Strands Agents SDK

Build model-agnostic AI agents with the Strands framework.

Installation pip install strands-agents strands-agents-tools

Or with npm

npm install @strands-agents/sdk

Quick Start from strands import Agent from strands.tools import tool

@tool def get_weather(city: str) -> str: """Get current weather for a city.""" # Implementation return f"Weather in {city}: 72°F, Sunny"

agent = Agent( model="anthropic.claude-3-sonnet", tools=[get_weather] )

response = agent("What's the weather in Seattle?") print(response)

TypeScript/JavaScript import { Agent, tool } from '@strands-agents/sdk';

const getWeather = tool({ name: 'get_weather', description: 'Get current weather for a city', parameters: { city: { type: 'string', description: 'City name' } }, handler: async ({ city }) => { return Weather in ${city}: 72°F, Sunny; } });

const agent = new Agent({ model: 'anthropic.claude-3-sonnet', tools: [getWeather] });

const response = await agent.run('What\'s the weather in Seattle?');

Model Agnostic

Strands works with any LLM:

from strands import Agent

Anthropic (default)

agent = Agent(model="anthropic.claude-3-sonnet")

OpenAI

agent = Agent(model="openai.gpt-4o")

Amazon Bedrock

agent = Agent(model="amazon.titan-text-premier")

Custom endpoint

agent = Agent( model="custom", endpoint="https://your-model-endpoint.com", api_key="..." )

Tool Definition Patterns Decorator Style from strands.tools import tool

@tool def search_database(query: str, limit: int = 10) -> list[dict]: """Search the product database.

Args:
    query: Search query string
    limit: Maximum results to return
"""
# Implementation
return results

Class Style from strands.tools import Tool

class DatabaseSearchTool(Tool): name = "search_database" description = "Search the product database"

def parameters(self):
    return {
        "query": {"type": "string", "description": "Search query"},
        "limit": {"type": "integer", "default": 10}
    }

def run(self, query: str, limit: int = 10):
    return self.db.search(query, limit)

ReAct Pattern

Built-in ReAct (Reasoning + Acting) support:

from strands import Agent, ReActStrategy

agent = Agent( model="anthropic.claude-3-sonnet", tools=[search_tool, calculate_tool], strategy=ReActStrategy( max_iterations=10, verbose=True ) )

Agent will reason through complex multi-step tasks

response = agent(""" Find the top 3 products in our database, calculate their average price, and recommend if we should adjust pricing. """)

Multi-Agent Systems from strands import Agent, MultiAgentOrchestrator

Specialist agents

researcher = Agent( name="researcher", model="anthropic.claude-3-sonnet", tools=[web_search, document_reader], system_prompt="You are a research specialist." )

analyst = Agent( name="analyst", model="anthropic.claude-3-sonnet", tools=[data_analyzer, chart_generator], system_prompt="You are a data analyst." )

writer = Agent( name="writer", model="anthropic.claude-3-sonnet", tools=[document_writer], system_prompt="You are a technical writer." )

Orchestrator

orchestrator = MultiAgentOrchestrator( agents=[researcher, analyst, writer], routing="supervisor" # or "round_robin", "intent" )

response = orchestrator.run( "Research AI trends, analyze the data, and write a report" )

Streaming Responses from strands import Agent

agent = Agent(model="anthropic.claude-3-sonnet")

Stream response

for chunk in agent.stream("Explain quantum computing"): print(chunk, end="", flush=True)

Memory Management from strands import Agent from strands.memory import ConversationMemory, SemanticMemory

agent = Agent( model="anthropic.claude-3-sonnet", memory=[ ConversationMemory(max_turns=10), SemanticMemory(embedding_model="text-embedding-3-small") ] )

Memory persists across calls

agent("My name is Alice") agent("What's my name?") # Remembers: "Your name is Alice"

AgentCore Integration

Use Strands with AWS Bedrock AgentCore:

from strands import Agent from strands.tools import tool import boto3

agentcore_client = boto3.client('bedrock-agentcore')

@tool def query_cloudwatch(metric_name: str, namespace: str) -> dict: """Query CloudWatch metrics via AgentCore Gateway.""" return agentcore_client.invoke_tool( tool_name="cloudwatch_query", parameters={"metric": metric_name, "namespace": namespace} )

agent = Agent( model="anthropic.claude-3-sonnet", tools=[query_cloudwatch] )

Official Use Cases

Strands is featured in AWS AgentCore samples:

A2A Multi-Agent Incident Response: Uses Strands for monitoring agent

cd amazon-bedrock-agentcore-samples/02-use-cases/A2A-multi-agent-incident-response

Monitoring agent uses Strands SDK for CloudWatch, logs, metrics

Resources Official Samples: https://github.com/awslabs/amazon-bedrock-agentcore-samples A2A Use Case: https://github.com/awslabs/amazon-bedrock-agentcore-samples/tree/main/02-use-cases/A2A-multi-agent-incident-response Integrations: https://github.com/awslabs/amazon-bedrock-agentcore-samples/tree/main/03-integrations

aws-strands

安装