langchain-scavio 2.5

LangChain integration for Scavio Search API -- real-time Google, Amazon, Walmart, YouTube, and Reddit data with knowledge graphs and structured results

langchain-scavio

LangChain integration for the Scavio Search API. Real-time structured data from Google, Amazon, Walmart, YouTube, and Reddit — all through a single package.

Why Scavio? Multi-platform coverage, structured knowledge graph data, and competitive pricing at $0.005/credit.

Installation

pip install langchain-scavio

Tools

Tool	Description
ScavioSearch	Google web search with knowledge graphs, PAA questions, news
ScavioAmazonSearch	Search Amazon product listings
ScavioAmazonProduct	Fetch full details for an Amazon product by ASIN
ScavioWalmartSearch	Search Walmart product listings
ScavioWalmartProduct	Fetch full details for a Walmart product by ID
ScavioYouTubeSearch	Search YouTube videos with duration/date/type filters
ScavioYouTubeMetadata	Fetch metadata for a YouTube video by video ID
ScavioRedditSearch	Search Reddit posts or comments with sort/pagination
ScavioRedditPost	Fetch a Reddit post's metadata and comment thread by URL

Quick Start

Get your API key at dashboard.scavio.dev.

import os
from langchain_scavio import ScavioSearch

os.environ["SCAVIO_API_KEY"] = "sk_live_..."

tool = ScavioSearch()
result = tool.invoke({"query": "best python web frameworks 2026"})

Use with a LangChain Agent

Scavio tools plug into the current create_agent API from langchain.agents:

from langchain.agents import create_agent
from langchain_scavio import (
    ScavioSearch,
    ScavioAmazonSearch, ScavioAmazonProduct,
    ScavioWalmartSearch,
    ScavioYouTubeSearch, ScavioYouTubeMetadata,
    ScavioRedditSearch, ScavioRedditPost,
)

agent = create_agent(
    "openai:gpt-4o",
    tools=[
        ScavioSearch(max_results=5),
        ScavioAmazonSearch(max_results=5),
        ScavioAmazonProduct(),
        ScavioWalmartSearch(max_results=5),
        ScavioYouTubeSearch(max_results=5),
        ScavioYouTubeMetadata(),
        ScavioRedditSearch(max_results=5),
        ScavioRedditPost(),
    ],
)

response = agent.invoke({
    "messages": [{"role": "user", "content": "Find me a Python book on Amazon under $30"}]
})

Async Support

All tools support async invocation:

result = await tool.ainvoke({"query": "async python frameworks"})

Configuration

Google Search

from langchain_scavio import ScavioSearch

tool = ScavioSearch(
    scavio_api_key="sk_live_...",       # or SCAVIO_API_KEY env var
    max_results=5,
    light_request=None,                  # None=light/1 credit, False=full/2 credits
    include_knowledge_graph=True,
    include_questions=True,
    include_related=False,
    country_code="us",
    language="en",
    search_type="classic",               # classic|news|maps|images|lens
    device="desktop",
)

Amazon

from langchain_scavio import ScavioAmazonSearch, ScavioAmazonProduct

search = ScavioAmazonSearch(
    max_results=5,
    pages=1,                             # number of result pages to fetch
    domain="com",                        # see supported marketplaces below
)

product = ScavioAmazonProduct()
result = product.invoke({"query": "B08N5WRWNW"})  # query = ASIN

Targeting a marketplace: use domain to pick which Amazon store to search — do not use a country code. Supported domains: com (US), co.uk (UK), ca, de, fr, es, it, co.jp, in, com.au, com.br, com.mx, nl, pl, se, sg, ae, sa, eg, cn, com.be, com.tr.

Walmart

from langchain_scavio import ScavioWalmartSearch, ScavioWalmartProduct

search = ScavioWalmartSearch(max_results=5)
result = search.invoke({
    "query": "air fryer",
    "sort_by": "price_low",              # best_match|price_low|price_high|best_seller
    "max_price": 5000,                   # in cents
    "fulfillment_speed": "2_days",       # today|tomorrow|2_days|anytime
})

product = ScavioWalmartProduct()
result = product.invoke({"product_id": "123456789"})

