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<!DOCTYPE html>
<html lang="en"><head>
<meta charset="utf-8"/>
<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
<title>Agent Park - Project Details</title>
<script src="https://cdn.tailwindcss.com?plugins=forms,typography"></script>
<link href="https://fonts.googleapis.com/css2?family=Space+Mono:ital,wght@0,400;0,700;1,400&amp;family=Inter:wght@300;400;500;600;700&amp;display=swap" rel="stylesheet"/>
<link href="https://fonts.googleapis.com/icon?family=Material+Icons" rel="stylesheet"/>
<script>
tailwind.config = {
darkMode: "class",
theme: {
extend: {
colors: {
primary: "#FFE100", // The vibrant yellow seen in headers and buttons
"primary-dark": "#e5cb00",
"background-light": "#F6F3EB", // Light beige background
"background-dark": "#121212", // Dark mode background
"surface-light": "#FFFFFF",
"surface-dark": "#1E1E1E",
"text-light": "#000000",
"text-dark": "#E0E0E0",
"border-light": "#000000",
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},
fontFamily: {
display: ["Space Mono", "monospace"], // For headers and buttons
body: ["Inter", "sans-serif"], // For body text
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'md': "2px",
},
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<style>::-webkit-scrollbar {
width: 8px;
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::-webkit-scrollbar-track {
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::-webkit-scrollbar-thumb {
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background-color: #475569;
}.prose h2, .prose h3 {
font-family: 'Space Mono', monospace;
font-weight: 700;
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</style>
</head>
<body class="bg-background-light dark:bg-background-dark text-text-light dark:text-text-dark font-body min-h-screen flex flex-col transition-colors duration-200">
<div class="bg-primary text-black text-xs font-bold py-2 text-center uppercase tracking-widest border-b border-border-light dark:border-border-dark font-display">
Discover the future of AI Agents <span class="material-icons text-sm align-middle ml-1">arrow_forward</span>
</div>
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<span class="material-icons text-2xl group-hover:rotate-12 transition-transform">smart_toy</span>
<span class="font-display font-bold text-lg tracking-tight">Agent Park</span>
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<a class="text-sm font-display font-bold hover:underline decoration-2 underline-offset-4" href="#">HOME</a>
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BACK TO PROJECTS
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<h1 class="font-display text-4xl md:text-6xl font-bold mb-6 leading-tight uppercase relative z-10">
AutoGPT X
</h1>
<div class="flex flex-wrap items-center gap-4 text-sm font-mono text-gray-600 dark:text-gray-400 mb-8 pb-8 border-b border-gray-300 dark:border-gray-700">
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<span>Added Oct 24, 2023</span>
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<span class="hidden sm:inline text-gray-300">|</span>
<div class="flex items-center gap-2">
<span class="material-icons text-base">category</span>
<span>Autonomous Agent</span>
</div>
<span class="hidden sm:inline text-gray-300">|</span>
<div class="flex items-center gap-2">
<span class="material-icons text-base">code</span>
<span>Open Source</span>
</div>
</div>
<div class="flex flex-wrap gap-3 mb-8">
<span class="px-3 py-1 border border-black dark:border-gray-500 text-xs font-display font-bold uppercase bg-white dark:bg-gray-800">Python</span>
<span class="px-3 py-1 border border-black dark:border-gray-500 text-xs font-display font-bold uppercase bg-white dark:bg-gray-800">LLM</span>
<span class="px-3 py-1 border border-black dark:border-gray-500 text-xs font-display font-bold uppercase bg-white dark:bg-gray-800">Automation</span>
<span class="px-3 py-1 border border-black dark:border-gray-500 text-xs font-display font-bold uppercase bg-white dark:bg-gray-800">GPT-4</span>
</div>
<div class="relative w-full aspect-video border-2 border-black dark:border-gray-600 bg-gray-100 dark:bg-gray-800 mb-10 overflow-hidden shadow-brutal dark:shadow-none">
<img alt="AutoGPT X Interface Abstract Visualization" class="w-full h-full object-cover" src="https://lh3.googleusercontent.com/aida-public/AB6AXuAvvUwvvv9eQigRJYV_SkkRfdJYe8PSpnsqNqriFIrtYb3ZxrfwipLWMSn-m8oQB9oT1LuynQY8q6o3m_GOFZGK0yM2nLxapj0EbrWKj49NN3JdO6I65VMXvnlM56uRAHXh-JKoIaFFlQRUg0hlhLlc7Nc_UfniYNUpu_0CX6OPsJwYhIRaAMeqjgLuWTfLA6cKNtkixtbNr9K7AI5puEIJjPLs7tQuUC5_7NjEiFSbBPFg_pO95-buoa59aO4_Qafu600RkDE9lDQ"/>
<div class="absolute bottom-4 right-4 bg-white dark:bg-black px-2 py-1 text-xs font-mono border border-black dark:border-gray-500">v2.0.1 Stable</div>
</div>
</header>
<article class="prose prose-lg dark:prose-invert max-w-none prose-headings:font-display prose-a:text-blue-600 dark:prose-a:text-blue-400 hover:prose-a:text-blue-800 dark:hover:prose-a:text-blue-300 prose-img:border-2 prose-img:border-black dark:prose-img:border-gray-600 prose-img:shadow-brutal dark:prose-img:shadow-none">
<p class="lead font-display text-xl mb-8">
AutoGPT X is an experimental open-source application showcasing the capabilities of the GPT-4 language model. This program, driven by GPT-4, chains together LLM "thoughts", to autonomously achieve whatever goal you set.
</p>
<h2>Overview</h2>
<p>
Unlike typical AI chatbots that require constant prompting, AutoGPT X takes a single goal and breaks it down into sub-tasks. It utilizes the internet for searching and gathering information, manages long-term and short-term memory, and can execute code or scripts to accomplish complex workflows.
</p>
<p>
As one of the first examples of GPT-4 running fully autonomously, AutoGPT X pushes the boundaries of what is possible with AI agents. It serves as a framework for building sophisticated AI assistants capable of performing real-world tasks with minimal human intervention.
</p>
<h3>Key Features</h3>
<ul class="list-disc pl-6 space-y-2 marker:text-black dark:marker:text-white">
<li><strong>Internet Access:</strong> Searches and gathers information autonomously.</li>
<li><strong>Long-Term Memory:</strong> Retains context over extended sessions.</li>
<li><strong>GPT-4 Powered:</strong> Leverages the reasoning capabilities of state-of-the-art LLMs.</li>
<li><strong>File Management:</strong> Can read and write files to your local system.</li>
<li><strong>Extensible:</strong> Plugin system allows for custom integrations.</li>
</ul>
<h3>Getting Started</h3>
<p>
To install AutoGPT X, you will need Python 3.10 or later. Clone the repository and install the dependencies:
</p>
<pre class="bg-gray-100 dark:bg-gray-800 border border-black dark:border-gray-600 p-4 font-mono text-sm overflow-x-auto"><code>git clone https://github.com/agentpark/autogpt-x.git
cd autogpt-x
pip install -r requirements.txt
python main.py</code></pre>
<blockquote>
"The goal of AutoGPT X is to make GPT-4 fully autonomous."
</blockquote>
<h2>Use Cases</h2>
<p>
Developers and enthusiasts are using AutoGPT X for a variety of tasks, including:
</p>
<ul class="list-disc pl-6 space-y-2 marker:text-black dark:marker:text-white">
<li>Automated market research and report generation.</li>
<li>Code debugging and autonomous software development.</li>
<li>Personal assistant tasks like booking flights or ordering food.</li>
<li>Content creation and social media management.</li>
</ul>
</article>
<div class="mt-12 pt-8 border-t border-gray-300 dark:border-gray-700 flex flex-col sm:flex-row justify-between items-center gap-6">
<div class="flex gap-4">
<button class="p-2 border border-black dark:border-gray-400 hover:bg-gray-100 dark:hover:bg-gray-800 transition-colors" title="Share on X">
<span class="material-icons text-lg">share</span>
</button>
<button class="p-2 border border-black dark:border-gray-400 hover:bg-gray-100 dark:hover:bg-gray-800 transition-colors" title="Copy Link">
<span class="material-icons text-lg">link</span>
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<div class="flex items-center gap-2">
<span class="text-sm font-display font-bold text-gray-500">DID THIS AGENT FIX THIS?</span>
<button class="px-4 py-2 bg-primary text-black font-display font-bold text-xs uppercase border border-black hover:bg-yellow-400 transition-colors shadow-[2px_2px_0px_0px_rgba(0,0,0,1)] active:shadow-none active:translate-x-[2px] active:translate-y-[2px]">Yes, it rocks</button>
</div>
</div>
</div>
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<h3 class="font-display font-bold text-lg mb-6 uppercase border-b-2 border-primary pb-2 inline-block">Project Links</h3>
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<span class="material-icons">language</span>
<span class="font-bold text-sm">Official Website</span>
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<span class="material-icons">description</span>
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</div>
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</a>
</div>
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<path d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-3.14-.95-3.14-.95-.43-.92-.1-1.25-.1-1.25.2-.05.41.08.95.08 2.76 1.89 3.78 1.89 3.78 1.69 2.88 4.44 2.05 5.53-.16.47-.86-.94-1.25-1.14-.42-.26-.89-.04-1.25.23-.89.65-2.18.95-3.3 1.01-.22.01-.44.05-.66.12-.26.11-.53.03-.72-.13-.22-.18-.44-.49-.44-.49-.25-.95-.08-1.25.23-.72.72-1.87 2.05-2.84 2.85-.13.11-.24.26-.24.42 0 .33.27.76.76.76 1.16 0 .87.72 1.96 2.05 2.4 2.82.2.06.43.09.65.05.31-.06.6-.17.87-.33.26-.16.48-.36.63-.57.23-.21.47-.44.64-.67.19-.26.35-.54.48-.83.11-.26.17-.54.17-.82 0-.47-.27-.91-.66-1.1-.67-.34-1.45-.51-2.32-.51-.88 0-1.67.18-2.35.53-.26.13-.5.25-.73.36-.22.1-.42.23-.58.37-.14.13-.26.26-.34.4-.07.14-.1.29-.1.44 0 .18.09.34.25.46.13.11.29.18.46.18.19 0 .38-.06.55-.17.15-.11.28-.24.39-.39.1-.15.18-.31.23-.48.05-.18.06-.36.06-.54 0-.3-.17-.57-.44-.74-.23-.17-.5-.26-.78-.26-.28 0-.55.09-.79.26-.23.17-.41.39-.54.64-.12.24-.17.5-.17.77 0 .26.17.5.43.67.25.17.57.26.89.26.31 0 .6-.09.85-.26.23-.17.41-.39.54-.64.12-.24.18-.5.18-.77 0-.26-.17-.5-.43-.67-.26-.17-.57-.26-.89-.26-.31 0-.6.09-.85.26-.23.17-.41.39-.54.64-.12.24-.17.5-.17.77zM8 15c-3.86 0-7-3.14-7-7s3.14-7 7-7 7 3.14 7 7-3.14 7-7 7z"/>
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<div class="font-display font-bold text-lg">34k</div>
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<div class="text-[9px] uppercase font-mono text-gray-500">Issues</div>
<div class="font-display font-bold text-lg">892</div>
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<div class="font-display font-bold text-lg">MIT</div>
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<span class="text-gray-500">Last commit</span>
<span class="font-display font-bold">2 days ago</span>
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<span class="material-icons text-2xl">rocket_launch</span>
</div>
<h3 class="font-display font-bold text-xl mb-2">Build Your Own Agent?</h3>
<p class="text-sm font-medium mb-6">Start using our API to build custom agents in minutes.</p>
<button class="w-full bg-black text-white font-display font-bold py-3 text-sm uppercase hover:bg-gray-800 transition-colors shadow-none">
Get Started Now
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<h3 class="font-display font-bold text-sm uppercase tracking-widest mb-4 text-gray-500 dark:text-gray-400">On this page</h3>
<ul class="space-y-3 text-sm border-l-2 border-gray-200 dark:border-gray-700 pl-4">
<li><a class="text-black dark:text-white font-bold border-l-2 border-black dark:border-white -ml-[18px] pl-4 block" href="#">Overview</a></li>
<li><a class="text-gray-600 dark:text-gray-400 hover:text-black dark:hover:text-white transition-colors block" href="#">Key Features</a></li>
<li><a class="text-gray-600 dark:text-gray-400 hover:text-black dark:hover:text-white transition-colors block" href="#">Getting Started</a></li>
<li><a class="text-gray-600 dark:text-gray-400 hover:text-black dark:hover:text-white transition-colors block" href="#">Use Cases</a></li>
</ul>
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<h3 class="font-display text-xl font-bold">BabyAGI</h3>
<span class="material-icons text-gray-400">psychology</span>
</div>
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Task-driven autonomous agent that uses OpenAI and Pinecone APIs to create, prioritize, and execute tasks.
