n8n

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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.

AI PDF OS: AI-Powered Document Intelligence

AI PDF OS is an AI-powered Document Intelligence Platform that transforms static PDFs into intelligent, interactive business assets. It combines contract analysis, risk detection, executive summaries, AI-powered document understanding, professional PDF editing, and QR-based document sharing in a single workspace. Users can upload contracts, agreements, reports, and other PDFs, and the platform automatically extracts key information, identifies important clauses, detects risks, and generates actionable insights. The integrated AI Copilot enables users to ask natural-language questions, receive contextual answers, and instantly understand complex documents. Powered by a multi-agent AI workflow, AI PDF OS performs document extraction, clause analysis, risk assessment, negotiation recommendations, and executive summary generation, helping organizations review contracts faster, improve compliance, and reduce risk. Beyond intelligence, the platform offers a professional PDF editing experience with annotations, highlights, comments, signatures, page management, and essential document tools. Built-in QR generation enables seamless document sharing and distribution. Key Features: AI Contract Intelligence, AI Copilot, Risk Detection, Executive Summaries, Clause Analysis, PDF Editing, Digital Signatures, QR Code Generation, Smart Document Sharing, and Multi-Agent AI Workflows. Built with Next.js, NestJS, PostgreSQL, Prisma, Apryse PDF Engine, and advanced AI models, AI PDF OS helps businesses save time, streamline document workflows, and make smarter decisions through AI-powered document intelligence. 🚀

AI E-Commerce Launch Command

Launching a product online normally takes a team 7-10 days: a researcher analyzes the market, a copywriter drafts content, an editor reviews and revises it multiple times, and a strategist builds the launch plan. AI E-Commerce Launch Command compresses this entire workflow into minutes using four specialized AI agents that collaborate through Band. The Research Agent analyzes market positioning, competitors, and audience pain points. The Content Agent drafts headlines, email sequences, and ad hooks based on that research. The Review Agent critically scores the content — if it scores below 7/10, it sends specific revision notes back to the Content Agent through Band, triggering a rewrite. This loop runs up to 3 times until quality is approved. Finally, the Campaign Agent assembles a complete launch brief: timeline, channel strategy, budget allocation, and KPIs. Every handoff, review, and revision happens visibly in a shared Band channel — agents tag each other, share structured context, and coordinate state in real time. This is built for ecommerce brands, agencies, and solo founders who need professional launch campaigns without hiring a full marketing team. Target users: ecommerce founders, dropshipping brands, and marketing agencies running frequent product launches who need fast, consistent, reviewed campaign content.

Aether Labs: Multi-Agent E-Commerce Orchestration

Aether Labs is a proof-of-concept AI Agent Orchestration platform that demonstrates how multiple specialized AI agents can collaborate to automate enterprise e-commerce workflows. Instead of relying on a single monolithic AI model, the system distributes responsibilities across dedicated agents responsible for product personalization, inventory validation, logistics calculations, and transaction security. Each agent produces structured outputs that are orchestrated through an n8n workflow before being passed to the next processing stage. The platform integrates OpenAI, LangChain, Supabase, and modern frontend technologies to showcase a transparent and modular architecture capable of supporting complex business operations. To improve explainability, the frontend displays an Agent Decision Timeline, allowing users to observe how each specialized agent contributes to the final outcome. The demonstration also includes live workflow execution, checkout interaction, and automatic database persistence. Developed as a hackathon prototype, Aether Labs explores how AI Agent Orchestration can provide a more maintainable, extensible, and enterprise-friendly alternative to traditional backend architectures while remaining transparent about its current prototype limitations and future roadmap.

