoyue722/llm-usage-mcp.gitcd llm-usage-mcpuv sync

Either way you get three console scripts:

• llm-usage — the multi-command CLI. See From the command line (CLI) below.
• llm-usage-mcp — the stdio MCP server.
• llm-usage-proxy — a back-compat alias; identical to llm-usage proxy.

The Quickstart below uses uv run … (the from-source workflow). If you installed from PyPI, the scripts are already on your PATH — drop the uv run prefix, and register the MCP server with claude mcp add llm-usage -- llm-usage-mcp.

2. Set at least one API key

You only need a key for the provider(s) you actually use; the proxy starts regardless and per-route requests return 503 configuration_error for any provider whose key is missing.

export ANTHROPIC_API_KEY=sk-ant-...# and/or:export OPENAI_API_KEY=sk-...export DEEPSEEK_API_KEY=sk-...export DASHSCOPE_API_KEY=sk-...   # Qwen

Full env-var reference: docs/configuration.md (or copy .env.example to .env and fill in).

3. Run the capture proxy

uv run llm-usage-proxy

It binds loopback-only (127.0.0.1:5525) — never reachable from the network. The proxy holds your API keys server-side; clients never need them.

4. Point your coding agent at the proxy

The proxy exposes one route per provider. Set the matching *_BASE_URL env var on the client side:

Example — launch Claude Code with calls routed through the proxy:

ANTHROPIC_BASE_URL=http://127.0.0.1:5525 claude

5. Confirm it's capturing

Make a call through your agent (or any client pointed at the proxy), then check it landed:

uv run llm-usage spend

Every call lands in ~/.llm-usage/usage.db with tokens, cost, latency, and a request_id for idempotency — and shows up in that headline. That's the whole loop: capture on one side, answers on the other.

Querying your spend

Once calls are being captured, you read them back two ways. Same data, same numbers — pick whichever fits the moment.

Ask your coding agent (MCP)

Register the MCP server with Claude Code:

claude mcp add llm-usage -- uv --directory $(pwd) run llm-usage-mcp

Then just ask, in plain English, inside that session:

How much did I spend on Anthropic today? Which provider is cheapest for a 10k-input / 2k-output call?

Claude picks the right tool and reads the numbers back. Seven tools are exposed over stdio; full param/return shapes are in docs/spec.md.

query_spend and usage_summary default to include_failed=false so partial-stream rows don't pollute totals; opt-in via the param.

From the command line (CLI)

The same questions, as a CLI — eight subcommands under one llm-usage console, for when typing is faster than asking your agent.

The examples below assume llm-usage is on your PATH — either source .venv/bin/activate or uv tool install .. Otherwise, prefix each command with uv run (e.g. uv run llm-usage spend).

$ llm-usage Local-first LLM spend capture + query, exposed over MCP.
 Commands   proxy      Run the local LLM capture proxy on 127.0.0.1.   compare    Project the cost of a hypothetical workload across every priced model.   models     Browse the local pricing catalog.   recommend  Recommend the cheapest priced model for a workload + budget.   spend      Show recorded spend over a calendar period.   status     Snapshot of the local install: DB, proxy, providers, pricing.   providers  List configured providers with key state, wire-format, model count.   about      Show version, author, license, and the project homepage.

Conventions that hold across every command:

• --json emits the same Pydantic shape the matching MCP tool returns. Pipe straight into jq.
• --color {auto,always,never} honors NO_COLOR and TTY detection. The palette is a warm, low-contrast dark theme — easy on the eyes at 11pm.
• Filter flags (--provider, --model) are case-insensitive on providers, case-sensitive on models, and repeatable where they act as whitelists.
• --version / -V prints the version and exits. --install-completion {bash|zsh|fish|powershell} installs a tab-completion script — one shell restart later, every flag is <Tab>-able.

compare

Rank every priced model by projected cost for an n-input / m-output call. Cheapest first, percent against the cheapest. Default view family-deduplicates rows that share both a model family root and an identical price — so gpt-5-mini and gpt-5-mini-2025-08-07 collapse to one row with ×2. Pass --all to see every catalog row.

