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Strands Agents

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

GitHub commit activityGitHub open issuesLicensePyPI versionPython versions

Docs ◆ Samples ◆ Tools ◆ Agent Builder

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, Llama, Ollama, 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
fromstrandsimportAgentfromstrands_toolsimportcalculatoragent=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 3.7 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:

fromstrandsimportAgent, tool@tooldefword_count(text: str) ->int:
"""Count words in text. This docstring is used by the LLM to understand the tool's purpose. """returnlen(text.split())
agent=Agent(tools=[word_count])
response=agent("How many words are in this sentence?")

MCP Support

Seamlessly integrate Model Context Protocol (MCP) servers:

fromstrandsimportAgentfromstrands.tools.mcpimportMCPClientfrommcpimportstdio_client, StdioServerParametersaws_docs_client=MCPClient(
lambda: stdio_client(StdioServerParameters(command="uvx", args=["awslabs.aws-documentation-mcp-server@latest"]))
)
withaws_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:

fromstrandsimportAgentfromstrands.modelsimportBedrockModelfromstrands.models.ollamaimportOllamaModelfromstrands.models.llamaapiimportLlamaAPIModel# Bedrockbedrock_model=BedrockModel(
model_id="us.amazon.nova-pro-v1:0",
temperature=0.3,
)
agent=Agent(model=bedrock_model)
agent("Tell me about Agentic AI")
# Ollamaollama_model=OllamaModel(
host="http://localhost:11434",
model_id="llama3"
)
agent=Agent(model=ollama_model)
agent("Tell me about Agentic AI")
# Llama APIllama_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:

fromstrandsimportAgentfromstrands_toolsimportcalculatoragent=Agent(tools=[calculator])
agent("What is the square root of 1764")

It's also available on GitHub via strands-agents/tools.

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.

⚠️ Preview Status

Strands Agents is currently in public preview. During this period:

  • APIs may change as we refine the SDK
  • We welcome feedback and contributions

About

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

strandsagents.com

Resources

Readme
License

Contributing

Contributing
Activity

Stars

1 star

Watchers

0 watching

Forks

0 forks
Report repository

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var __re = new RegExp('^' + "github\\.com" + '
GitHub - brianloyal/sdk-python: A model-driven approach to building AI agents in just a few lines of code. · GitHub
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Strands Agents

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

GitHub commit activityGitHub open issuesLicensePyPI versionPython versions
Docs
◆ Samples
◆ Tools
◆ Agent Builder
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, Llama, Ollama, 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
fromstrandsimportAgentfromstrands_toolsimportcalculatoragent=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 3.7 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:
fromstrandsimportAgent, tool@tooldefword_count(text: str) ->int:
"""Count words in text. This docstring is used by the LLM to understand the tool's purpose. """returnlen(text.split())
agent=Agent(tools=[word_count])
response=agent("How many words are in this sentence?")
MCP Support
Seamlessly integrate Model Context Protocol (MCP) servers:
fromstrandsimportAgentfromstrands.tools.mcpimportMCPClientfrommcpimportstdio_client, StdioServerParametersaws_docs_client=MCPClient(
lambda: stdio_client(StdioServerParameters(command="uvx", args=["awslabs.aws-documentation-mcp-server@latest"]))
)
withaws_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:
fromstrandsimportAgentfromstrands.modelsimportBedrockModelfromstrands.models.ollamaimportOllamaModelfromstrands.models.llamaapiimportLlamaAPIModel# Bedrockbedrock_model=BedrockModel(
model_id="us.amazon.nova-pro-v1:0",
temperature=0.3,
)
agent=Agent(model=bedrock_model)
agent("Tell me about Agentic AI")
# Ollamaollama_model=OllamaModel(
host="http://localhost:11434",
model_id="llama3"
)
agent=Agent(model=ollama_model)
agent("Tell me about Agentic AI")
# Llama APIllama_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:
fromstrandsimportAgentfromstrands_toolsimportcalculatoragent=Agent(tools=[calculator])
agent("What is the square root of 1764")
It's also available on GitHub via strands-agents/tools.
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.
⚠️ Preview Status
Strands Agents is currently in public preview. During this period:
  • APIs may change as we refine the SDK
  • We welcome feedback and contributions
About
A model-driven approach to building AI agents in just a few lines of code.
strandsagents.com
Resources
Readme
License
Contributing
Contributing
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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GitHub - brianloyal/sdk-python: A model-driven approach to building AI agents in just a few lines of code. · GitHub
Skip to content
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Strands Agents

