import { Callout, Tabs, Cards } from "@/docs/components";

SDKs & Libraries

<div className="status-badge status-stable">Stable</div>

Integrate elizaOS Cloud with official SDKs and community libraries.

Quick Start

Since elizaOS Cloud is 100% OpenAI API compatible, you can use any existing OpenAI SDK by simply changing the base URL. No special SDK required!

typescript

import OpenAI from 'openai';

const client = new OpenAI({
apiKey: process.env.ELIZA_API_KEY,
baseURL: 'https://elizacloud.ai/api/v1',
});

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

console.log(response.choices[0].message.content);

</Tabs.Tab> <Tabs.Tab>

python

from openai import OpenAI

client = OpenAI(
    api_key="eliza_xxx",
    base_url="https://elizacloud.ai/api/v1"
)

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

print(response.choices[0].message.content)

</Tabs.Tab> <Tabs.Tab>

bash

curl -X POST "https://elizacloud.ai/api/v1/chat/completions" \
  -H "Authorization: Bearer $ELIZA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

</Tabs.Tab> </Tabs>

JavaScript / TypeScript

Installation

bash

npm install openai
# or
bun add openai

Basic Usage

typescript

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.ELIZA_API_KEY,
  baseURL: "https://elizacloud.ai/api/v1",
});

Streaming

typescript

const stream = await client.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: "Tell me a story" }],
  stream: true,
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content || "");
}

Embeddings

typescript

const embedding = await client.embeddings.create({
  input: "Hello world",
  model: "text-embedding-3-small",
});

Python

Installation

bash

pip install openai

Basic Usage

python

from openai import OpenAI

client = OpenAI(
    api_key="eliza_xxx",
    base_url="https://elizacloud.ai/api/v1"
)

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

print(response.choices[0].message.content)

Streaming

python

stream = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

elizaOS-Specific APIs

For APIs that extend beyond OpenAI compatibility (images, video, voice), use direct HTTP calls:

Image Generation

typescript

const response = await fetch("https://elizacloud.ai/api/v1/generate-image", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.ELIZA_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    prompt: "A sunset over mountains",
    model: "google/gemini-2.5-flash-image",
  }),
});

const data = await response.json();
console.log(data.images[0].url);

Video Generation

typescript

const response = await fetch("https://elizacloud.ai/api/v1/generate-video", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.ELIZA_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    prompt: "A cat playing piano",
    model: "fal-ai/veo3",
  }),
});

const data = await response.json();
console.log(data.video.url);

Framework Integrations

Vercel AI SDK

typescript

import { generateText } from "ai";
import { createOpenAI } from "@ai-sdk/openai";

const openai = createOpenAI({
  apiKey: process.env.ELIZA_API_KEY,
  baseURL: "https://elizacloud.ai/api/v1",
});

const { text } = await generateText({
  model: openai("gpt-4o"),
  prompt: "Hello!",
});

LlamaIndex

python

from llama_index.llms.openai import OpenAI

llm = OpenAI(
    api_key="eliza_xxx",
    api_base="https://elizacloud.ai/api/v1",
    model="gpt-4o"
)

response = llm.complete("Hello!")

MCP Client

Connect to elizaOS Cloud MCP endpoint:

typescript

import { Client } from "@modelcontextprotocol/sdk";

const client = new Client({
  url: "https://elizacloud.ai/api/mcp",
  headers: {
    Authorization: `Bearer ${process.env.ELIZA_API_KEY}`,
  },
});

await client.initialize();

const tools = await client.listTools();
const result = await client.callTool("generate_image", {
  prompt: "A sunset",
});

Community Libraries

Since elizaOS Cloud is OpenAI-compatible, you can use any OpenAI SDK in your preferred language:

Language	Library	Installation
Go	`sashabaranov/go-openai`	`go get github.com/sashabaranov/go-openai`
Rust	`async-openai`	`cargo add async-openai`
Ruby	`ruby-openai`	`gem install ruby-openai`
PHP	`openai-php/client`	`composer require openai-php/client`

Simply configure the base URL to https://elizacloud.ai/api/v1 and use your elizaOS API key.

Feature Support

Feature	OpenAI SDK	Direct REST
Chat Completions	✅	✅
Streaming	✅	✅
Embeddings	✅	✅
Image Generation	❌	✅
Video Generation	❌	✅
Voice / TTS	❌	✅
Knowledge Base	❌	✅
MCP Tools	❌	✅
Agents API	❌	✅

<Callout type="info"> OpenAI SDK compatibility covers chat, embeddings, and streaming. For elizaOS-specific features like image/video generation, use direct REST calls. </Callout>

Contributing

We welcome SDK contributions! See our GitHub for guidelines.

Next Steps

<Cards> <Cards.Card title="API Reference" href="/docs/api"> Complete API documentation </Cards.Card> <Cards.Card title="Quickstart" href="/docs/quickstart"> Get started quickly </Cards.Card> <Cards.Card title="Examples" href="https://github.com/elizaOS/eliza/tree/main/examples" > Code examples </Cards.Card> </Cards>