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<span class="text-[10px] uppercase font-bold border border-gray-300 dark:border-gray-600 px-2 py-1">Python</span>
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View Details <span class="material-icons text-sm ml-1 group-hover:translate-x-1 transition-transform">arrow_forward</span>
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<h3 class="font-display text-xl font-bold">LangChain</h3>
<span class="material-icons text-gray-400">link</span>
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Building applications with LLMs through composability. The standard framework for chaining LLM capabilities.
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<span class="text-[10px] uppercase font-bold border border-gray-300 dark:border-gray-600 px-2 py-1">Framework</span>
<span class="text-[10px] uppercase font-bold border border-gray-300 dark:border-gray-600 px-2 py-1">Python</span>
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<span class="material-icons text-gray-400">bolt</span>
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A dedicated web platform to access the powers of AutoGPT and BabyAGI directly from your browser.
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display: ['"Space Mono"', "monospace"], // Monospace for headings like MotherDuck
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AI PROJECT <br class="hidden md:block"/> NAVIGATOR
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Discover and explore high-quality AI projects from across the web. Curated for developers and enthusiasts.
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<a class="px-4 py-2 bg-white dark:bg-surface-dark border border-black dark:border-gray-500 font-display text-sm font-bold hover:bg-primary hover:text-black transition-colors shadow-neobrutalism-sm" href="#">
Coding Assistant <span class="ml-1 opacity-60 text-xs">2</span>
</a>
<a class="px-4 py-2 bg-white dark:bg-surface-dark border border-black dark:border-gray-500 font-display text-sm font-bold hover:bg-primary hover:text-black transition-colors shadow-neobrutalism-sm" href="#">
Chat AI <span class="ml-1 opacity-60 text-xs">2</span>
</a>
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Data Analysis <span class="ml-1 opacity-60 text-xs">4</span>
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Image Gen <span class="ml-1 opacity-60 text-xs">2</span>
</a>
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Automation <span class="ml-1 opacity-60 text-xs">1</span>
</a>
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LLMs <span class="ml-1 opacity-60 text-xs">8</span>
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VIEW ALL <span class="material-icons text-sm ml-1">arrow_forward</span>
</a>
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<article class="bg-white dark:bg-surface-dark border-2 border-black dark:border-gray-600 p-6 shadow-neobrutalism hover:shadow-neobrutalism-hover hover:-translate-y-1 transition-all duration-200 flex flex-col h-full">
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<h3 class="font-display text-2xl font-bold">n8n</h3>
<span class="material-icons text-gray-400">auto_awesome</span>
</div>
<p class="text-gray-600 dark:text-gray-300 mb-6 flex-grow font-sans leading-relaxed">
Open-source workflow automation tool. Connect various services and APIs through a visual interface to build complex automations without code.
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<div class="flex flex-wrap gap-2 mb-4">
<span class="bg-gray-100 dark:bg-gray-700 px-2 py-1 text-xs font-mono border border-black dark:border-gray-500">Automation</span>
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</div>
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VIEW DETAILS
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</a>
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<h3 class="font-display text-2xl font-bold">Stable Diffusion</h3>
<span class="material-icons text-gray-400">image</span>
</div>
<p class="text-gray-600 dark:text-gray-300 mb-6 flex-grow font-sans leading-relaxed">
Open-source image generation model. Supports local deployment and custom training, enabling powerful creative workflows.
</p>
<div class="mt-auto">
<div class="flex flex-wrap gap-2 mb-4">
<span class="bg-gray-100 dark:bg-gray-700 px-2 py-1 text-xs font-mono border border-black dark:border-gray-500">Image Gen</span>
<span class="bg-gray-100 dark:bg-gray-700 px-2 py-1 text-xs font-mono border border-black dark:border-gray-500">Open Source</span>
</div>
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VIEW DETAILS
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</a>
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<h3 class="font-display text-2xl font-bold">Midjourney</h3>
<span class="material-icons text-gray-400">palette</span>
</div>
<p class="text-gray-600 dark:text-gray-300 mb-6 flex-grow font-sans leading-relaxed">
Powerful AI image generation tool. Create exquisite images through text descriptions with industry-leading artistic quality.
</p>
<div class="mt-auto">
<div class="flex flex-wrap gap-2 mb-4">
<span class="bg-gray-100 dark:bg-gray-700 px-2 py-1 text-xs font-mono border border-black dark:border-gray-500">Image Gen</span>
<span class="bg-gray-100 dark:bg-gray-700 px-2 py-1 text-xs font-mono border border-black dark:border-gray-500">Art</span>
</div>
<a class="inline-flex items-center font-display font-bold text-sm hover:text-secondary dark:hover:text-primary group" href="#">
VIEW DETAILS
<span class="material-icons text-sm ml-1 transform group-hover:translate-x-1 transition-transform">arrow_forward</span>
</a>
</div>
</article>
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<div class="flex justify-between items-start mb-4">
<h3 class="font-display text-2xl font-bold">ChatGPT</h3>
<span class="material-icons text-gray-400">chat</span>
</div>
<p class="text-gray-600 dark:text-gray-300 mb-6 flex-grow font-sans leading-relaxed">
Large language model developed by OpenAI. Supports conversation, writing, coding, and a wide variety of tasks.
</p>
<div class="mt-auto">
<div class="flex flex-wrap gap-2 mb-4">
<span class="bg-gray-100 dark:bg-gray-700 px-2 py-1 text-xs font-mono border border-black dark:border-gray-500">Chat AI</span>
<span class="bg-gray-100 dark:bg-gray-700 px-2 py-1 text-xs font-mono border border-black dark:border-gray-500">Coding Assistant</span>
</div>
<a class="inline-flex items-center font-display font-bold text-sm hover:text-secondary dark:hover:text-primary group" href="#">
VIEW DETAILS
<span class="material-icons text-sm ml-1 transform group-hover:translate-x-1 transition-transform">arrow_forward</span>
</a>
</div>
</article>
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<div class="flex justify-between items-start mb-4">
<h3 class="font-display text-2xl font-bold">Claude</h3>
<span class="material-icons text-gray-400">psychology</span>
</div>
<p class="text-gray-600 dark:text-gray-300 mb-6 flex-grow font-sans leading-relaxed">
AI assistant developed by Anthropic. Excels at analysis, writing, and coding tasks with a focus on safety and reliability.
</p>
<div class="mt-auto">
<div class="flex flex-wrap gap-2 mb-4">
<span class="bg-gray-100 dark:bg-gray-700 px-2 py-1 text-xs font-mono border border-black dark:border-gray-500">Chat AI</span>
<span class="bg-gray-100 dark:bg-gray-700 px-2 py-1 text-xs font-mono border border-black dark:border-gray-500">Analysis</span>
</div>
<a class="inline-flex items-center font-display font-bold text-sm hover:text-secondary dark:hover:text-primary group" href="#">
VIEW DETAILS
<span class="material-icons text-sm ml-1 transform group-hover:translate-x-1 transition-transform">arrow_forward</span>
</a>
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<div class="bg-white p-4 rounded-full border border-black mb-4">
<span class="material-icons text-3xl text-black">add</span>
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<h3 class="font-display text-2xl font-bold text-black mb-2">Submit Your Project</h3>
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Have an AI tool worth sharing? Add it to Agent Park.
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SUBMIT NOW
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VIEW ALL PROJECTS <span class="material-icons text-sm ml-1">arrow_forward</span>
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Get the latest AI tools and trends delivered straight to your inbox. No spam, just intelligence.
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design/projectlist.html
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<!DOCTYPE html>
<html lang="en"><head>
<meta charset="utf-8"/>
<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
<title>Agent Park - AI Project Navigator</title>
<script src="https://cdn.tailwindcss.com?plugins=forms,typography"></script>
<link href="https://fonts.googleapis.com/css2?family=Space+Mono:ital,wght@0,400;0,700;1,400&amp;family=Inter:wght@400;500;600;700&amp;display=swap" rel="stylesheet"/>
<link href="https://fonts.googleapis.com/icon?family=Material+Icons+Outlined" rel="stylesheet"/>
<script>
tailwind.config = {
darkMode: "class",
theme: {
extend: {
colors: {
primary: "#FFD700", // Gold/Yellow from the screenshots
"background-light": "#F5F2EB", // Warm off-white/beige from screenshots
"background-dark": "#121212", // Dark mode background
"surface-light": "#FFFFFF",
"surface-dark": "#1E1E1E",
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display: ["'Space Mono'", "monospace"], // Monospace for headings/brand
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<style>.border-thick {
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DISCOVER THE FUTURE OF AI AGENTS <span class="material-icons-outlined text-xs align-middle ml-1">arrow_forward</span>
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<span class="material-icons-outlined text-3xl dark:text-primary">smart_toy</span>
<span class="font-display font-bold text-xl tracking-tight">Agent Park</span>
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<a class="hover:underline decoration-2 underline-offset-4" href="#">Home</a>
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SUBMIT PROJECT
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</nav>
<main class="container mx-auto px-4 py-12 md:py-16 max-w-7xl relative">
<div class="absolute top-10 left-0 w-24 h-24 border-2 border-black dark:border-white/20 rounded-full opacity-20 pointer-events-none hidden lg:block"></div>
<div class="absolute top-20 right-10 w-32 h-20 border-2 border-black dark:border-white/20 rounded-[2rem] opacity-20 pointer-events-none transform rotate-12 hidden lg:block"></div>
<div class="text-center mb-16 relative z-10">
<h1 class="font-display text-5xl md:text-7xl font-bold mb-6 leading-tight tracking-tighter">
PROJECT <br class="md:hidden"/>NAVIGATOR
</h1>
<p class="text-lg md:text-xl text-gray-600 dark:text-gray-300 max-w-2xl mx-auto font-light leading-relaxed">
Discover and explore high-quality AI projects from across the web. <br class="hidden md:inline"/>Curated for developers and enthusiasts.
</p>
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<div class="flex flex-col md:flex-row gap-4 mb-8">
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<span class="material-icons-outlined absolute left-4 top-1/2 transform -translate-y-1/2 text-gray-400">search</span>
<input class="w-full pl-12 pr-4 py-3 bg-white dark:bg-black border-2 border-black dark:border-white/30 focus:border-primary focus:ring-0 font-display text-sm placeholder-gray-500" placeholder="Search AI projects..." type="text"/>
</div>
<div class="relative md:w-1/4">
<select class="w-full pl-4 pr-10 py-3 bg-white dark:bg-black border-2 border-black dark:border-white/30 focus:border-primary focus:ring-0 font-display text-sm appearance-none cursor-pointer">
<option>All Categories</option>
<option>Coding Assistant</option>
<option>Image Generation</option>
<option>Automation</option>
<option>LLMs</option>
</select>
<span class="material-icons-outlined absolute right-3 top-1/2 transform -translate-y-1/2 pointer-events-none text-gray-500">expand_more</span>
</div>
<button class="bg-primary border-2 border-black px-8 py-3 font-display font-bold text-sm uppercase hover:bg-yellow-400 transition-colors shadow-neo-sm active:shadow-none active:translate-x-[2px] active:translate-y-[2px] text-black">
Search
</button>
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<h3 class="font-display font-bold uppercase text-sm mb-4 border-b-2 border-black inline-block dark:border-primary pb-1">Browse by Tags</h3>
<div class="flex flex-wrap gap-3">
<button class="px-3 py-1 border border-black dark:border-white/40 bg-white dark:bg-transparent font-display text-xs hover:bg-black hover:text-white dark:hover:bg-primary dark:hover:text-black transition-colors flex items-center gap-2 group">
Coding Assistant <span class="bg-gray-200 dark:bg-gray-800 dark:group-hover:bg-black/20 px-1.5 py-0.5 text-[10px] rounded-sm group-hover:bg-white group-hover:text-black">12</span>
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Chat AI <span class="bg-gray-200 dark:bg-gray-800 dark:group-hover:bg-black/20 px-1.5 py-0.5 text-[10px] rounded-sm group-hover:bg-white group-hover:text-black">8</span>
</button>
<button class="px-3 py-1 border border-black dark:border-white/40 bg-black text-white dark:bg-primary dark:text-black font-display text-xs transition-colors flex items-center gap-2">
Data Analysis <span class="bg-gray-700 text-white dark:bg-black/20 dark:text-black px-1.5 py-0.5 text-[10px] rounded-sm">4</span>
</button>
<button class="px-3 py-1 border border-black dark:border-white/40 bg-white dark:bg-transparent font-display text-xs hover:bg-black hover:text-white dark:hover:bg-primary dark:hover:text-black transition-colors flex items-center gap-2 group">
Image Gen <span class="bg-gray-200 dark:bg-gray-800 dark:group-hover:bg-black/20 px-1.5 py-0.5 text-[10px] rounded-sm group-hover:bg-white group-hover:text-black">21</span>
</button>
<button class="px-3 py-1 border border-black dark:border-white/40 bg-white dark:bg-transparent font-display text-xs hover:bg-black hover:text-white dark:hover:bg-primary dark:hover:text-black transition-colors flex items-center gap-2 group">
Automation <span class="bg-gray-200 dark:bg-gray-800 dark:group-hover:bg-black/20 px-1.5 py-0.5 text-[10px] rounded-sm group-hover:bg-white group-hover:text-black">15</span>
</button>
<button class="px-3 py-1 border border-black dark:border-white/40 bg-white dark:bg-transparent font-display text-xs hover:bg-black hover:text-white dark:hover:bg-primary dark:hover:text-black transition-colors flex items-center gap-2 group">
LLMs <span class="bg-gray-200 dark:bg-gray-800 dark:group-hover:bg-black/20 px-1.5 py-0.5 text-[10px] rounded-sm group-hover:bg-white group-hover:text-black">34</span>
</button>
</div>
</div>
</div>
<div class="flex justify-between items-end mb-8 border-b border-black dark:border-white/20 pb-4">
<h2 class="font-display font-bold text-3xl uppercase tracking-tight">Featured Projects</h2>
<a class="font-display text-xs font-bold uppercase flex items-center gap-1 hover:text-gray-600 dark:hover:text-primary transition-colors" href="#">
View All <span class="material-icons-outlined text-sm">arrow_forward</span>
</a>
</div>
<div class="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-8">
<div class="col-span-1 md:col-span-2 bg-purple-200 dark:bg-purple-900/30 border-2 border-black dark:border-white/20 p-0 flex flex-col md:flex-row shadow-neo hover:shadow-neo-hover hover:-translate-y-1 transition-all duration-300 group overflow-hidden">
<div class="p-8 flex-1 flex flex-col justify-between relative z-10">
<div>
<div class="flex justify-between items-start mb-4">
<span class="bg-black text-white dark:bg-white dark:text-black font-display text-xs px-2 py-1 font-bold">TOP PICK</span>
<span class="material-icons-outlined text-2xl group-hover:rotate-12 transition-transform">auto_awesome</span>
</div>
<h3 class="font-display font-bold text-3xl mb-3">AutoGPT X</h3>
<p class="text-sm md:text-base mb-6 leading-relaxed opacity-90">
An experimental open-source application showcasing the capabilities of the GPT-4 language model. This program, driven by GPT-4, chains together LLM "thoughts", to autonomously achieve whatever goal you set.
