Adaline Gateway

The only fully local, production-grade Super SDK that provides a simple, unified, and powerful interface for calling more than 300+ LLMs.

• Production-ready and trusted by enterprises
• Fully local and NOT a proxy - deploy it anywhere
• Built-in batching, retries, caching, callbacks, and OpenTelemetry support
• Extensible with custom plugins for caching, logging, HTTP clients, and more - use it like building blocks to integrate with your infrastructure
• Supports plug-and-play providers - run fully custom providers while leveraging all the benefits of Adaline Gateway

Features

• 🔧 Strongly typed in TypeScript
• 📦 Isomorphic - works everywhere
• 🔒 100% local, private, and NOT a proxy
• 🛠️ Tool calling support across all compatible LLMs
• 📊 Batching for all requests with custom queue support
• 🔄 Automatic retries with exponential backoff
• ⏳ Caching with custom cache plug-in support
• 📞 Callbacks for comprehensive instrumentation and hooks
• 🔍 OpenTelemetry integration for existing infrastructure
• 🔌 Plug-and-play custom providers for local and custom models

Providers

Installation

Core packages

npm install @adaline/gateway @adaline/types

Provider packages

Dependencies for providers are optional. You can install them as needed. For example:

npm install @adaline/openai @adaline/anthropic @adaline/google @adaline/open-router @adaline/bedrock

Quickstart

Chat

This example demonstrates how to invoke an LLM with a simple text prompt in both non-streaming and streaming modes.

import { Gateway } from "@adaline/gateway";
import { OpenAI } from "@adaline/openai";
import { Config, ConfigType, MessageType } from "@adaline/types";

const OPENAI_API_KEY = "your-api-key"; // Replace with your OpenAI API key

const gateway = new Gateway();
const openai = new OpenAI();

const gpt4o = openai.chatModel({
  modelName: "gpt-4o",
  apiKey: OPENAI_API_KEY,
});

const config: ConfigType = Config().parse({
  temperature: 0.7,
  maxTokens: 300,
});

const messages: MessageType[] = [
  {
    role: "system",
    content: [{
      modality: "text",
      value: "You are a helpful assistant. You are extremely concise.",
    }],
  },
  {
    role: "user",
    content: [{
      modality: "text",
      value: `What is ${Math.floor(Math.random() * 100) + 1} + ${Math.floor(Math.random() * 100) + 1}?`,
    }],
  },
];

// * Complete chat
async function runCompleteChat() {
  const completeChat = await gateway.completeChat({
    model: gpt4o,
    config,
    messages,
    tools: [],
  });
  console.log(completeChat.provider.request); // HTTP Request sent to Provider
  console.log(completeChat.provider.response); // HTTP Response from Provider
  console.log(completeChat.cached); // Whether the response was cached
  console.log(completeChat.latencyInMs); // Latency in milliseconds
  console.log(completeChat.request); // Request in Gateway types
  console.log(completeChat.response); // Response in Gateway types, E.g.:
  // {
  //   "messages": [
  //     {
  //       "role": "assistant",
  //       "content": [
  //         {
  //           "modality": "text",
  //           "value": "<some-text-returned-by-LLM>"
  //         }
  //       ]
  //     }
  //   ],
  //   "usage": {
  //     "promptTokens": 138,
  //     "completionTokens": 573,
  //     "totalTokens": 711
  //   },
  //   "logProbs": []
  // }
  //
}

runCompleteChat();

// * Stream chat
async function runStreamChat() {
  const streamChat = await gateway.streamChat({
    model: gpt4o,
    config,
    messages,
    tools: [],
  });

  for await (const chunk of streamChat) {
    console.log(chunk.response);
  }
}

runStreamChat();

Word Embedding

This example demonstrates how to invoke an embedding model with text samples to generate word embeddings.

import { Gateway } from "@adaline/gateway";
import { OpenAI } from "@adaline/openai";
import { Config, ConfigType, EmbeddingRequestsType } from "@adaline/types";

const OPENAI_API_KEY = "your-api-key"; // Replace with your OpenAI API key

const gateway = new Gateway();
const openai = new OpenAI();

const textEmbedding3Large = openai.embeddingModel({
  modelName: "text-embedding-3-large",
  apiKey: OPENAI_API_KEY,
});

const config: ConfigType = Config().parse({
  encodingFormat: "float",
  dimensions: 255,
});

  const embeddingRequests: EmbeddingRequestsType = {
    modality: "text",
    requests: [
      "Hello world",
      "When the going gets tough, the tough get going. Or the tough pivots",
    ],
  };

async function runWordEmbedding() {
  const wordEmbedding = await gateway.getEmbeddings({
    model: textEmbedding3Large,
    config,
    embeddingRequests,
  });
  console.log(wordEmbedding.response);
}

runWordEmbedding();

