llm-warehouse 0.1.0

Auto-capture OpenAI and Anthropic LLM calls for warehousing and observability

LLM Warehouse

🏠 Auto-capture OpenAI and Anthropic LLM calls for warehousing

A lightweight Python library that automatically logs all your OpenAI and Anthropic API calls to various storage backends, including your own Flask app, Supabase, or local files.

🚀 Quick Start

Installation

pip install llm-warehouse

Or for the latest development version:

pip install git+https://github.com/sinanozdemir/llm-warehouse.git

Basic Usage

import llm_warehouse

# Option 1: Use with your own warehouse
llm_warehouse.patch(
    warehouse_url="https://your-warehouse.com",
    api_key="your-warehouse-api-key"
)

# Option 2: Use with Supabase
llm_warehouse.patch(
    supabase_url="https://your-project.supabase.co",
    supabase_key="your-supabase-anon-key"
)

# Option 3: Save to local file
llm_warehouse.patch(log_file="llm_calls.jsonl")

# Now use OpenAI/Anthropic normally - all calls are automatically logged!
import openai
client = openai.Client()
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hello!"}]
)

🎯 Automatic Setup (Recommended)

For automatic patching on import, set environment variables:

export LLM_WAREHOUSE_API_KEY="your-warehouse-api-key"
export LLM_WAREHOUSE_URL="https://your-warehouse.com"

Then just import any LLM library AFTER importing this package - logging happens automatically:

import llm_warehouse  # BEFORE openai or anthropic

import openai  # Automatically patched!
# or
import anthropic  # Automatically patched!

📊 What Gets Logged

• Request data: Model, messages, parameters
• Response data: Completions, token usage, timing
• Metadata: Timestamps, SDK method, streaming info
• Errors: API errors and exceptions

🔧 Configuration Options

🛡️ Environment Variables

Variable	Description
LLM_WAREHOUSE_API_KEY	Your warehouse API token (enables auto-patching)
LLM_WAREHOUSE_URL	Your warehouse URL

🔄 Programmatic Control

import llm_warehouse

# Enable logging
llm_warehouse.patch(warehouse_url="...", api_key="...")

# Disable logging
llm_warehouse.unpatch()

# Check status
if llm_warehouse.is_patched():
    print("LLM calls are being logged")

🏗️ Backend Options

Flask Warehouse Backend

Use with the included Flask app for full control:

llm_warehouse.patch(
    warehouse_url="https://your-warehouse.com",
    api_key="your-warehouse-api-key"
)

Supabase Backend

Direct integration with Supabase:

llm_warehouse.patch(
    supabase_url="https://your-project.supabase.co",
    supabase_key="your-supabase-anon-key"
)

Local File Backend

For development and testing:

llm_warehouse.patch(log_file="llm_calls.jsonl")

📦 Features

• ✅ Zero-configuration: Works out of the box with environment variables
• ✅ Multiple backends: Flask warehouse, Supabase, local files
• ✅ Async support: Full async/await compatibility
• ✅ Streaming support: Captures streaming responses
• ✅ Error handling: Logs API errors and exceptions
• ✅ Minimal overhead: Designed for production use
• ✅ Thread-safe: Works in multi-threaded applications

🧪 Development

git clone https://github.com/sinanozdemir/llm-warehouse.git
cd llm-warehouse/llm-warehouse-package
pip install -e ".[dev]"

Run tests:

pytest

Format code:

black llm_warehouse/
isort llm_warehouse/

📝 License

MIT License - see LICENSE file for details.