Category: Open Source

Agentic AI systems are rapidly expanding beyond the digital world and into the physical, where AI agents perceive, reason, and act in real environments. As AI systems increasingly interact with the physical world through robotics, autonomous vehicles, and smart infrastructure, a fundamental question emerges: how do we build agents that leverage massive cloud compute for […]

AWS is excited to continue our support for Model Context Protocol (MCP) as it moves under the Linux Foundation.  This move enables us, our partners, and our customers to be more confident in the long-term success of the protocol which has become a standard  component of agentic architectures. By open sourcing MCP in 2024, Anthropic […]

Modern AI can write code, compose symphonies, and solve complex reasoning problems. So why is it still so hard to get them to reliably do what you want? Building reliable AI agents that consistently perform complex tasks remains challenging. While modern language models excel at reasoning and problem-solving, translating that capability into predictable workflows often […]

AWS is excited to announce the awslabs/ml-container-creator open source project to simplify the process of building and deploying custom machine learning models on Amazon SageMaker. Some customers face challenges when trying to leverage the bring-your-own-container (BYOC) paradigm for hosting their predictive models on Amazon SageMaker AI‘s managed serving infrastructure. There are myriad ways to deploy […]

We’re excited to share that the Swift AWS Lambda Runtime project has officially moved to the AWS Labs organization. You can now find it here: 👉 https://github.com/awslabs/swift-aws-lambda-runtime This move marks a new chapter for the project, while maintaining full continuity with its roots. A thank you to the Swift community The Swift AWS Lambda Runtime […]

Jupyter notebooks have become a popular tool for data scientists, researchers, educators and analysts who need to experiment with code, visualize data, and document their findings. Many users run Jupyter on their laptops. This creates limitations to collaborate with a distributed team because users cannot securely provide direct access to their local JupyterLab application over […]

Today we are introducing CLI Agent Orchestrator (CAO, pronounced “kay-oh”), an open source, multi-agent orchestration framework that transforms how developers work with AI-powered CLI tools such as Amazon Q CLI and Claude Code. While individual developer CLI tools excel at focused tasks with sophisticated reasoning and autonomous execution, complex enterprise development projects often require coordination […]

Until recently, building AI agents meant wrestling with complex orchestration frameworks. Developers wrote elaborate state machines, predefined workflows, and extensive error-handling code to guide language models through multi-step tasks. We needed to build elaborate decision trees to handle “what if the API call fails?” or “what if the user asks something unexpected?” Despite this effort, […]

At AWS, we design cloud services that give customers the freedom to choose technology that best suits their needs. Our commitment to interoperability with open standards and open source technologies is a key reason customers choose AWS. This is one of the reasons why we launched Amazon DocumentDB (with MongoDB compatibility) in 2019. Amazon DocumentDB […]

Welcome to Part 4 of our blog series on Open Protocols for Agent Interoperability where we will cover the Agent-to-Agent (A2A) protocol, AWS’ involvement with the Linux Foundation-based open standard, and our support of A2A in the Strands Agents SDK. Here is what we’ve covered so far: Part 1: How the Model Context Protocol (MCP) […]