spendline 0.1.0

Track LLM tokens, cost, latency, and model usage in production.

Spendline Python SDK

Spendline tracks LLM usage in production with one line of code. The SDK captures tokens, model, latency, cost, timestamp, workflow ID, and request metadata without collecting prompt or completion text.

Install

pip install spendline

Quick Start

from openai import OpenAI
from spendline import track

client = OpenAI()

response = track(
    client.chat.completions.create(
        model="gpt-5-mini",
        messages=[{"role": "user", "content": "Say hello"}],
    ),
    workflow_id="support-bot",
    session_id="session-123",
    metadata={"feature": "chat", "environment": "production"},
)

print(response.choices[0].message.content)

Auto-Patch Supported Clients

from spendline import patch

patch()

LangChain

from spendline.langchain import SpendlineCallbackHandler

handler = SpendlineCallbackHandler(workflow_id="chatbot")

Supported Providers

Provider	Support
OpenAI	track() and patch()
Anthropic	track() and patch()
Google Gemini	track()
DeepSeek	track()
Bedrock	track()

Environment Variables

Variable	Purpose
SPENDLINE_API_KEY	Default ingest key
SPENDLINE_API_URL	Override API base URL
SPENDLINE_DISABLE	Disable tracking when true
SPENDLINE_LOG	Print tracked events locally

Privacy

Spendline does not collect:

• Prompt text
• Completion text
• Raw request bodies
• Secrets from your application context