Anthropic Claude Configuration

This guide explains how to configure Anthropic Claude models as backend inference providers. The semantic router accepts OpenAI-format requests and automatically translates them to Anthropic's Messages API format, returning responses in OpenAI format for seamless client compatibility.

Environment Setup

Setting the API Key

Anthropic API keys must be available as environment variables. Create a .env file or export directly:

export ANTHROPIC_API_KEY=sk-ant-api03-xxxxxxxxxxxx

Verifying the Environment

Before running the router, verify the API key is accessible:

# Should print your API key
echo $ANTHROPIC_API_KEY

# Test the key directly with Anthropic API
curl https://api.anthropic.com/v1/messages \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -H "content-type: application/json" \
  -d '{"model":"claude-sonnet-4-5","max_tokens":10,"messages":[{"role":"user","content":"Hi"}]}'

Quick Start with vllm-sr CLI

Step 1: Set Your API Key

Export your Anthropic API key as an environment variable:

export ANTHROPIC_API_KEY=sk-ant-api03-xxxxxxxxxxxx

Step 2: Initialize Configuration

mkdir my-router && cd my-router
vllm-sr init

Step 3: Configure Anthropic Models

Edit config.yaml with the minimal Anthropic setup:

version: "1"

listeners:
  - name: http
    address: 0.0.0.0
    port: 8080
    timeout: 300s

providers:
  default_model: "claude-sonnet-4-5"
  models:
    - name: "claude-sonnet-4-5"
      api_format: "anthropic"
      access_key: "${ANTHROPIC_API_KEY}"
      endpoints: [ ]

decisions:
  - name: "default"
    description: "Default routing to Claude"
    priority: 50
    rules:
      operator: "OR"
      conditions:
        - type: "domain"
          name: "other"
    modelRefs:
      - model: "claude-sonnet-4-5"

info

The ${ANTHROPIC_API_KEY} syntax references the environment variable. The key is expanded at runtime, keeping your credentials secure.

Step 4: Start the Router

vllm-sr serve config.yaml

Hybrid Configuration (vLLM + Anthropic)

Mix Anthropic models with local vLLM endpoints for cost optimization:

version: "1"

listeners:
  - name: http
    address: 0.0.0.0
    port: 8080
    timeout: 300s

providers:
  default_model: "Qwen/Qwen2.5-7B-Instruct"
  models:
    # Local model via vLLM for simple queries
    - name: "Qwen/Qwen2.5-7B-Instruct"
      endpoints:
        - name: "local-gpu"
          endpoint: "host.docker.internal:8000" # change this to your vLLM endpoint
          weight: 1

    # Cloud model via Anthropic API for complex queries
    - name: "claude-sonnet-4-5"
      api_format: "anthropic"
      access_key: "${ANTHROPIC_API_KEY}"
      endpoints: [ ]

decisions:
  - name: "simple_queries"
    description: "Use local model for simple queries"
    priority: 50
    rules:
      operator: "OR"
      conditions:
        - type: "domain"
          name: "other"
    modelRefs:
      - model: "Qwen/Qwen2.5-7B-Instruct"

  - name: "complex_queries"
    description: "Use Claude for complex queries"
    priority: 100
    rules:
      operator: "OR"
      conditions:
        - type: "domain"
          name: "computer_science"
        - type: "domain"
          name: "math"
    modelRefs:
      - model: "claude-sonnet-4-5"

Testing Hybrid Routing

Simple query → Routes to local vLLM (Qwen):

curl -X POST http://localhost:8080/v1/responses \
  -H "Content-Type: application/json" \
  -d '{
    "model": "auto",
    "input": "Where is Beijing?",
    "instructions": "You are a helpful assistant",
    "stream": false
  }'

Response (routed to Qwen/Qwen2.5-7B-Instruct):