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

GitHub commit activityGitHub open issuesLicensePyPI versionPython versions
Docs
◆ Samples
◆ Tools
◆ Agent Builder
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, Llama, Ollama, 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
fromstrandsimportAgentfromstrands_toolsimportcalculatoragent=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 3.7 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:
fromstrandsimportAgent, tool@tooldefword_count(text: str) ->int:
"""Count words in text. This docstring is used by the LLM to understand the tool's purpose. """returnlen(text.split())
agent=Agent(tools=[word_count])
response=agent("How many words are in this sentence?")
MCP Support
Seamlessly integrate Model Context Protocol (MCP) servers:
fromstrandsimportAgentfromstrands.tools.mcpimportMCPClientfrommcpimportstdio_client, StdioServerParametersaws_docs_client=MCPClient(
lambda: stdio_client(StdioServerParameters(command="uvx", args=["awslabs.aws-documentation-mcp-server@latest"]))
)
withaws_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:
fromstrandsimportAgentfromstrands.modelsimportBedrockModelfromstrands.models.ollamaimportOllamaModelfromstrands.models.llamaapiimportLlamaAPIModel# Bedrockbedrock_model=BedrockModel(
model_id="us.amazon.nova-pro-v1:0",
temperature=0.3,
)
agent=Agent(model=bedrock_model)
agent("Tell me about Agentic AI")
# Ollamaollama_model=OllamaModel(
host="http://localhost:11434",
model_id="llama3"
)
agent=Agent(model=ollama_model)
agent("Tell me about Agentic AI")
# Llama APIllama_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:
fromstrandsimportAgentfromstrands_toolsimportcalculatoragent=Agent(tools=[calculator])
agent("What is the square root of 1764")
It's also available on GitHub via strands-agents/tools.
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.
⚠️ Preview Status
Strands Agents is currently in public preview. During this period:
  • APIs may change as we refine the SDK
  • We welcome feedback and contributions
About
A model-driven approach to building AI agents in just a few lines of code.
strandsagents.com
Resources
Readme
License
Contributing
Contributing
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - brianloyal/sdk-python: A model-driven approach to building AI agents in just a few lines of code. · GitHub
Skip to content
BranchesTags
Repository files navigation
Strands Agents

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

GitHub commit activityGitHub open issuesLicensePyPI versionPython versions
Docs
◆ Samples
◆ Tools
◆ Agent Builder
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, Llama, Ollama, 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
fromstrandsimportAgentfromstrands_toolsimportcalculatoragent=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 3.7 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:
fromstrandsimportAgent, tool@tooldefword_count(text: str) ->int:
"""Count words in text. This docstring is used by the LLM to understand the tool's purpose. """returnlen(text.split())
agent=Agent(tools=[word_count])
response=agent("How many words are in this sentence?")
MCP Support
Seamlessly integrate Model Context Protocol (MCP) servers:
fromstrandsimportAgentfromstrands.tools.mcpimportMCPClientfrommcpimportstdio_client, StdioServerParametersaws_docs_client=MCPClient(
lambda: stdio_client(StdioServerParameters(command="uvx", args=["awslabs.aws-documentation-mcp-server@latest"]))
)
withaws_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:
fromstrandsimportAgentfromstrands.modelsimportBedrockModelfromstrands.models.ollamaimportOllamaModelfromstrands.models.llamaapiimportLlamaAPIModel# Bedrockbedrock_model=BedrockModel(
model_id="us.amazon.nova-pro-v1:0",
temperature=0.3,
)
agent=Agent(model=bedrock_model)
agent("Tell me about Agentic AI")
# Ollamaollama_model=OllamaModel(
host="http://localhost:11434",
model_id="llama3"
)
agent=Agent(model=ollama_model)
agent("Tell me about Agentic AI")
# Llama APIllama_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:
fromstrandsimportAgentfromstrands_toolsimportcalculatoragent=Agent(tools=[calculator])
agent("What is the square root of 1764")
It's also available on GitHub via strands-agents/tools.
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.
⚠️ Preview Status
Strands Agents is currently in public preview. During this period:
  • APIs may change as we refine the SDK
  • We welcome feedback and contributions
About
A model-driven approach to building AI agents in just a few lines of code.
strandsagents.com
Resources
Readme
License
Contributing
Contributing
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - brianloyal/sdk-python: A model-driven approach to building AI agents in just a few lines of code. · GitHub
Skip to content
BranchesTags
Repository files navigation
Strands Agents

