LangChain

Top Builders

Explore the top contributors showcasing the highest number of 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 Technologies Hackathon projects

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

RoboDk based Quantum state simulator

The Quantum‑Enhanced Robotics Simulator (QERS) is a fully‑functional digital testbed for designing, testing and validating robotic systems without physical hardware. Our goal is to narrow the reality gap between simulation and the real world by combining deterministic macro‑physics from engines like PyBullet with a quantum‑stochastic plugin that injects realistic noise via Qiskit. The simulator supports deterministic, stochastic and quantum‑perturbed stepping modes and exposes a FastAPI REST API for running jobs, retrieving metrics and managing assets. A Celery/Redis job system queues and executes simulation runs asynchronously, while the Next.js/Three.js web application provides a real‑time dashboard with a 3D viewport, scene tree, metrics panel and controls to toggle between classical domain randomization and quantum noise. Reality profiles define configurable dynamics, sensor and actuation parameters, enabling multi‑profile evaluation of policies. QERS computes gap metrics such as Gdyn, Gperc and Gperf and includes scripts for benchmarking across profiles and generating reports. Users can import URDFs, run batch simulations and compute performance drops and rank stability. Future phases will add mesh segmentation, an AI‑driven text‑to‑algorithm pipeline for generating planner and controller skeletons, and neural‑augmented simulation informed by real data. By combining quantum computing, domain randomization, residual learning and modern web technologies, QERS demonstrates a practical path to sim‑to‑real transfer and a production‑minded robotics startup.

InfernoBots

Our project is an AI-powered autonomous emergency response system built entirely in simulation, demonstrating how intelligent multi-robot teams can detect, assess, and respond to wildfire and disaster scenarios with least human intervention. The system integrates an aerial drone (DJI Mavic 2 Pro) and ground robots operating inside a realistic Webots simulation environment. The drone performs real-time visual surveillance using a Vision-Language Model (VLM) to detect fire, smoke, damaged vegetation, and injured humans. Instead of following pre-scripted paths, the drone uses a orchestrated agentic flow to reason about the scene, decide where to move next, prioritize threats, and select appropriate actions such as dropping water bombs to suppress flames or deploying first-aid kits and breathing masks for victims or navigating to next target around the scene of criticality. The ground robot supports logistics and follow-up operations, navigating autonomously to transport essential equipment based on the aerial assessment. A centralized supervisory AI coordinates task allocation, ensuring efficient parallel execution while avoiding collisions and redundant coverage. The system emphasizes adaptive decision-making, dynamic mission planning, and perception-driven autonomy. All behaviors are designed to be transferable to real robotic platforms, showcasing how AI reasoning combined with robotic control can enhance disaster response operations in real-world environments.

SANCTUMSIM

SANCTUM-SIM is a simulation-first robotics platform that demonstrates autonomous infrastructure management under real-world constraints. In a simulated geothermal and logistics facility, mobile inspection and maintenance robots operate in dynamic heat zones, energy limits, and failure-prone environments. Unlike scripted demos, each agent continuously evaluates tradeoffs between energy use, time, and operational risk using cost functions and a planner-based decision engine. Every action generates a structured, explainable rationale—showing why a robot rerouted, delayed, escalated, or conserved power. The system introduces sensor noise, randomized failures, and environmental volatility to demonstrate robustness, recovery, and graceful degradation. A browser dashboard visualizes agent states, decisions, and performance metrics in real time. SANCTUM-SIM enables energy operators and infrastructure teams to simulate, validate, and build trust in autonomous systems before real-world deployment.

NeuroPilot

People who could benefit from hands-free control, for example those with limited mobility, often cannot access it. NeuroPilot changes that: open-source BCI middleware that turns a Muse 2 headband into a real control interface. Muse 2 is already connected to the app, and we have a working integration with DJI Tello. Users train in a 3D simulation and in a training environment; when their brainwave pattern matches a chosen action (move left, right, up, down), the backend fires configurable webhooks or WebSockets. We accumulate 5 actions from the headband; YOLOv8 runs on the Tello camera feed to detect obstacles and Tello state. That batch and the vision data are sent to Gemini, which decides the movement. Every command sent to the DJI Tello is the result of an AI API call, so BCI intent is refined by vision and reasoning before it reaches the drone. The stack is software-only and simulation-first: a Next.js frontend, a FastAPI + WebSocket backend, a web Lab (Three.js), a machine/Connect dashboard for status and live feedback, and per-control webhook URLs. No lab hardware or locked-in vendors. Teams can integrate with existing sims, digital twins, or robots via HTTP.

SentinelOps AI Autonomous Robotics Engine

SentinelOps AI is a simulation-first robotics platform designed to demonstrate how autonomous systems reason, decide, and adapt in dynamic environments. The platform combines a robotics state simulator, an AI-driven decision engine, and structured event logging into a modular backend architecture built with FastAPI. Each simulation step updates the robot’s state, generates events, and triggers an AI reasoning loop that selects the next optimal action with confidence scoring. All decisions and events are logged for traceability, auditability, and system analysis. The goal of SentinelOps AI is to bridge the gap between artificial intelligence and robotics by showcasing clean API design, scalable backend structure, and autonomous control logic in a production-style system. The project emphasizes transparency, modularity, and intelligent automation as foundations for next-generation robotics platforms.

AstraBots Autonomous Simulation-Platform

In logistics, warehousing, and manufacturing, inefficiencies caused by human coordination, slow task execution, and operational errors cost billions annually. AstraBots solves this problem by providing a fully simulated, AI-powered robotics platform that can perform autonomous tasks, adapt to changing environments, and measure efficiency metrics in real time. Companies can simulate, test, and optimize workflows before investing in expensive hardware, dramatically reducing risk and cost. AstraBots combines a modern tech stack — FastAPI, Uvicorn, cloud-hosted simulations, and AI decision-making — to create a startup-ready platform. Robots in the simulation plan actions autonomously, react to environmental changes, and complete objectives without manual intervention. The platform tracks performance metrics like task efficiency, throughput, and reliability. This simulation-first approach allows rapid iteration, scalable deployment, and eventual integration with real robotic systems, making AstraBots the most versatile and cost-effective robotics solution for startups and enterprises globally.