> ## Documentation Index
> Fetch the complete documentation index at: https://assemblyai.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Generate Action Items with LLM Gateway

This tutorial will demonstrate how to use AssemblyAI's [LLM Gateway](/llm-gateway) framework to create action items from a transcript.

## Quickstart

<Tabs groupId="language">
  <Tab language="python" title="Python" default>
    ```python expandable theme={null}
    import requests
    import time

    API_KEY = "YOUR_API_KEY"
    audio_url = "https://storage.googleapis.com/aai-web-samples/meeting.mp4"

    # Step 1: Upload or provide audio URL and start transcription
    transcript_request = requests.post(
        "https://api.assemblyai.com/v2/transcript",
        headers={"authorization": API_KEY, "content-type": "application/json"},
        json={"audio_url": audio_url},
    )

    transcript_id = transcript_request.json()["id"]

    # Step 2: Poll until transcription completes
    while True:
        polling_response = requests.get(
            f"https://api.assemblyai.com/v2/transcript/{transcript_id}",
            headers={"authorization": API_KEY},
        )
        status = polling_response.json()["status"]

        if status == "completed":
            break
        elif status == "error":
            raise RuntimeError(f"Transcription failed: {polling_response.json()['error']}")
        else:
            print(f"Transcription status: {status}")
            time.sleep(3)

    # Step 3: Build the prompt
    prompt = """
    Here are guidelines to follow:
    - You are an expert at understanding transcripts of conversations, calls and meetings.
    - You are an expert at coming up with ideal action items based on the contents of the transcripts.
    - Action items are things that the transcript implies should get done.
    - Your action item ideas do not make stuff up that isn't relevant to the transcript.
    - You do not needlessly make up action items - you stick to important tasks.
    - You are useful, true and concise, and write in perfect English.
    - Your action items can be tied back to direct quotes in the transcript.
    - You do not cite the quotes the action items relate to.
    - The action items are written succinctly.
    - Please give useful action items based on the transcript.
    """

    answer_format = "Bullet Points"
    if answer_format:
        prompt += f"\nYour response should have the following format: {answer_format}"

    # Step 4: Send transcript ID to LLM Gateway
    headers = {"authorization": API_KEY}

    response = requests.post(
        "https://llm-gateway.assemblyai.com/v1/chat/completions",
        headers=headers,
        json={
            "model": "claude-sonnet-4-6",
            "messages": [
                {
                    "role": "user",
                    "content": f"{prompt}\n\n{{{{ transcript }}}}",
                }
            ],
            "transcript_id": transcript_id,
            "max_tokens": 1000,
        },
    )

    # Step 5: Print the LLM-generated action items
    response_json = response.json()
    print(response_json["choices"][0]["message"]["content"])
    ```
  </Tab>

  <Tab language="javascript" title="JavaScript">
    ```javascript expandable theme={null}
    const API_KEY = "YOUR_API_KEY";
    const headers = { authorization: API_KEY };
    const audioUrl = "https://storage.googleapis.com/aai-web-samples/meeting.mp4";

    // Step 1: Upload or provide audio URL and start transcription
    let res = await fetch("https://api.assemblyai.com/v2/transcript", {
      method: "POST",
      headers: { ...headers, "Content-Type": "application/json" },
      body: JSON.stringify({ audio_url: audioUrl }),
    });
    if (!res.ok) throw new Error(`Error: ${res.status}`);
    const transcriptRequest = await res.json();

    const transcriptId = transcriptRequest.id;

    // Step 2: Poll until transcription completes
    while (true) {
      res = await fetch(`https://api.assemblyai.com/v2/transcript/${transcriptId}`, { headers });
      if (!res.ok) throw new Error(`Error: ${res.status}`);
      const pollingResponse = await res.json();
      const status = pollingResponse.status;

      if (status === "completed") {
        break;
      } else if (status === "error") {
        throw new Error(`Transcription failed: ${pollingResponse.error}`);
      } else {
        console.log(`Transcription status: ${status}`);
        await new Promise((resolve) => setTimeout(resolve, 3000));
      }
    }

