n8n

Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

n8n

n8n is a fair-code workflow automation platform designed for "agentic workflows." It features a visual node editor and supports custom code, offering over 400 integrations to connect various applications and services. This platform enables users to automate complex tasks and build sophisticated workflows with ease, making it a powerful tool for developers and automators alike.

General
Author	n8n GmbH
Release Date	2019
Website	https://n8n.io/
Documentation	https://docs.n8n.io/
Technology Type	Workflow automation platform

Key Features

Visual Workflow Editor: Drag-and-drop interface for building complex workflows without writing extensive code.
400+ Integrations: Connects with a vast array of applications, databases, and APIs.
Custom Code Support: Allows for JavaScript/TypeScript functions within workflows for advanced customization.
Fair-Code Licensing: Provides transparency and community contributions while maintaining commercial viability.
Agentic Workflows: Designed to facilitate advanced automation scenarios often found in AI-driven applications.
Data Transformation: Tools for manipulating and transforming data between different steps in a workflow.

Start Building with n8n

n8n offers powerful capabilities for automating tasks and integrating systems. Its visual editor and extensive integrations make it accessible for rapidly building "agentic workflows." Developers can leverage n8n to connect AI models, manage data flows, and automate responses across various platforms. Explore the official documentation and community examples to see how n8n can enhance your projects.

👉 Start building with n8n 👉 n8n Integrations

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n8n AI Technologies Hackathon projects

Discover innovative solutions crafted with n8n 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.

MetaIntent

🔹 Project Vision Our vision is to empower businesses & designers to build their own local metaverse environments with full data protection & sovereignty, without relying on centralized intermediary platforms, making metaverse creation accessible & scalable as web publishing. 🔹 Core Innovation: In-Platform Metaverse Building Instead of searching external 3D libraries, users build metaverse environments directly inside the platform. By providing a natural language description, the AI agent generates the required 3D model & replaces or updates it inside the virtual environment in real time, reducing design time & eliminating dependency on external repositories. # No-Code Templates & Customization #We provide ready-made virtual environments & templates that companies can customize using drag-and-drop tools or AI-driven generation without technical expertise. #Cross-Device Deployment The platform is device-agnostic, enabling creation & publishing from desktops, mobile devices, & VR platforms, with global web access for users. 🔹 Monetization & Export Channels Businesses use environments as interactive digital export channels to showcase products & services, integrate online payments, & create multiple global revenue streams. 🔹 Data Protection & Local Deployment We enable local metaverse deployment with strong data governance, regulatory compliance, & full control over sensitive data & intellectual property. 🔹 Development Status & Roadmap We completed the system architecture & design phase & started implementing the core platform & AI agent modules, with a roadmap to transition from custom deployments to a scalable AI-powered platform. 🔹 Partnerships & Market Validation We established 2 strategic partnerships & are preparing pilot deployments to demonstrate platform value through real-world use cases & accelerate adoption. 🔹 Impact The platform enables economic empowerment, multiple income streams for creators & enterprises, globally, with full data sovereignty.

RoboGripAI

This project presents a simulation-first robotic system designed to perform structured physical tasks through reliable interaction with objects and its environment. The system focuses on practical task execution rather than complex physics modeling, ensuring repeatability, robustness, and measurable performance across varied simulated conditions. Simulation-first robotic system performing structured physical tasks such as pick-and-place, sorting, and simple assembly. Designed for repeatable execution under varied conditions, with basic failure handling, environmental interaction, and measurable performance metrics. A key emphasis of the system is reliability under dynamic conditions. The simulation introduces variations such as object position changes, minor environmental disturbances, and task sequence modifications. The robot is designed to adapt to these variations while maintaining consistent task success rates. Basic failure handling mechanisms are implemented, including reattempt strategies for failed grasps, collision avoidance corrections, and task state recovery protocols. The framework incorporates structured task sequencing and state-based control logic to ensure deterministic and repeatable behavior. Performance is evaluated using clear metrics such as task completion rate, execution time, grasp accuracy, recovery success rate, and system stability across multiple trials. The modular system design allows scalability for additional tasks or integration with advanced planning algorithms. By prioritizing repeatability, robustness, and measurable outcomes, this solution demonstrates practical robotic task automation in a controlled simulated environment, aligning with real-world industrial and research use cases. Overall, the project showcases a dependable robotic manipulation framework that bridges perception, decision-making, and action in a simulation-first setting, delivering consistent and benchmark-driven task execution.