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Use the official OpenAI and Anthropic SDKs, LangChain, the Vercel AI SDK and LiteLLM with the gateway. The base URL and key settings for each, with working code.

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Any library that lets you set a base URL for the OpenAI or Anthropic API works with the gateway. You change two settings: the base URL and the API key. Everything else (request shapes, streaming, tools, errors) stays as the library documents it.

OpenAI SDKs https://api.oneaiapikey.com/v1

Anthropic SDKs https://api.oneaiapikey.com

OpenAI SDK

Works with every model, in the chat completions format. The SDK also reads OPENAI_BASE_URL and OPENAI_API_KEY from the environment if you prefer not to pass them in code.

# pip install openai
import os
from openai import OpenAI

client = OpenAI(base_url="https://api.oneaiapikey.com/v1", api_key=os.environ["RELAYKIT_API_KEY"])
reply = client.chat.completions.create(
    model="claude-haiku-4-5",
    messages=[{"role": "user", "content": "Say hello in five words."}],
)
print(reply.choices[0].message.content)
// npm install openai
import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.oneaiapikey.com/v1", apiKey: process.env.RELAYKIT_API_KEY });
const reply = await client.chat.completions.create({
  model: "claude-haiku-4-5",
  messages: [{ role: "user", content: "Say hello in five words." }],
});
console.log(reply.choices[0].message.content);

Anthropic SDK

For Claude models in Anthropic’s own format, with prompt caching, extended thinking and every Messages feature. It also reads ANTHROPIC_BASE_URL and ANTHROPIC_API_KEY.

# pip install anthropic
import os
import anthropic

client = anthropic.Anthropic(base_url="https://api.oneaiapikey.com", api_key=os.environ["RELAYKIT_API_KEY"])
message = client.messages.create(
    model="claude-sonnet-5-5",
    max_tokens=512,
    messages=[{"role": "user", "content": "Say hello in five words."}],
)
print(message.content[0].text)
// npm install @anthropic-ai/sdk
import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({ baseURL: "https://api.oneaiapikey.com", apiKey: process.env.RELAYKIT_API_KEY });
const message = await client.messages.create({
  model: "claude-sonnet-5-5",
  max_tokens: 512,
  messages: [{ role: "user", content: "Say hello in five words." }],
});
console.log(message.content[0].text);

LangChain

ChatOpenAI works with every model; ChatAnthropic with Claude models. Leave use_responses_api off on ChatOpenAI unless the model is a GPT model, because the Responses API is fully supported only there.

# pip install langchain-openai langchain-anthropic
import os
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic

llm = ChatOpenAI(
    model="gpt-5.6-terra",
    base_url="https://api.oneaiapikey.com/v1",
    api_key=os.environ["RELAYKIT_API_KEY"],
)
print(llm.invoke("Say hello in five words.").content)

claude = ChatAnthropic(
    model="claude-sonnet-5-5",
    base_url="https://api.oneaiapikey.com",   # no /v1
    api_key=os.environ["RELAYKIT_API_KEY"],
)
// npm install @langchain/openai
import { ChatOpenAI } from "@langchain/openai";

const llm = new ChatOpenAI({
  model: "gpt-5.6-terra",
  apiKey: process.env.RELAYKIT_API_KEY,
  configuration: { baseURL: "https://api.oneaiapikey.com/v1" },
});
const reply = await llm.invoke("Say hello in five words.");
console.log(reply.content);

Vercel AI SDK

Use the OpenAI-compatible provider for any model. The plain @ai-sdk/openai provider also works, but openai("id") calls the Responses API; use openai.chat("id") to send chat completions instead.

AI SDK
// npm install ai @ai-sdk/openai-compatible @ai-sdk/anthropic
import { generateText, streamText } from "ai";
import { createOpenAICompatible } from "@ai-sdk/openai-compatible";
import { createAnthropic } from "@ai-sdk/anthropic";

// Any model, OpenAI format (chat completions)
const gateway = createOpenAICompatible({
  name: "gateway",
  baseURL: "https://api.oneaiapikey.com/v1",
  apiKey: process.env.RELAYKIT_API_KEY,
  includeUsage: true,          // token counts on streamed responses
});

const { text } = await generateText({
  model: gateway("claude-haiku-4-5"),
  prompt: "Say hello in five words.",
});

// Claude models, Anthropic format. This provider expects /v1 in the base URL.
const anthropic = createAnthropic({
  baseURL: "https://api.oneaiapikey.com/v1",
  apiKey: process.env.RELAYKIT_API_KEY,
});

const result = streamText({ model: anthropic("claude-sonnet-5-5"), prompt: "Write a haiku about latency." });
for await (const chunk of result.textStream) process.stdout.write(chunk);

LiteLLM

Prefix the model id with openai/ and set api_base to the OpenAI base URL.

LiteLLM SDK
# pip install litellm
import os
from litellm import completion

reply = completion(
    model="openai/claude-haiku-4-5",              # "openai/" tells LiteLLM to use the OpenAI format
    api_base="https://api.oneaiapikey.com/v1",
    api_key=os.environ["RELAYKIT_API_KEY"],
    messages=[{"role": "user", "content": "Say hello in five words."}],
)
print(reply.choices[0].message.content)

The same settings work in the LiteLLM proxy:

LiteLLM proxy
# config.yaml for the LiteLLM proxy
model_list:
  - model_name: claude-haiku-4-5
    litellm_params:
      model: openai/claude-haiku-4-5
      api_base: https://api.oneaiapikey.com/v1
      api_key: os.environ/RELAYKIT_API_KEY

Other languages

Libraries for Go, Java, .NET, Ruby, Rust and PHP that target the OpenAI API usually have a base URL or endpoint option. Set it to the OpenAI base URL above and pass your key as the API key. With plain HTTP, send Authorization: Bearer <key> to POST https://api.oneaiapikey.com/v1/chat/completions; the API reference lists every endpoint.

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