</p>
<div class="flex gap-2 flex-wrap mb-6">
<span class="border border-black dark:border-white/50 px-2 py-1 text-[10px] uppercase font-display font-bold bg-white/50 dark:bg-black/50">Automation</span>
<span class="border border-black dark:border-white/50 px-2 py-1 text-[10px] uppercase font-display font-bold bg-white/50 dark:bg-black/50">Agent</span>
</div>
</div>
<a class="inline-flex items-center font-display font-bold text-sm uppercase group-hover:translate-x-2 transition-transform" href="#">
View Details <span class="material-icons-outlined ml-1 text-base">arrow_forward</span>
</a>
</div>
<div class="w-full md:w-2/5 bg-purple-300 dark:bg-purple-800/50 border-t-2 md:border-t-0 md:border-l-2 border-black dark:border-white/20 relative overflow-hidden flex items-center justify-center min-h-[200px]">
<div class="absolute inset-0 bg-[radial-gradient(circle_at_center,_var(--tw-gradient-stops))] from-white/40 to-transparent"></div>
<span class="material-icons-outlined text-9xl opacity-20 rotate-12">psychology</span>
</div>
</div>
<div class="bg-white dark:bg-surface-dark border-2 border-black dark:border-white/20 p-6 shadow-neo hover:shadow-neo-hover hover:-translate-y-1 transition-all duration-300 flex flex-col justify-between group">
<div>
<div class="flex justify-between items-start mb-4 text-gray-400">
<span class="material-icons-outlined text-3xl text-black dark:text-white">hub</span>
</div>
<h3 class="font-display font-bold text-2xl mb-3">n8n</h3>
<p class="text-sm text-gray-600 dark:text-gray-400 mb-6 leading-relaxed">
Open-source workflow automation tool. Connect various services and APIs through a visual interface to build complex automations without code.
</p>
</div>
<div>
<div class="flex gap-2 flex-wrap mb-6">
<span class="bg-gray-100 dark:bg-gray-800 border border-gray-300 dark:border-gray-600 px-2 py-1 text-[10px] uppercase font-display font-bold text-gray-600 dark:text-gray-300">Automation</span>
<span class="bg-gray-100 dark:bg-gray-800 border border-gray-300 dark:border-gray-600 px-2 py-1 text-[10px] uppercase font-display font-bold text-gray-600 dark:text-gray-300">Workflow</span>
</div>
<div class="flex items-center justify-between">
<a class="inline-flex items-center font-display font-bold text-sm uppercase border-b-2 border-transparent hover:border-primary transition-colors pb-0.5" href="#">
View Details <span class="material-icons-outlined ml-1 text-sm">arrow_forward</span>
</a>
<!-- Neo-brutalism Stars Badge Design -->
<div class="inline-flex items-center gap-2 px-3 py-1.5 border-2 border-black dark:border-white/40 bg-white dark:bg-surface-dark shadow-neo-sm hover:shadow-neo transition-all cursor-pointer group/stars" title="42.5k stars on GitHub">
<svg class="w-4 h-4 text-primary" fill="currentColor" viewBox="0 0 16 16">
<path d="M8 .25a.75.75 0 01.673.418l1.882 3.815 4.21.612a.75.75 0 01.416 1.279l-3.046 2.97.719 4.192a.75.75 0 01-1.088.791L8 12.347l-3.766 1.98a.75.75 0 01-1.088-.79l.72-4.194L.818 6.374a.75.75 0 01.416-1.28l4.21-.611L7.327.668A.75.75 0 018 .25z"/>
</svg>
<span class="font-display font-bold text-sm text-black dark:text-white">42.5k</span>
</div>
</div>
</div>
</div>
<div class="bg-white dark:bg-surface-dark border-2 border-black dark:border-white/20 p-6 shadow-neo hover:shadow-neo-hover hover:-translate-y-1 transition-all duration-300 flex flex-col justify-between group">
<div>
<div class="flex justify-between items-start mb-4 text-gray-400">
<span class="material-icons-outlined text-3xl text-black dark:text-white">image</span>
</div>
<h3 class="font-display font-bold text-2xl mb-3">Stable Diffusion</h3>
<p class="text-sm text-gray-600 dark:text-gray-400 mb-6 leading-relaxed">
Open-source image generation model. Supports local deployment and custom training, enabling powerful creative workflows.
</p>
</div>
<div>
<div class="flex gap-2 flex-wrap mb-6">
<span class="bg-gray-100 dark:bg-gray-800 border border-gray-300 dark:border-gray-600 px-2 py-1 text-[10px] uppercase font-display font-bold text-gray-600 dark:text-gray-300">Image Gen</span>
<span class="bg-gray-100 dark:bg-gray-800 border border-gray-300 dark:border-gray-600 px-2 py-1 text-[10px] uppercase font-display font-bold text-gray-600 dark:text-gray-300">Open Source</span>
</div>
<a class="inline-flex items-center font-display font-bold text-sm uppercase border-b-2 border-transparent hover:border-primary transition-colors pb-0.5" href="#">
View Details <span class="material-icons-outlined ml-1 text-sm">arrow_forward</span>
</a>
</div>
</div>
<div class="bg-white dark:bg-surface-dark border-2 border-black dark:border-white/20 p-6 shadow-neo hover:shadow-neo-hover hover:-translate-y-1 transition-all duration-300 flex flex-col justify-between group">
<div>
<div class="flex justify-between items-start mb-4 text-gray-400">
<span class="material-icons-outlined text-3xl text-black dark:text-white">palette</span>
</div>
<h3 class="font-display font-bold text-2xl mb-3">Midjourney</h3>
<p class="text-sm text-gray-600 dark:text-gray-400 mb-6 leading-relaxed">
Powerful AI image generation tool. Create exquisite images through text descriptions with industry-leading artistic quality.
</p>
</div>
<div>
<div class="flex gap-2 flex-wrap mb-6">
<span class="bg-gray-100 dark:bg-gray-800 border border-gray-300 dark:border-gray-600 px-2 py-1 text-[10px] uppercase font-display font-bold text-gray-600 dark:text-gray-300">Image Gen</span>
<span class="bg-gray-100 dark:bg-gray-800 border border-gray-300 dark:border-gray-600 px-2 py-1 text-[10px] uppercase font-display font-bold text-gray-600 dark:text-gray-300">Art</span>
</div>
<a class="inline-flex items-center font-display font-bold text-sm uppercase border-b-2 border-transparent hover:border-primary transition-colors pb-0.5" href="#">
View Details <span class="material-icons-outlined ml-1 text-sm">arrow_forward</span>
</a>
</div>
</div>
<div class="bg-white dark:bg-surface-dark border-2 border-black dark:border-white/20 p-6 shadow-neo hover:shadow-neo-hover hover:-translate-y-1 transition-all duration-300 flex flex-col justify-between group">
<div>
<div class="flex justify-between items-start mb-4 text-gray-400">
<span class="material-icons-outlined text-3xl text-black dark:text-white">chat</span>
</div>
<h3 class="font-display font-bold text-2xl mb-3">ChatGPT</h3>
<p class="text-sm text-gray-600 dark:text-gray-400 mb-6 leading-relaxed">
Large language model developed by OpenAI. Supports conversation, writing, coding, and a wide variety of tasks.
</p>
</div>
<div>
<div class="flex gap-2 flex-wrap mb-6">
<span class="bg-gray-100 dark:bg-gray-800 border border-gray-300 dark:border-gray-600 px-2 py-1 text-[10px] uppercase font-display font-bold text-gray-600 dark:text-gray-300">Chat AI</span>
<span class="bg-gray-100 dark:bg-gray-800 border border-gray-300 dark:border-gray-600 px-2 py-1 text-[10px] uppercase font-display font-bold text-gray-600 dark:text-gray-300">Assistant</span>
</div>
<a class="inline-flex items-center font-display font-bold text-sm uppercase border-b-2 border-transparent hover:border-primary transition-colors pb-0.5" href="#">
View Details <span class="material-icons-outlined ml-1 text-sm">arrow_forward</span>
</a>
</div>
</div>
<div class="bg-primary dark:bg-primary border-2 border-black dark:border-black p-6 shadow-neo hover:shadow-neo-hover hover:-translate-y-1 transition-all duration-300 flex flex-col justify-center items-center text-center group">
<div class="bg-white dark:bg-black rounded-full w-12 h-12 flex items-center justify-center border-2 border-black mb-4">
<span class="material-icons-outlined text-2xl dark:text-primary">add</span>
</div>
<h3 class="font-display font-bold text-2xl mb-2 text-black">Submit Your Project</h3>
<p class="text-sm text-black mb-6 leading-relaxed px-4 font-medium">
Have an AI tool worth sharing? Add it to Agent Park.
</p>
<button class="bg-black text-white dark:bg-black dark:text-primary border-2 border-black dark:border-black px-6 py-2 font-display text-sm font-bold uppercase hover:bg-white hover:text-black dark:hover:bg-white dark:hover:text-black transition-colors">
Submit Now
</button>
</div>
</div>
<div class="mt-16 flex justify-center">
<div class="flex border-2 border-black dark:border-white/20 bg-white dark:bg-surface-dark shadow-neo dark:shadow-none">
<button class="px-4 py-2 border-r border-black dark:border-white/20 hover:bg-gray-100 dark:hover:bg-white/10 transition-colors disabled:opacity-50">
<span class="material-icons-outlined text-sm">arrow_back</span>
</button>
<button class="px-4 py-2 font-display font-bold text-sm bg-primary border-r border-black dark:border-white/20 text-black">1</button>
<button class="px-4 py-2 font-display font-bold text-sm border-r border-black dark:border-white/20 hover:bg-gray-100 dark:hover:bg-white/10 transition-colors">2</button>
<button class="px-4 py-2 font-display font-bold text-sm border-r border-black dark:border-white/20 hover:bg-gray-100 dark:hover:bg-white/10 transition-colors">3</button>
<button class="px-4 py-2 hover:bg-gray-100 dark:hover:bg-white/10 transition-colors">
<span class="material-icons-outlined text-sm">arrow_forward</span>
</button>
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</main>
<section class="border-t-2 border-black dark:border-white/20 flex flex-col md:flex-row">
<div class="w-full md:w-1/2 bg-primary dark:bg-yellow-600 p-12 md:p-20 border-b-2 md:border-b-0 md:border-r-2 border-black dark:border-white/20">
<h2 class="font-display font-bold text-4xl mb-4 text-black">STAY UPDATED</h2>
<p class="text-black mb-8 font-medium max-w-md">
Get the latest AI tools and trends delivered straight to your inbox. No spam, just intelligence.
