langchain-decodo

LangChain partner package for the Decodo web scraping API.

Provides two LangChain tools and a document loader that let LLM agents and RAG pipelines fetch live web content without managing proxies, JavaScript rendering, or anti-bot protection.

Installation

pip install langchain-decodo

Authentication

All classes read your Decodo API token from the DECODO_API_TOKEN environment variable, or you can pass it explicitly.

export DECODO_API_TOKEN="your-decodo-api-token"

Get a token from the Decodo Dashboard.

By default every class expects basic credentials (base64-encoded username:password). If your token is a plain API token, pass auth_mode="token".:

tool = DecodoWebScrapeTool(auth_mode="token")

Components

DecodoWebScrapeTool

Scrape any URL and return its full content as markdown or plain text. Handles JavaScript rendering, CAPTCHAs, and geo-blocking automatically.

from langchain_decodo import DecodoWebScrapeTool

tool = DecodoWebScrapeTool()  # reads DECODO_API_TOKEN from env
content = tool.run("https://example.com")
print(content)

Pass explicitly:

from pydantic import SecretStr
from langchain_decodo import DecodoWebScrapeTool

tool = DecodoWebScrapeTool(decodo_api_token=SecretStr("YOUR_TOKEN"))

DecodoSearchTool

Search Google, Amazon, or Reddit and return structured JSON results.

from langchain_decodo import DecodoSearchTool

tool = DecodoSearchTool()

# Google search (default)
results = tool.run({"query": "LangChain latest release", "engine": "google"})

# Amazon product search
results = tool.run({"query": "Python programming book", "engine": "amazon"})

# Reddit discussion search
results = tool.run({"query": "best web scraping libraries", "engine": "reddit"})

Returns a JSON string — a list of objects with content, url, and status_code fields.

Supported engines:

DecodoLoader

Load one or more URLs as LangChain Document objects for use in RAG pipelines.

from langchain_decodo import DecodoLoader

loader = DecodoLoader(
    urls=[
        "https://python.org/about/",
        "https://docs.python.org/3/whatsnew/3.12.html",
    ],
)
docs = loader.load()

for doc in docs:
    print(doc.metadata["url"], "—", len(doc.page_content), "chars")

Each Document has:

• page_content — scraped text/markdown.
• metadata["url"] — the source URL.
• metadata["source"] — same as url (LangChain convention).
• metadata["status_code"] — HTTP status from the target site.

LangChain agent example

pip install langchain langchain-openai langchain-decodo

from langchain.agents import create_agent
from langchain_decodo import DecodoSearchTool, DecodoWebScrapeTool

agent = create_agent(
    model="openai:gpt-4o-mini",
    tools=[DecodoWebScrapeTool(), DecodoSearchTool()],
)

result = agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": "What is the latest stable version of Python? Check python.org.",
            }
        ]
    }
)
print(result["messages"][-1].content)

RAG pipeline example

pip install langchain-openai langchain-text-splitters langchain-decodo numpy

from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_decodo import DecodoLoader

docs = DecodoLoader(urls=["https://python.org/about/"]).load()

splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = splitter.split_documents(docs)

store = InMemoryVectorStore.from_documents(chunks, OpenAIEmbeddings())
context = "\n\n".join(
    doc.page_content for doc in store.similarity_search("What is Python used for?", k=4)
)

llm = ChatOpenAI(model="gpt-4o-mini")
answer = llm.invoke(
    f"Answer using only this context:\n\n{context}\n\nQuestion: What is Python used for?"
)
print(answer.content)

Links

• Decodo website
• Decodo API documentation
• Decodo Dashboard
• LangChain documentation