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Get started with Deepgram LLMs

Send your first request to a Deepgram LLM and read the response.

Early Access. Deepgram LLMs are available only to current Deepgram contract customers enrolled in the Early Access program (talk to sales for access). Usage is free during Early Access, requests run on the global endpoint only, SDKs do not support the endpoint yet, and the API can change before general availability. Do not run production traffic on it.

This guide sends a single request to Think-Fast and prints the reply.

Before you begin

  • A current Deepgram contract with a project enrolled in Early Access. If you don’t have one, talk to sales.
  • A Deepgram API key from that project. To create one, see Creating Additional API Keys.
  • The key exported as an environment variable:
export DEEPGRAM_API_KEY="your-api-key"

No SDK support during Early Access. The Deepgram SDKs do not include /v1/think yet; SDK support is planned for general availability. The examples below call the endpoint with plain HTTP clients: requests for Python and the built-in fetch for JavaScript.

Send a request

Send a system message and a user message to /v1/think. Authenticate with the Token scheme.

import os
import requests
# Send the conversation to /v1/think, authenticating with your API key.
response = requests.post(
    "https://agent.deepgram.com/v1/think",
    headers={"Authorization": f"Token {os.environ['DEEPGRAM_API_KEY']}"},
    json={
        "model": "think-fast",
        # The full conversation: a system prompt and the caller's first message.
        "messages": [
            {
                "role": "system",
                "content": "You are a friendly voice agent. Reply in one or two short sentences.",
            },
            {"role": "user", "content": "Hi, can you check on my order?"},
        ],
    },
)
# Raise on a 4xx or 5xx, then print the assistant's reply.
response.raise_for_status()
print(response.json()["choices"][0]["message"]["content"])

Read the response

The endpoint returns a chat.completion object. The reply is in choices[0].message.content, and token counts are in usage.

{
  "id": "chatcmpl-…",
  "object": "chat.completion",
  "created": 1792786932,
  "model": "think-fast",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Sure! Could you please provide your order number…",
        "refusal": null
      },
      "finish_reason": "stop",
      "logprobs": null
    }
  ],
  "usage": {
    "prompt_tokens": 32,
    "completion_tokens": 19,
    "total_tokens": 51,
    "prompt_tokens_details": { "cached_tokens": 0 },
    "completion_tokens_details": { "reasoning_tokens": 0 }
  }
}

Keep the conversation going

The endpoint is stateless. To continue the conversation, append the assistant’s reply and the next user message to messages, then send the full list again.

Next steps