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

Docs ◆ Samples ◆ Tools ◆ Agent Builder

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.

• Lightweight & Flexible: Simple agent loop that just works and is fully customizable
• Model Agnostic: Support for Amazon Bedrock, Anthropic, Ollama, 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

# 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 3.7 Sonnet in the us-west-2 region. See the Quickstart Guide for details on configuring other model providers.

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

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?")

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")

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 # Bedrock bedrock_model = BedrockModel( model_id="us.amazon.nova-pro-v1:0", temperature=0.3, ) agent = Agent(model=bedrock_model) agent("Tell me about Agentic AI") # Ollama ollama_modal = OllamaModel( host="http://localhost:11434", model_id="llama3" ) agent = Agent(model=ollama_modal) 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
• LiteLLM
• LlamaAPI
• Ollama

Custom providers can be implemented using Custom Providers

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.

For detailed guidance & examples, explore our documentation:

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

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

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

Strands Agents is currently in public preview. During this period:

• APIs may change as we refine the SDK
• We welcome feedback and contributions