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.

AuditEye-II

AuditEye-II is an AI-powered auditing platform designed to reduce the time and effort required to review business documents. Instead of manually checking procurement records, invoices, receipts, expense reports, and supporting documents, users simply upload their files and organization policies, and the system determines the appropriate auditing workflow automatically. The platform supports AI-powered document auditing, cross-document comparison, and policy-aware compliance verification. When multiple related datasets are uploaded, AuditEye-II intelligently compares them to detect discrepancies, inconsistencies, missing information, and potential risks. When only a single document is available, the AI performs reasoning-based auditing to identify anomalies and generate actionable audit findings. The workflow is orchestrated using n8n and powered by Fireworks AI, enabling flexible routing between different AI models depending on the task. The architecture is modular, making it easy to expand with future capabilities such as OCR, knowledge-base retrieval (RAG), ERP integrations, and additional enterprise audit workflows. AuditEye-II was built as an MVP demonstrating how agentic AI can assist—not replace—auditors by accelerating document review, improving consistency, and providing structured, explainable audit insights while keeping humans in control of final decisions.

FlowForge: Natural Language to n8n Workflow JSON

FlowForge is an open source tool that converts natural language descriptions into importable n8n workflow JSON using a fine tuned Gemma model trained entirely on AMD hardware. The problem is simple. Building n8n automations by hand takes time and requires knowing the platform's node structure by heart. General purpose language models are surprisingly bad at this task since n8n workflow JSON has a strict schema with node types connections and parameters that must all line up correctly. We built a full pipeline from scratch. Two thousand seven hundred eleven real n8n templates were collected cleaned to eighteen hundred and enriched three times using an LLM to produce natural language prompts for each workflow giving us fifty four hundred training examples split into forty five hundred ninety training rows and eight hundred ten held out rows for evaluation. Training ran on a single AMD Radeon PRO W7900 with forty eight gigabytes of VRAM using LoRA rank sixteen on Gemma 3 4B with plain PyTorch and PEFT since Unsloth segfaults on this RDNA3 card and Gemma 4 currently cannot be LoRA trained at all because PEFT does not support its custom linear layers. This alone took real debugging and is documented in the README as a known issue others can avoid. We measured results honestly using a three gate evaluation harness checking whether output is valid JSON whether it matches the n8n schema and whether it actually imports into a live n8n instance. The base Gemma 3 4B model produces zero valid workflows on held out prompts. Our fine tuned model produces valid schema compliant workflows twenty percent of the time and critically whenever it produces valid JSON that JSON is always schema correct meaning the remaining failure mode is simply cutting off on the longest workflows rather than generating the wrong structure. The full stack includes a FastAPI backend anyone can clone the repository run the training pipeline on their own AMD hardware and build on top of what we made.

Weather Intelligence as a service (Wiaas)

WIaaS (Weather Intelligence as a Service) bridges the critical gap between raw atmospheric data and immediate industrial action. Moving beyond static weather dashboards, the platform integrates a high-performance FastAPI physics engine with an autonomous n8n multi-agent AI swarm. By computing 24-hour predictive matrices locally, WIaaS translates complex climate anomalies into automated, physics-constrained mitigation strategies. Architected to leverage the massive 192GB HBM3 memory of the AMD Instinct™ MI300X, it delivers zero-latency co-location of deep-physics models and collaborative reasoning—generating instant, actionable orchestration blueprints for utility providers, agricultural networks, and emergency responders. The platform emphasizes physics-grounded reasoning, ensuring that every recommendation is evaluated against real-world environmental constraints rather than relying solely on generative AI predictions. By integrating simulation, automation, and collaborative AI decision-making, WIaaS delivers transparent, scalable, and intelligent climate-risk management.

AutOps - Autonomous Operations

AutOps is an autonomous, AI-driven virtual system administrator designed for real-time monitoring, intelligent diagnosis, and automated remediation of server environments. Instead of relying on traditional ping monitors, AutOps acts as a 24/7 intelligent agent that watches over your containerized infrastructure. When a service goes down, it instantly fetches the relevant error logs, uses an embedded Large Language Model (LLM) to diagnose the exact root cause of the crash, and formulates a precise fix. Key Features: Discord Command Center: It transforms Discord into a secure, mobile-friendly command-and-control terminal using a custom Python bridge, allowing administrators to manage servers directly from their phones. Human-in-the-Loop (HITL) Safety: To prevent the AI from making destructive changes autonomously, AutOps enforces a strict safety mechanism. The AI generates a remediation proposal and sends an alert to the admin via Discord. The fix is only executed if the admin explicitly replies with an !approve command. Microservices Architecture: The system operates securely within Docker and is powered by n8n, a visual orchestration engine that routes complex background health checks, webhook events, and SSH command executions. Ultimately, AutOps reduces the tedious, multi-step troubleshooting process (connecting to a VPN, authenticating SSH, manually hunting for logs) into a simple mobile notification and a 5-second approval response—drastically lowering both the Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR) for server incidents.

ISEEKAIGO

Most AI companions today are built around static prompts layered on top of large language models. They can generate impressive responses, but they rarely evolve beyond the conversation in front of them. ISEEKAIGO is building the next generation of AI companionship by combining generative AI with persistent memory, human-inspired needs, autonomous behavior, and evolving cognition. Instead of simply responding, our AI learns from experience, self-learning, adapts over time, and develops its own behavioral context, making every interaction more personal and meaningful. Our vision extends beyond creating a better chatbot. We are building a foundation for Human-like AI that can interact with people over long periods while becoming increasingly capable through experience. By breaking this vision into scalable milestones, we aim to transform AI companionship into a platform for future autonomous AI applications such as building physical-AI core. We believe this project represents a meaningful step toward the future of Human-like AI, combining ambitious research with the ability to ship real products and bring advanced AI experiences to millions of users.