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docs/api-reference.md
-1049
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docs/api-testing-guide.md
-161
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# API 测试指南
## GET /api/events
获取所有事件:
```bash
curl http://localhost:3000/api/events
```
筛选特定年份:
```bash
curl "http://localhost:3000/api/events?year=2024"
```
限制返回数量:
```bash
curl "http://localhost:3000/api/events?limit=10"
```
分页:
```bash
curl "http://localhost:3000/api/events?offset=10&limit=10"
```
## POST /api/events
创建单个事件:
```bash
curl -X POST http://localhost:3000/api/events \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '[
{
"title": "事件标题",
"eventDate": "2023-03-14T00:00:00Z",
"description": "事件描述(10-500字)",
"imageUrl": "https://example.com/image.jpg"
}
]'
```
批量创建事件:
```bash
curl -X POST http://localhost:3000/api/events \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '[
{ "title": "事件1", "eventDate": "2023-01-01T00:00:00Z", "description": "描述1", "imageUrl": "https://example.com/1.jpg" },
{ "title": "事件2", "eventDate": "2023-02-01T00:00:00Z", "description": "描述2", "imageUrl": "https://example.com/2.jpg" }
]'
```
包含可选字段:
```bash
curl -X POST http://localhost:3000/api/events \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '[
{
"title": "GPT-4 Release",
"titleEn": "GPT-4 发布",
"eventDate": "2023-03-14T00:00:00Z",
"description": "OpenAI launches multimodal LLM",
"descriptionEn": "OpenAI 发布多模态大语言模型",
"imageUrl": "https://example.com/gpt4.jpg",
"sourceUrl": "https://openai.com/blog/gpt-4"
}
]'
```
## 验证规则
### 输入验证 (AIEventInputSchema)
- `title`: 1-200 字符（必填）
- `titleEn`: 最多 200 字符（可选）
- `eventDate`: ISO 8601 datetime 格式（必填）
- `description`: 10-500 字符（必填）
- `descriptionEn`: 最多 500 字符（可选）
- `imageUrl`: 有效 URL（必填）
- `sourceUrl`: 有效 URL（可选）
### 查询参数验证 (AIEventQuerySchema)
- `year`: 4位数字年份（可选）
- `limit`: 正整数（可选，默认 100）
- `offset`: 非负整数（可选，默认 0）
## 认证
所有 POST 请求必须在请求头中包含 API Key:
```
X-API-Key: YOUR_API_KEY
```
## 响应示例
### 成功响应 (POST)
```json
{
"created": 2,
"total": 2
}
```
### 成功响应 (GET)
```json
{
"events": [
{
"id": "cmkwn6q0300004jjz4lobtkpf",
"title": "Transformer论文",
"titleEn": null,
"eventDate": "2017-06-12T00:00:00.000Z",
"description": "Google团队发表Transformer架构",
"descriptionEn": null,
"imageUrl": "https://images.unsplash.com/photo-1677442136019-21780ecad995?w=800",
"sourceUrl": null,
"createdAt": "2026-01-27T13:38:39.268Z",
"updatedAt": "2026-01-27T13:38:39.268Z"
}
]
}
```
### 错误响应
```json
{
"error": "Validation failed",
"details": [
{
"code": "too_small",
"path": ["0", "description"],
"message": "String must contain at least 10 character(s)"
}
]
}
```
## 测试记录
### 2025-01-27 测试结果
- ✅ GET /api/events - 返回空列表
- ✅ POST /api/events - 单个事件创建成功
- ✅ GET /api/events - 验证事件已创建（1个事件）
- ✅ POST /api/events - 批量创建成功（2个事件）
- ✅ GET /api/events?year=2018 - 年份筛选成功（返回2个事件）
所有基础功能测试通过！
docs/discovery-workflow.md
-548
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@@ -1,548 +0,0 @@
# 项目发现工作流 (Project Discovery Workflow)
本文档详细说明了 AI 项目自动发现和收录的完整工作流程。
## 📋 工作流概览
```
┌─────────────────────────────────────────────────────────────────┐
│ 项目发现完整流程 │
└─────────────────────────────────────────────────────────────────┘
1. n8n 自动化平台
│
├─ 定期抓取 GitHub/Twitter/HackerNews 等平台
├─ 筛选符合条件的项目链接
│
▼
2. 调用去重检查 API (推荐)
POST /api/discovery/check-duplicates
│
├─ 检查 URL 是否已存在任务
├─ 检查 URL 对应项目是否已收录
├─ 过滤出需要创建的 URL
│
▼
3. 调用创建任务 API
POST /api/discovery/tasks
│
├─ 存入 ProjectDiscoveryTask 表
├─ 状态: PENDING
│
▼
4. 手动触发本地命令 (定期执行)
/discover-projects
│
├─ 通过 curl 获取待处理任务
├─ 调用 Content Explorer Agent
│ ├─ 使用 agent-browser 探索项目
│ ├─ 提取项目信息 (README, 代码结构等)
│ ├─ 生成结构化 JSON 数据
│ └─ 应用内容质量标准
│
├─ 调用 API Submitter Agent
│ ├─ 批量标记任务为 IN_PROGRESS
│ ├─ 提交探索数据到完成 API
│ └─ 自动重试失败的提交
│
▼
5. 完成任务并入库
POST /api/discovery/tasks/{id}/complete
│
├─ 验证数据格式 (Zod Schema)
├─ 多级去重检测 (GitHub/Website URL)
├─ 创建或更新 Project
├─ 创建 Tag 和关联关系
├─ 创建 ExternalLink
│
├─ 更新任务状态: COMPLETED/FAILED
│
▼
6. 数据已入库，可在前台展示
```
---
## 🔄 详细步骤说明
### 步骤 1: n8n 自动收集项目链接
**平台**: n8n 自动化平台 (独立部署)
**工作内容**:
- 定期抓取 GitHub Trending、Twitter、HackerNews 等平台
- 根据关键词筛选 AI 相关项目
- 提取项目的基本信息 (名称、链接、简介等)
**输出数据格式**:
```json
{
"sourceUrl": "https://github.com/langchain-ai/langchain",
"sourceType": "github_trending" // 或 "twitter", "hackernews" 等
}
```
---
### 步骤 2: 调用创建任务 API
**API 端点**: `POST /api/discovery/tasks`
**调用示例**:
```bash
curl -X POST https://your-domain.com/api/discovery/tasks \
-H "Content-Type: application/json" \
-H "x-api-key: YOUR_API_KEY" \
-d '{
"apiKey": "YOUR_API_KEY",
"tasks": [
{
"sourceUrl": "https://github.com/langchain-ai/langchain",
"sourceType": "github_trending"
},
{
"sourceUrl": "https://github.com/openai/openai-quickstart-python",
"sourceType": "github_trending"
}
]
}'
```
**数据库变更**:
- 在 `ProjectDiscoveryTask` 表中插入新记录
- `status` = `PENDING`
- `sourceUrl` 和 `sourceType` 来自 n8n
- `createdAt` = 当前时间
---
### 步骤 2.5: 调用去重检查 API（推荐）
**API 端点**: `POST /api/discovery/check-duplicates`
**调用示例**:
```bash
curl -X POST https://your-domain.com/api/discovery/check-duplicates \
-H "Content-Type: application/json" \
-d '{
"apiKey": "YOUR_API_KEY",
"urls": [
"https://github.com/langchain-ai/langchain",
"https://github.com/openai/openai-quickstart-python"
]
}'
```
**返回结果**:
```json
{
"success": true,
"results": [
{
"url": "https://github.com/langchain-ai/langchain",
"shouldCreate": false,
"reason": "Task already exists with status PENDING",
"existingTask": {
"id": "cmxxxxx",
"status": "PENDING",
"sourceUrl": "https://github.com/langchain-ai/langchain",
"createdAt": "2025-01-18T10:00:00Z",
"projectId": null
}
},
{
"url": "https://github.com/openai/openai-quickstart-python",
"shouldCreate": true,
"reason": "No existing task or project found"
}
],
"stats": {
"total": 2,
"shouldCreate": 1,
"duplicate": 1
}
}
```
**去重逻辑**（按优先级）:
1. **优先级 1**: 检查是否有 PENDING/IN_PROGRESS 的相同 URL 任务
- 如果存在 → `shouldCreate: false`
- 原因：任务已在处理中，避免重复探索
2. **优先级 2**: 检查是否有 COMPLETED/FAILED 的相同 URL 任务
- 如果存在 → `shouldCreate: false`
- 原因：任务已探索过，无需重复
3. **优先级 3**: 检查 URL 对应的项目是否已存在（通过 ExternalLink）
- 如果存在 → `shouldCreate: false`
- 原因：项目已通过其他来源收录
4. **默认**: 允许创建新任务
- `shouldCreate: true`
**n8n 集成建议**:
```javascript
// n8n Workflow 示例
const checkResponse = await fetch('https://your-domain.com/api/discovery/check-duplicates', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
apiKey: 'YOUR_API_KEY',
urls: collectedUrls // 从上一步收集的 URL 列表
})
})
const { results, stats } = await checkResponse.json()
// 过滤出应该创建任务的 URL
const urlsToCreate = results
.filter(r => r.shouldCreate)
.map(r => r.url)
// 只为不重复的 URL 创建任务
if (urlsToCreate.length > 0) {
await fetch('https://your-domain.com/api/discovery/tasks', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
apiKey: 'YOUR_API_KEY',
tasks: urlsToCreate.map(url => ({
sourceUrl: url,
sourceType: 'github_trending'
}))
})
})
}
console.log(`创建 ${urlsToCreate.length} 个新任务，跳过 ${stats.duplicate} 个重复任务`)
```
---
### 步骤 3: 手动触发本地命令
**执行环境**: 本地开发环境 (Local Development)
**执行命令**:
```bash
# 处理默认 10 个任务 (每批 3 个)
/discover-projects
# 处理指定数量的任务
/discover-projects 20
# 自定义批次大小
/discover-projects 9 --batch=2
# 处理所有待处理任务
/discover-projects all --batch=5
```
**命令执行流程**:
1. 通过 curl 调用 `GET /api/discovery/tasks?status=PENDING&limit=N`
2. 获取待处理的任务列表
3. 分批处理 (默认每批 3 个任务)
4. 为每个任务启动 **Content Explorer Agent**
---
### 步骤 3.1: Content Explorer Agent (项目探索)
**Agent 定义**: `.claude/agents/content-explorer-agent.md`
**核心能力**:
- 使用 `agent-browser` 子任务并行探索 GitHub 项目
- 访问项目主页、README、代码结构
- 提取项目元数据 (名称、描述、标签、链接等)
- 生成符合 `ProjectInputSchema` 的 JSON 数据
**内容质量标准** (参考: `.claude/schemas/project-content-template.md`):
- ✅ **描述**: 客观说明功能、突出价值、避免营销术语、10-500字
- ✅ **内容**: 从 README 提取并重新组织、符合中文表达习惯
- ✅ **链接**: 必须包含 GITHUB 链接、所有链接可访问
- ✅ **标签**: 1-10 个标签、按技术/应用/状态分类
- ✅ **动态数据**: Star/Fork 等动态数据不写入内容，使用 GitHub Badge
**输出数据格式**:
```json
{
"name": "LangChain",
"nameEn": "LangChain",
"description": "开发由 LLM 驱动的应用程序的框架，提供文档加载、文本分割、向量存储等核心组件",
"descriptionEn": "Framework for developing applications powered by language models",
"content": "## 核心功能\n\n- 文档加载: 支持 PDF、TXT、网页等多种格式\n- 文本分割: 智能分割长文本\n...",
"tags": [
{ "name": "LLM", "nameEn": "Large Language Model" },
{ "name": "框架", "nameEn": "Framework" }
],
"links": [
{ "type": "GITHUB", "url": "https://github.com/langchain-ai/langchain", "title": "GitHub 仓库" },
{ "type": "WEBSITE", "url": "https://python.langchain.com", "title": "官方文档" }
],
"status": "ACTIVE"
}
```
---
### 步骤 3.2: API Submitter Agent (数据提交)
**Agent 定义**: `.claude/agents/api-submitter-agent.md`
**核心能力**:
- 批量标记任务为 `IN_PROGRESS`
- 调用 `POST /api/discovery/tasks/{id}/complete` 提交数据
- 自动重试失败的提交 (指数退避，最多 3 次)
- 处理部分成功/失败情况
**提交流程**:
```
1. PATCH /api/discovery/tasks/{id} → status=IN_PROGRESS
↓
2. POST /api/discovery/tasks/{id}/complete → 提交探索数据
↓
3. 检查响应
├─ 成功 → 标记任务完成
├─ 失败 → 重试 (最多 3 次)
└─ 最终失败 → 记录错误信息
```
---
### 步骤 4: 完成任务并入库
**API 端点**: `POST /api/discovery/tasks/{id}/complete`
**处理逻辑** (参考: `src/app/api/discovery/tasks/[id]/complete/route.ts`):
1. **验证数据格式**: 使用 `ProjectInputSchema` 验证
2. **多级去重检测**:
- 优先级 1: GitHub URL 精确匹配
- 优先级 2: Website URL 精确匹配
- 优先级 3: slug 匹配
3. **创建或更新项目**:
- 如果存在重复项目 → 更新所有字段、标签、链接
- 如果不存在 → 创建新项目
4. **更新任务状态**:
- 成功 → `COMPLETED`
- 失败 → `FAILED` (记录错误信息)
**数据库变更**:
- `Project` 表: 创建或更新记录
- `Tag` 表: Upsert 标签
- `ProjectTag` 表: 创建关联关系
- `ExternalLink` 表: 创建或更新链接
- `ProjectDiscoveryTask` 表: 更新 `status`、`projectId`、`completedAt`
---
## 🗂️ 数据模型关系
```
ProjectDiscoveryTask (任务表)
├─ id: String (主键)
├─ status: TaskStatus (PENDING/IN_PROGRESS/COMPLETED/FAILED)
├─ sourceUrl: String (n8n 提供的原始 URL)
├─ sourceType: String (github_trending/twitter/hackernews)
├─ explorationData: Json (Agent 探索结果)
├─ explorationSummary: String (探索摘要)
├─ projectId: String (关联到 Project.id)
├─ errorMessage: String (失败原因)
└─ createdAt/startedAt/completedAt: DateTime
Project (项目表) ← 通过 projectId 关联
├─ id, name, nameEn, slug
├─ description, descriptionEn
├─ content, contentEn
├─ status (ACTIVE/ARCHIVED)
└─ 关联: tags, links, discoveryTasks
Tag (标签表)
└─ 通过 ProjectTag 多对多关联
ExternalLink (外部链接表)
└─ 通过 projectId 一对多关联
```
---
## 🔧 环境配置
### 环境变量
```env
# .env.local (本地开发)
WEBHOOK_API_KEY=sk_live_your_secure_api_key_min_32_chars # 用于调用完成 API
# 生产环境 (Vercel Dashboard 配置)
DATABASE_URL=postgres://...
