LlamaIndex

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

export DECODO_API_TOKEN=your_api_key

from llama_index.readers.decodo import DecodoWebReader

reader = DecodoWebReader(auth_mode="token")

# main.py
from llama_index.core import VectorStoreIndex
from llama_index.readers.decodo import DecodoWebReader

reader = DecodoWebReader(auth_mode="token")
documents = reader.load_data(
    [
        "https://quotes.toscrape.com",
        "https://quotes.toscrape.com/page/2/",
    ]
)

# Drop pages that failed to scrape before indexing them
documents = [doc for doc in documents if "error" not in doc.metadata]

index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()

print(query_engine.query("Which quotes are about books or reading?"))

from llama_index.readers.decodo import DecodoSearchReader

reader = DecodoSearchReader(auth_mode="token")
documents = reader.load_data("wireless keyboard", engine="amazon")

pip install llama-index-tools-decodo llama-index-llms-openai
export OPENAI_API_KEY=your_openai_key

# agent.py
import asyncio

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

tools = DecodoToolSpec(auth_mode="token").to_tool_list()

agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-5-mini"),
)


async def main():
    response = await agent.run(
        "Search Google for the 2025 NBA Finals, scrape the Wikipedia page about it and tell me who won."
    )
    print(response)


asyncio.run(main())