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(
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
- 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
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
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())
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:
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.
1 maintainer
Metadata
1 maintainer
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