WeChat Articles Scraper 微信公众号文章采集
Pricing
from $1.40 / 1,000 extracted wechat official accounts & articles insights harvesters
WeChat Articles Scraper 微信公众号文章采集
Scrape WeChat Official Account (微信公众号) articles, brand news, publication dates, author metadata, read counters, and full article text without requiring a Chinese phone number, VPN, or login.
Pricing
from $1.40 / 1,000 extracted wechat official accounts & articles insights harvesters
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Developer
David Sandor
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15 hours ago
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WeChat Official Accounts & Articles Insights Harvester 🚀
Scrapes WeChat Official Account (公众号) articles, brand news, publication dates, author metadata, read counters, and article body text without requiring a Chinese phone number or login.
⚡ Try it in 1-Click: Click the Run or Try for Free button on the right to test this Actor immediately with pre-configured parameters. No technical setup, passwords, or API keys required! 🎁 Free Starter Allowance: Test runs cost less than a fraction of a cent ($0.0001 container fee) so you can run test queries risk-free with your monthly Apify starter credits.
⚡ 1-Click API Quick-Start (cURL / Python / Dify / FastGPT / Coze)
Run directly via HTTP API without opening the browser form (支持直接通过 API 调用,无缝接入 Dify、Coze、FastGPT 与自动化脚本):
# 1-Click cURL Instant Run (Replace YOUR_APIFY_TOKEN)curl -X POST "https://api.apify.com/v2/acts/trusted_tech_spot~fast-wechat-articles-scraper/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \-H "Content-Type: application/json" \-d '{}'
🌟 Why Choose This Actor?
📊 No-Code Instant Export: Download your clean extracted data directly into Excel (.xlsx), CSV, JSON, or sync directly to Google Sheets with 1 click right from the Apify Console dataset tab. No coding required!
- ⚡ High-Speed & Resilient Extraction: Engineered with intelligent rate adaptation and defensive fallbacks to eliminate downtime or captchas.
- 💰 Predictable Pay-Per-Result Pricing: Only pay for the actual records extracted. Never pay for actor startup overhead, cold starts, or container idle seconds.
- 🧠 AI & RAG Ready: Seamlessly feeds vector databases (Pinecone, Qdrant, ChromaDB, Weaviate) and LLM agents with pre-calculated OpenAI token counts and semantic chunking.
- 🎯 Native Keyword Filtering: Target exact records automatically with
includeKeywordsandexcludeKeywordswithout client-side post-processing. - 📊 Instant Multi-Format Export: One-click download into JSON, CSV, Excel, XML, or HTML Table directly from Apify Console or API.
💡 国内/跨境电商用户快速上手 (Quick Guide for China & Cross-Border Teams):
- 本工具已内置自动抗封锁与动态解析,无需配置任何国外代理 (No Proxy Needed),亦无需提供个人手机号或 Cookie 凭据。
- 点击右侧或上方 "Run" 或 "Try for free",使用系统自带的预设参数即可在 2 秒内完成首批数据提取测试。
- 支持在控制台直接导出 Excel、CSV、JSON 等全格式数据,或无缝对接 Python / Node.js API。
🏆 Why Switch from NewRank & Xiguaji (新榜/西瓜数据)?
🌏 No Chinese Phone or Login Required: Bypasses the SMS phone verification wall completely. ⚡ Sub-2s Real-Time Article Extraction: Full text, read counts, author info, and publication timestamps. 💰 Transparent Pricing: No ¥3,000+/year Chinese enterprise SaaS lock-in.
🚀 How to Run in 3 Easy Steps
- Open the Actor: Click the Run button on the top right of this page.
- Review or Adjust Inputs: The form is organized into clean visual sections (Primary Settings, Filtering, and AI Vector Options). Run it with default values or customize to your target.
- Download Results: View your extracted dataset in real time and export it to Excel, CSV, or JSON.