{
  "id": "resp_c3c0724bddedf0ca2e262e7d",
  "object": "response",
  "model": "Qwen/Qwen2.5-7B-Instruct",
  "status": "completed",
  "output": [
    {
      "type": "message",
      "role": "assistant",
      "content": [
        {
          "type": "output_text",
          "text": "Beijing is located in northern China. It is the capital city of the People's Republic of China..."
        }
      ]
    }
  ],
  "usage": {
    "input_tokens": 22,
    "output_tokens": 93,
    "total_tokens": 115
  }
}

Complex query (computer science) → Routes to Anthropic (Claude):

curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "auto",
    "messages": [{"role": "user", "content": "Explain the basic rules of Weiqi"}],
    "stream": false
  }'

Response (routed to claude-sonnet-4-5):

{
  "id": "msg_01EGW1bYrBedQt8vw1uxyR3z",
  "object": "chat.completion",
  "model": "claude-sonnet-4-5",
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "# Basic Rules of Weiqi (Go)\n\n## Objective\nControl more territory than your opponent..."
      }
    }
  ],
  "usage": {
    "completion_tokens": 337,
    "prompt_tokens": 21,
    "total_tokens": 358
  }
}

tip

Use model: "auto" to let the router automatically select the best model based on your decision rules. Simple queries go to the cost-effective local model, while complex queries are routed to Claude.

Sending Requests

Once configured, send standard OpenAI-format requests:

curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4-5",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Explain quantum computing in simple terms."}
    ],
    "max_tokens": 1024
  }'

The router will:

1. Parse the OpenAI-format request
2. Convert it to Anthropic Messages API format
3. Call the Anthropic API
4. Convert the response back to OpenAI format
5. Return the response to the client

Response API Support

The router also supports the OpenAI Response API (/v1/responses) with Anthropic backends. Requests are translated to Chat Completions format, sent to Claude, and responses are converted back to Response API format.

Enabling Response API

Add the response_api configuration to your config file:

response_api:
  enabled: true
  store_backend: "memory"
  ttl_seconds: 86400
  max_responses: 1000

Sending Response API Requests

curl -X POST http://localhost:8080/v1/responses \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4-5",
    "input": "What is the difference between threads and processes?",
    "instructions": "You are a helpful assistant"
  }'

Example Response

{
  "id": "resp_54a277277aa864ee6e18754d",
  "object": "response",
  "created_at": 1768803933,
  "model": "claude-sonnet-4-5",
  "status": "completed",
  "output": [
    {
      "type": "message",
      "id": "item_594ea35de68d22a8a3563cc7",
      "role": "assistant",
      "content": [
        {
          "type": "output_text",
          "text": "# Threads vs Processes\n\n## **Process**\n- Independent execution unit with its own memory space..."
        }
      ],
      "status": "completed"
    }
  ],
  "output_text": "# Threads vs Processes...",
  "usage": {
    "input_tokens": 21,
    "output_tokens": 311,
    "total_tokens": 332
  },
  "instructions": "You are a helpful assistant"
}

Supported Parameters

The following OpenAI parameters are translated to Anthropic equivalents:

OpenAI Parameter	Anthropic Equivalent	Notes
model	model	Model name passed directly
messages	messages + system	System messages extracted separately
max_tokens	max_tokens	Required by Anthropic (defaults to 4096)
max_completion_tokens	max_tokens	Alternative to max_tokens
temperature	temperature	0.0 to 1.0
top_p	top_p	Nucleus sampling
stop	stop_sequences	Stop sequences
stream	—	Not supported (see limitations below)

Current Limitations

Streaming Not Supported

The Anthropic backend currently only supports non-streaming responses. If you send a request with stream: true, the router will return an error:

{
  "error": {
    "message": "Streaming is not supported for Anthropic models. Please set stream=false in your request.",
    "type": "invalid_request_error",
    "code": 400
  }
}

Workaround: Ensure all requests to Anthropic models use stream: false or omit the stream parameter entirely (defaults to false).

# Correct - non-streaming request
curl -X POST http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4-5",
    "messages": [{"role": "user", "content": "Hello"}],
    "stream": false
  }'

tip

If your application requires streaming responses, consider using a local vLLM endpoint or an OpenAI-compatible API that supports streaming, and configure decision-based routing to direct streaming-critical workloads to those backends.