# AWS Plugin for Vision Agents
AWS (Bedrock) LLM integration for Vision Agents framework with support for both standard and realtime interactions.
## Installation
```bash
pip install vision-agents-plugins-aws
```
## Usage
### Standard LLM Usage
This example shows how to use qwen3 on bedrock for the LLM.
```python
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"),
tts=cartesia.TTS(),
stt=deepgram.STT(),
turn_detection=smart_turn.TurnDetection(buffer_duration=2.0, confidence_threshold=0.5),
)
```
The full example is available in example/aws_qwen_example.py
### Realtime Text/Image Usage
Nova sonic audio realtime STS is also supported:
```python
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(),
)
```
## Running the examples
Create a `.env` file, or cp .env.example to .env and fill in
```
STREAM_API_KEY=your_stream_api_key_here
STREAM_API_SECRET=your_stream_api_secret_here
AWS_BEARER_TOKEN_BEDROCK=
AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=
FAL_KEY=
CARTESIA_API_KEY=
DEEPGRAM_API_KEY=
```
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"description": "# AWS Plugin for Vision Agents\n\nAWS (Bedrock) LLM integration for Vision Agents framework with support for both standard and realtime interactions.\n\n## Installation\n\n```bash\npip install vision-agents-plugins-aws\n```\n\n## Usage\n\n### Standard LLM Usage\n\nThis example shows how to use qwen3 on bedrock for the LLM.\n\n```python\nagent = Agent(\n edge=getstream.Edge(),\n agent_user=User(name=\"Friendly AI\"),\n instructions=\"Be nice to the user\",\n llm=aws.LLM(model=\"qwen.qwen3-32b-v1:0\"),\n tts=cartesia.TTS(),\n stt=deepgram.STT(),\n turn_detection=smart_turn.TurnDetection(buffer_duration=2.0, confidence_threshold=0.5),\n)\n```\n\nThe full example is available in example/aws_qwen_example.py\n\n### Realtime Text/Image Usage\n\nNova sonic audio realtime STS is also supported:\n\n```python \nagent = Agent(\n edge=getstream.Edge(),\n agent_user=User(name=\"Story Teller AI\"),\n instructions=\"Tell a story suitable for a 7 year old about a dragon and a princess\",\n llm=aws.Realtime(),\n)\n```\n\n## Running the examples\n\nCreate a `.env` file, or cp .env.example to .env and fill in\n\n```\nSTREAM_API_KEY=your_stream_api_key_here\nSTREAM_API_SECRET=your_stream_api_secret_here\n\nAWS_BEARER_TOKEN_BEDROCK=\nAWS_ACCESS_KEY_ID=\nAWS_SECRET_ACCESS_KEY=\n\nFAL_KEY=\nCARTESIA_API_KEY=\nDEEPGRAM_API_KEY=\n```",
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