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Basic Information

Strands Agents SDK for Python is a developer-focused library that provides a model-driven approach to building and running AI agents with minimal code. It is intended for creating everything from simple conversational assistants to complex autonomous and multi-agent workflows, and it scales from local development to production deployment. The SDK exposes a simple, customizable agent loop, Python-based tool integration via decorators, hot reloading of tools from a directory, and native support for the Model Context Protocol (MCP) to access many pre-built tools. It is model-agnostic and ships with built-in providers and adapters for multiple model hosts. The README includes quickstart installation instructions via pip and notes about provider configuration such as AWS credentials when using the default Bedrock provider.

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2779
App URL
https://github.com/strands-agents/sdk-python
Github Repository
https://github.com/strands-agents/sdk-python/blob/main/README.md

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App Details

Features
The project emphasizes a lightweight and flexible agent loop, model-agnostic provider support, and advanced capabilities such as multi-agent systems, autonomous agents, and streaming responses. It includes built-in MCP client support to list and use tools served by MCP servers, and offers example tools packaged separately for experimentation. Tool creation is simplified with a Python decorator interface and hot reloading from a tools directory for rapid iteration. Multiple model providers are supported out of the box, including Amazon Bedrock, Anthropic, LiteLLM, LlamaAPI, Ollama, OpenAI, and Writer, with an API for implementing custom providers. The README and docs provide guides for quickstart, agent loop concepts, examples, API reference, and production deployment.
Use Cases
For developers building AI-enabled applications, this SDK reduces boilerplate and accelerates prototyping by enabling agents to be created in a few lines of code and extended with Python tools. Hot reloading and decorator-based tools speed up local development and testing. Model-agnostic abstractions let teams switch or experiment with different model providers without extensive rewrites. MCP integration and the optional tools package let agents leverage a rich set of external capabilities without custom integration work. Documentation, examples, and deployment guidance help users move from experimentation to production while provider adapters and configuration notes address practical concerns like credentials and model access.

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