hong-lab-ai-cost 0.1.7

Lightweight API cost tracker for research labs

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

API Keys (Recommended: Use Environment Variables)

You do NOT need to hardcode API keys in your code. Each provider's SDK automatically reads from environment variables — just export them in your shell or .env file:

# OpenAI (including third-party proxies)
export OPENAI_API_KEY="sk-..."

# Google Gemini
export GEMINI_API_KEY="..."

# Anthropic
export ANTHROPIC_API_KEY="sk-ant-..."

This way, your code stays clean and your keys are never exposed in source files. The hong-lab-ai-cost SDK does not handle API keys at all — it only wraps the client for cost tracking. Key management is entirely handled by each provider's own SDK.

💡 Tip: Add these exports to your ~/.bashrc, ~/.zshrc, or use a .env file with python-dotenv to load them automatically.

Usage

OpenAI

from openai import OpenAI
from hong_lab_ai_cost import CostTracker

tracker = CostTracker(project="MyProject", user="kyle", email="[e-mail]")
client = tracker.wrap(OpenAI())  # Reads OPENAI_API_KEY from environment

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="[e-mail]")
client = tracker.wrap(genai.Client())  # Reads GEMINI_API_KEY from environment

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="[e-mail]")
client = tracker.wrap(anthropic.Anthropic())  # Reads ANTHROPIC_API_KEY from environment

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="[e-mail]")
# OPENAI_API_KEY is read from environment; only base_url needs to be specified
client = tracker.wrap(OpenAI(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	https://api.honglab.dev
Storage dir	storage_dir=	—	.cost-tracker/

Example .cost-tracker.yaml:

project: DentalVLM
user: kyle
email: [e-mail]

Remote Sync

Remote sync is enabled by default — all usage records are automatically uploaded to https://api.honglab.dev. You do not need to configure anything extra.

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.

To disable remote sync (local-only mode), explicitly set remote_url to an empty string:

tracker = CostTracker(project="MyProject", user="kyle", email="[e-mail]", remote_url="")

Or via environment variable:

export COST_TRACKER_REMOTE_URL=""

Pricing

The SDK includes a built-in pricing catalog (pricing_catalog.yaml) covering 170+ paid API models from OpenAI, Google Gemini, and Anthropic.

Prices are sourced from LiteLLM's community-maintained database (GitHub 60k+ stars, actively maintained) as a baseline. The SDK uses these prices to automatically calculate costs based on token usage — no manual lookup needed.

Override or Add Custom Pricing

You can override any model's pricing or add new models not in the catalog:

from hong_lab_ai_cost import CostTracker

tracker = CostTracker(project="MyProject", user="kyle", email="[e-mail]")

# Override an existing model's pricing (USD per 1M tokens)
tracker.pricing.set("gpt-4o", "openai", input_per_1m=2.50, output_per_1m=10.00)

# Add a model not in the catalog (e.g., a self-hosted or proxy model)
tracker.pricing.set("my-local-llama", "custom", input_per_1m=0.00, output_per_1m=0.00)
tracker.pricing.set("deepseek-v3", "deepseek", input_per_1m=0.27, output_per_1m=1.10)

Overrides take effect immediately and apply for the lifetime of the CostTracker instance.

Unknown Models

If you use a model that is not in the catalog and has not been manually configured, the SDK will:

1. Log a warning (once per model, not spammed):

   ⚠️  Unknown model 'my-new-model' — cost recorded as $0.00.
      To fix, add pricing via tracker.pricing.set('my-new-model', 'provider', input_per_1m, output_per_1m)
      or update pricing_catalog.yaml.
   

2. Record the usage with cost = $0.00 — token counts are still tracked, so you can retroactively calculate costs.

Update the Pricing Catalog

To pull the latest model prices from LiteLLM and update the bundled catalog:

python -m hong_lab_ai_cost.sync_pricing

This merges new prices into pricing_catalog.yaml. Any custom entries you added to the YAML file are preserved.

Note: This updates the YAML file inside the installed package. After syncing, you should rebuild and republish the SDK to distribute the updated prices to all users.

License

MIT