Langchain AI technology page Top Builders

Explore the top contributors showcasing the highest number of Langchain AI technology page app submissions within our community.

LangChain

Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you are able to combine them with other sources of computation or knowledge. This library is aimed at assisting in the development of those types of applications.

General
Repository	https://github.com/hwchase17/langchain
Type	Large Language Model framework

LangChain - Resources

Resources to get stared with LangChain

LangChain GitHub Repository
Documentation Documentation for LangChain

LangChain - Use cases

Use cases for LangChain

Question Answering over specific documents Documentation for Question Answering over specific documents
Chatbots Documentation for Chatbots build with LangChain
Agents Documentation for Agents systems build with LangChain

LangChain - Example Projects

Implementations of LangChain

Question Answering on Financial Data PDFs Chat with financial documents (quarterly investor update PDFs) in this case
Code Understanding - Chat with GitHub repo Chat with any number of codebases with context
Question Answering over Notion Database Ask questions to your Notion database in natural language
Chat-LangChain Implementation of a locally hosted chatbot specifically focused on question answering over the LangChain documentation
GPT+WolframAlpha Demonstrates a conversational agent implemented with OpenAI GPT3 and LangChain

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Langchain AI technology page Hackathon projects

Discover innovative solutions crafted with Langchain AI technology page, developed by our community members during our engaging hackathons.

solat montoring ai with drones

Here is a **long description** for your project idea: **Solar Panel Monitoring System with Drone Simulation, CNN-Based Defect Detection, and YOLOv9 Dataset Integration via Web Interface**. --- ## 🌞 Long Description: Solar Panel Monitoring with AI and Drones This project presents a simulated **intelligent solar panel monitoring system** that integrates **drone-based inspection**, **deep learning-based defect detection**, and a **web-based visualization interface**. The goal is to demonstrate how modern AI techniques can automate and enhance the monitoring of solar panel infrastructure to ensure optimal performance and early fault detection. --- ### 🚁 1. Drone Simulation and Image Capture The system includes a simulated **DroneAPI** which emulates: - **Takeoff and landing operations** - **Autonomous navigation** to predefined solar panel locations - **Capture of RGB images** representing visual data - **Capture of thermal images** mimicking heat distribution across panels This simulation framework can easily be adapted to real drone APIs such as DJI or Parrot for real-world deployment. --- ### 🧠 2. Defect Detection using Deep Learning (CNN) To analyze the condition of the solar panels: - A **Convolutional Neural Network (CNN)** is used to classify RGB images into **three categories**: - `Normal` - `Crack` - `Dirt` The CNN is designed to be lightweight and fast, ideal for real-time edge deployment or drone-based processing. Thermal images are further analyzed for **hot spots**, which may indicate malfunctioning cells or overheating, using threshold-based anomaly detection. --- ### 📊 3. Automated Report Generation After inspecting all panels: - The system generates a visual report using `matplotlib` which includes: - A histogram showing the distribution of detected defects - A bar chart indicating the number of hot spots per panel - The report and all captured images are saved in the `static/` folder and can be accessed from the web interfac

AI Requirements Engineer Assistant

The AI Requirements Engineer Assistant addresses a critical pain point in software development: poorly defined and analyzed requirements that lead to project delays, cost overruns, and failed implementations. Requirements engineering is often manual, error-prone, and lacks standardization across teams and organizations. Our solution leverages Large Language Models through the Groq API to automatically analyze requirements documents, identify ambiguities and inconsistencies, prioritize features based on business value and dependencies, and generate visual representations like use case diagrams. The tool helps product managers, business analysts, and developers collaborate more effectively by providing a shared understanding of requirements quality and completeness. Key differentiators include multi-format document support (PDF, DOCX, TXT), interactive natural language Q&A about requirements, and a clean, intuitive interface that requires no AI expertise to use. Unlike generic AI assistants, our tool is specifically designed for requirements engineering with specialized prompts and output formatting tailored to software development needs. Target users include software development teams, business analysts, product managers, and quality assurance professionals who want to improve requirements quality early in the development lifecycle, reducing costly changes and misunderstandings later.