    // Step 3: Build the prompt
    let prompt = `
    Here are guidelines to follow:
    - You are an expert at understanding transcripts of conversations, calls and meetings.
    - You are an expert at coming up with ideal action items based on the contents of the transcripts.
    - Action items are things that the transcript implies should get done.
    - Your action item ideas do not make stuff up that isn't relevant to the transcript.
    - You do not needlessly make up action items - you stick to important tasks.
    - You are useful, true and concise, and write in perfect English.
    - Your action items can be tied back to direct quotes in the transcript.
    - You do not cite the quotes the action items relate to.
    - The action items are written succinctly.
    - Please give useful action items based on the transcript.
    `;

    const answerFormat = "Bullet Points";
    if (answerFormat) {
      prompt += `\nYour response should have the following format: ${answerFormat}`;
    }

    // Step 4: Send transcript ID to LLM Gateway
    res = await fetch("https://llm-gateway.assemblyai.com/v1/chat/completions", {
      method: "POST",
      headers: { ...headers, "Content-Type": "application/json" },
      body: JSON.stringify({
        model: "claude-sonnet-4-6",
        messages: [
          {
            role: "user",
            content: `${prompt}\n\n{{ transcript }}`,
          },
        ],
        transcript_id: transcriptId,
        max_tokens: 1000,
      }),
    });
    if (!res.ok) throw new Error(`Error: ${res.status}`);
    const response = await res.json();

    // Step 5: Print the LLM-generated action items
    console.log(response.choices[0].message.content);
    ```
  </Tab>
</Tabs>

## Getting Started

Before we begin, make sure you have an AssemblyAI account and an API key. You can [sign up for an AssemblyAI account](https://www.assemblyai.com/dashboard/home) and get your API key from your dashboard.

Find more details on the current LLM Gateway pricing in the AssemblyAI [pricing page](https://www.assemblyai.com/pricing).

## Step-by-Step Instructions

In this guide, we will prompt LLM Gateway to generate action items from our transcript.

Install the required packages:

<Tabs groupId="language">
  <Tab language="python" title="Python" default>
    ```bash theme={null}
    pip install requests
    ```
  </Tab>
</Tabs>

Import the necessary libraries and set AssemblyAI API key.

<Tabs groupId="language">
  <Tab language="python" title="Python" default>
    ```python theme={null}
    import requests
    import time

    API_KEY = "YOUR_API_KEY"
    ```
  </Tab>

  <Tab language="javascript" title="JavaScript">
    ```javascript theme={null}
    const API_KEY = "YOUR_API_KEY";
    const headers = { authorization: API_KEY };
    ```
  </Tab>
</Tabs>

Use AssemblyAI to transcribe a file and save the transcript.

<Tabs groupId="language">
  <Tab language="python" title="Python" default>
    ```python expandable theme={null}
    # Step 1: Upload or provide audio URL and start transcription
    audio_url = "https://storage.googleapis.com/aai-web-samples/meeting.mp4"

    transcript_request = requests.post(
        "https://api.assemblyai.com/v2/transcript",
        headers={"authorization": API_KEY, "content-type": "application/json"},
        json={"audio_url": audio_url},
    )

    transcript_id = transcript_request.json()["id"]

    # Step 2: Poll until transcription completes
    while True:
        polling_response = requests.get(
            f"https://api.assemblyai.com/v2/transcript/{transcript_id}",
            headers={"authorization": API_KEY},
        )
        status = polling_response.json()["status"]

        if status == "completed":
            break
        elif status == "error":
            raise RuntimeError(f"Transcription failed: {polling_response.json()['error']}")
        else:
            print(f"Transcription status: {status}")
            time.sleep(3)
    ```
  </Tab>

  <Tab language="javascript" title="JavaScript">
    ```javascript expandable theme={null}
    // Step 1: Upload or provide audio URL and start transcription
    const audioUrl = "https://storage.googleapis.com/aai-web-samples/meeting.mp4";

    let res = await fetch("https://api.assemblyai.com/v2/transcript", {
      method: "POST",
      headers: { ...headers, "Content-Type": "application/json" },
      body: JSON.stringify({ audio_url: audioUrl }),
    });
    if (!res.ok) throw new Error(`Error: ${res.status}`);
    const transcriptRequest = await res.json();

    const transcriptId = transcriptRequest.id;

    // Step 2: Poll until transcription completes
    while (true) {
      res = await fetch(`https://api.assemblyai.com/v2/transcript/${transcriptId}`, { headers });
      if (!res.ok) throw new Error(`Error: ${res.status}`);
      const pollingResponse = await res.json();
      const status = pollingResponse.status;

      if (status === "completed") {
        break;
      } else if (status === "error") {
        throw new Error(`Transcription failed: ${pollingResponse.error}`);
      } else {
        console.log(`Transcription status: ${status}`);
        await new Promise((resolve) => setTimeout(resolve, 3000));
      }
    }
    ```
  </Tab>
</Tabs>

Provide detailed instructions to prompt LLM Gateway to create action items from the transcript.

