Integration

LlamaIndex + Reddit Comments Tree

Search Reddit Comments Tree from your LlamaIndex agent with Scavio. Get comment text, author, score in structured JSON.

What is LlamaIndex?

Data framework for building RAG pipelines and LLM applications over custom data. Connects LLMs to external data sources.

Searching Reddit Comments Tree with LlamaIndex

This integration lets your LlamaIndex agent search Reddit Comments Tree in real time via the Scavio API. The agent gets back structured JSON with comment text, author, score, depth — ready for reasoning and decision-making.

Setup

Bash
pip install llama-index requests

Code Example

Here is a complete LlamaIndex agent that searches Reddit Comments Tree using Scavio:

Python
from llama_index.core.tools import FunctionTool
from llama_index.llms.openai import OpenAI
from llama_index.core.agent import ReActAgent
import requests

def search_reddit_comments_tree(query: str) -> str:
    """Search Reddit Comments Tree for real-time results."""
    response = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": "your_scavio_api_key", "Content-Type": "application/json"},
        json={"query": query},
    )
    return str(response.json())

tool = FunctionTool.from_defaults(fn=search_reddit_comments_tree)
llm = OpenAI(model="gpt-4o")
agent = ReActAgent.from_tools([tool], llm=llm, verbose=True)
response = agent.chat("r/ChatGPT best prompt for summarizing long articles")
print(response)

Full Working Example

A production-ready example with error handling:

Python
from llama_index.core.tools import FunctionTool
from llama_index.llms.openai import OpenAI
from llama_index.core.agent import ReActAgent
import requests

def search_reddit_comments_tree(query: str) -> str:
    """Search Reddit Comments Tree for real-time results."""
    response = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": "your_scavio_api_key", "Content-Type": "application/json"},
        json={"query": query},
    )
    return str(response.json())

tool = FunctionTool.from_defaults(fn=search_reddit_comments_tree)
llm = OpenAI(model="gpt-4o")
agent = ReActAgent.from_tools([tool], llm=llm, verbose=True)
response = agent.chat("r/ChatGPT best prompt for summarizing long articles")
print(response)

Pricing

Scavio offers a free tier with 500 credits/month (1 credit per search). No credit card required. This is enough to build and test your LlamaIndex integration. Paid plans start at $30/month for higher volumes.

Frequently Asked Questions

Install Scavio and create a LlamaIndex tool that calls the Scavio API. Register the tool with your agent and your LlamaIndex agent will have access to real-time search across Google, Amazon, YouTube, and Walmart.

Scavio works with LlamaIndex via custom tool definitions. The integration takes under 10 minutes to set up. See the code example above for the full setup.

Once connected, your LlamaIndex agent can search Google (web, news, images, shopping, maps), Amazon (12 marketplaces), YouTube (videos, transcripts, channels), and Walmart. All from a single API key.

Scavio has a free tier with 500 credits/month (1 credit per search). This is enough to build and test your LlamaIndex integration. Paid plans start at $30/month. There is no per-seat or per-agent pricing.

Yes. The Scavio API returns live Reddit Comments Tree results with comment text, author, score in structured JSON. Your LlamaIndex agent can use this data to make informed decisions based on current information.

Add Real-Time Search to LlamaIndex

Get your free Scavio API key and connect LlamaIndex to Google, Amazon, YouTube, Walmart, and Reddit. 500 free credits/month.