litellm-claude-code

🥈Silver

LiteLLM Claude Code Provider offers an OpenAI-compatible API interface for Claude Code. It enables developers to integrate Claude's capabilities into existing workflows using familiar OpenAI SDK patterns. Ideal for operations teams looking to automate code-related tasks, this provider supports Docker containers and connects to Claude agents.

1450Updated Today

Intermediate30min to implementautomation

Saves ~30 min per use

Quick InstallView Source

git clone https://github.com/cabinlab/litellm-claude-code.git

Works with:

Claude

Overview

About This Skill

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/cabinlab/litellm-claude-code

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

I'm integrating LiteLLM Claude Code Provider into my [COMPANY]'s [INDUSTRY] workflow. I need to automate [SPECIFIC CODE TASK] using Docker containers. Provide me with a step-by-step guide and sample code snippets for this integration.

Example Output

## Integration Guide for LiteLLM Claude Code Provider

### Step 1: Set Up Docker Container

```bash
# Pull the LiteLLM Docker image
docker pull litellm/litellm

# Run the container with Claude Code Provider
docker run -p 8000:8000 litellm/litellm --model claude-code
```

### Step 2: Install OpenAI SDK

```bash
pip install openai
```

### Step 3: Configure API Key

```python
import os
os.environ['OPENAI_API_KEY'] = 'your-api-key'
```

### Step 4: Make API Requests

```python
from openai import OpenAI

client = OpenAI(api_key=os.environ['OPENAI_API_KEY'])
response = client.chat.completions.create(
    model='claude-code',
    messages=[{'role': 'user', 'content': 'Write a Python function to analyze sales data.'}]
)
print(response.choices[0].message.content)
```

### Step 5: Handle Responses

```python
# Example response handling
if response.choices[0].finish_reason == 'stop':
    print('Task completed successfully.')
else:
    print('Task encountered an error.')
```