AWS Strands Agents SDK

<div align="center"> <div> <a href="https://strandsagents.com"> <img src="https://strandsagents.com/latest/assets/logo-github.svg" alt="Strands Agents" width="55px" height="105px"> </a> </div> <h1> Strands Agents </h1> <h2> A model-driven approach to building AI agents in just a few lines of code. </h2> <div align="center"> </div> <p> <a href="https://strandsagents.com/">Documentation</a> ◆ <a href="https://github.com/strands-agents/samples">Samples</a> ◆ <a href="https://github.com/strands-agents/tools">Tools</a> ◆ <a href="https://github.com/strands-agents/agent-builder">Agent Builder</a> ◆ <a href="https://github.com/strands-agents/mcp-server">MCP Server</a> </p> </div>

Strands Agents is a simple yet powerful SDK that takes a model-driven approach to building and running AI agents. From simple conversational assistants to complex autonomous workflows, from local development to production deployment, Strands Agents scales with your needs.

This monorepo contains the Python SDK, TypeScript SDK, documentation site, and supporting packages:

Directory	Description
strands-py/	Python SDK — agent loop, model providers, tools (PyPI)
strands-wasm/	WebAssembly bindings for running Python tools from TypeScript agents
strands-py-wasm/	Python host for WASM components (bridges WIT interfaces to Python)
strandly/	Developer CLI for local builds, codegen, and workspace tooling
site/	Documentation site built with Astro/Starlight (strandsagents.com)
designs/	Design proposals for significant features (RFC-style)

Feature Overview

• Lightweight & Flexible: Simple agent loop that just works and is fully customizable
• Model Agnostic: Support for Amazon Bedrock, Anthropic, Gemini, LiteLLM, Llama, Ollama, OpenAI, Writer, and custom providers
• Advanced Capabilities: Multi-agent systems, autonomous agents, and streaming support
• Built-in MCP: Native support for Model Context Protocol (MCP) servers, enabling access to thousands of pre-built tools

Quick Start

# Install Strands Agents
pip install strands-agents strands-agents-tools

from strands import Agent
from strands_tools import calculator
agent = Agent(tools=[calculator])
agent("What is the square root of 1764")

Note: For the default Amazon Bedrock model provider, you'll need AWS credentials configured and model access enabled for Claude 4 Sonnet in the us-west-2 region. See the Quickstart Guide for details on configuring other model providers.

Installation

Ensure you have Python 3.10+ installed, then:

# Create and activate virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows use: .venv\Scripts\activate

# Install Strands and tools
pip install strands-agents strands-agents-tools

Features at a Glance

Python-Based Tools

Easily build tools using Python decorators:

from strands import Agent, tool

@tool
def word_count(text: str) -> int:
    """Count words in text.

    This docstring is used by the LLM to understand the tool's purpose.
    """
    return len(text.split())

agent = Agent(tools=[word_count])
response = agent("How many words are in this sentence?")

Hot Reloading from Directory: Enable automatic tool loading and reloading from the ./tools/ directory:

from strands import Agent

# Agent will watch ./tools/ directory for changes
agent = Agent(load_tools_from_directory=True)
response = agent("Use any tools you find in the tools directory")

MCP Support

Seamlessly integrate Model Context Protocol (MCP) servers:

from strands import Agent
from strands.tools.mcp import MCPClient
from mcp import stdio_client, StdioServerParameters

aws_docs_client = MCPClient(
    lambda: stdio_client(StdioServerParameters(command="uvx", args=["awslabs.aws-documentation-mcp-server@latest"]))
)

with aws_docs_client:
   agent = Agent(tools=aws_docs_client.list_tools_sync())
   response = agent("Tell me about Amazon Bedrock and how to use it with Python")

Multiple Model Providers

Support for various model providers:

from strands import Agent
from strands.models import BedrockModel
from strands.models.ollama import OllamaModel
from strands.models.llamaapi import LlamaAPIModel
from strands.models.gemini import GeminiModel
from strands.models.llamacpp import LlamaCppModel

# Bedrock
bedrock_model = BedrockModel(