hl897tech

hl897tech / xhs_content_agent

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这是我开发的一个基于大模型的小红书内容运营智能 Agent 系统。 该系统可以实现从数据采集到内容生成，再到自动发布的完整流程，主要功能包括： 使用 Playwright 爬取小红书热门内容 分析爆款规律（关键词、标签、标题结构） 基于分析结果自动生成选题 使用大模型生成完整文案（标题、正文、标签、互动语） 调用图像模型生成配图 支持通过 API 或 MCP 一键发布内容 技术栈包括： FastAPI、LangChain、OpenAI（GPT + 图像模型）、Playwright、Pydantic 等。 该项目的核心思路是将数据分析与大模型生成结合，实现内容生产的自动化与智能化。 适用于： 内容运营、自媒体自动化、AI Agent 应用开发等场景。

13
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69% credibility
Found Apr 03, 2026 at 13 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

An AI assistant that collects and analyzes popular Little Red Book posts, generates similar content with text and images, publishes to the platform, and logs data to a spreadsheet app.

How It Works

1
🔍 Discover the tool

You find a helpful assistant that studies popular posts on Little Red Book to help you create your own engaging content.

2
⚙️ Get it ready

You set up the assistant on your computer and connect your Little Red Book account plus your AI thinking service.

3
📱 Sign into Little Red Book

You log in once so the assistant can access your account safely for posting later.

4
📊 Gather popular examples

The assistant collects real top-performing posts on topics you choose, like beauty tips or student life hacks.

5
🧠 Analyze trends

It studies likes, comments, and styles to spot what makes posts go viral.

6
✨ Create new posts

AI generates fresh titles, heartfelt text, catchy tags, and perfect matching pictures just like the hits.

7
📤 Publish and save

You review, hit publish to share on Little Red Book, and it saves everything to your organized list.

🎉 Content live!

Your new post is out there gaining likes, and you have a full plan for more viral successes.

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AI-Generated Review

What is xhs_content_agent?

This Python FastAPI agent automates Xiaohongshu (Little Red Book) content ops: it scrapes hot notes via Playwright browser agent, analyzes viral patterns like keywords, tags, and title structures, then uses OpenAI GPT and image models to generate topics, full posts (titles, body, hashtags, CTAs), and matching visuals. Publish one-click via API or MCP protocol. Turns data-driven insights into ready-to-post content, skipping manual grinding for self-media teams.

Why is it gaining traction?

Full pipeline in one repo beats piecing together LangChain chains or custom scrapers—MCP server slots right into agent github claude, cursor, or vscode copilot cli workflows as a playwright agent llm. Devs dig the agent github repo simplicity: feed audience/tone params, get publishable assets. Playwright agent mcp hooks make it a quick win for browser automation fans.

Who should use this?

Self-media operators posting daily to Xiaohongshu, content agencies scaling topic ideation and gen, AI agent devs building social bots for Chinese platforms. Ideal for those chaining playwright agent browser crawls to LLM output without boilerplate.

Verdict

Worth forking for Xiaohongshu automation—solid API endpoints and MCP tools deliver real end-to-end value. At 13 stars and 0.7% credibility score, it's early-stage raw; add tests and expand docs to mature.

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