Amazon Bedrock and Deepgram Voice Agent

This guide walks you through two methods to use Amazon Bedrock with Deepgram Voice Agent:

1. Native Integration (Recommended) - Use the built-in aws_bedrock provider type
2. Proxy Server - Route requests through a proxy server for advanced use cases

Before you Begin

Method 1: Native Integration (Recommended)

Prerequisites

• An Amazon Bedrock service account with appropriate permissions
• AWS credentials (Access Key ID and Secret Access Key)
• Access to desired Bedrock models in your AWS account

Configuration

Configure your Voice Agent with the aws_bedrock provider type. You can use either IAM credentials or STS (temporary) credentials:

Using IAM Credentials

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{
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  "agent": {
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    "think": {
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      "provider": {
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        "type": "aws_bedrock",
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        "model": "us.anthropic.claude-3-5-sonnet-20241022-v2:0",
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        "temperature": 0.7,
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        "credentials": {
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          "type": "iam",
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          "region": "us-east-2",
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          "access_key_id": "{{your_access_key_id}}",
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          "secret_access_key": "{{your_secret_access_key}}"
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        }
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      },
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      "endpoint": {
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        "url": "https://bedrock-runtime.us-east-2.amazonaws.com/"
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      }
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    }
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  }
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}

Using STS (Temporary) Credentials

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{
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  "agent": {
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    "think": {
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      "provider": {
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        "type": "aws_bedrock",
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        "model": "us.anthropic.claude-3-5-sonnet-20241022-v2:0",
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        "temperature": 0.7,
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        "credentials": {
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          "type": "sts",
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          "region": "us-east-2",
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          "access_key_id": "{{your_temporary_access_key_id}}",
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          "secret_access_key": "{{your_temporary_secret_access_key}}",
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          "session_token": "{{your_session_token}}"
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        }
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      },
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      "endpoint": {
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        "url": "https://bedrock-runtime.us-east-2.amazonaws.com/"
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      }
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    }
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  }
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}

Method 2: Proxy Server Integration

For advanced use cases or if you need additional processing between Deepgram and Bedrock, you can use a proxy server.

Prerequisites

You will need:

• An understanding of Python and using Python virtual environments.
• An Amazon Bedrock service account
• A Deepgram Voice Agent. Here’s our guide on building the voice agent.
• ngrok to allow access to a local server OR your own hosted server

Architecture Overview (Proxy Method)

How it works:

• The proxy logs and forwards agent.think payloads to Bedrock
• Bedrock handles LLM logic and returns structured responses
• Deepgram converts the response into speech back to the user

Set Up the Proxy

Clone the proxy repo

$
git clone https://github.com/deepgram-devs/deepgram-voice-agent-client-llm-proxy.git
>
cd deepgram-voice-agent-client-llm-proxy
>
python3 -m venv venv
>
source venv/bin/activate
>
pip install -r requirements.txt

Configure the environment

$
cp .env.example .env

Specifying Bedrock provider details

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AGENT_ID=your_bedrock_agent_id
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AGENT_ALIAS_ID=your_bedrock_agent_alias_id
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AWS_ACCESS_KEY_ID=your_aws_access_key_id
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AWS_SECRET_ACCESS_KEY=your_aws_secret_access_key
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AWS_REGION=us-east-1

Start the server

$
python app.py

Using ngrok

Be sure to set the port correctly to 5000 by running:

$
ngrok http 5000

Configure Deepgram Voice Agent (Proxy Method)

In your Deepgram Voice Agent settings, update the provider, model, and endpoint URL for agent.think. See more examples of configuration here.

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"agent": {
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  "think": {
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    "provider": {
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      "type": "open_ai",
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      "model": "gpt-4o-mini",
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      "temperature": 0.7
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    },
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    "endpoint": {
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      "url": "{{host}}/v1/chat/completions",
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      "headers": {
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        "authorization": "Bearer {{token}}"
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      }
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    }
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  }
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}

Test the Integration

1. Launch the proxy and ngrok
2. Deploy your Deepgram Voice Agent with the updated config
3. Start a call or session
4. Observe agent.think payloads and Bedrock responses in proxy logs
5. Confirm LLM responses originate from Bedrock (e.g., function calls reflected)