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docs(claude_non_anthropic_models.md): add tutorial showing non anthropic model connection to claude code
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Krrish Dholakia
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3 changed files with 319 additions and 7 deletions
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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# Claude MCP Quickstart
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# Use Claude Code with MCPs
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This tutorial shows how to connect MCP servers to Claude Code via LiteLLM Proxy.
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:::info
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This tutorial is based on [Anthropic's official LiteLLM configuration documentation](https://docs.anthropic.com/en/docs/claude-code/llm-gateway#litellm-configuration). This integration allows you to use any LiteLLM supported model through Claude Code with centralized authentication, usage tracking, and cost controls.
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:::
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Note: LiteLLM supports OAuth for MCP servers as well. [Learn more](https://docs.litellm.ai/docs/mcp#mcp-oauth)
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## Connecting MCP Servers
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@ -95,4 +91,3 @@ d. Start Oauth flow via Claude Code
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e. Once completed, you should see this success message:
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<img src={require('../../img/oauth_2_success.png').default} alt="OAuth 2.0 Success" style={{ width: '500px', height: 'auto' }} />
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316
docs/my-website/docs/tutorials/claude_non_anthropic_models.md
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316
docs/my-website/docs/tutorials/claude_non_anthropic_models.md
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import Image from '@theme/IdealImage';
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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# Use Claude Code with Non-Anthropic Models
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This tutorial shows how to use Claude Code with non-Anthropic models like OpenAI, Gemini, and other LLM providers through LiteLLM proxy.
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:::info
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LiteLLM automatically translates between different provider formats, allowing you to use any supported LLM provider with Claude Code while maintaining the Anthropic Messages API format.
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:::
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## Prerequisites
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- [Claude Code](https://docs.anthropic.com/en/docs/claude-code/overview) installed
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- API keys for your chosen providers (OpenAI, Vertex AI, etc.)
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## Installation
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First, install LiteLLM with proxy support:
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```bash
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pip install 'litellm[proxy]'
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```
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## Configuration
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### 1. Setup config.yaml
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Create a configuration file with your preferred non-Anthropic models:
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<Tabs>
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<TabItem value="openai" label="OpenAI">
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```yaml
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model_list:
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# OpenAI GPT-4o
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- model_name: gpt-4o
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litellm_params:
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model: openai/gpt-4o
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api_key: os.environ/OPENAI_API_KEY
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# OpenAI GPT-4o-mini
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- model_name: gpt-4o-mini
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litellm_params:
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model: openai/gpt-4o-mini
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api_key: os.environ/OPENAI_API_KEY
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```
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Set your environment variables:
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```bash
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export OPENAI_API_KEY="your-openai-api-key"
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export LITELLM_MASTER_KEY="sk-1234567890" # Generate a secure key
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```
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</TabItem>
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<TabItem value="gemini" label="Google AI Studio">
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```yaml
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model_list:
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# Google Gemini
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- model_name: gemini-3.0-flash-exp
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litellm_params:
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model: gemini/gemini-3.0-flash-exp
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api_key: os.environ/GEMINI_API_KEY
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```
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Set your environment variables:
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```bash
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export GEMINI_API_KEY="your-gemini-api-key"
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export LITELLM_MASTER_KEY="sk-1234567890" # Generate a secure key
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```
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</TabItem>
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<TabItem value="vertex_ai" label="Vertex AI">
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```yaml
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model_list:
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# Google Gemini
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- model_name: vertex-gemini-3-flash-preview
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litellm_params:
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model: vertex_ai/gemini-3-flash-preview
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vertex_credentials: os.environ/VERTEX_FILE_PATH_ENV_VAR # os.environ["VERTEX_FILE_PATH_ENV_VAR"] = "/path/to/service_account.json"
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vertex_project: "my-test-project"
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vertex_location: "us-east-1"
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# Anthropic Claude
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- model_name: anthropic-vertex
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litellm_params:
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model: vertex_ai/claude-3-sonnet@20240229
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vertex_ai_project: "my-test-project"
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vertex_ai_location: "us-east-1"
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vertex_credentials: os.environ/VERTEX_FILE_PATH_ENV_VAR # os.environ["VERTEX_FILE_PATH_ENV_VAR"] = "/path/to/service_account.json"
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```
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Set your environment variables:
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```bash
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export VERTEX_FILE_PATH_ENV_VAR="/path/to/service_account.json"
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export LITELLM_MASTER_KEY="sk-1234567890"
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```
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</TabItem>
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<TabItem value="multi" label="Azure OpenAI">
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```yaml
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model_list:
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# Azure OpenAI
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- model_name: azure-gpt-4
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litellm_params:
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model: azure/gpt-4
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api_key: os.environ/AZURE_API_KEY
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api_base: os.environ/AZURE_API_BASE
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api_version: "2024-02-01"
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```
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Set your environment variables:
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```bash
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export AZURE_API_KEY="your-azure-api-key"
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export AZURE_API_BASE="https://your-resource.openai.azure.com"
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export LITELLM_MASTER_KEY="sk-1234567890"
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```
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</TabItem>
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</Tabs>
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### 2. Start LiteLLM Proxy
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```bash
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litellm --config /path/to/config.yaml
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# RUNNING on http://0.0.0.0:4000
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```
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### 3. Verify Setup
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Test that your proxy is working correctly:
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<Tabs>
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<TabItem value="openai-test" label="OpenAI">
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```bash
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curl -X POST http://0.0.0.0:4000/v1/messages \
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-H "Authorization: Bearer $LITELLM_MASTER_KEY" \
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-H "Content-Type: application/json" \
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-d '{
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"model": "gpt-4o",
