decodo-llamaindex

LlamaIndex integration for the Decodo Web Scraping API. Load live web pages and search-engine results into your RAG pipelines, or give LlamaIndex agents real-time browsing capabilities — through two Python packages.

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Features

Component	Description
DecodoWebReader	Scrape one or more URLs → list[Document]
DecodoSearchReader	Run Google / Amazon / Reddit search → list[Document]
DecodoToolSpec	LlamaIndex tool spec for agent use (scrape + search)

Decodo handles JavaScript rendering, anti-bot bypassing, CAPTCHA solving, and proxy rotation automatically.

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Installation

pip install llama-index-readers-decodo llama-index-tools-decodo

To use the examples you will also need an LLM provider package, e.g.:

pip install llama-index-llms-openai llama-index-embeddings-openai

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Authentication

Get your API token from the Decodo dashboard and export it:

export DECODO_API_TOKEN="your_token_here"

All classes read DECODO_API_TOKEN from the environment by default. You can also pass it explicitly. A Unified API key (a plain hex string) needs auth_mode="token":

reader = DecodoWebReader(api_token="your_token_here")
reader = DecodoWebReader(api_token="your_api_key", auth_mode="token")

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Usage

DecodoWebReader — scrape URLs into Documents

import os
from llama_index.readers.decodo import DecodoWebReader

reader = DecodoWebReader()  # reads DECODO_API_TOKEN from env

docs = reader.load_data([
    "https://news.ycombinator.com",
    "https://en.wikipedia.org/wiki/Large_language_model",
])

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

DecodoSearchReader — fetch search results into Documents

from llama_index.readers.decodo import DecodoSearchReader

reader = DecodoSearchReader()

# Google Search
google_docs = reader.load_data("open source LLMs 2025", engine="google")

# Amazon product search
amazon_docs = reader.load_data("mechanical keyboard", engine="amazon")

# Reddit
reddit_docs = reader.load_data("r/MachineLearning", engine="reddit")

RAG Pipeline

import os
from llama_index.core import VectorStoreIndex
from llama_index.core.settings import Settings
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.readers.decodo import DecodoWebReader

Settings.llm = OpenAI(model="gpt-4o")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")

# 1. Load documents
reader = DecodoWebReader()
docs = reader.load_data([
    "https://en.wikipedia.org/wiki/Retrieval-augmented_generation",
    "https://en.wikipedia.org/wiki/Transformer_(deep_learning_architecture)",
])

# 2. Build index
index = VectorStoreIndex.from_documents(docs)

# 3. Query
engine = index.as_query_engine()
response = engine.query("How does RAG work and why is it useful?")
print(response)

See examples/rag_pipeline.ipynb for a complete walkthrough.

LlamaIndex Agent with DecodoToolSpec

import asyncio
from llama_index.core.agent.workflow import ReActAgent
from llama_index.llms.openai import OpenAI
from llama_index.tools.decodo import DecodoToolSpec

spec = DecodoToolSpec()
tools = spec.to_tool_list()

agent = ReActAgent(tools=tools, llm=OpenAI(model="gpt-4o"))


async def main() -> None:
    response = await agent.run(
        "Search Google for 'Python async best practices 2025' "
        "and summarise the top recommendations."
    )
    print(response)


asyncio.run(main())

See examples/agent_example.py for a runnable script.

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API Reference

The integration ships as two packages. Each takes api_token, auth_mode and timeout.

Argument	Default	Description
api_token	DECODO_API_TOKEN env var	Decodo credential
auth_mode	"basic"	"basic" sends Authorization: Basic to /v2/scrape; "token" sends Authorization: Bearer to /unified/v1/scrape. A Unified API key (plain hex string) needs "token".
timeout	180.0	HTTP timeout in seconds

DecodoWebReader (llama-index-readers-decodo)

Method	Returns	Description
load_data(urls, continue_on_error=True)	list[Document]	Scrape each URL; failed URLs become error Documents unless continue_on_error=False

Document metadata: url, status_code, source

DecodoSearchReader (llama-index-readers-decodo)

Method	Returns	Description
load_data(query, engine="google", num_results=10)	list[Document]	Run a search; raises RuntimeError if Decodo returns no results or only failed ones

Supported engine values: "google", "amazon", "reddit" (Google with a site:reddit.com filter).

Document metadata: query, engine, target, url, status_code, source

DecodoToolSpec (llama-index-tools-decodo)

Function	Signature	Description
scrape_url	(url: str) -> str	Fetch a web page as markdown
search_web	(query: str, num_results: int = 10) -> list[dict]	Google search
search_amazon	(query: str, num_results: int = 10) -> list[dict]	Amazon product search
search_reddit	(query: str, num_results: int = 10) -> list[dict]	Google search with a site:reddit.com filter

Search functions return dicts with url, content and status_code, and raise RuntimeError if Decodo returns no results or only failed ones. Convert to LlamaIndex tools with spec.to_tool_list().

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Project Layout

├── llama-index-readers-decodo/   # DecodoWebReader, DecodoSearchReader
├── llama-index-tools-decodo/     # DecodoToolSpec
└── examples/
    ├── rag_pipeline.ipynb
    └── agent_example.py

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License

MIT © Decodo