A standalone agent runner that executes tasks using MCP (Model Context Protocol) tools via Anthropic Claude, AWS BedRock and OpenAI APIs. It enables AI agents to run autonomously in cloud environments and interact with various systems securely.
AWS Bedrock AgentCore with Langbase
An AI agent for admissions that delivers a seamless admissions counselor experience that understands student needs, answers questions, and facilitates advisor connections.
Opensource full fledged Ai recipes backed with python, angular, react
The open standard for AI agent integrity. Evaluate, enforce, and prove that autonomous agents are adversarially coherent, environmentally portable, and verifiably assured.
LingFlow 是一个基于 Go 语言构建的 WebSocket 实时 AI 聊天服务框架，核心理念是通过 S3 动态技能加载 赋能 LLM（大语言模型），实现可热更新的领域知识注入。系统采用 事件溯源（Event Sourcing） 模式管理会话生命周期，支持流式响应（思考过程 + 最终回复），并提供完整的认证、安全防护和多云部署能力。
DEMO for a simple multi-agent collaboration solution on AWS with new Bedrock capabilities
Production-grade document-intelligence RAG agent template — AWS Bedrock Knowledge Bases, OpenSearch-backed metadata/audit, dual-mode JWT + Azure AD SSO, multi-team isolation, and LibreOffice document conversion. Advanced tier of the Document Intel Agent Template family.
CASCADE is an event-driven orchestrator that autonomously resolves complex travel disruptions in sub-second SLA windows. It resolves concurrent seat contention under heavy load, preserves persistent cross-session agent memory, predicts route risks, and guarantees deterministic recovery with immutable audit trails for enterprise logistics.
AI errand & task concierge built with Strands Agents SDK + AWS Bedrock, with Google Calendar sync. FastAPI backend, vanilla JS frontend. Built for the AWS Agents for Humans Hackathon.
Agentic AI approach connecting 3 agents to do real-time credit score evaluations, interest rate calculation based on payment history, and auto-generation of letter of decisions
AI-driven document intelligence platform leveraging AWS Bedrock Knowledge Base and RAG architecture. Stack Highlights Bedrock (Claude 3.5 Sonnet) for LLM orchestration Aurora Serverless PostgreSQL with pgvector for embeddings S3 for document management Terraform for infrastructure provisioning Python for automation and chat workflows Deliver
You’ll explore foundational AI concepts and then dive deep into building real-world GenAI applications. From there, the book guides you into the realm of Agentic AI, detailing how to design intelligent agents capable of perception, reasoning, planning, decision-making, and dynamic collaboration.
A small code snippet as a part of "How to Write an Agent" Blog Post
Document Analyzer is a Django web application for uploading documents, extracting text, generating AI summaries, classifying document types, searching content with PostgreSQL full-text search, and chatting against a single document or a multi-document notebook.
Agent-driven AWS solution that autonomously analyzes, understands, and structures complex contract PDFs into actionable, machine-readable outputs
This repo has code form AI Agent setup on aws bedrok
A revolutionary AI-powered business intelligence platform specifically designed for Managed Service Providers (MSPs). Prism Insights features six collaborative AI agents that work together to optimize client profitability, software licensing, sales pipeline, resource allocation, departmental spending, and vendor management.
Document Entity Extraction using AWS Bedrock
Use Python and the Strands library to create very low-code agents that interact with AWS Bedrock models and services. I'll also use the built-in Strands tools to give the agent more capabilities to: make HTTP calls, lookup the current time, and invoke the AWS CLI
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