strands-agents 1.10.0

A model-driven approach to building AI agents in just a few lines of code

Strands Agents

A model-driven approach to building AI agents in just a few lines of code.

Documentation ◆ Samples ◆ Python SDK ◆ Tools ◆ Agent Builder ◆ MCP Server

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.

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(
  model_id="us.amazon.nova-pro-v1:0",
  temperature=0.3,
  streaming=True, # Enable/disable streaming
)
agent = Agent(model=bedrock_model)
agent("Tell me about Agentic AI")

# Google Gemini
gemini_model = GeminiModel(
  api_key="your_gemini_api_key",
  model_id="gemini-2.5-flash",
  params={"temperature": 0.7}
)
agent = Agent(model=gemini_model)
agent("Tell me about Agentic AI")

# Ollama
ollama_model = OllamaModel(
  host="http://localhost:11434",
  model_id="llama3"
)
agent = Agent(model=ollama_model)
agent("Tell me about Agentic AI")

# Llama API
llama_model = LlamaAPIModel(
    model_id="Llama-4-Maverick-17B-128E-Instruct-FP8",
)
agent = Agent(model=llama_model)
response = agent("Tell me about Agentic AI")

Built-in providers:

• Amazon Bedrock
• Anthropic
• Gemini
• Cohere
• LiteLLM
• llama.cpp
• LlamaAPI
• MistralAI
• Ollama
• OpenAI
• SageMaker
• Writer

Custom providers can be implemented using Custom Providers

Example tools

Strands offers an optional strands-agents-tools package with pre-built tools for quick experimentation:

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

It's also available on GitHub via strands-agents/tools.

Documentation

For detailed guidance & examples, explore our documentation:

• User Guide
• Quick Start Guide
• Agent Loop
• Examples
• API Reference
• Production & Deployment Guide

Contributing ❤️

We welcome contributions! See our Contributing Guide for details on:

• Reporting bugs & features
• Development setup
• Contributing via Pull Requests
• Code of Conduct
• Reporting of security issues

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Security

See CONTRIBUTING for more information.