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

Opensource full fledged Ai recipes backed with python, angular, react

An AI agent for admissions that delivers a seamless admissions counselor experience that understands student needs, answers questions, and facilitates advisor connections.

DEMO for a simple multi-agent collaboration solution on AWS with new Bedrock capabilities

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

Document Entity Extraction using AWS Bedrock

🔍 Build a production-grade RAG system for heavy machinery support using AWS Bedrock and PostgreSQL to enhance information retrieval and decision-making.

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.

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