# How does an 8k-in / 2k-out call price out today?$ llm-usage compare --in 8000 --out 2000
# Just OpenAI's models:$ llm-usage compare --in 8000 --out 2000 --model gpt-5-mini --model gpt-5-nano
# Same projection, JSON for a script:$ llm-usage compare --in 8000 --out 2000 --json | jq '.ranked[0]'

[llm-usage compare ranking models by projected cost]

models

Catalog browser. Sibling of compare, but answers "what does this model charge?" rather than "what would my workload cost?". Rates per million tokens, sorted alphabetically by provider by default; switch with --sort input or --sort output to find the cheapest in either axis. Cache rates are hidden until you ask (--cache) because most models don't have them and empty columns waste width.

# Full catalog, deduped.$ llm-usage models
# OpenAI's nano models only, with cache rates:$ llm-usage models --provider openai --match nano --cache
# Cheapest input rate first — quick "what's the floor right now?":$ llm-usage models --sort input

recommend

Picks one. Filters by --provider, --model, and --budget, then returns the cheapest match plus two runner-ups. The reasoning string explains what it assumed and what got chosen, so you can sanity-check rather than trust blindly.

# Cheapest priced model, full stop.$ llm-usage recommend
# Anything Anthropic that fits under one cent for a 1k/1k call:$ llm-usage recommend --provider anthropic --budget 0.01
# Of these three specific candidates, which wins?$ llm-usage recommend --model gpt-5-mini --model claude-sonnet-4-6 --model qwen-max

v1 ranks by cost only. --task is optional and surfaces in the reasoning text; it doesn't drive selection (the tool isn't an LLM and can't interpret free text).

spend

Read the SQLite. The default view is a usage_summary headline — total dollars, top-3 providers, top-3 models, largest single call. Pass --group-by to switch into rollup mode.

# Headline for this week.$ llm-usage spend
# This month grouped by model, JSON for a dashboard:$ llm-usage spend --period month --group-by model --json | jq
# Spend on a specific project tag, day-by-day:$ llm-usage spend --group-by day --project my-side-thing

Period boundaries are calendar UTC: today = since 00:00 UTC, week = since Monday, month = since the 1st, year = since January 1st. Failed / partial-stream rows are excluded by default; opt in with --include-failed.

[llm-usage spend headline — totals, top providers, largest call]

status

One screen, four sections: Database, Capture proxy, Providers, Pricing. The "is everything actually working?" command. Read-only — running it on a fresh install before you've ever booted the proxy or MCP server prints database not initialized rather than silently creating the file.

$ llm-usage status
# Skip the network probe (offline, CI, slow link):$ llm-usage status --no-net
# Machine-readable for a healthcheck script:$ llm-usage status --json

providers

Per-provider configuration view. Wider than the status Providers block: adds the wire-format flag (openai-compat: yes/no) and an optional --models expansion that lists every priced model under each provider.

$ llm-usage providers$ llm-usage providers --models   # expand each provider with its model list

about

The front-door panel: version, author, license, and the project homepage. The human-facing companion to --version — fields are read from the installed package metadata, so they match what PyPI shows.

$ llm-usage about
# Machine-readable, for a script or an issue template:$ llm-usage about --json

Supported providers

More on the way. Google Gemini, AWS Bedrock, Moonshot (Kimi), Zhipu GLM, MiniMax, and others are scoped in docs/post_v1_providers.md.

Where prices come from. Pricing is a vendored, trimmed snapshot of LiteLLM's pricing JSON, refreshed weekly by a GitHub Action (refresh-pricing.yml). Models LiteLLM doesn't carry yet are filled in locally via pricing_overrides.json.

Configuration

Everything is env vars (or a .env file at the repo root). Defaults are sane — nothing is required to start the proxy. Full reference: docs/configuration.md. The three you're most likely to touch:

Docker

A minimal Dockerfile is included only for automated MCP registry validation (e.g. Glama), which verifies that the packaged server boots and responds to MCP introspection. The recommended way to run the server is still uvx llm-usage-mcp locally — this is a local-first tool, not a hosted service.

License

MIT.