LlamaIndex Integration

Integrate Scavio with LlamaIndex to give your RAG pipelines and agents real-time web search across Google, Google News, Reddit, YouTube, and Amazon -- returned as clean Document objects ready to index or reason over.

Fresh data for RAG

The llama-index-tools-scavio package turns every Scavio search into LlamaIndex Documents -- a cost-effective Tavily and SerpAPI alternative with broader platform coverage.

Introduction

The llama-index-tools-scavio package provides a ScavioToolSpec with five search tools. Hand them to any LlamaIndex agent, or call them directly in a data pipeline.

Step-by-Step Integration Guide

Step 1: Install the package

Bash

pip install llama-index-tools-scavio

Step 2: Set your API key

Get a key at dashboard.scavio.dev (free credits, no card), then set it as an environment variable:

Bash

export SCAVIO_API_KEY=sk_live_...

Step 3: Basic usage

Python

from llama_index.tools.scavio import ScavioToolSpec

tool_spec = ScavioToolSpec()  # reads SCAVIO_API_KEY

docs = tool_spec.search("best real-time search API for AI agents", max_results=5)
for doc in docs:
    print(doc.text, doc.metadata["url"])

Available Tools

ScavioToolSpec exposes these functions, each returning a list of Document objects:

Tool	Description
`search`	Google SERP -- real-time organic web results
`news`	Google News -- recent articles on a topic
`reddit_search`	Reddit posts -- community discussion and sentiment
`youtube_search`	YouTube -- videos, channels, playlists
`amazon_search`	Amazon -- product listings

Use with an agent

Python

from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
from llama_index.tools.scavio import ScavioToolSpec

agent = FunctionAgent(
    tools=ScavioToolSpec().to_tool_list(),
    llm=OpenAI(model="gpt-5.5"),
    system_prompt="You are a research assistant. Use Scavio for fresh web data.",
)

response = await agent.run(
    "What are people on Reddit saying about Tavily alternatives?"
)

Advanced Example

Load live search results straight into a vector index:

Python

from llama_index.core import VectorStoreIndex
from llama_index.tools.scavio import ScavioToolSpec

docs = ScavioToolSpec().search("open-source agent frameworks 2026", max_results=10)
index = VectorStoreIndex.from_documents(docs)
answer = index.as_query_engine().query("Which framework is most popular and why?")
print(answer)

Every endpoint via MCP

Need Walmart, TikTok, Instagram, Maps, Shopping, and more? Point any LlamaIndex agent at Scavio's hosted MCP server for the full catalog.

Benefits of Scavio + LlamaIndex

Document-native: results drop straight into RAG.
Agent-ready: to_tool_list() and go.
Multi-platform: web, news, social, shopping in one key.
Cost-effective: most calls cost a single credit.

Next Steps

Python SDK -- the client the tools wrap
MCP Integration -- the full tool catalog

LlamaIndex Integration

Fresh data for RAG

The llama-index-tools-scavio package turns every Scavio search into LlamaIndex Documents -- a cost-effective Tavily and SerpAPI alternative with broader platform coverage.

Introduction

The llama-index-tools-scavio package provides a ScavioToolSpec with five search tools. Hand them to any LlamaIndex agent, or call them directly in a data pipeline.

Step-by-Step Integration Guide

Step 1: Install the package

Bash

pip install llama-index-tools-scavio

Step 2: Set your API key

Get a key at dashboard.scavio.dev (free credits, no card), then set it as an environment variable:

Bash

export SCAVIO_API_KEY=sk_live_...

Step 3: Basic usage

Python

from llama_index.tools.scavio import ScavioToolSpec

tool_spec = ScavioToolSpec()  # reads SCAVIO_API_KEY

docs = tool_spec.search("best real-time search API for AI agents", max_results=5)
for doc in docs:
    print(doc.text, doc.metadata["url"])

Available Tools

ScavioToolSpec exposes these functions, each returning a list of Document objects:

Tool	Description
`search`	Google SERP -- real-time organic web results
`news`	Google News -- recent articles on a topic
`reddit_search`	Reddit posts -- community discussion and sentiment
`youtube_search`	YouTube -- videos, channels, playlists
`amazon_search`	Amazon -- product listings

Use with an agent

Python

from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
from llama_index.tools.scavio import ScavioToolSpec

agent = FunctionAgent(
    tools=ScavioToolSpec().to_tool_list(),
    llm=OpenAI(model="gpt-5.5"),
    system_prompt="You are a research assistant. Use Scavio for fresh web data.",
)

response = await agent.run(
    "What are people on Reddit saying about Tavily alternatives?"
)

Advanced Example

Load live search results straight into a vector index:

Python

from llama_index.core import VectorStoreIndex
from llama_index.tools.scavio import ScavioToolSpec

docs = ScavioToolSpec().search("open-source agent frameworks 2026", max_results=10)
index = VectorStoreIndex.from_documents(docs)
answer = index.as_query_engine().query("Which framework is most popular and why?")
print(answer)

Every endpoint via MCP

Need Walmart, TikTok, Instagram, Maps, Shopping, and more? Point any LlamaIndex agent at Scavio's hosted MCP server for the full catalog.

Benefits of Scavio + LlamaIndex

Document-native: results drop straight into RAG.
Agent-ready: to_tool_list() and go.
Multi-platform: web, news, social, shopping in one key.
Cost-effective: most calls cost a single credit.

Next Steps

Python SDK -- the client the tools wrap
MCP Integration -- the full tool catalog