<Tabs groupId="language">
  <Tab language="python" title="Python" default>
    ```python theme={null}
    prompt = f"""
            Here are guidelines to follow:
            - You are an expert at understanding transcripts of conversations, calls and meetings.
            - You are an expert at coming up with ideal action items based on the contents of the transcripts.
            - Action items are things that the transcript implies should get done.
            - Your action item ideas do not make stuff up that isn't relevant to the transcript.
            - You do not needlessly make up action items - you stick to important tasks.
            - You are useful, true and concise, and write in perfect English.
            - Your action items can be tied back to direct quotes in the transcript.
            - You do not cite the quotes the action items relate to.
            - The action items are written succinctly.
            - Please give useful action items based on the transcript.
            - Your response should be formatted in bullet points.
            """
    ```
  </Tab>

  <Tab language="javascript" title="JavaScript">
    ```javascript theme={null}
    let prompt = `
            Here are guidelines to follow:
            - You are an expert at understanding transcripts of conversations, calls and meetings.
            - You are an expert at coming up with ideal action items based on the contents of the transcripts.
            - Action items are things that the transcript implies should get done.
            - Your action item ideas do not make stuff up that isn't relevant to the transcript.
            - You do not needlessly make up action items - you stick to important tasks.
            - You are useful, true and concise, and write in perfect English.
            - Your action items can be tied back to direct quotes in the transcript.
            - You do not cite the quotes the action items relate to.
            - The action items are written succinctly.
            - Please give useful action items based on the transcript.
            - Your response should be formatted in bullet points.
            `;
    ```
  </Tab>
</Tabs>

Generate the custom action items using LLM Gateway.

<Tabs groupId="language">
  <Tab language="python" title="Python" default>
    ```python theme={null}
    answer_format = "Bullet Points"
    if answer_format:
        prompt += f"\nYour response should have the following format: {answer_format}"
    ```
  </Tab>

  <Tab language="javascript" title="JavaScript">
    ```javascript theme={null}
    const answerFormat = "Bullet Points";
    if (answerFormat) {
      prompt += `\nYour response should have the following format: ${answerFormat}`;
    }
    ```
  </Tab>
</Tabs>

Prompt LLM Gateway using the transcript results and return the response.

<Tabs groupId="language">
  <Tab language="python" title="Python" default>
    ```python expandable theme={null}
    # Step 4: Send transcript ID to LLM Gateway
    headers = {"authorization": API_KEY}

    response = requests.post(
        "https://llm-gateway.assemblyai.com/v1/chat/completions",
        headers=headers,
        json={
            "model": "claude-sonnet-4-6",
            "messages": [
                {
                    "role": "user",
                    "content": f"{prompt}\n\n{{{{ transcript }}}}",
                }
            ],
            "transcript_id": transcript_id,
            "max_tokens": 1000,
        },
    )

    # Step 5: Print the LLM-generated action items
    response_json = response.json()
    print(response_json["choices"][0]["message"]["content"])

    ```
  </Tab>

  <Tab language="javascript" title="JavaScript">
    ```javascript expandable theme={null}
    // Step 4: Send transcript ID to LLM Gateway
    let res = await fetch("https://llm-gateway.assemblyai.com/v1/chat/completions", {
      method: "POST",
      headers: { ...headers, "Content-Type": "application/json" },
      body: JSON.stringify({
        model: "claude-sonnet-4-6",
        messages: [
          {
            role: "user",
            content: `${prompt}\n\n{{ transcript }}`,
          },
        ],
        transcript_id: transcriptId,
        max_tokens: 1000,
      }),
    });
    if (!res.ok) throw new Error(`Error: ${res.status}`);
    const response = await res.json();

    // Step 5: Print the LLM-generated action items
    console.log(response.choices[0].message.content);
    ```
  </Tab>
</Tabs>