llmspend 0.1.2

Know where your AI money goes. Track LLM API costs per feature, per model, per user.

LLMSpend

Know where your AI money goes.

Track LLM API costs per feature, per model, per user. 2 lines of code. Zero dependencies. Local-first.

pip install llmspend

Quick Start

import anthropic
from llmspend import monitor

# Wrap your client — that's it
client = monitor.wrap(anthropic.Anthropic(), project="my-app")

# Use it exactly as before
response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1000,
    messages=[{"role": "user", "content": "Hello"}]
)
# Cost, tokens, and latency are now tracked automatically

Works with OpenAI too:

import openai
from llmspend import monitor

client = monitor.wrap(openai.OpenAI(), project="my-app")
# All chat.completions.create calls are now tracked

Tag by Feature

See exactly which part of your app is burning money:

response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1000,
    messages=[{"role": "user", "content": query}],
    llmspend={"feature": "chatbot", "user_id": "u_123"}
)

CLI

# Cost summary (last 24h by default)
llmspend stats

# Last 7 days, grouped by feature
llmspend stats --last 7d --by feature

# Most expensive individual calls
llmspend top

# Export all events as JSON
llmspend export

Output:

  LLMSpend — Last 7d
  ──────────────────────────────────────────────────────
  Total: $12.4320 across 2,847 calls

  Group                      Calls       Cost    Avg ms
  ───────────────────────── ────── ────────── ────────
  chatbot                     1204   $7.2100     1180ms
  search                       893   $3.8900      640ms
  summarizer                   750   $1.3320      380ms

Local Dashboard

llmspend dashboard

Opens a local web dashboard at localhost:8888 — cost breakdown by model, feature, and time. Auto-refreshes. No account needed.

How It Works

1. monitor.wrap() patches client.messages.create (Anthropic) or client.chat.completions.create (OpenAI)
2. Every API call is intercepted — tokens, cost, latency, and your tags are recorded
3. Events flush to a local SQLite database at ~/.llmspend/events.db every 5 seconds via a background thread
4. Zero overhead on your API calls — logging happens asynchronously after the response returns

What Gets Tracked

Per API call:

• Provider, model, timestamp
• Input/output tokens
• Cost in USD (calculated from published pricing)
• Latency in ms
• Your custom tags (feature, user_id, or any key-value pair)

What is never tracked:

• Prompt content
• Response content
• API keys

Supported Models

Anthropic: Claude Opus 4, Sonnet 4, Haiku 4.5, and all dated variants

OpenAI: GPT-4o, GPT-4.1, o3, o4-mini, and all variants

Cost calculation uses prefix matching — claude-haiku-4-5-20251001 matches the claude-haiku-4 pricing tier. Unknown models are tracked with null cost (tokens and latency still recorded).

Configuration

from llmspend import monitor

# Default: logs to ~/.llmspend/events.db
monitor.configure()

# Custom local path
monitor.configure(local_path="/var/log/my-app/llmspend.db")

# Future: send to hosted dashboard
monitor.configure(backend_url="https://llmspend.dev", api_key="ls_...")

Design Principles

• Never crash your app. All tracking runs in try/except. If LLMSpend fails, your API call still works.
• Never store prompts. Only metadata (tokens, cost, timing). Your data stays private.
• Zero dependencies. Pure Python stdlib. No requests, no aiohttp, no protobuf.
• Local-first. Works offline. No account required. Your data stays on your machine.

Hosted Version

Coming soon at llmspend.dev — team dashboards, alerts, budget caps, and cost forecasting.

License

MIT — use it however you want.