ChatTogetherAI

Together AI offers an API to query 50+ leading open-source models in a couple lines of code.

This guide will help you getting started with ChatTogetherAI chat models. For detailed documentation of all ChatTogetherAI features and configurations head to the API reference.

Overview

Integration details

Class	Package	Local	Serializable	PY support	Package downloads	Package latest
ChatTogetherAI	@langchain/community	❌	✅	✅

Model features

See the links in the table headers below for guides on how to use specific features.

Tool calling	Structured output	JSON mode	Image input	Audio input	Video input	Token-level streaming	Token usage	Logprobs
✅	✅	✅	✅	✅	✅	✅	✅	✅

Setup

To access ChatTogetherAI models you’ll need to create a Together account, get an API key here, and install the @langchain/community integration package.

Credentials

Head to api.together.ai to sign up to TogetherAI and generate an API key. Once you’ve done this set the TOGETHER_AI_API_KEY environment variable:

export TOGETHER_AI_API_KEY="your-api-key"

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"

Installation

The LangChain ChatTogetherAI integration lives in the @langchain/community package:

tip

See this section for general instructions on installing integration packages.

npm

npm i @langchain/community @langchain/core

yarn

yarn add @langchain/community @langchain/core

pnpm

pnpm add @langchain/community @langchain/core

Instantiation

Now we can instantiate our model object and generate chat completions:

import { ChatTogetherAI } from "@langchain/community/chat_models/togetherai";

const llm = new ChatTogetherAI({
  model: "mistralai/Mixtral-8x7B-Instruct-v0.1",
  temperature: 0,
  // other params...
});

NOTE: if you do not provide a model, LangChain would attempt to use “meta-llama/Llama-3.3-70B-Instruct-Turbo-Free” as the default model.

Invocation

const aiMsg = await llm.invoke([
  [
    "system",
    "You are a helpful assistant that translates English to French. Translate the user sentence.",
  ],
  ["human", "I love programming."],
]);
aiMsg;

AIMessage {
  "id": "chatcmpl-9rT9qEDPZ6iLCk6jt3XTzVDDH6pcI",
  "content": "J'adore la programmation.",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "completionTokens": 8,
      "promptTokens": 31,
      "totalTokens": 39
    },
    "finish_reason": "stop"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 31,
    "output_tokens": 8,
    "total_tokens": 39
  }
}

console.log(aiMsg.content);

J'adore la programmation.

Chaining

We can chain our model with a prompt template like so:

import { ChatPromptTemplate } from "@langchain/core/prompts";

const prompt = ChatPromptTemplate.fromMessages([
  [
    "system",
    "You are a helpful assistant that translates {input_language} to {output_language}.",
  ],
  ["human", "{input}"],
]);

const chain = prompt.pipe(llm);
await chain.invoke({
  input_language: "English",
  output_language: "German",
  input: "I love programming.",
});

AIMessage {
  "id": "chatcmpl-9rT9wolZWfJ3xovORxnkdf1rcPbbY",
  "content": "Ich liebe das Programmieren.",
  "additional_kwargs": {},
  "response_metadata": {
    "tokenUsage": {
      "completionTokens": 6,
      "promptTokens": 26,
      "totalTokens": 32
    },
    "finish_reason": "stop"
  },
  "tool_calls": [],
  "invalid_tool_calls": [],
  "usage_metadata": {
    "input_tokens": 26,
    "output_tokens": 6,
    "total_tokens": 32
  }
}

Behind the scenes, TogetherAI uses the OpenAI SDK and OpenAI compatible API, with some caveats:

API reference

For detailed documentation of all ChatTogetherAI features and configurations head to the API reference: https://api.js.langchain.com/classes/langchain_community_chat_models_togetherai.ChatTogetherAI.html

Related

• Chat model conceptual guide
• Chat model how-to guides