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https://github.com/strands-agents/sdk-python

A model-driven approach to building AI agents in just a few lines of code.
https://github.com/strands-agents/sdk-python

agentic agentic-ai agents ai anthropic autonomous-agents bedrock genai litellm llama llm machine-learning mcp multi-agent-systems ollama openai opentelemetry python strands-agents

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

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README 

          


  


    

      Strands Agents

    

  



  


    Strands Agents

  



  


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

  



  


    GitHub commit activity

    GitHub open issues

    GitHub open pull requests

    License

    PyPI version

    Python versions

  


  

  


    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



```bash

# Install Strands Agents

pip install strands-agents strands-agents-tools

```



```python

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](https://strandsagents.com/) for details on configuring other model providers.



## Installation



Ensure you have Python 3.10+ installed, then:



```bash

# 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:



```python

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:



```python

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:



```python

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:



```python

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](https://strandsagents.com/latest/user-guide/concepts/model-providers/amazon-bedrock/)

 - [Anthropic](https://strandsagents.com/latest/user-guide/concepts/model-providers/anthropic/)

 - [Gemini](https://strandsagents.com/latest/user-guide/concepts/model-providers/gemini/)

 - [Cohere](https://strandsagents.com/latest/user-guide/concepts/model-providers/cohere/)

 - [LiteLLM](https://strandsagents.com/latest/user-guide/concepts/model-providers/litellm/)

 - [llama.cpp](https://strandsagents.com/latest/user-guide/concepts/model-providers/llamacpp/)

 - [LlamaAPI](https://strandsagents.com/latest/user-guide/concepts/model-providers/llamaapi/)

 - [MistralAI](https://strandsagents.com/latest/user-guide/concepts/model-providers/mistral/)

 - [Ollama](https://strandsagents.com/latest/user-guide/concepts/model-providers/ollama/)

 - [OpenAI](https://strandsagents.com/latest/user-guide/concepts/model-providers/openai/)

 - [SageMaker](https://strandsagents.com/latest/user-guide/concepts/model-providers/sagemaker/)

 - [Writer](https://strandsagents.com/latest/user-guide/concepts/model-providers/writer/)



Custom providers can be implemented using [Custom Providers](https://strandsagents.com/latest/user-guide/concepts/model-providers/custom_model_provider/)



### Example tools



Strands offers an optional strands-agents-tools package with pre-built tools for quick experimentation:



```python

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](https://github.com/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](https://strandsagents.com/latest/documentation/docs/user-guide/concepts/experimental/bidirectional-streaming/quickstart) guide. 



**Supported Model Providers:**

- Amazon Nova Sonic (v1, v2)

- Google Gemini Live

- OpenAI Realtime API



**Quick Example:**



```python

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:**



```python

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](https://strandsagents.com/)

- [Quick Start Guide](https://strandsagents.com/latest/user-guide/quickstart/)

- [Agent Loop](https://strandsagents.com/latest/user-guide/concepts/agents/agent-loop/)

- [Examples](https://strandsagents.com/latest/examples/)

- [API Reference](https://strandsagents.com/latest/api-reference/agent/)

- [Production & Deployment Guide](https://strandsagents.com/latest/user-guide/deploy/operating-agents-in-production/)



## Contributing ❤️



We welcome contributions! See our [Contributing Guide](CONTRIBUTING.md) 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](LICENSE) file for details.



## Security



See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information.