WEBHOOK_API_KEY=sk_live_your_secure_api_key_min_32_chars
```
### 依赖服务
1. **n8n 平台**:
- 独立部署 (自托管或云服务)
- 配置定时工作流 (Workflow)
- 存储 `WEBHOOK_API_KEY` 用于 API 调用
2. **本地开发环境**:
- Node.js 18+
- pnpm 包管理器
- Claude Code CLI (支持斜杠命令)
3. **生产环境**:
- Vercel (Next.js 部署)
- Neon PostgreSQL (数据库)
---
## 📊 执行监控
### 查看待处理任务
```bash
# 查询待处理任务数量
curl https://your-domain.com/api/discovery/tasks?status=PENDING&limit=100
# 查询进行中的任务
curl https://your-domain.com/api/discovery/tasks?status=IN_PROGRESS
# 查询失败的任务 (需要重试)
curl https://your-domain.com/api/discovery/tasks?status=FAILED
```
### 重试失败任务
```bash
# 手动重试失败的任务
/discover-projects 10 --status=FAILED
```
---
## 🎯 质量保障
### 内容质量标准 (详细参考: `.claude/schemas/project-content-template.md`)
1. **描述要求**:
- 清晰说明项目的核心功能
- 突出项目的独特价值
- 避免使用营销术语 ("最好"、"第一"、"革命性" 等)
- 字数控制在 10-500 字
2. **内容要求**:
- 从项目 README 提取并重新组织
- 避免机械翻译，符合中文表达习惯
- 支持 Markdown 格式
- 动态数据 (Star/Fork) 不写入内容
3. **链接要求**:
- 必须包含 GITHUB 链接
- 所有链接必须可访问
- 链接类型必须正确 (WEBSITE/GITHUB/HUGGINGFACE/PAPER)
4. **标签要求**:
- 1-10 个标签
- 按技术栈/应用领域/开发状态分类
- 中英文对应
### 数据验证
所有提交的数据必须通过 `ProjectInputSchema` 验证 (详见 `src/lib/validations.ts`):
```typescript
ProjectInputSchema {
name: string (1-200字符, 必填)
description: string (10-500字符, 必填)
tags: Tag[] (1-10个, 必填)
links: ExternalLink[] (1-10个, 必填)
nameEn?: string (1-200字符)
descriptionEn?: string
content?: string (Markdown, 最多10000字符)
contentEn?: string
status?: "ACTIVE" | "ARCHIVED"
source?: string
}
```
---
## 🚀 快速开始
### 第一次使用
1. **配置 n8n 工作流**:
- 创建新的 n8n Workflow
- 配置定时触发器 (如每天凌晨 2 点)
- 添加 HTTP Request 节点调用 `POST /api/discovery/tasks`
2. **本地执行命令**:
```bash
# 启动开发服务器 (如果未运行)
pnpm dev
# 执行项目发现命令
/discover-projects
```
3. **查看结果**:
- 访问 https://your-domain.com 查看新收录的项目
- 检查数据库确认数据正确性
### 日常维护
1. **定期执行命令** (建议每天 1-2 次):
```bash
/discover-projects 20
```
2. **监控失败任务**:
- 检查 `status=FAILED` 的任务
- 分析错误原因 (网络问题、数据格式等)
- 必要时手动重试
3. **优化内容质量**:
- 随机抽查已收录项目的描述和内容
- 调整 Agent 的提示词以提升质量
---
## 🔍 故障排查
### 常见问题
1. **任务长时间处于 IN_PROGRESS 状态**:
- 可能原因: Agent 探索超时、网络问题
- 解决方案: 手动更新任务状态为 PENDING 后重试
2. **大量任务失败**:
- 检查 `errorMessage` 字段
- 常见原因: 数据格式不正确、URL 无法访问、API Key 错误
3. **重复项目被创建**:
- 检查去重逻辑是否正常工作
- 确认 `ExternalLink` 表的索引 `idx_link_type_url` 存在
4. **n8n 无法调用 API**:
- 检查 `WEBHOOK_API_KEY` 是否正确配置
- 确认 API 端点可访问
- 查看 n8n 执行日志
---
## 📚 相关文档
- **API 参考**: `docs/api-reference.md`
- **数据模型**: `prisma/schema.prisma`
- **验证规则**: `src/lib/validations.ts`
- **Agent 定义**: `.claude/agents/content-explorer-agent.md`
- **Agent 定义**: `.claude/agents/api-submitter-agent.md`
- **内容质量标准**: `.claude/schemas/project-content-template.md`
- **项目主文档**: `CLAUDE.md`
---
## 📝 更新日志
- **2025-01-18**: 创建文档，记录完整的项目发现工作流
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# Timeline E2E 测试指南
## 使用 chrome-devtools-mcp 测试
### 1. 启动测试环境
```bash
# 确保开发服务器运行
pnpm dev
```
### 2. 页面加载测试
使用 `new_page` 或 `navigate_page`:
```
URL: http://localhost:3000/timeline
预期: 页面成功加载，中文路径重定向到 /zh/timeline
```
使用 `take_snapshot`:
- 验证页面结构正确，包含 header 和 timeline sections
- 验证导航菜单包含"AI 时间轴"链接
### 3. 数据验证
使用 `evaluate_script`:
```javascript
() => {
const yearSections = document.querySelectorAll('section');
const eventCards = document.querySelectorAll('.stack-card');
return {
yearCount: yearSections.length,
eventCount: eventCards.length,
hasHeader: document.querySelector('h1') !== null
};
}
```
预期结果:
```json
{
"yearCount": 2,
"eventCount": 3,
"hasHeader": true
}
```
### 4. 无控制台错误
使用 `list_console_messages` with `types: ["error", "warn"]`:
- Expected: 空数组或仅资源预加载警告（可忽略）
### 5. 响应式测试
使用 `resize_page`:
- 桌面: 1920x1080
- 移动: 375x667 (iPhone SE)
验证: 布局在不同尺寸下正常显示，移动端显示汉堡菜单
### 6. 截图对比
使用 `take_screenshot`:
- 保存路径: `tests/screenshots/timeline-page.png`
- 手动对比与设计原型
- 验证视觉风格符合 Neo-brutalism 设计
## 测试记录
### 2025-01-27 测试结果
- ✅ 页面加载成功（自动重定向到 /zh/timeline）
- ✅ 数据渲染正确（2年3事件：2018年2个，2017年1个）
- ✅ 年份降序排列（2018 → 2017）
- ✅ 导航菜单"AI 时间轴"链接正常
- ✅ 无控制台错误（仅1个资源预加载警告）
- ✅ 移动端响应式布局正常
- ✅ 桌面端截图已保存
### 测试数据
**2018年事件（2个）:**
1. BERT发布 - Google发布BERT预训练模型 (2018/10/11)
2. GPT-1发布 - OpenAI发布第一代GPT模型 (2018/6/11)
**2017年事件（1个）:**
1. Transformer论文 - Google团队发表Transformer架构 (2017/6/12)
### 测试环境
- Node.js: v22
- Next.js: 15.1.11
- 浏览器: Chrome (chrome-devtools-mcp)
- 测试时间: 2025-01-27 21:40
### 已知问题
无
### 后续优化
- 添加更多历史事件数据
- 实现搜索和筛选功能
- 添加事件详情页面
- 优化移动端卡片间距
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# n8n Tag Janitor Workflow
## Overview
Daily automated workflow to clean up duplicate/similar tags using AI semantic analysis.
## Workflow Structure
[Cron] → [HTTP GET /api/tags] → [Code: Preprocess] → [AI: Generate Plan] → [Code: Validate JSON] → [HTTP POST /api/tags/maintenance] → [Notification]
## Node Configuration
### 1. Schedule Trigger (Cron)
- Trigger: Daily at 03:00 UTC
- Timezone: UTC
### 2. HTTP Request - Fetch Tags
- Method: GET
- URL: `{{$env.SITE_BASE_URL}}/api/tags`
- Response: JSON with `.tags` array
### 3. Code Node - Preprocess
Purpose: Format tags for LLM input, handle chunking if >200 tags
```javascript
const tags = $input.first().json.tags;
const formatted = tags.map((t) => ({
id: t.id,
name: t.name,
nameEn: t.nameEn || "",
projectCount: t._count.projects,
}));
// Sort by projectCount desc for prioritization
formatted.sort((a, b) => b.projectCount - a.projectCount);
return [{ json: { tags: formatted, total: formatted.length } }];
```
### 4. AI Node - Generate Merge Plan
Model: GPT-4o / Claude 3.5 Sonnet
Temperature: 0.1 (deterministic)
Prompt Template:
```
You are a tag management expert. Analyze these tags and identify:
1. Semantic duplicates that should be merged (e.g., "机器学习" and "ML" → keep "机器学习" with nameEn "Machine Learning")
2. Tags missing English names that need nameEn补全
Tags (JSON):
{{$json.tags}}
Output STRICT JSON (no markdown, no explanation):
{
"merges": [
{
"target": { "name": "保留的标签名", "nameEn": "Canonical English Name" },
"sourceTagIds": ["id1", "id2"]
}
],
"updates": [
{ "tagId": "id", "nameEn": "English Name" }
]
}
Rules:
- Keep the tag with higher projectCount as target
- For merges, target can be { "id": "existing_id" } if keeping existing tag, or { "name": "...", "nameEn": "..." } to create new
- Only include tags that NEED action (empty arrays if nothing to do)
- nameEn should be proper English, not pinyin
- Common tech terms: 机器学习=Machine Learning, 深度学习=Deep Learning, 自然语言处理=NLP
```
### 5. Code Node - Validate & Parse
```javascript
const response = $input.first().json;
let plan;
try {
plan = typeof response === "string" ? JSON.parse(response) : response;
} catch (e) {
throw new Error("Invalid JSON from AI: " + e.message);
}
// Validate structure
if (!Array.isArray(plan.merges)) plan.merges = [];
if (!Array.isArray(plan.updates)) plan.updates = [];
// Self-merge check: target.id cannot be in sourceTagIds
for (const merge of plan.merges) {
if (merge.target.id && merge.sourceTagIds.includes(merge.target.id)) {
throw new Error("Self-merge detected: " + merge.target.id);
}
}
return [{ json: plan }];
```
### 6. HTTP Request - Execute Maintenance
- Method: POST
- URL: `{{$env.SITE_BASE_URL}}/api/tags/maintenance`
- Body:
```json
{
"apiKey": "{{$env.WEBHOOK_API_KEY}}",
"updates": {{$json.updates}},
"merges": {{$json.merges}}
}
```
### 7. Notification (Slack/Email/Webhook)
Send summary:
- Tags merged: X
- Tags deleted: Y
- Names updated: Z
- Errors: [list]
## Environment Variables Required
- `SITE_BASE_URL`: https://your-site.com
- `WEBHOOK_API_KEY`: API key for authentication
## Chunking Strategy (for >200 tags)
1. Split tags into batches of 100
2. Process each batch sequentially
3. Aggregate results before final notification
## Error Handling
- Retry HTTP requests 3x with exponential backoff
- On AI parse failure: skip and alert
- On maintenance failure: log error, continue with notification
## Testing
1. Dry run: Comment out HTTP POST node, check AI output only
2. Real run: Enable all nodes, monitor /api/tags count before/after
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# Frontier Signals Workflow
## Goal
Aggregate frontier discussions from multiple platforms, keep only **AI Agent-related** signals via AI filtering, and ingest into `POST /api/webhook/signals`.