YouTube

from langchain_scavio import ScavioYouTubeSearch, ScavioYouTubeMetadata

search = ScavioYouTubeSearch(max_results=5)
result = search.invoke({
    "query": "python tutorial",
    "duration": "medium",                # short|medium|long
    "upload_date": "this_month",         # last_hour|today|this_week|this_month|this_year
    "sort_by": "view_count",             # relevance|date|view_count|rating
    "video_type": "video",               # video|channel|playlist
})

metadata = ScavioYouTubeMetadata()
result = metadata.invoke({"video_id": "dQw4w9WgXcQ"})

Reddit

Reddit endpoints cost 2 credits each and typically take 5-15 seconds (JS rendering required).

from langchain_scavio import ScavioRedditSearch, ScavioRedditPost

search = ScavioRedditSearch(max_results=5)
result = search.invoke({
    "query": "langchain",
    "sort": "top",                       # new|relevance|hot|top|comments
    "type": "posts",                     # posts|comments
})

# Paginate by passing back the previous response's nextCursor
next_page = search.invoke({
    "query": "langchain",
    "sort": "top",
    "cursor": result["data"]["nextCursor"],
})

post = ScavioRedditPost()
result = post.invoke({
    "url": "https://www.reddit.com/r/programming/comments/abc123/example_post/"
})
# result["data"]["post"] + result["data"]["comments"] (flat list with `depth`)

Agent-Controllable Parameters

ScavioSearch

Parameter	Type	Description
query	str	Search query
search_type	classic|news|maps|images|lens	Type of search
country_code	str	ISO 3166-1 alpha-2
language	str	ISO 639-1
device	desktop|mobile	Device type
page	int	Result page number

ScavioAmazonSearch

Parameter	Type	Description
query	str	Product search query
domain	str	Amazon marketplace — the only way to select a store (com, co.uk, de, co.jp, ...)
sort_by	str	featured|most_recent|price_low_to_high|price_high_to_low|average_review|bestsellers
start_page	int	Page number
category_id	str	Category filter
merchant_id	str	Seller filter
language / currency	str	Localization
zip_code	str	Local pricing

ScavioWalmartSearch

Parameter	Type	Description
query	str	Product search query
sort_by	str	best_match|price_low|price_high|best_seller
min_price / max_price	int	Price range in cents
fulfillment_speed	str	today|tomorrow|2_days|anytime
delivery_zip	str	Delivery ZIP code

ScavioYouTubeSearch

Parameter	Type	Description
query	str	Search query
upload_date	str	last_hour|today|this_week|this_month|this_year
video_type	str	video|channel|playlist
duration	str	short|medium|long
sort_by	str	relevance|date|view_count|rating
hd / subtitles / live	bool	Content filters

ScavioRedditSearch

Parameter	Type	Description
query	str	Reddit search query (1-500 chars)
type	str	posts|comments
sort	str	new|relevance|hot|top|comments
cursor	str	Opaque pagination cursor from prior response's nextCursor

ScavioRedditPost

Parameter	Type	Description
url	str	Full Reddit post URL (www., old., or new. subdomains accepted)

Error Handling

• Empty results raise ToolException with actionable suggestions for the LLM
• API errors return {"error": "message"} without crashing the agent
• handle_tool_error=True ensures LangChain passes errors to the LLM as context

Architecture

ScavioBaseAPIWrapper                      # Auth, headers, sync/async HTTP POST
  +-- ScavioSearchAPIWrapper              # -> /api/v1/google
  +-- ScavioAmazonSearchAPIWrapper        # -> /api/v1/amazon/search
  +-- ScavioAmazonProductAPIWrapper       # -> /api/v1/amazon/product
  +-- ScavioWalmartSearchAPIWrapper       # -> /api/v1/walmart/search
  +-- ScavioWalmartProductAPIWrapper      # -> /api/v1/walmart/product
  +-- ScavioYouTubeSearchAPIWrapper       # -> /api/v1/youtube/search
  +-- ScavioYouTubeMetadataAPIWrapper     # -> /api/v1/youtube/metadata
  +-- ScavioRedditSearchAPIWrapper        # -> /api/v1/reddit/search
  +-- ScavioRedditPostAPIWrapper          # -> /api/v1/reddit/post

Each tool splits parameters into init-only (developer-controlled, e.g. max_results, domain) and LLM-controllable (passed via args_schema at invocation time, e.g. query, sort_by).

Migrating from Tavily

- from langchain_tavily import TavilySearch
+ from langchain_scavio import ScavioSearch

- tool = TavilySearch(max_results=5)
+ tool = ScavioSearch(max_results=5)

See the full migration guide for parameter mapping and feature comparison.

License

MIT