List Supported Models

import { OpenAI } from "@adaline/openai";

const openai = new OpenAI();

const openaiChatModels = openai.chatModelLiterals();
console.log(openaiChatModels); // Array of chat model names

const openaiEmbeddingModels = openai.embeddingModelLiterals();
console.log(openaiEmbeddingModels); // Array of embedding model names

const o4MiniSchema = openai.chatModelSchemas()["o4-mini"]; // Schema for the o4-mini chat model
console.log(o4MiniSchema.name);
console.log(o4MiniSchema.description);
console.log(o4MiniSchema.maxInputTokens);
console.log(o4MiniSchema.maxOutputTokens);
console.log(o4MiniSchema.roles);
console.log(o4MiniSchema.modalities);
console.log(o4MiniSchema.config); 

Prompt Types

@adaline/types contains the core types used to build prompts and process LLM responses.

Config

A ConfigType is used to configure parameters of an LLM for a request. The valid fields for ConfigType depend on the specific LLM being used. Refer to the documentation of the LLM provider for details. Gateway internally transforms the ConfigType to and from the LLM provider's schema.

Note: ConfigType is a lenient and forgiving type; any unknown fields will be ignored during transformation.

// Example of a config
const config: ConfigType = Config().parse({
  temperature: 0.7,
  maxTokens: 1000,
  // Any unknown fields will be ignored
  unknownField: "This will be ignored"
});

The exact schema for ConfigType for each LLM can be found programmatically using the provider's SDK:

import { OpenAI } from "@adaline/openai";

const openai = new OpenAI();

const o4MiniSchema = openai.chatModelSchemas()["o4-mini"];
console.log(o4MiniSchema.config.def); // Verbose description of the Config for this LLM
console.log(o4MiniSchema.config.schema); // Zod type to validate Config for this LLM

Message

A MessageType is the core component of a prompt sent to the LLM provider and the response received from it. Gateway internally transforms the MessageType to and from the LLM provider's schema.

// Example of a message
const message: MessageType = {
  role: "system",
  content: [
    {
      modality: "text",
      value: "Hello, how can I help you today?",
    }
  ]
};

// Example of a message
const completeMessage: MessageType = {
  role: "assistant",
  content: [
    {
      modality: "text",
      value: "I've analyzed the image you sent and found the following information:",
    },
    {
      modality: "reasoning",
      value: {
        type: "thinking",
        thinking: "The image appears to contain a chart with quarterly sales data. I should point out the key trends.",
        signature: "reasoning-signature-456"
      },
    },
    {
      modality: "tool-call",
      index: 0,
      id: "call_987654321",
      name: "analyze_chart",
      arguments: '{"chart_type": "bar", "data_points": ["Q1", "Q2", "Q3", "Q4"]}',
    }
  ],
};

Role

A RoleEnumType is an enumeration of possible message roles. Gateway internally transforms these roles to and from the LLM provider's accepted roles. For example, the "assistant" role in Gateway will be transformed to the "model" role for Google's Gemini models.

// Example of a role
const role: RoleEnumType = "assistant";

Content

A ContentType is the core part of a message that contains the actual content (text, images, etc.) being sent to or received from the LLM provider.

Text Content

A TextContentType represents text content in a message being sent to or received from the LLM provider.

// Example of text content
const textContent: TextContentType = {
  modality: "text",
  value: "Hello, how can I help you today?"
};

Image Content

An ImageContentType represents image content in a message being sent to or received from the LLM provider.

// Example of base64 image content
const base64ImageContent: ImageContentType = {
  modality: "image",
  detail: "high",
  value: {
    type: "base64",
    base64: "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8z8BQDwAEhQGAhKmMIQAAAABJRU5ErkJggg==",
    mediaType: "png"
  },
};

// Example of URL image content
const urlImageContent: ImageContentType = {
  modality: "image",
  detail: "medium",
  value: {
    type: "url",
    url: "https://example.com/image.jpg"
  },
};

The value field can be one of two types:

Base64ImageContentValueType:

// Example of base64 image content value
const base64ImageContent: Base64ImageContentValueType = {
  type: "base64",
  base64: "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8/x8AAwMCAO+ip1sAAAAASUVORK5CYII=",
  mediaType: "png"
};

UrlImageContentValueType:

// Example of URL image content value
const urlImageContent: UrlImageContentValueType = {
  type: "url",
  url: "https://example.com/image.png"
};

Tool Call Content

A ToolCallContentType represents a tool call in a message being sent to or received from the LLM provider.

// Example of tool call content
const toolCallContent: ToolCallContentType = {
  modality: "tool-call",
  index: 0,
  id: "call_123456789",
  name: "search_database",
  arguments: '{"query": "user information", "limit": 10}',
};

Tool Response Content

A ToolResponseContentType represents a tool response in a message being sent to or received from the LLM provider.