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3P Agentic Frameworks

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Basic Information

This repository provides examples and reference architectures for building autonomous, agentic AI systems on AWS. It is intended to show how to combine popular open source agent frameworks with AWS services to create production-ready agent applications across industry verticals. The content demonstrates multiple layers of the agent stack including foundational models, orchestration, memory, tools, observability and evals, and deployment patterns. The repository aggregates sample projects and notebooks that cover use cases such as agent memory for insurance, serverless multi-agent systems implementing A2A protocols, infrastructure security auditing crews, customer support automation with Amazon Bedrock and LangGraph, multi-agent collaboration examples, LLM routing, and Vision QA solutions using LlamaIndex. The README also points to related AWS blog posts and hands-on workshops to help practitioners learn and adopt these patterns.

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App URL
https://github.com/aws-samples/3P-Agentic-Frameworks
Github Repository
https://github.com/aws-samples/3P-Agentic-Frameworks/blob/main/README.md

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App Details

Features
Collection of curated examples and reference architectures that illustrate end-to-end agentic solutions on AWS. Multiple sample projects and notebooks demonstrate specific capabilities such as agent memory, multi-agent orchestration, serverless A2A protocols, security auditor crews, customer support agents built with Amazon Bedrock, LangGraph and Mistral models, LLM routing patterns, and vision question-answering with LlamaIndex. Coverage of agent stack concerns including foundational models, orchestration, memory, tool integration, observability and evaluation. Links to AWS blog articles and hands-on workshops that expand on design patterns and deployment. Guidance for contributions, a license (MIT-0), and support pointers like issues, wiki and documentation. Emphasis on production-ready considerations and deployment patterns for AWS services.
Use Cases
This repository helps architects, ML engineers and developers learn how to assemble and deploy agentic AI systems on AWS by providing concrete, working examples and reference designs. Users can study domain-specific samples such as insurance memory agents, customer support automation, and security auditing crews to understand design trade-offs and integration points. The included notebooks and examples show how to combine orchestration frameworks, model routing and memory components with AWS services and models like Amazon Bedrock, LangGraph integrations, Mistral, LlamaIndex and SageMaker AI. Workshops and AWS blog references support hands-on learning for observability, evals and production patterns. The repo reduces time-to-prototype and offers patterns for scaling, testing and evaluating multi-agent solutions while providing contribution and issue channels for community support.

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