# sdk-python **Repository Path**: yanggan2021/sdk-python ## Basic Information - **Project Name**: sdk-python - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-12-02 - **Last Updated**: 2025-12-02 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README
Strands Agents

Strands Agents

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

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DocumentationSamplesPython SDKToolsAgent BuilderMCP 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). ## 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/) - [Examples](https://strandsagents.com/latest/examples/) ## Local examples shipped with this repository The `examples/` folder in this repository contains small, runnable examples for quick experimentation. - `examples/siliconflow_example.py` — Demonstrates calling Silicon Flow's OpenAI-compatible endpoint using `OpenAIModel` (set `SILICONFLOW_API_KEY`). - `examples/ollama_local_example.py` — Demonstrates calling a local Ollama instance using `OllamaModel` (default host is http://localhost:11434). See `examples/README.md` for full instructions on installing dependencies and running the 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.