Strands Agents - Python SDK
A model-driven approach to building AI agents in just a few lines of code.
Documentation ◆ Samples ◆ Tools ◆ 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
Connect to 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")
The SDK works with both major versions of the mcp package through a built-in compatibility layer, so most code that uses MCPClient runs unchanged on either version. A fresh install resolves to mcp 2.x, and pinning mcp<2 keeps you on 1.x. See docs/MCP_VERSIONS.md for support status, the behavior differences on 2.x, and migration notes.
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
Build voice agents that talk with users in real time. A BidiAgent holds a persistent connection to a speech model, streams audio both ways, runs tools mid-conversation, and stops speaking when the user interrupts.
Install the extra for your provider. Local audio also needs the PortAudio system library:
# Amazon Bedrock Nova Sonic (Python 3.12+)
pip install "strands-agents[bidi,bidi-io,bidi-pyaudio]"
# Google Gemini Live
pip install "strands-agents[bidi-google,bidi-io,bidi-pyaudio]"
# OpenAI Realtime API
pip install "strands-agents[bidi-openai,bidi-io,bidi-pyaudio]"
This agent listens on your microphone, answers through your speakers, and prints transcripts to the terminal:
import asyncio
from strands.bidi.agent import BidiAgent
from strands.bidi.io import AudioIO
from strands.bidi.models import BedrockNovaSonicModel
async def main():
model = BedrockNovaSonicModel(model_id="amazon.nova-2-5-sonic")
agent = BidiAgent(model=model)
audio_io = AudioIO()
await agent.run(inputs=[audio_io.input()], outputs=[audio_io.output()])
if __name__ == "__main__":
asyncio.run(main())
run() keeps the conversation open until you press Ctrl+C or a tool calls agent.cancel(). See the bidirectional streaming quickstart for model configuration, custom I/O, and more.
Documentation
For detailed guidance & examples, explore our documentation:
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.
1 maintainer
Metadata
1 maintainer
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