Thanks to visit codestin.com
Credit goes to docs.together.ai

Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.together.ai/llms.txt

Use this file to discover all available pages before exploring further.

Run leading open-source models on Together AI with a few lines of code. Follow the steps below to get started.

​
Step 1: Create an API key

Register for an account if you don’t have one. Then go to your project’s API keys page, select Create key, give it a name, and copy the value.
New keys are only shown once, so make sure to save it somewhere safe.
Next, export the key as an environment variable in your terminal: 
export TOGETHER_API_KEY="your_api_key"
The SDK reads TOGETHER_API_KEY automatically when you call Together(). Pass api_key= to the constructor to override it.

​
Step 2: Install the SDK

Together AI publishes official SDKs for Python and TypeScript. Install the SDK with your preferred package manager: 
uv init # optional
uv add together
You can also call the REST API directly from any language.

​
Step 3: Run your first query

The example below sends a chat completion request and prints the response: 
from together import Together

client = Together()  # reads TOGETHER_API_KEY from environment

response = client.chat.completions.create(
    model="openai/gpt-oss-20b",
    messages=[
        {
            "role": "user",
            "content": "What are the top 3 things to do in New York?",
        }
    ],
)

print(response.choices[0].message.content)
To stream tokens as they arrive, add stream=True (Python) or stream: true (TypeScript/cURL) and iterate over the response chunks: 
stream = client.chat.completions.create(
    model="openai/gpt-oss-20b",
    messages=[
        {
            "role": "user",
            "content": "What are the top 3 things to do in New York?",
        }
    ],
    stream=True,
)

for chunk in stream:
    if chunk.choices:
        print(chunk.choices[0].delta.content or "", end="", flush=True)
Congrats! You just made your first request to Together AI. The same client works for multi-turn conversations, function calling, structured outputs, image generation, and audio transcription.

​
Next steps

Choose a model

Browse the catalog of models for chat, coding, vision, and reasoning.

Dedicated endpoints

Reserve GPUs for steady traffic or fine-tuned models.

Fine-tune a model

Train a model on your own data with LoRA, DPO, or full fine-tuning.

GPU clusters

Run large-scale training and custom workloads on dedicated GPU clusters.