llm-usage-mcp

<!-- mcp-name: io.github.zhaoyue722/llm-usage-mcp --> <p align="center"> <img src="https://raw.githubusercontent.com/zhaoyue722/llm-usage-mcp/main/docs/assets/watch-pom.png" alt="llm-usage-mcp" width="140"> </p> <h1 align="center">llm-usage-mcp</h1> <p align="center"><em>your LLM spend watchdog</em></p> <p align="center"> <a href="https://github.com/zhaoyue722/llm-usage-mcp/actions/workflows/ci.yml"><img src="https://github.com/zhaoyue722/llm-usage-mcp/actions/workflows/ci.yml/badge.svg" alt="CI"></a> <a href="https://github.com/zhaoyue722/llm-usage-mcp/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-MIT-blue.svg" alt="License: MIT"></a> <a href="https://www.python.org/downloads/"><img src="https://img.shields.io/badge/python-3.13%2B-blue.svg" alt="Python 3.13+"></a> </p> <p align="center">English | <a href="https://github.com/zhaoyue722/llm-usage-mcp/blob/main/README.zh.md">中文</a></p> 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).  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:  ## 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](#supported-providers). ## Quickstart Two minutes from `git clone` to your first captured call. This part is about **capture** — getting calls recorded. [Reading the data back](#querying-your-spend) comes next. ### 1. Install Install from PyPI with [uv](https://docs.astral.sh/uv/) (or `pipx`) — this puts the three console scripts on your `PATH`: ```bash uv tool install llm-usage-mcp # or: pipx install llm-usage-mcp ``` Prefer to hack on it? Clone and sync from source instead: ```bash git clone https://github.com/zhaoyue722/llm-usage-mcp.git cd llm-usage-mcp uv sync ``` Either way you get three console scripts: - `llm-usage` — the multi-command CLI. See [From the command line (CLI)](#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. ```bash 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`](https://github.com/zhaoyue722/llm-usage-mcp/blob/main/docs/configuration.md) (or copy [`.env.example`](https://github.com/zhaoyue722/llm-usage-mcp/blob/main/.env.example) to `.env` and fill in). ### 3. Run the capture proxy ```bash 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: | Provider | Client env var | Value | |---|---|---| | Anthropic | `ANTHROPIC_BASE_URL` | `http://127.0.0.1:5525` | | OpenAI | `OPENAI_BASE_URL` | `http://127.0.0.1:5525/openai/v1` | | DeepSeek | `DEEPSEEK_BASE_URL` (or any OpenAI-SDK base-url override) | `http://127.0.0.1:5525/deepseek/v1` | | Qwen | DashScope OpenAI-compatible base | `http://127.0.0.1:5525/qwen/v1` | Example — launch Claude Code with calls routed through the proxy: ```bash 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: ```bash 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: ```bash 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`](https://github.com/zhaoyue722/llm-usage-mcp/blob/main/docs/spec.md). | Tool | Purpose | |---|---| | `query_spend` | Totals + per-group rollups over a time window (group by provider / model / project / tag / day). | | `usage_summary` | Headline summary for `today` / `week` / `month` / `year` — totals, top-N providers + models, largest call. | | `compare_providers` | Given a hypothetical workload (tokens in / out), rank every priced model by cost. | | `recommend_provider` | Pick the cheapest priced model that fits a stated budget. | | `get_pricing` | Inspect the vendored pricing snapshot. | | `list_providers` | List providers + their models + OpenAI-compatibility flag. | | `record_usage` | Manual write path — log a call when the capture proxy isn't in the picture. | `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`). ```text $ 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. ``` | Command | The question it answers | |---|---| | [`compare`](#compare) | Given a workload, who's cheapest? | | [`models`](#models) | What do they actually charge per million tokens? | | [`recommend`](#recommend) | I've got $0.04 left — which model won't bankrupt me? | | [`spend`](#spend) | How much did I just spend? | | [`status`](#status) | Is everything actually working? | | [`providers`](#providers) | What's configured locally? | | [`about`](#about) | What is this, and where do I report a bug? | | `proxy` | Run the capture proxy (same as `llm-usage-proxy`). | 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. ```bash # 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]' ```  #### `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. ```bash # 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. ```bash # 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. ```bash # 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`.  #### `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. ```bash $ 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. ```bash $ 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. ```bash $ llm-usage about # Machine-readable, for a script or an issue template: $ llm-usage about --json ``` ## Supported providers | Provider | Auth | Non-streaming | Streaming | Cache pricing | |---|---|---|---|---| | Anthropic | `x-api-key` | yes | yes | `cache_creation` + `cache_read` | | OpenAI | `Bearer` | yes | yes | nested `prompt_tokens_details.cached_tokens` | | DeepSeek | `Bearer` | yes | yes | `prompt_cache_hit_tokens` / `_miss_tokens` | | Qwen (DashScope) | `Bearer` | yes | yes | usually omitted on the OpenAI-compat endpoint | **More on the way.** Google Gemini, AWS Bedrock, Moonshot (Kimi), Zhipu GLM, MiniMax, and others are scoped in [`docs/post_v1_providers.md`](https://github.com/zhaoyue722/llm-usage-mcp/blob/main/docs/post_v1_providers.md). **Where prices come from.** Pricing is a vendored, trimmed snapshot of [LiteLLM's pricing JSON](https://github.com/BerriAI/litellm/blob/main/litellm/model_prices_and_context_window_backup.json), refreshed weekly by a GitHub Action ([`refresh-pricing.yml`](.github/workflows/refresh-pricing.yml)). Models LiteLLM doesn't carry yet are filled in locally via [`pricing_overrides.json`](src/llm_usage/core/pricing_data/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`](https://github.com/zhaoyue722/llm-usage-mcp/blob/main/docs/configuration.md). The three you're most likely to touch: | Variable | Default | Purpose | |---|---|---| | `LLM_USAGE_DB_URL` | `sqlite:///$HOME/.llm-usage/usage.db` | Where the local DB lives. | | `LLM_USAGE_PROXY_PORT` | `5525` | Capture proxy port (loopback only). | | `LLM_USAGE_<PROVIDER>_BASE_URL` | each provider's official endpoint | Point a provider at a reverse proxy / gateway — handy in network-restricted regions. | ## License [MIT](https://github.com/zhaoyue722/llm-usage-mcp/blob/main/LICENSE).