📥 Input Parameters
All parameters come pre-filled with sensible defaults so you can run the Actor right away:
| Parameter | Type | Required | Default / Prefill | Description |
|---|---|---|---|---|
Keyword or Product URL (keywordOrUrl) | string | No | "Tencent" | WeChat article URL or search keyword/account name (e.g., "Tencent" or "TechTrends") |
Maximum Records to Fetch (limit) | integer | No | 3 | Maximum number of articles to return |
Include Keywords (Filter) (includeKeywords) | string | No | "" | Filter results to only include items containing these comma-separated keywords |
Exclude Keywords (Filter) (excludeKeywords) | string | No | "" | Filter out results containing these comma-separated keywords |
Enable AI / RAG Token Chunking (enableRagEnrichment) | boolean | No | true | Automatically compute OpenAI token counts and generate semantic vector chunks for RAG pipelines |
Preferred Output Format (outputFormat) | string | No | "JSON" | Format results for preferred downstream consumer (e.g. JSON, Markdown, Flat) |
📊 Realistic Output Preview (Tabular Format)
| Article Title (文章标题) | Official Account (公众号) | Publish Date (发布时间) | Author (作者) | Reads (阅读数) |
|---|---|---|---|---|
| 2026跨境电商出海趋势全解析 | 跨境观察局 | 2026-10-12 | 行业分析师 | 10,000+ |
| AI智能体在数字化零售中的落地案例 | 科技前沿观察 | 2026-10-10 | 张明 | 8,420 |
| 华南供应链采购峰会特别报道 | 供应链数字化 | 2026-10-08 | 专栏编辑 | 5,610 |
📤 Sample Output Data
Data is saved directly to the default dataset. Below is a realistic example of the structured fields returned:
{"query": "Sample query","totalArticles": 42,"articles": ["Sample Record 1","Sample Record 2"],"_sentimentScore": 0.25,"_rag": {"estimatedTokens": 380,"chunksCount": 1,"ingestionReady": true}}
💻 Developer API & AI Framework Integration
Node.js (apify-client)
import { ApifyClient } from 'apify-client';const client = new ApifyClient({token: 'YOUR_APIFY_TOKEN',});// Run actor with pre-configured parametersconst run = await client.actor('trusted_tech_spot/fast-wechat-articles-scraper').call();// Fetch structured items from the datasetconst { items } = await client.dataset(run.defaultDatasetId).listItems();console.log(`Extracted ${items.length} records:`, items);
Python (apify-client)
from apify_client import ApifyClientclient = ApifyClient("YOUR_APIFY_TOKEN")# Run actor with default parametersrun = client.actor("trusted_tech_spot/fast-wechat-articles-scraper").call()# Fetch dataset itemsitems = client.dataset(run["defaultDatasetId"]).list_items().itemsprint(f"Extracted {len(items)} records:", items)
LangChain Vector Ingestion (AI & RAG)
from apify_client import ApifyClientfrom langchain_community.vectorstores import Chromafrom langchain_openai import OpenAIEmbeddingsfrom langchain_core.documents import Document# 1. Fetch pre-chunked dataset from Apifyclient = ApifyClient("YOUR_APIFY_TOKEN")run = client.actor("trusted_tech_spot/fast-wechat-articles-scraper").call()items = client.dataset(run["defaultDatasetId"]).list_items().items# 2. Extract semantic chunks directly into LangChain documentsdocs = []for item in items:chunks = item.get("_rag", {}).get("chunks", [str(item)])for chunk in chunks:docs.append(Document(page_content=chunk, metadata={"source": "fast-wechat-articles-scraper"}))# 3. Ingest into ChromaDB / Pinecone vector storevector_store = Chroma.from_documents(docs, OpenAIEmbeddings())print(f"Successfully indexed {len(docs)} chunks into Vector DB!")
LlamaIndex Knowledge Ingestion
from apify_client import ApifyClientfrom llama_index.core import VectorStoreIndex, Documentclient = ApifyClient("YOUR_APIFY_TOKEN")run = client.actor("trusted_tech_spot/fast-wechat-articles-scraper").call()items = client.dataset(run["defaultDatasetId"]).list_items().items# Wrap clean extracted text into LlamaIndex documentsdocuments = [Document(text=str(item)) for item in items]index = VectorStoreIndex.from_documents(documents)query_engine = index.as_query_engine()response = query_engine.query("Summarize key findings from this extracted data")print(response)
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💬 Technical Support & Custom Demands
Need custom extraction fields, high-volume dedicated scraping, enterprise data feeds, or encountered an edge-case?
- 🐛 Open an Issue: Open a ticket directly on the Actor Issues Tab for 24-hour engineer response.
- ✉️ Direct Email Support: Reach out directly to contact@trustedtechspot.com for custom scraping jobs, private proxy setups, or SLA contracts.
- 💡 Feature Requests: Request new export columns, filtering rules, or platform endpoints anytime.
🔒 Privacy & Enterprise Compliance
- No Passwords or Cookies Required: Safely executes without requesting your credentials, private tokens, or session cookies.
- Enterprise Grade Sanitization: Automatically scrubs sensitive headers and tracking parameters before saving data.