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amazon bedrock agentcore samples

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Basic Information

This repository provides example code, notebooks, and tutorials demonstrating Amazon Bedrock AgentCore capabilities for deploying, operating, and integrating AI agents securely at scale. It is an educational and experimental collection that shows how AgentCore Runtime, Gateway, Memory, Identity, built-in Tools, and Observability combine to accelerate agent development and production readiness. The samples include interactive tutorials, end-to-end use cases, and framework integrations so developers can learn to convert APIs and services into MCP-compatible tools, manage agent memory and identity, run agents in a serverless runtime, and instrument agents for monitoring. The content is aimed at developers and architects seeking hands-on examples and patterns rather than turnkey production systems. The repository also documents prerequisites and environment setup for running the provided notebooks and examples.

Links

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App URL
https://github.com/awslabs/amazon-bedrock-agentcore-samples
Github Repository
https://github.com/awslabs/amazon-bedrock-agentcore-samples/blob/main/README.md

App Details

Features
A curated set of folders with notebook-based tutorials, end-to-end use cases, and integration examples. Demonstrations of AgentCore components: Runtime for secure serverless deployment, Gateway to expose APIs and services as MCP-compatible tools, Memory for managed agent memory, Identity for access management, and Observability for tracing and monitoring. Built-in tools include a Code Interpreter and a Browser Tool to extend agent capabilities. Integration examples show how to work with popular agentic frameworks such as Strands Agents, LangChain, and CrewAI. Includes practical setup instructions, requirement specifications (Python 3.10, Docker or Finch, Jupyter), and sample projects to illustrate component composition and deployment workflows.
Use Cases
The repository helps developers learn to design, build, and operate agentic applications by providing concrete, runnable examples that illustrate core AgentCore capabilities and patterns. It accelerates onboarding by showing how to deploy agents into a serverless runtime, expose services as agent tools via the Gateway, implement persistent and customizable memory, and manage identity and access. Observability examples demonstrate how to trace and debug agent workflows to maintain quality at scale. Integration samples reduce friction for teams using existing frameworks by showing interoperability with Strands Agents, LangChain, and CrewAI. The materials and scripts shorten experimentation time and provide reference implementations for adapting AgentCore features into real projects, while noting the examples are for learning and not production use.

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