sdk-python

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

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Strands Agents is a lightweight and flexible SDK that takes a model-driven approach to building and running AI agents. It supports various model providers, offers advanced capabilities like multi-agent systems and streaming support, and comes with built-in MCP server support. Users can easily create tools using Python decorators, integrate MCP servers seamlessly, and leverage multiple model providers for different AI tasks. The SDK is designed to scale from simple conversational assistants to complex autonomous workflows, making it suitable for a wide range of AI development needs.

README:

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:

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.

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