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"max_tokens": 1000,
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"messages": [{"role": "user", "content": "What is the capital of France?"}]
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}'
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```
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</TabItem>
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<TabItem value="gemini-test" label="Google AI Studio">
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```bash
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curl -X POST http://0.0.0.0:4000/v1/messages \
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-H "Authorization: Bearer $LITELLM_MASTER_KEY" \
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-H "Content-Type: application/json" \
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-d '{
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"model": "gemini-3.0-flash-exp",
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"max_tokens": 1000,
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"messages": [{"role": "user", "content": "What is the capital of France?"}]
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}'
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```
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</TabItem>
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<TabItem value="vertex-test" label="Vertex AI">
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```bash
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curl -X POST http://0.0.0.0:4000/v1/messages \
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-H "Authorization: Bearer $LITELLM_MASTER_KEY" \
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-H "Content-Type: application/json" \
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-d '{
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"model": "gemini-3.0-flash-exp",
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"max_tokens": 1000,
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"messages": [{"role": "user", "content": "What is the capital of France?"}]
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}'
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```
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</TabItem>
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<TabItem value="azure-test" label="Azure OpenAI">
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```bash
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curl -X POST http://0.0.0.0:4000/v1/messages \
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-H "Authorization: Bearer $LITELLM_MASTER_KEY" \
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-H "Content-Type: application/json" \
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-d '{
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"model": "azure-gpt-4",
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"max_tokens": 1000,
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"messages": [{"role": "user", "content": "What is the capital of France?"}]
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}'
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```
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</TabItem>
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</Tabs>
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### 4. Configure Claude Code
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Configure Claude Code to use your LiteLLM proxy:
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```bash
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export ANTHROPIC_BASE_URL="http://0.0.0.0:4000"
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export ANTHROPIC_AUTH_TOKEN="$LITELLM_MASTER_KEY"
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```
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:::tip
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The `LITELLM_MASTER_KEY` gives Claude Code access to all proxy models. You can also create virtual keys in the LiteLLM UI to limit access to specific models.
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:::
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### 5. Use Claude Code with Non-Anthropic Models
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Start Claude Code and specify which model to use:
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```bash
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# Use OpenAI GPT-4o
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claude --model gpt-4o
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# Use OpenAI GPT-4o-mini for faster responses
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claude --model gpt-4o-mini
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# Use Google Gemini
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claude --model gemini-3.0-flash-exp
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# Use Vertex AI Gemini
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claude --model vertex-gemini-3-flash-preview
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# Use Vertex AI Anthropic Claude
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claude --model anthropic-vertex
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# Use Azure OpenAI
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claude --model azure-gpt-4
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```
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## How It Works
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LiteLLM acts as a unified interface that:
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1. **Receives requests** from Claude Code in Anthropic Messages API format
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2. **Translates** the request to the target provider's format (OpenAI, Gemini, etc.)
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3. **Forwards** the request to the actual provider
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4. **Translates** the response back to Anthropic Messages API format
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5. **Returns** the response to Claude Code
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This allows you to use Claude Code's interface with any LLM provider supported by LiteLLM.
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## Advanced Features
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### Load Balancing and Fallbacks
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Configure multiple deployments with automatic fallback:
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```yaml
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model_list:
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- model_name: gpt-4o # virtual model name
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litellm_params:
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model: openai/gpt-4o
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api_key: os.environ/OPENAI_API_KEY
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- model_name: gpt-4o # same virtual name
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litellm_params:
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model: azure/gpt-4o
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api_key: os.environ/AZURE_API_KEY
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api_base: os.environ/AZURE_API_BASE
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router_settings:
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routing_strategy: simple-shuffle # Load balance between deployments
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num_retries: 2
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timeout: 30
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```
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### Usage Tracking and Budgets
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Track usage and set budgets through the LiteLLM UI:
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```yaml
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litellm_settings:
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master_key: os.environ/LITELLM_MASTER_KEY
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database_url: "postgresql://..." # Enable database for tracking
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general_settings:
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store_model_in_db: true
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```
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Start the proxy with the UI:
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```bash
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litellm --config /path/to/config.yaml --detailed_debug
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```
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Access the UI at `http://0.0.0.0:4000/ui` to:
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- View usage analytics
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- Set budget limits per user/key
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- Monitor costs across different providers
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- Create virtual keys with specific permissions
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## Supported Providers
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LiteLLM supports 100+ providers. Here are some popular ones for use with Claude Code:
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- **OpenAI**: GPT-4o, GPT-4o-mini, o1, o3-mini
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- **Google**: Gemini 2.0 Flash, Gemini 1.5 Pro/Flash
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- **Azure OpenAI**: All OpenAI models via Azure
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- **AWS Bedrock**: Llama, Mistral, and other models
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- **Vertex AI**: Gemini, Claude, and other models on Google Cloud
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- **Groq**: Fast inference for Llama and Mixtral
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- **Together AI**: Llama, Mixtral, and other open source models
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- **Deepseek**: Deepseek-chat, Deepseek-coder
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[View full list of supported providers →](https://docs.litellm.ai/docs/providers)
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@ -122,6 +122,7 @@ const sidebars = {
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items: [
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"tutorials/claude_responses_api",
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"tutorials/claude_mcp",
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"tutorials/claude_non_anthropic_models",
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]
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},
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"tutorials/cost_tracking_coding",
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