Ghost Agent

Ghost Agent is an innovative autonomous agent designed to revolutionize competitive intelligence and strategic analysis. By leveraging advanced AI technologies, including Mistral AI's large language model and RAG (Retrieval Augmented Generation), Ghost Agent autonomously collects, analyzes, and synthesizes public information about companies and their competitive landscape. The system operates through a sophisticated three-tier architecture: a Data Collection Engine that performs automated web scraping, LinkedIn analysis, and real-time data aggregation; an Intelligence Processing Core utilizing advanced NLP, vector database (ChromaDB), and RAG systems for contextual analysis; and a Strategic Recommendation Engine that generates actionable insights and visual representations. What sets Ghost Agent apart is its ability to not just collect data, but to understand market dynamics and generate actionable strategic insights. The system continuously learns from new data, improving its analysis capabilities and recommendations over time. By automating the entire process from data collection to strategic recommendation, Ghost Agent reduces analysis time from weeks to minutes while providing comprehensive, data-driven insights that would typically require extensive manual research and expert consultation. Built with modern technologies (FastAPI, React, TypeScript), the platform ensures scalability, real-time processing, and an intuitive user experience, making professional-grade strategic analysis accessible to businesses of all sizes and effectively democratizing access to high-quality competitive intelligence.

SupplyShield - Smart Risk Detection

SupplyShield 2.0 is an AI-powered logistics intelligence system designed to monitor global shipments in real time, detect risks proactively, and automate decision-making and communication to reduce costly disruptions. Supply chains today face unpredictable events — weather delays, port strikes, roadblocks, and geopolitical risks — yet most systems rely on manual reporting, slow updates, and siloed communication. These gaps lead to delayed actions, misinformed stakeholders, and massive financial losses. SupplyShield 2.0 solves this with a full-stack AI-driven dashboard that uses real-time inputs (shipment logs, weather, news) to identify potential disruptions. It uses Claude LLM via Anthropic to summarize risks, suggest optimal decisions (like rerouting or expediting), explain severity levels, and even draft professional messages or Slack updates for stakeholders — automatically. The system is built using Streamlit for interactive visualization, LangChain + LangGraph for smart logic flows, and ChromaDB to store historical decisions and learn from them. It integrates OpenWeatherMap, GNews, and Slack Webhooks, providing a 360° view of each shipment — including weather at its current location, breaking news alerts, AI-generated risk summaries, and cost comparisons between action choices (e.g., penalty vs air freight vs rerouting). Users can upload new shipment entries or choose from samples. The app allows for one-click PDF and CSV report exports and features an internal chatbot interface that remembers the current session. It is built with modular architecture, real-time visuals (Plotly maps and charts), memory-driven chat, and customizable alerts. By automating insight, communication, and contingency logic, SupplyShield 2.0 helps companies reduce operational losses, speed up decision-making, and improve reliability in their global supply operations. It combines the power of LLMs with logistics domain intelligence to build the next-generation smart supply chain layer.

AYAZ-

AYAZ is an AI-driven automation platform designed specifically for small- and mid-sized businesses (SMBs), leveraging advanced LangChain and LangGraph SupervisorAgent workflows to integrate and streamline the Google Workspace ecosystem—Gmail, Calendar, Meet, Drive, Docs, and Sheets—into a single intuitive interface. By eliminating the cumbersome need to constantly switch between applications, AYAZ significantly reduces productivity losses due to context-switching, saving users substantial time and effort daily. Users interact with AYAZ through natural language commands, effortlessly scheduling meetings, managing emails, creating documents, and organizing data without manual repetition. Unlike existing tools such as Zapier, Reclaim.ai, or Notion AI, AYAZ provides a deeply integrated, cross-app solution that understands context, intelligently automating complex tasks through real-time coordination of multiple specialized AI agents. Its unique multi-agent architecture ensures adaptability and seamless collaboration across Google Workspace tools. AYAZ empowers SMBs to scale efficiently, reduce operational overhead, and increase team productivity by automating routine workflows. Ultimately, AYAZ positions itself as an essential productivity companion, driving SMBs towards greater operational agility, cost efficiency, and enabling teams to focus on high-value strategic tasks.

Telemedicine BOT

Our project, Telemedicine_BOT, is a web-based chatbot designed to help users quickly find nearby hospitals, check the availability of doctors and emergency beds, and get health-related guidance. It is especially useful in rural areas where people often don’t have quick access to hospitals or doctor contact information during emergencies. The chatbot is powered by a smart backend using FastAPI, LangChain, Chainlit, and vector search with FAISS, making it responsive and intelligent in understanding user needs. Users just need a phone or computer with internet access to use it. This solution is designed by a team of software engineers working together to build a meaningful healthtech product. It not only saves critical time during emergencies but also reduces costs by avoiding unnecessary travel and wait times. In the future, we plan to add features like live video calls with doctors, image-based disease identification, and offline SMS alerts for low-internet areas. The project has strong real-world potential, especially in countries like Pakistan, where many communities still lack fast access to medical care. Our goal is to build a tool that can help save lives, improve healthcare reach, and create a reliable digital bridge between patients and hospitals.