## Workflow
- Workflow name: `前沿信号聚合（多源+AI Agent过滤）`
- Workflow ID: `bAxNZKGq2ApUUiw9`
- Status: `active`
- Trigger: every 4 hours (`Schedule Trigger`)
- Activated at: `2026-02-23`
## Source Research (Endpoints + Extracted Elements)
| Source | Endpoint | Node Type | Extracted Elements |
| --- | --- | --- | --- |
| Hacker News | `https://hacker-news.firebaseio.com/v0/topstories.json` + `.../item/{id}.json` | HTTP Request | `title`, `url`, `text`, `score`, `descendants`, `time` |
| GitHub | `https://api.github.com/search/issues` | HTTP Request | `title`, `html_url`, `body`, `comments`, `reactions`, `labels`, `updated_at` |
| arXiv | `https://export.arxiv.org/api/query?...` | RSS Read | `title`, `link`, `content/contentSnippet`, `pubDate/isoDate`, `categories` |
| Reddit | `https://www.reddit.com/r/LocalLLaMA/new.json?limit=40` | HTTP Request | `title`, `permalink`, `selftext/url`, `ups`, `num_comments`, `created_utc` |
| Product Hunt | `https://www.producthunt.com/feed` | RSS Read | `title`, `link`, `content/contentSnippet`, `published/updated` |
| Hugging Face | `https://huggingface.co/blog/feed.xml` | RSS Read | `title`, `link`, `content/contentSnippet`, `published/updated` |
## Why These Nodes
- `Schedule Trigger`: periodic ingestion
- `HTTP Request` / `RSS Read`: source fetching with stable machine-readable endpoints
- `Code`: per-source normalization and schema-safe cleanup
- `Merge (append)`: multi-source union
- `Remove Duplicates`: `source + sourceUrl` dedupe before/after AI
- `Limit`: cap candidate volume before AI
- `LLM Chain + Structured Output Parser`: relevance filtering + structured sections
- `If`: keep only `shouldKeep=true`
- `HTTP Request` (POST): write to `/api/webhook/signals`
## Target Contract Mapping
The workflow emits payload compatible with `SignalWebhookPayloadSchema`:
- `apiKey`: manually configured in node `构建 Webhook Payload`
- `signals[]`:
- `source` -> one of:
- `hacker_news`
- `github`
- `arxiv`
- `hugging_face`
- `reddit`
- `product_hunt`
- `sourceUrl`, `title`, `summary`, `topic`, `tags`, `sections`, `engagement`, `hotScore`, `isHot`, `publishedAt`, `isActive`
Sections are constrained to:
- style: `focus | debate | evidence | action | risk`
- max 6 sections, max 6 items per section
## AI Filtering Policy
The AI node does **not** score ideas by novelty/reliability/feasibility.
It only decides whether a signal is about AI Agent topics and then structures content for reading efficiency.
- Keep (`shouldKeep=true`) if discussion is materially agent-related
- Drop (`shouldKeep=false`) if clearly unrelated to AI Agent
- Drop low-discussion or low-value question items (`shouldKeep=false`)
- If information is insufficient, default to drop
## High-Heat Gate（AI 前硬过滤）
`筛掉占位候选` 节点会在 AI 前做硬性筛选，减少低价值输入：
- source-level minimum engagement:
- `hacker_news >= 35`
- `github >= 8`
- `reddit >= 25`
- source-level freshness window:
- `hacker_news <= 72h`
- `github <= 168h`
- `reddit/arxiv/hugging_face/product_hunt <= 120h`
- low-value question filtering:
- 求助型单问题 + 短摘要 + 低互动，直接丢弃
- per-source candidate cap（AI 前限流）:
- `hacker_news 6`, `github 6`, `reddit 6`, `arxiv 4`, `hugging_face 4`, `product_hunt 4`
- strict keyword gate for low-discussion sources:
- `arxiv/hugging_face/product_hunt` 必须命中 agent 相关关键词（如 `agent`, `agentic`, `multi-agent`, `tool calling`, `mcp`, `智能体`）才进入 AI
## HOT 判定（流程内）
`清洗并映射入库字段` Code 节点会基于**互动量 + 时间新鲜度**计算：
- `hotScore`：0-100
- `isHot`：布尔值，用于页面 HOT 标签与排序优先
该判定只衡量讨论热度，不评价观点对错或质量。
## DB 升级（新增 HOT 字段）
由于当前仓库默认忽略 `prisma/migrations/*_*` 目录，建议在数据库手动执行一次：
```sql
ALTER TABLE "signals"
ADD COLUMN IF NOT EXISTS "hotScore" INTEGER NOT NULL DEFAULT 0,
ADD COLUMN IF NOT EXISTS "isHot" BOOLEAN NOT NULL DEFAULT false;
CREATE INDEX IF NOT EXISTS "idx_signal_hot_score" ON "signals"("hotScore");
CREATE INDEX IF NOT EXISTS "idx_signal_active_hot_sort" ON "signals"("isActive", "isHot", "hotScore", "publishedAt");
```
## Manual Configuration (No Env)
This workflow is intentionally configured without `$env` usage.
- Node `构建 Webhook Payload`:
- set `apiKey` to your real webhook key (replace placeholder string)
- Node `发送到 Signals Webhook`:
- set the target URL to your actual API base (current default is `https://agentpark.fun/api/webhook/signals`)
## Notes
- Reddit source uses JSON API with explicit `User-Agent` headers to reduce 403 blocking risk.
- `n8n_test_workflow` cannot trigger schedule-only workflows via API; runtime verification should be done by waiting for scheduled execution or manual run in n8n UI.
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# n8n 历史数据初始化 Workflow
## 概述
此 workflow 用于一次性收集和初始化 2017-2025 年的 AI 重大事件数据。
## Workflow 结构
### Node 1: Cron 触发器(手动触发)
- 节点类型: `Manual Trigger`
- 用途: 开发测试时手动运行
### Node 2: 设置年份列表
- 节点类型: `Code`
- 用途: 定义要处理的年份列表
```javascript
// 返回年份数组
return [
{ year: 2017 },
{ year: 2018 },
{ year: 2019 },
{ year: 2020 },
{ year: 2021 },
{ year: 2022 },
{ year: 2023 },
{ year: 2024 },
{ year: 2025 },
];
```
### Node 3: 搜索 Agent(循环每年)
- 节点类型: `Loop Over Items`
- 用途: 遍历每个年份
### Node 4: Web Search - Agent 1
- 节点类型: `HTTP Request`
- 方法: POST
- URL: `http://localhost:3000/api/web-search` (或 MCP 端点)
- Headers:
```json
{
"Content-Type": "application/json"
}
```
- Body:
```json
{
"search_query": "AI breakthrough {{ $json.year }} LLM release transformer model",
"search_recency_filter": "noLimit",
"content_size": "high"
}
```
### Node 5: 筛选 Agent - Agent 2
- 节点类型: `Code`
- 用途: 根据权威来源筛选
```javascript
const trustedDomains = [
'arxiv.org',
'openai.com',
'anthropic.com',
'google.ai',
'meta.ai',
'deepmind.com',
'research.google',
];
const items = $input.all();
const filtered = items.filter(item => {
const url = item.json.url || '';
return trustedDomains.some(domain => url.includes(domain));
});
return filtered;
```
### Node 6: 格式化 Agent - Agent 3
- 节点类型: `Code`
- 用途: 转换为 API 格式
```javascript
const items = $input.all();
const formatted = items.map(item => {
const publishedDate = item.json.published_date || new Date().toISOString();
return {
json: {
title: item.json.title || 'Untitled',
eventDate: new Date(publishedDate).toISOString(),
description: (item.json.description || item.json.snippet || '').substring(0, 500),
imageUrl: item.json.image_url || 'https://images.unsplash.com/photo-1677442136019-21780ecad995?w=800',
sourceUrl: item.json.url,
},
};
});
return formatted;
```
### Node 7: 提交到 API
- 节点类型: `HTTP Request`
- 方法: POST
- URL: `http://localhost:3000/api/events`
- Headers:
```json
{
"Content-Type": "application/json",
"X-API-Key": "={{ $env.WEBHOOK_API_KEY }}"
}
```
- Body: `={{ $json }}` (发送整个数组)
### Node 8: 错误处理
- 节点类型: `IF`
- 条件: 检查上一个节点的 status code
- On True: 记录成功
- On False: 发送错误邮件
## 环境变量
在 n8n 中设置:
- `WEBHOOK_API_KEY`: 你的 API 密钥(从 .env.local 获取)
- `API_ENDPOINT`: `http://localhost:3000/api/events` (开发) 或生产 URL
## 测试步骤
1. 在 n8n UI 中创建此 workflow
2. 手动触发运行
3. 检查数据库: `pnpm prisma studio`
4. 验证事件已正确创建
## 数据质量标准
### 标题要求
- 清晰描述事件
- 1-200 字符
- 避免营销术语
### 描述要求
- 客观描述功能和价值
- 10-500 字符
- 突出技术亮点
### 日期要求
- ISO 8601 格式
- 准确的发布日期
### 链接要求
- 必须包含 sourceUrl（权威来源）
- 链接可访问
- 优先 arxiv.org、openai.com 等
## 权威来源列表
- 学术论文: arxiv.org
- 官方博客: openai.com, anthropic.com, google.ai, meta.ai
- 研究机构: deepmind.com, research.google
- 新闻媒体: techcrunch.com, theverge.com (需人工审核)
## 注意事项
1. **去重**: workflow 会自动跳过重复的事件（基于 sourceUrl）
2. **图片**: 如果没有图片，使用默认占位图
3. **错误处理**: 失败的事件会被记录，不会中断整个流程
4. **数据验证**: API 会验证所有字段，不符合标准的数据会被拒绝
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# n8n 增量更新 Workflow
## 概述
此 workflow 每周一自动运行，收集最近 7 天的新 AI 事件。
## Workflow 结构
### Node 1: Cron 触发器
- 节点类型: `Cron`
- 表达式: `0 9 * * 1` (每周一早上 9:00)
- 时区: Asia/Shanghai
### Node 2: Web Search - Agent 1
- 节点类型: `HTTP Request`
- URL: `http://localhost:3000/api/web-search` (或 MCP 端点)
- Body:
```json
{
