sample agentic frameworks on aws

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This repository provides examples and reference architectures for building autonomous, agentic AI systems on AWS. It collects sample projects and notebooks that demonstrate how to integrate popular open source agentic frameworks and models with AWS services to create production-ready agent applications across different industry verticals. The README highlights layered concerns of the agent stack including foundational models, orchestration, memory, tools, observability and evaluation, and deployment patterns. Included examples span use cases such as agent memory for insurance, a serverless multi-agent A2A protocol, an AWS infrastructure security auditing crew, customer support automation using Amazon Bedrock with LangGraph and Mistral models, and vision question-answering with LlamaIndex. The repository also points to AWS blog posts and hands-on workshops that explain patterns and integrations and is published under the MIT-0 license with contribution guidance and security notes.

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Curated examples and reference architectures that show how to build and deploy agentic systems on AWS. Demonstrations cover multiple layers: foundational model integration, orchestration and multi-agent collaboration, memory patterns, tool and connector usage, observability and evals, and deployment best practices. Integrations and tools referenced include Amazon Bedrock, Amazon SageMaker AI, LangGraph, Langfuse, CrewAI, Mistral models, LlamaIndex, Model Context Protocol, and related notebooks and workshops. The repo collects domain examples such as insurance memory agents, serverless multi-agent implementations, security auditing crews, customer support agents, and vision QA agents. It links to AWS blog walkthroughs and workshops for deeper learning. The project includes contributing and security guidance and is distributed under the MIT-0 license.
Use Cases
The repository helps architects and developers accelerate building agentic AI solutions by providing concrete, AWS-aligned examples and patterns that can be adapted to real projects. Teams can study sample implementations to understand how to integrate LLMs and multimodal models with orchestration frameworks, design memory and tool interfaces, implement observability and evaluation workflows, and apply deployment patterns for production readiness. The included use cases and notebooks serve as starting points for domain adaptation, and the linked AWS blogs and workshops provide additional step-by-step guidance. Contribution and security guidance reduce onboarding friction for collaborators. Overall this repo reduces research overhead when adopting multi-agent and agent orchestration techniques on AWS.

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