ll2cz 0.6.2

Transform LiteLLM database data into CloudZero AnyCost CBF format

ll2cz - LiteLLM to CloudZero ETL Tool

Transform LiteLLM database data into CloudZero AnyCost CBF format for cost tracking and analysis.

Features

• Extract usage data from LiteLLM PostgreSQL or SQLite database
• Transform data into CloudZero Billing Format (CBF)
• Cost comparison analysis between SpendLogs and user tables (v0.4.0+)
• Dual data source support with --source option: transaction-level SpendLogs or daily aggregated user tables
• SQLite support for testing and offline analysis (v0.6.1+)
• Analysis mode with beautiful terminal output using Rich
• Multiple output options: CSV files or direct CloudZero API streaming
• Built with modern Python tools: uv, ruff, pytest, polars, httpx

Installation

From PyPI (Recommended)

# Install with uv (recommended)
uv add ll2cz

# Or install with pip
pip install ll2cz

From Source

git clone https://github.com/Cloudzero/cloudzero-litellm-toolkit.git
cd cloudzero-litellm-toolkit
uv sync

Configuration

ll2cz supports configuration files to avoid repeating common settings. You can store your database connection, CloudZero API credentials, and other settings in ~/.ll2cz/config.yml.

Create Configuration File

# Create an example configuration file
ll2cz config example

# Check current configuration status
ll2cz config status

This creates ~/.ll2cz/config.yml with the following structure:

database_url: postgresql://user:password@host:5432/litellm_db
cz_api_key: your-cloudzero-api-key
cz_connection_id: your-connection-id

Configuration Priority

CLI arguments always take priority over configuration file values:

1. CLI arguments (highest priority)
2. Configuration file (~/.ll2cz/config.yml)
3. Default values (lowest priority)

CLI Commands

# Show version
ll2cz --version

# Show help
ll2cz --help

# Configuration management
ll2cz config example     # Create example config file
ll2cz config status      # Show current config status
ll2cz config edit        # Interactively edit configuration

Usage

Transform Mode

Transform LiteLLM data to CloudZero CBF format:

# Display data on screen (formatted table) - PostgreSQL
ll2cz transform --input "postgresql://user:pass@host:5432/litellm_db" --screen

# Display data on screen - SQLite
ll2cz transform --input "sqlite://path/to/litellm.sqlite" --screen

# Export to CSV file
ll2cz transform --input "postgresql://user:pass@host:5432/litellm_db" --output data.csv

# Limit records for screen display
ll2cz transform --screen --limit 25

Transmit Mode

Send data directly to CloudZero AnyCost API:

# Send today's data
ll2cz transmit day

# Send specific day's data (DD-MM-YYYY format)
ll2cz transmit day 15-01-2024

# Send current month's data
ll2cz transmit month

# Send specific month's data (MM-YYYY format)
ll2cz transmit month 01-2024

# Send all available data (batched by day)
ll2cz transmit all

# Test mode - show payloads without sending (5 records only)
ll2cz transmit day --test

# Use append mode (sum operation instead of replace_hourly)
ll2cz transmit day --append

# Specify timezone for date handling
ll2cz transmit day --timezone "US/Eastern"

# Limit number of records to process
ll2cz transmit month --limit 1000

Analysis Mode

Analyze your LiteLLM database data:

# General data analysis
ll2cz analyze data --limit 10000

# Show raw table data
ll2cz analyze data --show-raw --table all

# Show specific table only
ll2cz analyze data --show-raw --table user

# CZRN (CloudZero Resource Name) analysis
ll2cz analyze czrn --limit 10000

# Spend analysis
ll2cz analyze spend --limit 10000

# Database schema analysis
ll2cz analyze schema --output schema_docs.md

# Refresh cache from server (use cache commands)
ll2cz cache refresh

# Save analysis to JSON
ll2cz analyze data --json analysis.json

Cache Management

Manage local data cache for offline operation:

# Check cache status (local only)
ll2cz cache status

# Check cache status with remote server verification
ll2cz cache status --remote-check

# Clear local cache
ll2cz cache clear

# Force refresh cache from server
ll2cz cache refresh

Data Transformation

The tool transforms LiteLLM usage logs into CloudZero's CBF format with the following mappings:

• spend → cost
• total_tokens → usage_quantity
• model, user_id, call_type → dimensions
• metadata fields → additional dimensions
• Duration calculated from startTime and endTime

Technology Stack

This project follows modern Python best practices:

• Python 3.12 - Latest Python version
• uv - Fast Python package manager
• Polars - High-performance DataFrames (instead of pandas)
• httpx - Modern HTTP client (instead of requests)
• Rich - Beautiful terminal output and formatting
• Typer - Modern CLI framework with rich help formatting
• PyYAML - YAML configuration file support
• Pathlib - Modern filesystem path operations
• pytest - Testing framework
• ruff - Fast Python linter and formatter

Development

# Setup development environment
uv sync --dev

# Run tests
uv run pytest

# Run linting
uv run ruff check src/ tests/

# Fix linting issues
uv run ruff check --fix src/ tests/

# Build package
uv build

Testing with SQLite

For testing and development, you can use SQLite instead of PostgreSQL:

# Create a test SQLite database with sample data
python scripts/create_test_sqlite.py

# Use the test database
ll2cz transmit all --test --input "sqlite://test.sqlite"

The test database includes:

• Sample organizations, teams, and users
• API keys with realistic naming
• 30 days of usage data
• Multiple model providers (OpenAI, Anthropic, etc.)

Requirements

• Python ≥ 3.12
• PostgreSQL or SQLite database with LiteLLM data
• CloudZero API key and connection ID (for streaming mode)

License

Apache 2.0 - see LICENSE file for details.