"search_query": "AI news LLM release model launch this week",
"search_recency_filter": "oneWeek"
}
```
### Node 3: 筛选 Agent - Agent 2
- 节点类型: `Code`
- 用途: 筛选 + 去重(查询数据库避免重复)
```javascript
const trustedDomains = [
'arxiv.org',
'openai.com',
'anthropic.com',
'google.ai',
'meta.ai',
'deepmind.com',
];
// 过滤权威来源
const items = $input.all();
const filtered = items.filter(item => {
const url = item.json.url || '';
return trustedDomains.some(domain => url.includes(domain));
});
// TODO: 添加数据库查询去重
// 这里可以调用 GET /api/events 检查 sourceUrl 是否已存在
return filtered;
```
### Node 4: 格式化 Agent - Agent 3
- 节点类型: `Code`
- 代码: 同历史 workflow
### Node 5: 提交到 API
- 节点类型: `HTTP Request`
- 配置: 同历史 workflow
### Node 6: 发送通知邮件
- 节点类型: `Send Email`
- 条件: 仅在创建新事件时发送
- 内容:
```
主题: AI Timeline - 新事件已添加
本次更新添加了 {{ $json.created }} 个新事件。
查看: https://your-domain.com/timeline
```
### Node 7: 错误处理
- 节点类型: `Error Trigger`
- 动作: 发送错误邮件到管理员
## 测试
1. 修改 Cron 为手动触发进行测试
2. 验证只有新事件被添加
3. 检查邮件通知是否正常发送
4. 确认错误处理工作正常
## 数据质量保证
### 自动筛选规则
1. **来源可信**: 仅来自权威域名
2. **时效性**: 仅最近 7 天的内容
3. **去重**: 基于 sourceUrl 自动去重
### 人工审核流程
建议在自动导入后进行人工审核：
1. 检查标题是否准确
2. 验证描述是否客观
3. 确认图片是否合适
4. 测试链接是否可访问
## 邮件通知配置
### 成功通知
当有新事件添加时发送：
- 收件人: 内容团队
- 主题: "AI Timeline - {{ count }} 个新事件已添加"
- 内容: 包含事件列表和链接
### 错误通知
当 workflow 失败时发送：
- 收件人: 技术团队
- 主题: "⚠️ AI Timeline Workflow 失败"
- 内容: 错误详情和日志
## 监控指标
建议监控以下指标：
- 每周添加的事件数量
- workflow 执行时间
- 失败率和错误类型
- 去重率
## 优化建议
1. **AI 辅助筛选**: 使用 AI 模型评估新闻相关性
2. **多源聚合**: 整合多个搜索 API
3. **智能去重**: 基于标题相似度去重
4. **自动翻译**: 自动生成英文翻译（titleEn, descriptionEn）
docs/n8n/project-tag-reset-workflow.json
-435
View File
@@ -1,435 +0,0 @@
{
"name": "Project Tag Reset - Multi AI Classifier",
"nodes": [
{
"parameters": {},
"id": "manual-trigger",
"name": "手动触发",
"type": "n8n-nodes-base.manualTrigger",
"typeVersion": 1,
"position": [
0,
0
]
},
{
"parameters": {
"url": "https://www.agentpark.fun/api/tags",
"options": {}
},
"id": "fetch-tags",
"name": "获取标签池",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.4,
"position": [
220,
0
]
},
{
"parameters": {
"url": "https://www.agentpark.fun/api/projects",
"sendQuery": true,
"specifyQuery": "keypair",
"queryParameters": {
"parameters": [
{
"name": "limit",
"value": "100"
},
{
"name": "page",
"value": "1"
},
{
"name": "sort",
"value": "latest"
}
]
},
"options": {
"pagination": {
"pagination": {
"paginationMode": "updateAParameterInEachRequest",
"parameters": {
"parameters": [
{
"type": "qs",
"name": "page",
"value": "={{($response.body.pagination.page ?? $response.body.pagination.currentPage ?? 1) + 1}}"
}
]
},
"paginationCompleteWhen": "other",
"completeExpression": "={{!$response.body.pagination || (($response.body.pagination.page ?? $response.body.pagination.currentPage ?? 1) >= ($response.body.pagination.totalPages ?? 1))}}",
"limitPagesFetched": true,
"maxRequests": 20,
"requestInterval": 0
}
}
}
},
"id": "fetch-projects",
"name": "获取项目列表",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.4,
"position": [
440,
0
]
},
{
"parameters": {
"jsCode": "const tagsResponse = $('获取标签池').first().json;\nconst project = $input.first().json.projects;\n\nif (!tagsResponse?.success || !Array.isArray(tagsResponse?.tags)) {\n throw new Error('Failed to fetch tags from /api/tags');\n}\n\nif (!project || typeof project !== 'object' || !project.slug) {\n throw new Error('Invalid project item after split');\n}\n\nconst allowedCategories = [\n 'FIXED_PROJECT_TYPE',\n 'TECH_STACK',\n 'AI_PARADIGM',\n 'PRODUCT_FORM',\n 'DOMAIN_SCENARIO',\n];\n\nconst tagPools = Object.fromEntries(\n allowedCategories.map((category) => [\n category,\n tagsResponse.tags\n .filter((tag) => tag.category === category)\n .map((tag) => ({\n id: tag.id,\n slug: tag.slug,\n name: tag.name,\n nameEn: tag.nameEn || '',\n })),\n ])\n);\n\nreturn [\n {\n json: {\n projectSlug: project.slug,\n projectName: project.name,\n projectNameEn: project.nameEn || '',\n projectDescription: project.description,\n projectDescriptionEn: project.descriptionEn || '',\n currentTags: Array.isArray(project.tags)\n ? project.tags.map((tag) => ({\n slug: tag.slug,\n name: tag.name,\n nameEn: tag.nameEn || '',\n }))\n : [],\n tagPools,\n },\n },\n];"
},
"id": "prepare-classification-items",
"name": "准备分类输入",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
660,
0
]
},
{
"parameters": {
"model": "gpt-4o-mini",
"messages": {
"values": [
{
"role": "system",
"content": "You classify projects into FIXED_PROJECT_TYPE. Return STRICT JSON only."
},
{
"role": "user",
"content": "=Project:\\n- slug: {{$json.projectSlug}}\\n- name: {{$json.projectName}}\\n- nameEn: {{$json.projectNameEn}}\\n- description: {{$json.projectDescription}}\\n- descriptionEn: {{$json.projectDescriptionEn}}\\n- currentTags: {{JSON.stringify($json.currentTags)}}\\n\\nCandidate pool (FIXED_PROJECT_TYPE):\\n{{JSON.stringify($json.tagPools.FIXED_PROJECT_TYPE)}}\\n\\nTask:\\n- Choose exactly ONE best slug from candidate pool.\\n\\nOutput strict JSON only:\\n{\\n \"selected\": [\"one-slug\"]\\n}"
}
]
},
"options": {
"temperature": 0.1
}
},
"id": "ai-fixed-project-type",
"name": "AI 固定项目分类",
"type": "@n8n/n8n-nodes-langchain.openAi",
"typeVersion": 1.8,
"position": [
880,
0
]
},
{
"parameters": {
"model": "gpt-4o-mini",
"messages": {
"values": [
{
"role": "system",
"content": "You classify DOMAIN_SCENARIO tags. Return STRICT JSON only."
},
{
"role": "user",
"content": "=Project:\\n- slug: {{$json.projectSlug}}\\n- name: {{$json.projectName}}\\n- description: {{$json.projectDescription}}\\n- currentTags: {{JSON.stringify($json.currentTags)}}\\n\\nCandidate pool (DOMAIN_SCENARIO):\\n{{JSON.stringify($json.tagPools.DOMAIN_SCENARIO)}}\\n\\nTask:\\n- Choose 1 to 3 slugs from candidate pool.\\n- Keep only the most representative domains for this project.\\n\\nOutput strict JSON only:\\n{\\n \"selected\": [\"slug1\", \"slug2\"]\\n}"
}
]
},
"options": {
"temperature": 0.1
}
},
"id": "ai-domain-scenario",
"name": "AI 领域场景分类",
"type": "@n8n/n8n-nodes-langchain.openAi",
"typeVersion": 1.8,
"position": [
1100,
0
]
},
{
"parameters": {
"model": "gpt-4o-mini",
"messages": {
"values": [
{
"role": "system",
"content": "You classify PRODUCT_FORM tags. Return STRICT JSON only."
},
{
"role": "user",
"content": "=Project:\\n- slug: {{$json.projectSlug}}\\n- name: {{$json.projectName}}\\n- description: {{$json.projectDescription}}\\n- currentTags: {{JSON.stringify($json.currentTags)}}\\n\\nCandidate pool (PRODUCT_FORM):\\n{{JSON.stringify($json.tagPools.PRODUCT_FORM)}}\\n\\nTask:\\n- Choose 1 to 3 slugs from candidate pool that best represent product form.\\n\\nOutput strict JSON only:\\n{\\n \"selected\": [\"slug1\", \"slug2\"]\\n}"
}
]
},
"options": {
"temperature": 0.1
}
},
"id": "ai-product-form",
"name": "AI 产品形态分类",
"type": "@n8n/n8n-nodes-langchain.openAi",
"typeVersion": 1.8,
"position": [
1320,
0
]
},
{
"parameters": {
"model": "gpt-4o-mini",
"messages": {
"values": [
{
"role": "system",
"content": "You classify TECH_STACK tags. Return STRICT JSON only."
},
{
"role": "user",
"content": "=Project:\\n- slug: {{$json.projectSlug}}\\n- name: {{$json.projectName}}\\n- description: {{$json.projectDescription}}\\n- currentTags: {{JSON.stringify($json.currentTags)}}\\n\\nCandidate pool (TECH_STACK):\\n{{JSON.stringify($json.tagPools.TECH_STACK)}}\\n\\nTask:\\n- Choose 1 to 8 slugs from candidate pool for the main technologies used by this project.\\n\\nOutput strict JSON only:\\n{\\n \"selected\": [\"slug1\", \"slug2\"]\\n}"
}
]
},
"options": {
"temperature": 0.1
}
},
"id": "ai-tech-stack",
"name": "AI 技术栈分类",
"type": "@n8n/n8n-nodes-langchain.openAi",
"typeVersion": 1.8,
"position": [
1540,
0
]
},
{
"parameters": {
"model": "gpt-4o-mini",
"messages": {
"values": [
{
"role": "system",
"content": "You classify AI_PARADIGM tags. Return STRICT JSON only."
