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, LiteLLM, Llama, Ollama, OpenAI, 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 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:
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?")
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
# 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")
# 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.
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.
⚠️ 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
Metadata
Release files for strands-agents 0.3.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| strands_agents-0.3.0.tar.gz | 196.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| strands_agents-0.3.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 337.7 kB
Release files / strands_agents-0.3.0.tar.gz
| Download URL | strands_agents-0.3.0.tar.gz |
|---|---|
| Size | 196.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
77574aec23b9d366c18b45fd830a5c8d8c470738fcb5ef8cf0110024d832c402
|
|
BLAKE2b-256 checksum How to use checksums |
84d0bdb73ffab397a287dcc703e2ebd020979c43580f96e91d3c712a380cbae1
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jul 11, 2025.
Transparency logRelease files / strands_agents-0.3.0-py3-none-any.whl
| Download URL | strands_agents-0.3.0-py3-none-any.whl |
|---|---|
| Size | 141.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
c2dcceb5f27151694d7ace52d822b4d793aa26e87331aff71abbc1ddcb99f4aa
|
|
BLAKE2b-256 checksum How to use checksums |
6a3b1673c7401baa7130b6b1fc4a78f133f0a15b219e85d8684bf70ffe1013c0
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jul 11, 2025.
Transparency log