LLMSpend: Open-source cost tracker for Anthropic and OpenAI SDKs

What LLMSpend does

LLMSpend is a Python package that monitors API usage costs for Anthropic and OpenAI SDKs. It was created because the Anthropic dashboard only shows total spend without breaking it down by feature. The tool tracks tokens, cost, and latency per call, grouping data by feature, model, user, or project.

How to use it

Install with pip install llmspend. Integration requires two lines of code:

from llmspend import monitor
client = monitor.wrap(anthropic.Anthropic(), project="my-app")

Then add an llmspend parameter to track specific features:

response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1000,
    messages=[{"role": "user", "content": query}],
    llmspend={"feature": "chatbot"}
)

Reporting and dashboard

From the terminal, run llmspend stats --last 7d --by feature to get output like:

Total: $4.2100 across 847 calls
chatbot 512 $2.8900 1180ms
summarizer 335 $1.3200 640ms

Run llmspend dashboard to open a local web dashboard at localhost:8888.

Technical details

• Local SQLite storage — no account needed, no data leaves your machine
• Works with both Anthropic and OpenAI SDKs
• Zero dependencies (pure Python standard library)
• Never stores prompts or responses — only tracks cost metrics
• No prompt logging, tracing, or evaluations — focused solely on cost tracking
• MIT licensed, open source on GitHub

The tool was built entirely with Claude Code in a single session, with Claude writing the monkey-patching logic, pricing engine, CLI, and web dashboard.