llmtracer-sdk 2.0.10

Auto-track LLM cost, latency, and usage. Two lines of code, every provider.

LLM Tracer — Python SDK

Track cost, latency, and token usage across OpenAI, Anthropic, and Google Gemini — in one line of code.

Install

pip install llmtracer-sdk

Quick Start

import llmtracer

llmtracer.init(api_key="lt_...")

# That's it. All OpenAI, Anthropic, and Google Gemini calls are now tracked automatically.

No wrappers, no callbacks, no code changes. The SDK auto-patches your provider clients at import time.

View your dashboard at llmtracer.dev.

What Gets Captured

Every LLM call is automatically tracked with:

• Provider, model, tokens (input + output), latency, cost
• Google Gemini: thinking tokens (2.5 models), tool tokens, cached tokens
• Anthropic: cache creation + read tokens
• OpenAI: reasoning tokens (o1/o3/o4), cached tokens
• Caller file, function, and line number
• Auto-flush on process exit (no manual flush needed)

Environment Variable Pattern

import os
import llmtracer

llmtracer.init(
    api_key=os.environ["LLMTRACER_API_KEY"],
    debug=True,  # prints token counts to console
)

Trace Context and Tags

with llmtracer.trace(tags={"feature": "chat", "user_id": "u_sarah"}):
    response = client.chat.completions.create(...)

Tags appear in the dashboard's Breakdown page and Top Tags card. Use them to answer questions like "which user costs the most?" or "which feature should I optimize?"

Tagging Patterns

Pattern	Tag	Example
Track cost by feature	feature	"chat", "search", "summarize"
Track cost by user	user_id	"u_sarah", "u_mike"
Track cost by customer (B2B)	customer	"acme-corp", "initech"
Track cost by conversation	conversation_id	"conv_abc123"
Track environment	env	"production", "staging"

Supported Providers

Provider	Package	Auto-patched
OpenAI	openai	✅
Anthropic	anthropic	✅
Google Gemini	google-genai	✅

LangChain Support

If you use LangChain with ChatOpenAI, ChatAnthropic, or ChatGoogleGenerativeAI, the underlying SDK calls are auto-captured. No callback handler needed — just llmtracer.init() and you're done.

Debug Mode

Enable debug=True to print token counts to the console:

llmtracer.init(api_key="lt_...", debug=True)

[llmtracer] openai gpt-4o | 1,247 in → 384 out | $0.0094 | 1.2s
[llmtracer] anthropic claude-sonnet-4-5 | 2,100 in → 512 out (cache_read: 1,800) | $0.0031 | 0.8s
[llmtracer] google gemini-2.5-pro | 900 in → 280 out (thinking: 1,420) | $0.0067 | 2.1s

Requirements

• Python 3.8+
• Works with any version of openai, anthropic, or google-genai SDKs

Zero Dependencies

The core SDK uses only Python stdlib (urllib.request, threading, hashlib).

License

MIT