---
title: "Scrape Slack with LangChain | EnConvert"
description: "Scrape Slack pages into a LangChain pipeline with EnConvert Perceive, returning clean Markdown with navigation and sidebars stripped in a single batch call"
canonical: "https://www.enconvert.com/solutions/scrape-slack-with-langchain-enconvert"
locale: "en"
---

# How to Scrape Slack with LangChain

A developer building a LangChain pipeline that reads Slack pages needs clean page text before ingesting it into a vector store. Slack pages mix message content with channel names, timestamps, reaction counts, member lists, and sidebar navigation, and scraping them by hand requires CSS selectors that break when Slack updates its layout. EnConvert Perceive renders up to 10 Slack pages in a single batch call and returns clean Markdown for each one, with navigation and sidebars stripped by default. The extracted Markdown is ready to load into LangChain Documents and index with no HTML parsing.

## Example

```python
import os

import requests
from langchain_community.vectorstores import FAISS
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings

BASE_URL = "https://api.enconvert.com/v2"
headers = {"X-API-Key": os.environ["ENCONVERT_API_KEY"], "Content-Type": "application/json"}

page_urls = [url.strip() for url in os.environ["SLACK_PAGE_URLS"].split(",")]  # public Slack page URLs

if len(page_urls) > 10:
    raise ValueError("This example covers inline batches of 10 URLs or fewer")

response = requests.post(f"{BASE_URL}/perceive/batch", headers=headers,
    json={"urls": page_urls, "options": {"outputs": ["markdown"]}}, timeout=300)

if response.status_code == 402:
    raise RuntimeError("Monthly ops allowance exhausted")
response.raise_for_status()

documents = []
for result in response.json()["items"]:
    page_url = result["url"]
    if result.get("status") != "completed":
        print(f"Skipped {page_url}: {result.get('status')}")
        continue
    if result.get("is_blocked"):
        print(f"Skipped {page_url}: blocked page")
        continue
    render_quality = result.get("render_quality", 1.0)
    if render_quality < 0.40:
        print(f"Skipped {page_url}: low render quality ({render_quality:.2f})")
        continue
    markdown_url = result["outputs"]["markdown"]["url"]
    markdown = requests.get(markdown_url, timeout=60)
    markdown.raise_for_status()
    documents.append(Document(
        page_content=markdown.text,
        metadata={"source_url": page_url, "render_quality": render_quality},
    ))

if not documents:
    raise RuntimeError("No Slack pages passed the render checks")

embeddings = OpenAIEmbeddings(
    model="text-embedding-3-small", api_key=os.environ["OPENAI_API_KEY"]
)
vectorstore = FAISS.from_documents(documents, embeddings)
results = vectorstore.similarity_search("What decisions and action items are discussed in these Slack pages?", k=3)
for doc in results:
    print(doc.metadata["source_url"])
    print(doc.page_content[:300])
```

## Output

Perceive returns clean Markdown for each URL in the batch, with navigation and sidebars stripped by default, delivered as pre-signed URLs that you fetch once per page. Scraping Slack pages by hand needs CSS selectors that break when Slack updates its layout, and passing raw HTML into a LangChain Document fills the vector store index with navigation and boilerplate that degrades retrieval quality.

## Get started

[Get free API key — 500 ops per month, no card required.](https://www.enconvert.com/auth?mode=signup)

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