vision-agents-plugins-aws 0.4.2

AWS (Bedrock) LLM integration for Vision Agents

AWS Plugin for Vision Agents

AWS (Bedrock) integration for Vision Agents framework with support for standard LLM, realtime with Nova Sonic, and text-to-speech with automatic session resumption.

Installation

uv add "vision-agents[aws]"
# or directly
uv add vision-agents-plugins-aws

Usage

Standard LLM Usage

The AWS plugin supports various Bedrock models including Qwen, Claude, and others. Claude models also support vision/image inputs.

from vision_agents.core import Agent, User
from vision_agents.plugins import aws, getstream, cartesia, deepgram, smart_turn

agent = Agent(
    edge=getstream.Edge(),
    agent_user=User(name="Friendly AI"),
    instructions="Be nice to the user",
    llm=aws.LLM(
        model="qwen.qwen3-32b-v1:0",
        region_name="us-east-1"
    ),
    tts=cartesia.TTS(),
    stt=deepgram.STT(),
    turn_detection=smart_turn.TurnDetection(buffer_duration=2.0, confidence_threshold=0.5),
)

For vision-capable models like Claude:

llm = aws.LLM(
    model="anthropic.claude-3-haiku-20240307-v1:0",
    region_name="us-east-1"
)

# Send image with text
response = await llm.converse(
    messages=[{
        "role": "user",
        "content": [
            {"image": {"format": "png", "source": {"bytes": image_bytes}}},
            {"text": "What do you see in this image?"}
        ]
    }]
)

Realtime Audio Usage

AWS Nova 2 Sonic provides realtime speech-to-speech capabilities with automatic reconnection logic. The default model is amazon.nova-2-sonic-v1:0.

from vision_agents.core import Agent, User
from vision_agents.plugins import aws, getstream

agent = Agent(
    edge=getstream.Edge(),
    agent_user=User(name="Story Teller AI"),
    instructions="Tell a story suitable for a 7 year old about a dragon and a princess",
    llm=aws.Realtime(
        model="amazon.nova-2-sonic-v1:0",
        region_name="us-east-1",
        voice_id="matthew"  # See available voices in AWS Nova documentation
    ),
)

The Realtime implementation includes automatic reconnection logic that reconnects after periods of silence or when approaching connection time limits.

See example/aws_realtime_nova_example.py for a complete example.

Text-to-Speech (TTS)

AWS Polly TTS is available for converting text to speech:

from vision_agents.plugins import aws

tts = aws.TTS(
    region_name="us-east-1",
    voice_id="Joanna",  # AWS Polly voice ID
    engine="neural",  # 'standard' or 'neural'
    text_type="text",  # 'text' or 'ssml'
    language_code="en-US"
)

# Use in agent
agent = Agent(
    llm=aws.LLM(model="qwen.qwen3-32b-v1:0"),
    tts=tts,
    # ... other components
)

Function Calling

Standard LLM (aws.LLM)

The standard LLM implementation fully supports function calling. Register functions using the @llm.register_function decorator:

from vision_agents.plugins import aws

llm = aws.LLM(
    model="qwen.qwen3-32b-v1:0",
    region_name="us-east-1"
)


@llm.register_function(
    name="get_weather",
    description="Get the current weather for a given city"
)
async def get_weather(city: str) -> dict:
    """Get weather information for a city."""
    return {
        "city": city,
        "temperature": 72,
        "condition": "Sunny"
    }

Realtime (aws.Realtime)

The Realtime implementation fully supports function calling with AWS Nova 2 Sonic. Register functions using the @llm.register_function decorator:

from vision_agents.plugins import aws

llm = aws.Realtime(
    model="amazon.nova-2-sonic-v1:0",
    region_name="us-east-1",
    voice_id="matthew"
)


@llm.register_function(
    name="get_weather",
    description="Get the current weather for a given city"
)
async def get_weather(city: str) -> dict:
    """Get weather information for a city."""
    return {
        "city": city,
        "temperature": 72,
        "condition": "Sunny"
    }

# The function will be automatically called when the model decides to use it

See example/aws_realtime_function_calling_example.py for a complete example.

Configuration

Environment Variables

Create a .env file with the following variables:

STREAM_API_KEY=your_stream_api_key_here
STREAM_API_SECRET=your_stream_api_secret_here

AWS_BEDROCK_API_KEY=
AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=
AWS_REGION=us-east-1

CARTESIA_API_KEY=
DEEPGRAM_API_KEY=

Make sure your .env file is configured before running the examples.