Strands Agents – An Open-source python SDK for building agents

Strands Agents – An Open-source python SDK for building agents

According to Gartner, over a third of all enterprise apps will be powered by Agentic AI by 2028.  This evolution isn’t a roadmap of the future. It’s already happening today.

There are many ways of building agents on the AWS platform. The key proposition is to meet the customer, wherever they are in their agentic AI journey, whether through out-of-the-box agents, custom development or options to build DIY agents, or a combination of these.

·     You can use out-of-the-box Specialized Agents with Amazon Q – customers who are looking to immediately deploy agentic experiences with minimal technical overhead, Amazon Q Business and Amazon Q Developer allows you to immediately test and deploy agentic AI or further customize to meet specific needs of your business.

·   Or build your Agentic AI application with Gaurdrails and fully managed for you using Amazon Bedrock. Fully Managed agents that you can build, that integrates with your systems and data, and tools, giving you the flexibility to test different foundation models in a secure managed environment, with a comprehensive toolset to build, deploy, operate, maintain, and scale trusted, high-performing AI agents in Amazon Bedrock.

·      Or DIY agents by providing a model-driven approach to building AI agents in just a few lines of code, using Strands Agents.

 

What are Strands Agents?

Strands Agents is an Open-source python SDK for building agents using just a few lines of code. It takes a model-driven approach and uses the automated reasoning capabilities of models to build agents. It allows the agent to perform complex, multistep reasoning and actions, and is built for developers by developers, and open-sourced by AWS.  

·     Strands simplify agent development by embracing the capabilities of models to plan, chain thoughts, call tools, and reflect. Like the two strands of DNA, Strands connects two core pieces of the agent together: the model and the tools.

·       Get started quickly: With Strands, developers can simply define a prompt and a list of tools in code to build an agent, then test it locally and deploy it to the cloud.

·     Model driven approach: Strands plans the agent's next steps and executes tools using the advanced reasoning capabilities of models.

·     Highly flexible: For more complex agent use cases, developers can customize their agent's behavior in Strands.

·       Model Agnostic: Strands can run anywhere and can support any model with reasoning and tool use capabilities, including models in Amazon Bedrock, Anthropic, Ollama, and other providers through LiteLLM.

·   Deploy anywhere: Deploy and run agents in any environment where you run Python applications and deploy on ECS, Lambda, and EC2.

·       Built-in MCP: Native support for Model Context Protocol (MCP) servers, enabling access to thousands of pre-built tools. Strands also provides a natively a number of pre-built tools, examples: image_reader to process and analyze images,  use_aws to interact with AWS services and  http_request to make API calls, fetch web data, and call local HTTP servers.

Core Working Principle: At the heart of Strands' capabilities lies the agentic loop, a continuous cycle where an agent interacts with its model and tools to accomplish a task prompted by the user. This loop leverages the remarkable advancements of LLMs), which can now reason, plan, and select tools with native proficiency.

In each iteration of the loop, Strands engages the LLM with the user's prompt, agent context, and a description of the available tools. The LLM can respond in various ways, including in natural language for the end user, outlining a series of steps, reflecting on previous actions, or selecting one or more tools to utilize. When the LLM chooses a tool, Strands seamlessly executes it and returns the result to the LLM. Once the task is complete, Strands delivers the agent's final outcome.

 

Join the Strands Agents community

Strands Agents is an open-source project licensed under the Apache License 2.0. Contributions are welcome to the project, where developers can add support for additional models and tools, they can collaborate on new features or expand the documentation. If they find a bug,  or have a suggestion, or have something to contribute, they can join the project on GitHub.