Skip to main content

Lightweight API cost tracker for research labs

Project description

Hong Lab AI Cost Tracker

A lightweight Python SDK that transparently tracks LLM API costs. Wrap your existing client with tracker.wrap() — costs are logged automatically.

Supports OpenAI, Google Gemini, Anthropic, and third-party proxies (e.g. apiyihe.org).

Installation

pip install hong-lab-ai-cost

# With specific provider support
pip install "hong-lab-ai-cost[openai]"
pip install "hong-lab-ai-cost[all]"       # OpenAI + Gemini + Anthropic

Usage

OpenAI

from openai import OpenAI
from hong_lab_ai_cost import CostTracker

tracker = CostTracker(project="MyProject", user="kyle", email="kyle@aucklanduni.ac.nz")
client = tracker.wrap(OpenAI(api_key="sk-..."))

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello!"}],
)

print(tracker.summary())

Google Gemini

from google import genai
from hong_lab_ai_cost import CostTracker

tracker = CostTracker(project="MyProject", user="kyle", email="kyle@aucklanduni.ac.nz")
client = tracker.wrap(genai.Client(api_key="..."))

response = client.models.generate_content(model="gemini-2.5-flash", contents="Hello!")

Anthropic

import anthropic
from hong_lab_ai_cost import CostTracker

tracker = CostTracker(project="MyProject", user="kyle", email="kyle@aucklanduni.ac.nz")
client = tracker.wrap(anthropic.Anthropic(api_key="..."))

response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}],
)

Third-party Proxy

from openai import OpenAI
from hong_lab_ai_cost import CostTracker

tracker = CostTracker(project="MyProject", user="kyle", email="kyle@aucklanduni.ac.nz")
client = tracker.wrap(OpenAI(api_key="...", base_url="https://z.apiyihe.org/v1"))

response = client.chat.completions.create(model="gpt-4o-mini", messages=[...])

Manual Recording

For unsupported providers, record usage manually:

tracker.record(model="llama-3-8b", prompt_tokens=1000, completion_tokens=500)

Configuration

All settings can be provided via constructor arguments, environment variables, or a .cost-tracker.yaml file (priority: constructor > env > yaml > defaults).

Setting Constructor Env Variable Default
Project name project= COST_TRACKER_PROJECT "default"
User name user= COST_TRACKER_USER None
Email email= COST_TRACKER_EMAIL None
Remote API remote_url= COST_TRACKER_REMOTE_URL None (local only)
Storage dir storage_dir= .cost-tracker/

Example .cost-tracker.yaml:

project: DentalVLM
user: kyle
email: kyle@aucklanduni.ac.nz
remote_url: https://api.honglab.dev

Remote Sync

By default, the SDK only saves records locally. To enable automatic upload to a remote server, configure remote_url:

Method 1: Constructor argument

tracker = CostTracker(
    project="MyProject",
    user="kyle",
    email="kyle@aucklanduni.ac.nz",
    remote_url="https://api.honglab.dev",  # Add this to enable upload
)

Method 2: Environment variable

export COST_TRACKER_REMOTE_URL="https://api.honglab.dev"

Method 3: Config file .cost-tracker.yaml

remote_url: https://api.honglab.dev

Records are uploaded to POST {remote_url}/api/v1/usage/batch with X-Lab-User and X-Lab-Email headers. The server validates these against a whitelist.

If the upload fails (network error, server down), the record is kept locally and retried on the next tracker.flush() or at process exit.

License

MIT

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

hong_lab_ai_cost-0.1.1.tar.gz (14.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

hong_lab_ai_cost-0.1.1-py3-none-any.whl (15.0 kB view details)

Uploaded Python 3

File details

Details for the file hong_lab_ai_cost-0.1.1.tar.gz.

File metadata

  • Download URL: hong_lab_ai_cost-0.1.1.tar.gz
  • Upload date:
  • Size: 14.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.12

File hashes

Hashes for hong_lab_ai_cost-0.1.1.tar.gz
Algorithm Hash digest
SHA256 261c1d4484b02560d806477023dfab21efbfb2d170874ea7eb0f677d0ffe3014
MD5 5c9fb9069001a34f2f7341c714045524
BLAKE2b-256 b85d4ad4850aa9260cc3fa6e11a59b795c407f4c9a8e3d1a1408e7a19443346c

See more details on using hashes here.

File details

Details for the file hong_lab_ai_cost-0.1.1-py3-none-any.whl.

File metadata

File hashes

Hashes for hong_lab_ai_cost-0.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 7ec95986dd4c95f2c7fa4b4e2ee4df8b19ac188eba07feef9497c9b0b3e89ed0
MD5 f50151a042e4b6fb6d34b662b4c6c24d
BLAKE2b-256 13fabe8700e480b62db66afba8c35295ab4006f13c1640d6133665926499fec1

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page