},
{
"role": "user",
"content": "=Project:\\n- slug: {{$json.projectSlug}}\\n- name: {{$json.projectName}}\\n- description: {{$json.projectDescription}}\\n- currentTags: {{JSON.stringify($json.currentTags)}}\\n\\nCandidate pool (AI_PARADIGM):\\n{{JSON.stringify($json.tagPools.AI_PARADIGM)}}\\n\\nTask:\\n- Choose 0 to 5 slugs from candidate pool for AI paradigm.\\n\\nOutput strict JSON only:\\n{\\n \"selected\": [\"slug1\", \"slug2\"]\\n}"
}
]
},
"options": {
"temperature": 0.1
}
},
"id": "ai-ai-paradigm",
"name": "AI 技术范式分类",
"type": "@n8n/n8n-nodes-langchain.openAi",
"typeVersion": 1.8,
"position": [
1760,
0
]
},
{
"parameters": {
"jsCode": "function parseNodeJson(nodeJson) {\n const content = nodeJson?.message?.content || nodeJson?.text || nodeJson?.response || nodeJson;\n\n if (typeof content === 'string') {\n const trimmed = content.trim();\n try {\n return JSON.parse(trimmed);\n } catch {\n const jsonMatch = trimmed.match(/\\{[\\s\\S]*\\}/);\n if (jsonMatch) {\n return JSON.parse(jsonMatch[0]);\n }\n return { selected: [] };\n }\n }\n\n if (typeof content === 'object' && content !== null) {\n return content;\n }\n\n return { selected: [] };\n}\n\nfunction normalizeSelected(parsed, pool, minCount, maxCount) {\n const poolSlugs = pool.map((tag) => String(tag.slug).toLowerCase());\n const allowed = new Set(poolSlugs);\n const selectedRaw = Array.isArray(parsed?.selected) ? parsed.selected : [];\n const selected = [];\n\n for (const slug of selectedRaw) {\n const normalizedSlug = String(slug || '').trim().toLowerCase();\n if (!normalizedSlug || !allowed.has(normalizedSlug) || selected.includes(normalizedSlug)) {\n continue;\n }\n selected.push(normalizedSlug);\n if (selected.length >= maxCount) {\n break;\n }\n }\n\n if (selected.length < minCount) {\n return poolSlugs.slice(0, minCount);\n }\n return selected;\n}\n\nconst preparedItems = $items('准备分类输入', 0);\nconst fixedItems = $items('AI 固定项目分类', 0);\nconst domainItems = $items('AI 领域场景分类', 0);\nconst productItems = $items('AI 产品形态分类', 0);\nconst techItems = $items('AI 技术栈分类', 0);\nconst paradigmItems = $items('AI 技术范式分类', 0);\n\nif (\n preparedItems.length !== fixedItems.length ||\n preparedItems.length !== domainItems.length ||\n preparedItems.length !== productItems.length ||\n preparedItems.length !== techItems.length ||\n preparedItems.length !== paradigmItems.length\n) {\n throw new Error('AI node output item count mismatch');\n}\n\nconst output = [];\nfor (let i = 0; i < preparedItems.length; i++) {\n const base = preparedItems[i].json;\n const pools = base.tagPools;\n\n const fixed = normalizeSelected(\n parseNodeJson(fixedItems[i].json),\n pools.FIXED_PROJECT_TYPE,\n 1,\n 1\n );\n const domains = normalizeSelected(\n parseNodeJson(domainItems[i].json),\n pools.DOMAIN_SCENARIO,\n 1,\n 3\n );\n const productForms = normalizeSelected(\n parseNodeJson(productItems[i].json),\n pools.PRODUCT_FORM,\n 1,\n 3\n );\n const techStack = normalizeSelected(\n parseNodeJson(techItems[i].json),\n pools.TECH_STACK,\n 1,\n 8\n );\n const paradigms = normalizeSelected(\n parseNodeJson(paradigmItems[i].json),\n pools.AI_PARADIGM,\n 0,\n 5\n );\n\n output.push({\n json: {\n projectSlug: base.projectSlug,\n selectedTagSlugsByCategory: {\n FIXED_PROJECT_TYPE: fixed,\n TECH_STACK: techStack,\n AI_PARADIGM: paradigms,\n PRODUCT_FORM: productForms,\n DOMAIN_SCENARIO: domains,\n },\n },\n });\n}\n\nreturn output;"
},
"id": "build-reset-payload",
"name": "组装重置载荷",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1980,
0
]
},
{
"parameters": {
"method": "POST",
"url": "https://www.agentpark.fun/api/tags/reset-projects",
"sendBody": true,
"specifyBody": "json",
"jsonBody": "={\n \"apiKey\": \"{{$env.WEBHOOK_API_KEY}}\",\n \"dryRun\": false,\n \"replaceAllCategories\": true,\n \"projects\": [\n {\n \"projectSlug\": \"{{$json.projectSlug}}\",\n \"selectedTagSlugsByCategory\": {{JSON.stringify($json.selectedTagSlugsByCategory)}}\n }\n ]\n}",
"options": {}
},
"id": "reset-project-tags",
"name": "调用标签重置接口",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.4,
"position": [
2200,
0
]
},
{
"parameters": {
"jsCode": "const results = $input.all().map((item) => item.json);\nconst summary = {\n totalRequests: results.length,\n successRequests: results.filter((r) => r.success === true).length,\n failedRequests: results.filter((r) => r.success !== true).length,\n timestamp: new Date().toISOString(),\n};\n\nreturn [{ json: { summary, results } }];"
},
"id": "summarize-results",
"name": "汇总执行结果",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
2420,
0
]
},
{
"parameters": {
"operation": "splitOutItems",
"fieldToSplitOut": "projects",
"include": "noOtherFields",
"options": {}
},
"id": "split-projects",
"name": "拆分项目列表",
"type": "n8n-nodes-base.itemLists",
"typeVersion": 3.1,
"position": [
560,
0
]
}
],
"connections": {
"手动触发": {
"main": [
[
{
"node": "获取标签池",
"type": "main",
"index": 0
}
]
]
},
"获取标签池": {
"main": [
[
{
"node": "获取项目列表",
"type": "main",
"index": 0
}
]
]
},
"获取项目列表": {
"main": [
[
{
"node": "拆分项目列表",
"type": "main",
"index": 0
}
]
]
},
"准备分类输入": {
"main": [
[
{
"node": "AI 固定项目分类",
"type": "main",
"index": 0
}
]
]
},
"AI 固定项目分类": {
"main": [
[
{
"node": "AI 领域场景分类",
"type": "main",
"index": 0
}
]
]
},
"AI 领域场景分类": {
"main": [
[
{
"node": "AI 产品形态分类",
"type": "main",
"index": 0
}
]
]
},
"AI 产品形态分类": {
"main": [
[
{
"node": "AI 技术栈分类",
"type": "main",
"index": 0
}
]
]
},
"AI 技术栈分类": {
"main": [
[
{
"node": "AI 技术范式分类",
"type": "main",
"index": 0
}
]
]
},
"AI 技术范式分类": {
"main": [
[
{
"node": "组装重置载荷",
"type": "main",
"index": 0
}
]
]
},
"组装重置载荷": {
"main": [
[
{
"node": "调用标签重置接口",
"type": "main",
"index": 0
}
]
]
},
"调用标签重置接口": {
"main": [
[
{
"node": "汇总执行结果",
"type": "main",
"index": 0
}
]
]
},
"拆分项目列表": {
"main": [
[
{
"node": "准备分类输入",
"type": "main",
"index": 0
}
]
]
}
},
"settings": {
"executionOrder": "v1"
},
"meta": {
"templateCredsSetupCompleted": true
}
}
docs/n8n/project-tag-reset-workflow.md
-52
View File
@@ -1,52 +0,0 @@
# Project Tag Reset Workflow
## Goal
Reset every project's tags according to the new taxonomy, using **one dedicated AI node per tag category**:
- `FIXED_PROJECT_TYPE`
- `DOMAIN_SCENARIO`
- `PRODUCT_FORM`
- `TECH_STACK`
- `AI_PARADIGM`
Each AI node must select only from its own category pool.
## Files
- Workflow JSON: `docs/n8n/project-tag-reset-workflow.json`
- API endpoint used by workflow: `POST /api/tags/reset-projects`
## Required Environment Variables in n8n
- `WEBHOOK_API_KEY`: same key configured on Next.js server
## Fixed Base URL
- All workflow API URLs are hardcoded to `https://www.agentpark.fun`
## Execution Flow
1. `手动触发`
2. `获取标签池` (`HTTP GET /api/tags`) 读取标签池
3. `获取项目列表` (`HTTP GET /api/projects`) 使用 Query 参数（`limit=100,page=1,sort=latest`）+ HTTP 节点内置分页拉取全部项目
4. `拆分项目列表` 将每页 `projects[]` 拆分为“单项目一条记录”
5. `准备分类输入` 仅做轻量字段整理（不再负责分页/HTTP 请求）
6. 五个独立 AI 节点分别分类：
- `AI 固定项目分类`
- `AI 领域场景分类`
- `AI 产品形态分类`
- `AI 技术栈分类`
- `AI 技术范式分类`
7. `组装重置载荷` 归一化 AI 输出并生成 `selectedTagSlugsByCategory`
8. `调用标签重置接口` (`HTTP POST /api/tags/reset-projects`) 回写标签
9. `汇总执行结果` 输出成功/失败统计
## Notes
- Workflow uses HTTP node built-in pagination for `/api/projects` (`limit=100`) to process full project volume.
- `page` 不再写死在 URL 上，避免出现重复请求同一页导致的 identical response 停止问题。
- Pagination and project fan-out no longer rely on complex Code-node network logic.
- Workflow no longer depends on `SITE_BASE_URL`; base URL is fixed to `https://www.agentpark.fun`.
- Endpoint supports `dryRun`. You can set `"dryRun": true` first in the request node for safe validation.
- Endpoint performs category validation and rejects cross-category slug usage.
docs/n8n/tag-janitor-workflow.json
-151
View File
@@ -1,151 +0,0 @@
{
"name": "Tag Janitor - Daily Cleanup",
"nodes": [
{
"parameters": {
"rule": {
"interval": [
{
"triggerAtHour": 3
}
]
}
},
"id": "schedule",
"name": "Daily 3AM UTC",
"type": "n8n-nodes-base.scheduleTrigger",
"typeVersion": 1.2,
"position": [0, 0]
},
{
"parameters": {
"url": "={{$env.SITE_BASE_URL}}/api/tags",
"options": {}
},
"id": "fetch-tags",
"name": "Fetch Tags",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [220, 0]
},
{
"parameters": {
"jsCode": "const response = $input.first().json;\nif (!response.success) {\n throw new Error('Failed to fetch tags: ' + JSON.stringify(response));\n}\n\nconst tags = response.tags;\nconst formatted = tags.map(t => ({\n id: t.id,\n name: t.name,\n nameEn: t.nameEn || '',\n projectCount: t._count?.projects || 0\n}));\n\n// Sort by project count descending\nformatted.sort((a, b) => b.projectCount - a.projectCount);\n\nreturn [{ json: { tags: formatted, total: formatted.length } }];"
},
"id": "preprocess",
"name": "Preprocess Tags",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [440, 0]
},
{
"parameters": {
"model": "gpt-4o",
"messages": {
"values": [
{
"role": "system",
"content": "You are a tag management expert. Analyze tags and identify semantic duplicates for merging and missing English names for completion. Output STRICT JSON only."
},
{
"role": "user",
"content": "Analyze these tags and identify:\n1. Semantic duplicates to merge (e.g., \"机器学习\" and \"ML\" → keep higher projectCount)\n2. Tags missing nameEn that need English names\n\nTags ({{$json.total}} total):\n{{JSON.stringify($json.tags)}}\n\nOutput STRICT JSON (no markdown):\n{\n \"merges\": [\n {\n \"target\": { \"name\": \"保留的标签\", \"nameEn\": \"Canonical Name\" },\n \"sourceTagIds\": [\"id1\", \"id2\"]\n }\n ],\n \"updates\": [\n { \"tagId\": \"id\", \"nameEn\": \"English Name\" }\n ]\n}\n\nRules:\n- Keep tag with higher projectCount as merge target\n- target can use { \"id\": \"existing\" } to keep existing tag\n- Only include tags needing action (empty arrays if none)\n- nameEn must be proper English, not pinyin"
}
]
},
"options": {
"temperature": 0.1
}
},
"id": "ai-analyze",
"name": "AI Analyze Tags",
"type": "@n8n/n8n-nodes-langchain.openAi",
"typeVersion": 1.8,
"position": [660, 0]
},
{
"parameters": {
"jsCode": "const response = $input.first().json;\nlet plan;\n\ntry {\n const content = response.message?.content || response.text || response;\n plan = typeof content === 'string' ? JSON.parse(content) : content;\n} catch (e) {\n throw new Error('Failed to parse AI response: ' + e.message);\n}\n\nif (!Array.isArray(plan.merges)) plan.merges = [];\nif (!Array.isArray(plan.updates)) plan.updates = [];\n\nfor (const merge of plan.merges) {\n if (merge.target.id && merge.sourceTagIds.includes(merge.target.id)) {\n throw new Error('Self-merge detected: ' + merge.target.id);\n }\n}\n\nif (plan.merges.length === 0 && plan.updates.length === 0) {\n return [{ json: { skipped: true, reason: 'No changes needed' } }];\n}\n\nreturn [{ json: plan }];"
},
"id": "validate-json",
"name": "Validate JSON",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [880, 0]
},
{
"parameters": {
"conditions": {
"boolean": [
{
"value1": "={{$json.skipped}}",
"value2": true
}
]
}
},
"id": "check-skip",
"name": "Check Skip",
"type": "n8n-nodes-base.if",
"typeVersion": 2,
"position": [1100, 0]
},
{
"parameters": {
"method": "POST",
"url": "={{$env.SITE_BASE_URL}}/api/tags/maintenance",
"sendBody": true,
"specifyBody": "json",
"jsonBody": "={\n \"apiKey\": \"{{$env.WEBHOOK_API_KEY}}\",\n \"updates\": {{JSON.stringify($json.updates)}},\n \"merges\": {{JSON.stringify($json.merges)}}\n}",
"options": {}
},
"id": "execute-maintenance",
"name": "Execute Maintenance",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [1320, -100]
},
{
"parameters": {
"jsCode": "const maintenanceResult = $('Execute Maintenance').first()?.json || {};\nconst skipped = $('Check Skip').first()?.json?.skipped;\n\nif (skipped) {\n return [{ json: {\n status: 'skipped',\n message: 'No changes needed',\n timestamp: new Date().toISOString()\n }}];\n}\n\nconst result = maintenanceResult.result || {};\n\nreturn [{ json: {\n status: maintenanceResult.success ? 'success' : 'failed',\n updatedCount: result.updatedCount || 0,\n mergedCount: result.mergedCount || 0,\n deletedTagCount: result.deletedTagCount || 0,\n timestamp: new Date().toISOString(),\n error: maintenanceResult.error || null\n}}];"
},
"id": "summarize",
"name": "Summarize Results",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [1540, 0]
}
],
"connections": {
"Daily 3AM UTC": {
"main": [[{ "node": "Fetch Tags", "type": "main", "index": 0 }]]
},
"Fetch Tags": {
"main": [[{ "node": "Preprocess Tags", "type": "main", "index": 0 }]]
},
"Preprocess Tags": {
"main": [[{ "node": "AI Analyze Tags", "type": "main", "index": 0 }]]
},
"AI Analyze Tags": {
"main": [[{ "node": "Validate JSON", "type": "main", "index": 0 }]]
},
"Validate JSON": {
"main": [[{ "node": "Check Skip", "type": "main", "index": 0 }]]
},
"Check Skip": {
"main": [
[{ "node": "Execute Maintenance", "type": "main", "index": 0 }],
[{ "node": "Summarize Results", "type": "main", "index": 0 }]
]
},
"Execute Maintenance": {
"main": [[{ "node": "Summarize Results", "type": "main", "index": 0 }]]
}
},
"settings": {
"executionOrder": "v1"
},
"meta": {
"templateCredsSetupCompleted": true
}
}