@elizaos/plugin-elizacloud

ElizaOS Cloud plugin - Multi-model AI generation with text, image, and audio support.

Available in TypeScript, Python, and Rust with uniform APIs.

Installation

TypeScript / JavaScript

npm install @elizaos/plugin-elizacloud
# or
bun add @elizaos/plugin-elizacloud

Python

pip install elizaos-plugin-elizacloud

Rust

[dependencies]
elizaos-plugin-elizacloud = "1.7.4"

API Comparison

All three implementations provide the same model handlers with uniform naming conventions:

Model Type	TypeScript	Python	Rust
TEXT_SMALL	handleTextSmall	handle_text_small	handle_text_small
TEXT_LARGE	handleTextLarge	handle_text_large	handle_text_large
OBJECT_SMALL	handleObjectSmall	handle_object_small	handle_object_small
OBJECT_LARGE	handleObjectLarge	handle_object_large	handle_object_large
TEXT_EMBEDDING	handleTextEmbedding	handle_text_embedding	handle_text_embedding
TEXT_EMBEDDING (batch)	handleBatchTextEmbedding	handle_batch_text_embedding	handle_batch_text_embedding
IMAGE	handleImageGeneration	handle_image_generation	handle_image_generation
IMAGE_DESCRIPTION	handleImageDescription	handle_image_description	handle_image_description
TEXT_TO_SPEECH	handleTextToSpeech	handle_text_to_speech	handle_text_to_speech
TRANSCRIPTION	handleTranscription	handle_transcription	handle_transcription
TEXT_TOKENIZER_ENCODE	handleTokenizerEncode	handle_tokenizer_encode	handle_tokenizer_encode
TEXT_TOKENIZER_DECODE	handleTokenizerDecode	handle_tokenizer_decode	handle_tokenizer_decode

Type Definitions

All implementations share the same type structures:

Type	Description
ElizaCloudConfig	Client configuration (API key, models, etc.)
TextGenerationParams	Parameters for text generation
ObjectGenerationParams	Parameters for structured JSON generation
TextEmbeddingParams	Parameters for embeddings (single or batch)
ImageGenerationParams	Parameters for image generation
ImageDescriptionParams	Parameters for image description
ImageDescriptionResult	Result from image description
TextToSpeechParams	Parameters for TTS
TranscriptionParams	Parameters for audio transcription
TokenizeTextParams	Parameters for tokenization
DetokenizeTextParams	Parameters for detokenization

Configuration

Get your API key from https://www.elizacloud.ai/dashboard/api-keys

Setting	Description	Default
api_key / ELIZAOS_CLOUD_API_KEY	Your API key (format: eliza_xxxxx)	Required
base_url / ELIZAOS_CLOUD_BASE_URL	Base URL for API requests	https://www.elizacloud.ai/api/v1
small_model / ELIZAOS_CLOUD_SMALL_MODEL	Small/fast model	gpt-5.4-mini
large_model / ELIZAOS_CLOUD_LARGE_MODEL	Large/powerful model	claude-sonnet-4.6
embedding_model / ELIZAOS_CLOUD_EMBEDDING_MODEL	Embedding model	text-embedding-3-small
embedding_dimensions	Embedding vector size	1536

Usage Examples

TypeScript

import { elizaOSCloudPlugin } from "@elizaos/plugin-elizacloud";

// Register the plugin with your agent
const agent = new Agent({
  plugins: [elizaOSCloudPlugin],
});

// Use models via runtime
const text = await runtime.useModel(ModelType.TEXT_LARGE, {
  prompt: "What is the meaning of life?",
});

// Structured object generation
const obj = await runtime.useModel(ModelType.OBJECT_LARGE, {
  prompt: "Generate a user profile with name and age",
});

const embedding = await runtime.useModel(ModelType.TEXT_EMBEDDING, {
  text: "Hello, world!",
});

// Tokenization
const tokens = await runtime.useModel(ModelType.TEXT_TOKENIZER_ENCODE, {
  prompt: "Hello tokenizer!",
  modelType: ModelType.TEXT_SMALL,
});

Python

import asyncio
from elizaos_plugin_elizacloud import (
    ElizaCloudClient,
    ElizaCloudConfig,
    TextGenerationParams,
    ObjectGenerationParams,
    handle_object_large,
    handle_tokenizer_encode,
    TokenizeTextParams,
)

async def main():
    config = ElizaCloudConfig(api_key="eliza_xxxxx")

    async with ElizaCloudClient(config) as client:
        # Text generation
        text = await client.generate_text(
            TextGenerationParams(prompt="Hello!"),
            model_size="large",
        )
        print(text)

    # Structured object generation
    obj = await handle_object_large(
        config,
        ObjectGenerationParams(prompt="Generate a user profile"),
    )
    print(obj)

    # Tokenization
    tokens = await handle_tokenizer_encode(
        config,
        TokenizeTextParams(prompt="Hello tokenizer!"),
    )
    print(f"Tokens: {tokens}")

asyncio.run(main())

Rust

use elizaos_plugin_elizacloud::{
    ElizaCloudClient, ElizaCloudConfig, TextGenerationParams,
    ObjectGenerationParams, TokenizeTextParams,
    handle_object_large, handle_tokenizer_encode,
};

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    let config = ElizaCloudConfig::new("eliza_xxxxx");
    let client = ElizaCloudClient::new(config.clone())?;

    // Text generation
    let text = client.generate_text_large(TextGenerationParams {
        prompt: "What is the meaning of life?".to_string(),
        ..Default::default()
    }).await?;
    println!("{}", text);

    // Structured object generation
    let obj = handle_object_large(config.clone(), ObjectGenerationParams {
        prompt: "Generate a user profile".to_string(),
        ..Default::default()
    }).await?;
    println!("{}", obj);

    // Tokenization
    let tokens = handle_tokenizer_encode(config, TokenizeTextParams {
        prompt: "Hello tokenizer!".to_string(),
        ..Default::default()
    }).await?;
    println!("Tokens: {:?}", tokens);

    Ok(())
}

Features

Feature	Description
Text Generation	Small (fast) and large (powerful) model support
Object Generation	Structured JSON output from natural language
Text Embeddings	Single and batch embedding with rate limit handling
Image Generation	DALL-E style image generation
Image Description	Vision model for describing images
Text-to-Speech	Multiple voice options
Audio Transcription	Whisper-based audio transcription
Tokenization	Token counting, encoding, and decoding

Development

Building

# TypeScript
bun run build

# Python
cd python && pip install -e ".[dev]"

# Rust
cd rust && cargo build --release

Testing

# TypeScript
npx vitest typescript/

# Python
cd python && pytest tests/

# Rust
cd rust && cargo test

Linting

# TypeScript
bun run format

# Python
cd python && ruff check . && ruff format .

# Rust
cd rust && cargo clippy && cargo fmt

Publishing

This package is published to:

• npm: @elizaos/plugin-elizacloud
• PyPI: elizaos-plugin-elizacloud
• crates.io: elizaos-plugin-elizacloud

Publishing happens automatically via GitHub Actions when the version in package.json changes.

Required Secrets

Add these secrets to your GitHub repository:

• NPM_TOKEN - npm access token
• PYPI_TOKEN - PyPI API token
• CRATES_IO_TOKEN - crates.io API token

License

MIT