Strands Agents

Strands Agents SDK

Build AI agents using the model-driven Strands Agents SDK. Agents consist of three components: a model, tools, and a prompt.

Installation

pip install strands-agents strands-agents-tools --break-system-packages

Basic Agent

from strands import Agent

agent = Agent(system_prompt="You are a helpful assistant.")
response = agent("Hello, how can you help me?")
print(response)

Custom Tools

Use @tool decorator to convert Python functions into agent tools:

from strands import Agent, tool

@tool
def calculate_area(length: float, width: float) -> float:
    """Calculate rectangle area.
    
    Args:
        length: Rectangle length
        width: Rectangle width
    
    Returns:
        Area of the rectangle
    """
    return length * width

agent = Agent(tools=[calculate_area])
agent("What is the area of a 5x3 rectangle?")

Key requirements for tools:

• Clear docstring (used by LLM to understand when/how to use the tool)
• Type hints for all parameters and return value
• Descriptive parameter names

Class-Based Tools

For tools that share state or resources:

from strands import Agent, tool

class DatabaseTools:
    def __init__(self, connection_string: str):
        self.conn = self._connect(connection_string)
    
    def _connect(self, conn_str):
        return {"connected": True, "db": conn_str}
    
    @tool
    def query(self, sql: str) -> dict:
        """Execute SQL query.
        
        Args:
            sql: SQL query to execute
        """
        return {"results": f"Query: {sql}", "connection": self.conn}

db = DatabaseTools("postgres://...")
agent = Agent(tools=[db.query])

Model Providers

Default is Amazon Bedrock with Claude. Configure alternatives:

from strands import Agent
from strands.models import BedrockModel
from strands.models.ollama import OllamaModel
from strands.models.anthropic import AnthropicModel

# Amazon Bedrock (default)
agent = Agent(model=BedrockModel(
    model_id="us.anthropic.claude-sonnet-4-20250514-v1:0",
    region_name="us-west-2"
))

# Anthropic API (set ANTHROPIC_API_KEY env var)
agent = Agent(model=AnthropicModel(
    client_args={"api_key": "<KEY>"},
    model_id="claude-sonnet-4-20250514",
    max_tokens=1028
))

# Ollama (local)
agent = Agent(model=OllamaModel(
    host="http://localhost:11434",
    model_id="llama3.1"
))

MCP Integration

Connect to Model Context Protocol servers for external tools:

from strands import Agent
from strands.tools.mcp import MCPClient

# Stdio transport
mcp = MCPClient(transport="stdio", command="npx", args=["-y", "@modelcontextprotocol/server-filesystem"])

agent = Agent(tools=[mcp])
agent("List files in the current directory")

Multi-Agent Patterns

Agents as Tools

Wrap specialized agents as tools for an orchestrator:

from strands import Agent, tool

@tool
def research_assistant(query: str) -> str:
    """Research factual information.
    
    Args:
        query: Research question
    """
    researcher = Agent(system_prompt="You are a research expert.")
    return str(researcher(query))

@tool  
def code_assistant(task: str) -> str:
    """Write and explain code.
    
    Args:
        task: Coding task description
    """
    coder = Agent(system_prompt="You are a coding expert.")
    return str(coder(task))

orchestrator = Agent(
    system_prompt="Route tasks to the appropriate specialist.",
    tools=[research_assistant, code_assistant]
)

Swarm

Autonomous agent collaboration with shared context:

from strands import Agent
from strands.multiagent import Swarm

researcher = Agent(name="researcher", system_prompt="You research topics thoroughly.")
analyst = Agent(name="analyst", system_prompt="You analyze data and findings.")
writer = Agent(name="writer", system_prompt="You write clear reports.")

swarm = Swarm([researcher, analyst, writer])
result = swarm("Research AI trends and write a summary report")

Swarms enable emergent intelligence through:

• Shared working memory
• Autonomous handoffs between agents
• Dynamic task delegation

Graph

Deterministic workflows with defined execution order:

from strands import Agent
from strands.multiagent import GraphBuilder

researcher = Agent(name="researcher", system_prompt="Research the topic.")
reviewer = Agent(name="reviewer", system_prompt="Review and fact-check.")
writer = Agent(name="writer", system_prompt="Write the final output.")

builder = GraphBuilder()
builder.add_node(researcher, "research")
builder.add_node(reviewer, "review") 
builder.add_node(writer, "write")
builder.add_edge("research", "review")
builder.add_edge("review", "write")
builder.set_entry_point("research")

graph = builder.build()
result = graph("Write a report on quantum computing")

Conditional Edges

def needs_revision(state):
    return "needs revision" in state.results.get("review", {}).get("output", "").lower()

builder.add_edge("review", "research", condition=needs_revision)  # Loop back
builder.add_edge("review", "write", condition=lambda s: not needs_revision(s))

Streaming

Callback Handler

from strands import Agent
from strands.handlers import PrintingCallbackHandler

agent = Agent(callback_handler=PrintingCallbackHandler())
agent("Tell me a story")

Async Iterator

import asyncio
from strands import Agent

async def stream_response():
    agent = Agent()
    async for event in agent.stream_async("Tell me a story"):
        if hasattr(event, 'data'):
            print(event.data, end="", flush=True)

asyncio.run(stream_response())

Session Management

Persist conversations across sessions:

from strands import Agent
from strands.session import FileSessionManager

session_mgr = FileSessionManager(session_id="user-123", base_dir="./sessions")
agent = Agent(session_manager=session_mgr)

agent("Remember my name is Alice")
# Later session...
agent("What's my name?")  # Recalls "Alice"

Built-in Tools

The strands-agents-tools package provides:

• calculator - Math operations
• current_time - Get current time
• http_request - Make HTTP requests
• file_read, file_write, editor - File operations
• python_repl - Execute Python code
• shell - Run shell commands
• memory - Store/retrieve information
• retrieve - RAG retrieval
• mcp_client - Dynamic MCP server connections

from strands import Agent
from strands_tools import calculator, current_time, file_read

agent = Agent(tools=[calculator, current_time, file_read])

Pattern Selection Guide

References

For detailed API documentation and advanced patterns, see:

• references/model-providers.md - Complete model provider configurations
• references/multi-agent-patterns.md - Advanced multi-agent architectures