Connect Your Code

Change the base URL. Add the X-Majordomo-Key header. Nothing else.

OpenAI SDK

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.gomajordomo.com/v1",
    api_key="your-openai-api-key",
    default_headers={"X-Majordomo-Key": "mdm_sk_your_key_here"},
)

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

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://gateway.gomajordomo.com/v1',
  apiKey: process.env.OPENAI_API_KEY,
  defaultHeaders: { 'X-Majordomo-Key': 'mdm_sk_your_key_here' },
});

const response = await client.chat.completions.create({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: 'Hello!' }],
});

curl -X POST https://gateway.gomajordomo.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "X-Majordomo-Key: mdm_sk_your_key_here" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{"model": "gpt-4o", "messages": [{"role": "user", "content": "Hello!"}]}'

Streaming works unchanged — the gateway proxies the SSE stream transparently.

Anthropic SDK

import anthropic

client = anthropic.Anthropic(
    base_url="https://gateway.gomajordomo.com",
    api_key="your-anthropic-api-key",
)

response = client.messages.create(
    model="claude-sonnet-4-6",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}],
    extra_headers={"X-Majordomo-Key": "mdm_sk_your_key_here"},
)

import Anthropic from '@anthropic-ai/sdk';

const client = new Anthropic({
  baseURL: 'https://gateway.gomajordomo.com',
  apiKey: process.env.ANTHROPIC_API_KEY,
});

const response = await client.messages.create(
  {
    model: 'claude-sonnet-4-6',
    max_tokens: 1024,
    messages: [{ role: 'user', content: 'Hello!' }],
  },
  { headers: { 'X-Majordomo-Key': 'mdm_sk_your_key_here' } },
);

curl -X POST https://gateway.gomajordomo.com/v1/messages \
  -H "Content-Type: application/json" \
  -H "X-Majordomo-Key: mdm_sk_your_key_here" \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-sonnet-4-6",
    "max_tokens": 1024,
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Prompt caching tokens (cache_read_input_tokens, cache_creation_input_tokens) are tracked and priced separately.

Pydantic AI

No adapter needed — point Pydantic AI’s underlying provider client at the gateway and add the X-Majordomo-Key header. The same pattern works for any framework that lets you configure the base URL and headers of its HTTP client.

from anthropic import AsyncAnthropic
from pydantic_ai import Agent
from pydantic_ai.models.anthropic import AnthropicModel
from pydantic_ai.providers.anthropic import AnthropicProvider

# Route the underlying client through the gateway
client = AsyncAnthropic(
    base_url="https://gateway.gomajordomo.com",
    api_key="your-anthropic-api-key",
    default_headers={"X-Majordomo-Key": "mdm_sk_your_key_here"},
)

model = AnthropicModel(
    "claude-sonnet-4-6",
    provider=AnthropicProvider(anthropic_client=client),
)

agent = Agent(model=model, system_prompt="You are a helpful assistant.")
result = await agent.run("Summarize this document: ...")

Add per-request metadata by passing extra headers through the model settings — for example AnthropicModelSettings(extra_headers={"X-Majordomo-Feature": "document-summarizer"}) on the agent.run(...) call.

majordomo-llm

A unified async Python client for OpenAI, Anthropic, Gemini, and more — with built-in cost tracking, structured output, and multi-provider cascade. Route it through the gateway by passing base_url and default_headers to get_llm_instance.

pip install majordomo-llm

from majordomo_llm import get_llm_instance, LLMCascade

GATEWAY = "https://gateway.gomajordomo.com"
HEADERS = {"X-Majordomo-Key": "mdm_sk_your_key_here"}

# Basic completion
llm = get_llm_instance(
    "anthropic",
    "claude-sonnet-4-6",
    base_url=GATEWAY,
    default_headers=HEADERS,
)
response = await llm.get_response("Summarize this document: ...")
print(response.content)
print(f"Cost: ${response.total_cost:.6f}")

# Structured output
from pydantic import BaseModel

class Sentiment(BaseModel):
    label: str
    score: float

result = await llm.get_structured_json_response(
    response_model=Sentiment,
    user_prompt="Analyze: 'This product is excellent'",
)
print(result.content.label)   # "positive"
print(result.content.score)   # 0.95

# Multi-provider cascade — falls back on failure
cascade = LLMCascade(
    [("anthropic", "claude-sonnet-4-6"), ("openai", "gpt-4o")],
    base_url=GATEWAY,
    default_headers=HEADERS,
)
response = await cascade.get_response("Hello!")

GitHub →

Metadata tagging

Add any X-Majordomo-* header to tag requests for cost attribution. Works with all SDKs above.

# OpenAI
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[...],
    extra_headers={
        "X-Majordomo-Feature": "document-review",
        "X-Majordomo-Team": "legal",
        "X-Majordomo-User-Id": "user_123",
    },
)

# Anthropic — same pattern, add to extra_headers
response = client.messages.create(
    ...,
    extra_headers={
        "X-Majordomo-Key": "mdm_sk_your_key_here",
        "X-Majordomo-Feature": "contract-analysis",
        "X-Majordomo-Team": "legal",
    },
)

See Cost Attribution for how to query this data.

Getting Started

How It Works

Track & Analyze

Control

Enterprise

Reference

Connect Your Code

OpenAI SDK

Anthropic SDK

Pydantic AI

majordomo-llm

Metadata tagging

​OpenAI SDK

​Anthropic SDK

​Pydantic AI

​majordomo-llm

​Metadata tagging

OpenAI SDK

Anthropic SDK

Pydantic AI

majordomo-llm

Metadata tagging