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

GitHub commit activityGitHub open issuesLicensePyPI versionPython versions
Docs
◆ Samples
◆ Tools
◆ Agent Builder
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, Llama, Ollama, 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
fromstrandsimportAgentfromstrands_toolsimportcalculatoragent=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 3.7 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:
fromstrandsimportAgent, tool@tooldefword_count(text: str) ->int:
"""Count words in text. This docstring is used by the LLM to understand the tool's purpose. """returnlen(text.split())
agent=Agent(tools=[word_count])
response=agent("How many words are in this sentence?")
MCP Support
Seamlessly integrate Model Context Protocol (MCP) servers:
fromstrandsimportAgentfromstrands.tools.mcpimportMCPClientfrommcpimportstdio_client, StdioServerParametersaws_docs_client=MCPClient(
lambda: stdio_client(StdioServerParameters(command="uvx", args=["awslabs.aws-documentation-mcp-server@latest"]))
)
withaws_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:
fromstrandsimportAgentfromstrands.modelsimportBedrockModelfromstrands.models.ollamaimportOllamaModelfromstrands.models.llamaapiimportLlamaAPIModel# Bedrockbedrock_model=BedrockModel(
model_id="us.amazon.nova-pro-v1:0",
temperature=0.3,
)
agent=Agent(model=bedrock_model)
agent("Tell me about Agentic AI")
# Ollamaollama_model=OllamaModel(
host="http://localhost:11434",
model_id="llama3"
)
agent=Agent(model=ollama_model)
agent("Tell me about Agentic AI")
# Llama APIllama_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:
fromstrandsimportAgentfromstrands_toolsimportcalculatoragent=Agent(tools=[calculator])
agent("What is the square root of 1764")
It's also available on GitHub via strands-agents/tools.
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.
⚠️ Preview Status
Strands Agents is currently in public preview. During this period:
  • APIs may change as we refine the SDK
  • We welcome feedback and contributions
About
A model-driven approach to building AI agents in just a few lines of code.
strandsagents.com
Resources
Readme
License
Contributing
Contributing
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
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GitHub - brianloyal/sdk-python: A model-driven approach to building AI agents in just a few lines of code. · GitHub
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Strands Agents

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

GitHub commit activityGitHub open issuesLicensePyPI versionPython versions
Docs
◆ Samples
◆ Tools
◆ Agent Builder
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, Llama, Ollama, 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
fromstrandsimportAgentfromstrands_toolsimportcalculatoragent=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 3.7 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:
fromstrandsimportAgent, tool@tooldefword_count(text: str) ->int:
"""Count words in text. This docstring is used by the LLM to understand the tool's purpose. """returnlen(text.split())
agent=Agent(tools=[word_count])
response=agent("How many words are in this sentence?")
MCP Support
Seamlessly integrate Model Context Protocol (MCP) servers:
fromstrandsimportAgentfromstrands.tools.mcpimportMCPClientfrommcpimportstdio_client, StdioServerParametersaws_docs_client=MCPClient(
lambda: stdio_client(StdioServerParameters(command="uvx", args=["awslabs.aws-documentation-mcp-server@latest"]))
)
withaws_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:
fromstrandsimportAgentfromstrands.modelsimportBedrockModelfromstrands.models.ollamaimportOllamaModelfromstrands.models.llamaapiimportLlamaAPIModel# Bedrockbedrock_model=BedrockModel(
model_id="us.amazon.nova-pro-v1:0",
temperature=0.3,
)
agent=Agent(model=bedrock_model)
agent("Tell me about Agentic AI")
# Ollamaollama_model=OllamaModel(
host="http://localhost:11434",
model_id="llama3"
)
agent=Agent(model=ollama_model)
agent("Tell me about Agentic AI")
# Llama APIllama_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:
fromstrandsimportAgentfromstrands_toolsimportcalculatoragent=Agent(tools=[calculator])
agent("What is the square root of 1764")
It's also available on GitHub via strands-agents/tools.
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.
⚠️ Preview Status
Strands Agents is currently in public preview. During this period:
  • APIs may change as we refine the SDK
  • We welcome feedback and contributions
About
A model-driven approach to building AI agents in just a few lines of code.
strandsagents.com
Resources
Readme
License
Contributing
Contributing
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - brianloyal/sdk-python: A model-driven approach to building AI agents in just a few lines of code. · GitHub
Skip to content
BranchesTags
Repository files navigation
Strands Agents