RevSignal: Ops Intelligence for Startups

The problem: every founder knows their company has operational bottlenecks, but finding exactly where, and what is actually automatable, takes weeks and an expensive consultant. Most tools surface firmographics and market noise, which is true but useless for deciding what to fix. What it does: you give it one company domain. A multi-step agent reads the hard-to-reach live web (reviews, forums, job posts, code, public company data, funding portals) and returns a grounded operational brief. What is in the brief: operational pain signals, where each one is an internal bottleneck paired with the AI automation that resolves it; a where-to-start recommendation; company structure (headcount, roles, customers, funding); and funding opportunities researched for the company's own country, analysed for eligibility and coverage. Every claim carries a verbatim quote and a source link, and a grounding critic removes anything it cannot cite, so there are no hallucinations by construction. Proof it generalises: we ran it on five companies across five verticals and five countries, a US dev tool, an Estonian analytics startup, a Finnish consumer-health brand. Same engine, a different playbook each time, with funding always resolving to the correct country. How we built it: Bright Data for resilient live-web access (SERP, Web Unlocker, MCP), Xiaomi MiMo V2.5 Pro for the reasoning chain, and n8n for orchestration. Who we are: RevSignal is built by ArtificialArtz. It is the free Layer 1 of our AI-transformation model: the cited brief is the lead magnet, then a paid Audit, then a delivered Managed Ops Sprint. Our moat is the bridge from diagnosis to delivery, and we will run it on our own pipeline every day. Live demo: demo.artificialartz.xyz/revsignal

IntelScout AI — Live GTM Intelligence

IntelScout AI solves a critical problem for enterprise go-to-market teams: competitive intelligence that is fast, live, and immediately actionable.Traditional tools rely on static databases that are outdated the moment they are published. IntelScout AI takes a fundamentally different approach. When a user submits a research request, the platform triggers a live Bright Data SERP API crawl bypassing georestrictions, CAPTCHAs, and IP blocks that defeat ordinary scrapers. Depending on the selected depth, the system pulls 2, 10, or 30 pages of real-time web results scoped to the exact company, market, and focus areas the user specified.This raw data flows into an n8n workflow where it is passed to a Gemini AI agent with a structured prompt. The AI analyzes pricing signals, product launches, partnershipannouncements, hiring patterns, funding activity, and regional expansion moves then returns a fully structured JSON containing a sentiment score, categorized opportunities, risk signals, and a complete narrative report.A JavaScript processing node normalizes and validates this output before it is delivered back to the frontend via webhook response. The result renders as a visual dashboard: a Chart.js sentiment graph, color-coded metric cards, opportunity and risk insight panels, and a full intelligence report with section headers and sourced bullet points.The platform includes persistent user profiles, full research history stored locally, and a clean multi-page interface covering research, history, about, and contact all served from a single HTML file with no backend dependency beyond the n8n workflow.IntelScout AI was built as a working SaaS prototype demonstrating that Bright Data's live data infrastructure, combined with AI reasoning and n8n's workflow automation, can replace expensive static intelligence subscriptions with a system that is faster, more accurate, and fully auditable.

DUAL-BROKER SOTA ENGINE

Dual-Broker SOTA Engine is an automated trading system capturing real-time arbitrage between TradFi and Web3 prediction markets (Polymarket). The project proves that combining robust web scraping with low-latency LLM intelligence creates a secure, enterprise-grade engine. **Bright Data: Bypassing the Web's Toughest Blocks** Arbitrage demands real-time data from highly protected platforms like Yahoo Finance and Polymarket, where stale data leads to losses. The engine implements a resilient 3-tier extraction fallback powered by Bright Data: - **Bright Data Scraping Browser (CDP):** Renders JS-heavy, dynamic order books and scrapes depth snapshots via Puppeteer. - **Web Unlocker:** Bypasses advanced browser fingerprinting and CAPTCHAs on news feeds to guarantee a 99.9% extraction success rate. - **Residential Proxies:** Rotates IPs across a massive pool, ensuring high-frequency scraping runs continuously without rate-limiting or bans. Standardized via a Bright Data MCP Server, this stack transforms the open web into a structured enterprise data feed. **AI/ML API: High-Concurrency, Cost-Effective Swarm Intelligence** Running financial forecasts in real-time requires a consensus mechanism that is fast and affordable. The engine deploys a 50-persona Bayesian Swarm Consensus powered by the AI/ML API: - **Ultra-Low Latency:** AI/ML API orchestrates up to 50 parallel LLM persona requests simultaneously, converging the decision matrix in under 5 seconds. - **Economic Viability:** Leveraging top-tier models (DeepSeek-V4-Pro) via the gateway keeps token costs at a fraction of a cent. - **Real-Time P&L Safeguards:** The dashboard integrates with AI/ML API's billing API to track consumption and prove positive net profitability. With Apache Flink streaming and a Saga-based transaction sandbox for atomic execution, the engine proves that web data unlocked by Bright Data and reasoned by AI/ML API is ready for enterprise production.