crashlens-logger 1.0.5

CLI tool to generate structured LLM logs for CrashLens cost detection

CrashLens Logger 🧠💸

Structured Token & Cost Logs for OpenAI / Anthropic Usage

⚠️ Are you burning money on GPT calls without knowing where or why?
CrashLens Logger captures cost, tokens, and prompts in JSON logs — for FinOps, audits, or debugging.

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Purpose

CrashLens Logger is a Python package for generating structured, machine-readable logs of LLM (Large Language Model) API usage.
It helps you:

• Track prompt, model, and token usage for every AI call
• Automatically calculate cost using standard model pricing
• Output logs in newline-delimited JSON (NDJSON) for easy analysis, monitoring, and cost tracking

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Real Use Cases

• 🔍 Debug fallback loops by logging all model calls with prompt/token trace
• 💰 Auto-generate cost reports across agents & users
• 🧠 Analyze which prompts are burning tokens (and why)
• 🛡️ Audit LLM usage for compliance or security

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Installation

pip install --upgrade crashlens_logger

This will install or upgrade to the latest version.

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Quick Start

from crashlens_logger import CrashLensLogger
import uuid
from datetime import datetime
import openai

logger = CrashLensLogger()

def call_and_log():
    trace_id = str(uuid.uuid4())
    start_time = datetime.utcnow().isoformat() + "Z"
    prompt = "What are the main tourist attractions in Rome?"
    model = "gpt-3.5-turbo"
    response = openai.ChatCompletion.create(
        model=model,
        messages=[{"role": "user", "content": prompt}]
    )
    end_time = datetime.utcnow().isoformat() + "Z"
    usage = response["usage"]
    logger.log_event(
        traceId=trace_id,
        startTime=start_time,
        endTime=end_time,
        input={"model": model, "prompt": prompt},
        usage=usage,
        output_file="logs.jsonl"  # <-- This will create/append to logs.jsonl
    )

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Where Do Logs Go?

By default, logs are printed to stdout in newline-delimited JSON (NDJSON) format.

If you provide the output_file parameter (recommended), logs will also be appended to that file (e.g., logs.jsonl).

• If the file does not exist, it will be created automatically.
• If the file exists, each new log will be appended as a new line.

You can redirect output to a file as well:

python your_script.py > logs.jsonl

But using output_file="logs.jsonl" is the most robust and portable way to persist logs.

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Example Output

{
  "traceId": "trace_norm_01",
  "startTime": "2025-07-22T10:30:05Z",
  "input": {"model": "gpt-3.5-turbo", "prompt": "What are the main tourist attractions in Rome?"},
  "usage": {"prompt_tokens": 10, "completion_tokens": 155, "total_tokens": 165},
  "cost": 0.0002375
}

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What Gets Calculated Automatically?

• total_tokens: If you provide prompt_tokens and completion_tokens in usage, the logger adds total_tokens.
• cost: If you provide model, prompt_tokens, and completion_tokens, the logger calculates cost using standard pricing.

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Troubleshooting

• Cannot resolve host: Check your internet connection or DNS.
• pip cache issues: Try pip install --no-cache-dir crashlens_logger
• Permission errors: Use a virtual environment or add --user to your pip command.
• Module not found: Ensure you’re using the correct Python environment.
• File not created: Make sure you are passing output_file="logs.jsonl" to log_event and that your process has write permissions.

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Roadmap

• Token pricing overrides
• File/DB exporters
• SDK instrumentation helpers
• Pydantic validation for log structure

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Testing

Run tests with:

pytest

100% coverage on core logging logic.

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License

MIT License