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

GitHub commit activityGitHub open issuesLicensePyPI versionPython versions
Docs
◆ Samples
◆ Tools
◆ Agent Builder
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, Llama, Ollama, 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
fromstrandsimportAgentfromstrands_toolsimportcalculatoragent=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 3.7 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:
fromstrandsimportAgent, tool@tooldefword_count(text: str) ->int:
"""Count words in text. This docstring is used by the LLM to understand the tool's purpose. """returnlen(text.split())
agent=Agent(tools=[word_count])
response=agent("How many words are in this sentence?")
MCP Support
Seamlessly integrate Model Context Protocol (MCP) servers:
fromstrandsimportAgentfromstrands.tools.mcpimportMCPClientfrommcpimportstdio_client, StdioServerParametersaws_docs_client=MCPClient(
lambda: stdio_client(StdioServerParameters(command="uvx", args=["awslabs.aws-documentation-mcp-server@latest"]))
)
withaws_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:
fromstrandsimportAgentfromstrands.modelsimportBedrockModelfromstrands.models.ollamaimportOllamaModelfromstrands.models.llamaapiimportLlamaAPIModel# Bedrockbedrock_model=BedrockModel(
model_id="us.amazon.nova-pro-v1:0",
temperature=0.3,
)
agent=Agent(model=bedrock_model)
agent("Tell me about Agentic AI")
# Ollamaollama_model=OllamaModel(
host="http://localhost:11434",
model_id="llama3"
)
agent=Agent(model=ollama_model)
agent("Tell me about Agentic AI")
# Llama APIllama_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:
fromstrandsimportAgentfromstrands_toolsimportcalculatoragent=Agent(tools=[calculator])
agent("What is the square root of 1764")
It's also available on GitHub via strands-agents/tools.
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.
⚠️ Preview Status
Strands Agents is currently in public preview. During this period:
  • APIs may change as we refine the SDK
  • We welcome feedback and contributions
About
A model-driven approach to building AI agents in just a few lines of code.
strandsagents.com
Resources
Readme
License
Contributing
Contributing
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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var enabled = true;
function applyDarkMode() {
if (!enabled) return;
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if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
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filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
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/* Restore images, videos, iframes, canvas */
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filter: invert(1) hue-rotate(180deg) !important;
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[data-theme="light"], [data-theme="light"],
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})();
})();
GitHub - brianloyal/sdk-python: A model-driven approach to building AI agents in just a few lines of code. · GitHub
Skip to content
BranchesTags
Repository files navigation
Strands Agents

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

GitHub commit activityGitHub open issuesLicensePyPI versionPython versions
Docs
◆ Samples
◆ Tools
◆ Agent Builder
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, Llama, Ollama, 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
fromstrandsimportAgentfromstrands_toolsimportcalculatoragent=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 3.7 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:
fromstrandsimportAgent, tool@tooldefword_count(text: str) ->int:
"""Count words in text. This docstring is used by the LLM to understand the tool's purpose. """returnlen(text.split())
agent=Agent(tools=[word_count])
response=agent("How many words are in this sentence?")
MCP Support
Seamlessly integrate Model Context Protocol (MCP) servers:
fromstrandsimportAgentfromstrands.tools.mcpimportMCPClientfrommcpimportstdio_client, StdioServerParametersaws_docs_client=MCPClient(
lambda: stdio_client(StdioServerParameters(command="uvx", args=["awslabs.aws-documentation-mcp-server@latest"]))
)
withaws_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:
fromstrandsimportAgentfromstrands.modelsimportBedrockModelfromstrands.models.ollamaimportOllamaModelfromstrands.models.llamaapiimportLlamaAPIModel# Bedrockbedrock_model=BedrockModel(
model_id="us.amazon.nova-pro-v1:0",
temperature=0.3,
)
agent=Agent(model=bedrock_model)
agent("Tell me about Agentic AI")
# Ollamaollama_model=OllamaModel(
host="http://localhost:11434",
model_id="llama3"
)
agent=Agent(model=ollama_model)
agent("Tell me about Agentic AI")
# Llama APIllama_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:
fromstrandsimportAgentfromstrands_toolsimportcalculatoragent=Agent(tools=[calculator])
agent("What is the square root of 1764")
It's also available on GitHub via strands-agents/tools.
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.
⚠️ Preview Status
Strands Agents is currently in public preview. During this period:
  • APIs may change as we refine the SDK
  • We welcome feedback and contributions
About
A model-driven approach to building AI agents in just a few lines of code.
strandsagents.com
Resources
Readme
License
Contributing
Contributing
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages