Using Writer with AWS Strands Agents

pip install 'strands-agents[writer]'

from strands import Agent
from strands.models.writer import WriterModel
from strands_tools import calculator

model = WriterModel(
    client_args={"api_key": "<WRITER_API_KEY>"},
    model_id="palmyra-x5",
)

agent = Agent(model=model, tools=[calculator])
response = agent("What is 2+2")
print(response)

model = WriterModel(
    client_args={
        "api_key": "<WRITER_API_KEY>",
        "timeout": 30,
        "base_url": "https://api.writer.com/v1",
        # Additional client configuration options
    },
    model_id="palmyra-x5"
)

export WRITER_API_KEY="your_api_key_here"

model = WriterModel(model_id="palmyra-x5")

from strands import Agent
from strands.models.writer import WriterModel
from my_tools import web_search, email_sender  # Custom tools from your local module

model = WriterModel(
    client_args={"api_key": "<WRITER_API_KEY>"},
    model_id="palmyra-x5",
)

agent = Agent(
    model=model,
    tools=[web_search, email_sender],
    system_prompt="You are an enterprise assistant that helps automate business workflows."
)

response = agent("Research our competitor's latest product launch and draft a summary email for the leadership team")

from strands import Agent
from strands.models.writer import WriterModel

# Use specialized finance model for financial analysis
model = WriterModel(
    client_args={"api_key": "<WRITER_API_KEY>"},
    model_id="palmyra-fin"
)

agent = Agent(
    model=model,
    system_prompt="You are a financial analyst assistant. Provide accurate, data-driven analysis."
)

# Replace the placeholder with your actual financial report content
actual_report = """
[Your quarterly earnings report content would go here - this could include:
- Revenue figures
- Profit margins
- Growth metrics
- Risk factors
- Market analysis
- Any other financial data you want analyzed]
"""

response = agent(f"Analyze the key financial risks in this quarterly earnings report: {actual_report}")

from strands import Agent
from strands.models.writer import WriterModel

# Use Palmyra X5 for processing very long documents
model = WriterModel(
    client_args={"api_key": "<WRITER_API_KEY>"},
    model_id="palmyra-x5",
    temperature=0.2
)

agent = Agent(
    model=model,
    system_prompt="You are a document analysis assistant that can process and summarize lengthy documents."
)

# Can handle documents up to 1M tokens
# Replace the placeholder with your actual document content
actual_transcripts = """
[Meeting transcript content would go here - this could be thousands of lines of text
from meeting recordings, documents, or other long-form content that you want to analyze]
"""

response = agent(f"Summarize the key decisions and action items from these meeting transcripts: {actual_transcripts}")

from strands import Agent
from strands.models.writer import WriterModel
from pydantic import BaseModel
from typing import List

# Define a structured schema for creative content
class MarketingCampaign(BaseModel):
    campaign_name: str
    target_audience: str
    key_messages: List[str]
    call_to_action: str
    tone: str
    estimated_engagement: float

# Use Palmyra X5 for creative marketing content
model = WriterModel(
    client_args={"api_key": "<WRITER_API_KEY>"},
    model_id="palmyra-x5",
    temperature=0.8  # Higher temperature for creative output
)

agent = Agent(
    model=model,
    system_prompt="You are a creative marketing strategist. Generate innovative marketing campaigns with structured data."
)

# Generate structured marketing campaign
response = agent.structured_output(
    output_model=MarketingCampaign,
    prompt="Create a marketing campaign for a new eco-friendly water bottle targeting young professionals aged 25-35."
)

print(f"Campaign Name: {response.campaign_name}\nTarget Audience: {response.target_audience}\nKey Messages: {response.key_messages}\nCall to Action: {response.call_to_action}\nTone: {response.tone}\nEstimated Engagement: {response.estimated_engagement}")

from strands import Agent
from strands.models.writer import WriterModel

# Use Palmyra X5 for vision tasks
model = WriterModel(
    client_args={"api_key": "<WRITER_API_KEY>"},
    model_id="palmyra-x5"
)

agent = Agent(
    model=model,
    system_prompt="You are a visual analysis assistant. Provide detailed, accurate descriptions of images and extract relevant information."
)

# Read the image file
with open("path/to/image.png", "rb") as image_file:
    image_data = image_file.read()

messages = [
    {
        "role": "user",
        "content": [
            {
                "image": {
                    "format": "png",
                    "source": {
                        "bytes": image_data
                    }
                }
            },
            {
                "text": "Analyze this image and describe what you see. What are the key elements, colors, and any text or objects visible?"
            }
        ]
    }
]

# Create an agent with the image message
vision_agent = Agent(model=model, messages=messages)

# Analyze the image
response = vision_agent("What are the main features of this image and what might it be used for?")

print(response)