Together AI

Integrate Together AI models through the Adaline Proxy for automatic telemetry and observability.

Together AI

Integrate Together AI open-source models through the Adaline Proxy to automatically capture telemetry — requests, responses, token usage, latency, and costs — with minimal code changes.

Supported Models

Together AI accepts any model name available on the Together AI platform. Popular models include:

Chat Models

Model	Description
meta-llama/Llama-3.1-405B-Instruct-Turbo	Llama 3.1 405B
meta-llama/Llama-3.1-70B-Instruct-Turbo	Llama 3.1 70B
meta-llama/Llama-3.1-8B-Instruct-Turbo	Llama 3.1 8B
mistralai/Mixtral-8x7B-Instruct-v0.1	Mixtral 8x7B MoE
Qwen/Qwen2-72B-Instruct	Qwen 2 72B
deepseek-ai/DeepSeek-R1	DeepSeek R1 reasoning model

Embedding Models

Together AI also supports embedding models. Pass any Together AI embedding model name.

Together AI uses a flexible model routing system — any model available on the Together AI models page can be used.

Proxy Base URL

https://gateway.adaline.ai/v1/together-ai/

Prerequisites

1. A Together AI API key
2. An Adaline API key, project ID, and prompt ID

Chat Completions

Complete Chat

Python

from together import Together

client = Together(
    base_url="https://gateway.adaline.ai/v1/together-ai",
    api_key="your-together-ai-api-key",
    supplied_headers={
        "adaline-api-key": "your-adaline-api-key",
        "adaline-project-id": "your-project-id",
        "adaline-prompt-id": "your-prompt-id",
    },
)

response = client.chat.completions.create(
    model="meta-llama/Llama-2-7b-chat-hf",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What are the benefits of open source AI models?"}
    ],
    extra_headers={
        "adaline-trace-name": "togetherai-chat-completion"  # Optional
    }
)

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

import OpenAI from "openai";

// Together AI is OpenAI-compatible
const client = new OpenAI({
  apiKey: "your-together-ai-api-key",
  baseURL: "https://gateway.adaline.ai/v1/together-ai/",
});

const response = await client.chat.completions.create(
  {
    model: "meta-llama/Llama-2-7b-chat-hf",
    messages: [
      { role: "system", content: "You are a helpful assistant." },
      { role: "user", content: "What are the benefits of open source AI models?" },
    ],
  },
  {
    headers: {
      "adaline-api-key": "your-adaline-api-key",
      "adaline-project-id": "your-project-id",
      "adaline-prompt-id": "your-prompt-id",
      "adaline-trace-name": "togetherai-chat-completion",
    },
  }
);

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

Together AI’s Python SDK uses supplied_headers for persistent headers set at client initialization, and extra_headers for per-request headers.

Stream Chat

Python

from together import Together

client = Together(
    base_url="https://gateway.adaline.ai/v1/together-ai",
    api_key="your-together-ai-api-key",
    supplied_headers={
        "adaline-api-key": "your-adaline-api-key",
        "adaline-project-id": "your-project-id",
        "adaline-prompt-id": "your-prompt-id",
    },
)

stream = client.chat.completions.create(
    model="meta-llama/Llama-2-7b-chat-hf",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain the concept of distributed computing."}
    ],
    stream=True,
    extra_headers={
        "adaline-trace-name": "togetherai-stream-chat"  # Optional
    }
)

for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

Next Steps

• Multi-Step Workflows — RAG pipelines, multi-step generation, and conversational agents
• Headers Reference — Complete header documentation

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