Python (Unified Mode) | Enterprise Router

Setup

pip install openai

from openai import OpenAI
client = OpenAI(
    api_key="sk-proxy-YOUR_KEY_HERE",
    base_url="https://api.enterpriserouter.com/v1"
)

Basic chat

OpenAI

response = client.chat.completions.create(
    model="openai/gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

Anthropic

response = client.chat.completions.create(
    model="anthropic/claude-sonnet-4-6",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

Gemini

response = client.chat.completions.create(
    model="google/gemini-2.5-flash",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)

Streaming

Works identically across all providers:

stream = client.chat.completions.create(
    model="anthropic/claude-sonnet-4-6",
    messages=[{"role": "user", "content": "Write a short poem about code."}],
    stream=True
)
for chunk in stream:
    content = chunk.choices[0].delta.content
    if content:
        print(content, end="")

System messages

response = client.chat.completions.create(
    model="anthropic/claude-sonnet-4-6",
    messages=[
        {"role": "system", "content": "You are a senior software engineer. Be concise."},
        {"role": "user", "content": "What's the difference between a mutex and a semaphore?"}
    ]
)

System messages are translated to each provider’s native format automatically (e.g., Anthropic’s system parameter, Gemini’s system_instruction).

Tool calling

The same tool definition works for every provider:

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get weather for a city",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {"type": "string"}
                },
                "required": ["city"]
            }
        }
    }
]
response = client.chat.completions.create(
    model="google/gemini-2.5-flash",
    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
    tools=tools,
    tool_choice="auto"
)
# Standard OpenAI tool_calls format, regardless of provider
tool_call = response.choices[0].message.tool_calls[0]
print(f"{tool_call.function.name}({tool_call.function.arguments})")

Multi-turn conversation

messages = [
    {"role": "system", "content": "You are a helpful math tutor."},
    {"role": "user", "content": "What is a derivative?"}
]
response = client.chat.completions.create(
    model="openai/gpt-4o",
    messages=messages
)
messages.append(response.choices[0].message)
messages.append({"role": "user", "content": "Can you give me an example?"})
response = client.chat.completions.create(
    model="openai/gpt-4o",
    messages=messages
)
print(response.choices[0].message.content)

Switching providers

The power of unified mode: swap providers by changing one string. Your code, prompts, tools, and response handling all stay the same.

models = [
    "openai/gpt-4o",
    "anthropic/claude-sonnet-4-6",
    "google/gemini-2.5-flash",
]
for model in models:
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": "What is 2+2?"}]
    )
    print(f"{model}: {response.choices[0].message.content}")