Strands Agents - Python SDK

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

Documentation ◆ Samples ◆ 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(
  client_args={
    "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
• OpenAI Responses API
• 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.

Bidirectional Streaming

⚠️ Experimental Feature: Bidirectional streaming is currently in experimental status. APIs may change in future releases as we refine the feature based on user feedback and evolving model capabilities.

Build real-time voice and audio conversations with persistent streaming connections. Unlike traditional request-response patterns, bidirectional streaming maintains long-running conversations where users can interrupt, provide continuous input, and receive real-time audio responses. Get started with your first BidiAgent by following the Quickstart guide.

Supported Model Providers:

• Amazon Nova Sonic (v1, v2)
• Google Gemini Live
• OpenAI Realtime API

Installation:

# Server-side only (no audio I/O dependencies)
pip install strands-agents[bidi]

# With audio I/O support (includes PyAudio dependency)
pip install strands-agents[bidi,bidi-io]

Quick Example:

import asyncio
from strands.experimental.bidi import BidiAgent
from strands.experimental.bidi.models import BidiNovaSonicModel
from strands.experimental.bidi.io import BidiAudioIO, BidiTextIO
from strands.experimental.bidi.tools import stop_conversation
from strands_tools import calculator

async def main():
    # Create bidirectional agent with Nova Sonic v2
    model = BidiNovaSonicModel()
    agent = BidiAgent(model=model, tools=[calculator, stop_conversation])

    # Setup audio and text I/O (requires bidi-io extra)
    audio_io = BidiAudioIO()
    text_io = BidiTextIO()

    # Run with real-time audio streaming
    # Say "stop conversation" to gracefully end the conversation
    await agent.run(
        inputs=[audio_io.input()],
        outputs=[audio_io.output(), text_io.output()]
    )

if __name__ == "__main__":
    asyncio.run(main())

Note: BidiAudioIO and BidiTextIO require the bidi-io extra. For server-side deployments where audio I/O is handled by clients (browsers, mobile apps), install only strands-agents[bidi] and implement custom input/output handlers using the BidiInput and BidiOutput protocols.

Configuration Options:

from strands.experimental.bidi.models import BidiNovaSonicModel

# Configure audio settings and turn detection (v2 only)
model = BidiNovaSonicModel(
    provider_config={
        "audio": {
            "input_rate": 16000,
            "output_rate": 16000,
            "voice": "matthew"
        },
        "turn_detection": {
            "endpointingSensitivity": "MEDIUM"  # HIGH, MEDIUM, or LOW
        },
        "inference": {
            "max_tokens": 2048,
            "temperature": 0.7
        }
    }
)

# Configure I/O devices
audio_io = BidiAudioIO(
    input_device_index=0,  # Specific microphone
    output_device_index=1,  # Specific speaker
    input_buffer_size=10,
    output_buffer_size=10
)

# Text input mode (type messages instead of speaking)
text_io = BidiTextIO()
await agent.run(
    inputs=[text_io.input()],  # Use text input
    outputs=[audio_io.output(), text_io.output()]
)

# Multi-modal: Both audio and text input
await agent.run(
    inputs=[audio_io.input(), text_io.input()],  # Speak OR type
    outputs=[audio_io.output(), text_io.output()]
)

Documentation

For detailed guidance & examples, explore our documentation:

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

Development

pip install hatch
hatch test        # run unit tests
hatch fmt         # format & lint

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

Stay in touch with the team

Come meet the Strands team and other users on Discord

License

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

Security

See CONTRIBUTING for more information.