langchain-scavio 2.7

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

langchain-scavio

20 LangChain tools for real-time search across Google, Amazon, Walmart, YouTube, Reddit, and TikTok -- structured data with knowledge graphs, all through a single package.

pip install langchain-scavio

Get your free API key at dashboard.scavio.dev.

Why Scavio over Tavily?

	Scavio	Tavily	SerpAPI
Platforms	Google, Amazon, Walmart, YouTube, Reddit, TikTok	Google only	Google + others
Tools	20	1	1 per wrapper
Knowledge graphs	Yes	No	Partial
Product data (price, rating, reviews)	Yes	No	No
Pricing	$0.005/credit	$0.01/search	$0.05/search
Amazon marketplace coverage	23 countries	--	--
LangChain async	Yes	Yes	Yes

What Can You Build?

• Shopping agents -- search Amazon and Walmart, compare prices, find deals across 23 marketplaces
• Product research agents -- Google reviews + Amazon listings + YouTube reviews + Reddit opinions in one query
• Content research agents -- YouTube trends + Reddit sentiment + Google news in a single workflow
• Brand monitoring -- track what Reddit and Google say about any topic in real time
• Social media agents -- TikTok profile analytics, hashtag tracking, video comments, and trend discovery

Quick Start

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"})

All 20 Tools

Tool	Description
ScavioSearch	Google web search with knowledge graphs, PAA questions, news
ScavioAmazonSearch	Search Amazon product listings across 23 marketplaces
ScavioAmazonProduct	Fetch full details for an Amazon product by ASIN
ScavioWalmartSearch	Search Walmart product listings with price/fulfillment filters
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
ScavioTikTokProfile	Look up a TikTok user profile by username or sec_user_id
ScavioTikTokUserPosts	Fetch a TikTok user's posted videos with statistics
ScavioTikTokVideo	Fetch details for a single TikTok video
ScavioTikTokVideoComments	Fetch comments on a TikTok video
ScavioTikTokCommentReplies	Fetch replies to a specific comment on a TikTok video
ScavioTikTokSearchVideos	Search TikTok videos by keyword with sort/time filters
ScavioTikTokSearchUsers	Search TikTok users by keyword
ScavioTikTokHashtag	Look up TikTok hashtag info (video count, views)
ScavioTikTokHashtagVideos	Fetch TikTok videos for a specific hashtag
ScavioTikTokUserFollowers	Fetch a TikTok user's followers
ScavioTikTokUserFollowings	Fetch accounts a TikTok user is following

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,
    ScavioTikTokSearchVideos, ScavioTikTokProfile, ScavioTikTokVideo,
)

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(),
        ScavioTikTokSearchVideos(max_results=5),
        ScavioTikTokProfile(),
        ScavioTikTokVideo(),
    ],
)

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`)

TikTok

from langchain_scavio import (
    ScavioTikTokProfile, ScavioTikTokUserPosts, ScavioTikTokVideo,
    ScavioTikTokVideoComments, ScavioTikTokCommentReplies,
    ScavioTikTokSearchVideos, ScavioTikTokSearchUsers,
    ScavioTikTokHashtag, ScavioTikTokHashtagVideos,
    ScavioTikTokUserFollowers, ScavioTikTokUserFollowings,
)

# Look up a user profile (returns sec_uid needed by other tools)
profile = ScavioTikTokProfile()
result = profile.invoke({"username": "tiktok"})
sec_uid = result["data"]["user"]["sec_uid"]

# Fetch their recent posts
posts = ScavioTikTokUserPosts(max_results=5)
result = posts.invoke({"sec_user_id": sec_uid, "sort_type": "1"})  # popular

# Search videos by keyword
search = ScavioTikTokSearchVideos(max_results=5)
result = search.invoke({
    "keyword": "python tutorial",
    "sort_type": "1",                        # 0=relevance, 1=most likes
    "publish_time": "30",                    # 0=all, 1=day, 7=week, 30=month
})

# Get video details and comments
video = ScavioTikTokVideo()
result = video.invoke({"video_id": "7123456789012345678"})

comments = ScavioTikTokVideoComments(max_results=10)
result = comments.invoke({"video_id": "7123456789012345678"})

# Hashtag research
hashtag = ScavioTikTokHashtag()
result = hashtag.invoke({"hashtag_name": "python"})
hashtag_id = result["data"]["challengeInfo"]["challenge"]["id"]

hashtag_videos = ScavioTikTokHashtagVideos(max_results=5)
result = hashtag_videos.invoke({"hashtag_id": hashtag_id})

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)

ScavioTikTokProfile

Parameter	Type	Description
username	str	TikTok handle without @ (provide this or sec_user_id)
sec_user_id	str	Secure user ID from a previous lookup

ScavioTikTokUserPosts

Parameter	Type	Description
sec_user_id	str	Secure user ID from a profile lookup
cursor	str	Pagination cursor (from data.max_cursor)
count	int	Results per page (1-30, default 20)
sort_type	str	0=latest (default), 1=popular

ScavioTikTokVideo

Parameter	Type	Description
video_id	str	TikTok video identifier

ScavioTikTokVideoComments

Parameter	Type	Description
video_id	str	TikTok video identifier
cursor	str	Pagination cursor
count	int	Results per page (1-50, default 20)

ScavioTikTokCommentReplies

Parameter	Type	Description
video_id	str	TikTok video identifier
comment_id	str	Comment ID from the comments endpoint
cursor	str	Pagination cursor
count	int	Results per page (1-50, default 20)

ScavioTikTokSearchVideos

Parameter	Type	Description
keyword	str	Search query (1-500 chars)
cursor	str	Pagination offset
count	int	Results per page (1-30, default 20)
sort_type	str	0=relevance (default), 1=most likes
publish_time	str	0=all, 1=day, 7=week, 30=month, 90=3mo, 180=6mo

ScavioTikTokSearchUsers

Parameter	Type	Description
keyword	str	Search query (1-500 chars)
cursor	str	Pagination offset
count	int	Results per page (1-30, default 20)

ScavioTikTokHashtag

Parameter	Type	Description
hashtag_name	str	Hashtag text without # (provide this or hashtag_id)
hashtag_id	str	Numeric hashtag identifier

ScavioTikTokHashtagVideos

Parameter	Type	Description
hashtag_id	str	Hashtag ID from the hashtag info endpoint
cursor	str	Pagination cursor
count	int	Results per page (1-30, default 20)

ScavioTikTokUserFollowers / ScavioTikTokUserFollowings

Parameter	Type	Description
sec_user_id	str	Secure user ID from a profile lookup
count	int	Results per page (1-20, default 20)
page_token	str	Pagination token from data.next_page_token
min_time	int	Pagination field from data.min_time

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
  +-- ScavioTikTokProfileAPIWrapper       # -> /api/v1/tiktok/profile
  +-- ScavioTikTokUserPostsAPIWrapper     # -> /api/v1/tiktok/user/posts
  +-- ScavioTikTokVideoAPIWrapper         # -> /api/v1/tiktok/video
  +-- ScavioTikTokVideoCommentsAPIWrapper # -> /api/v1/tiktok/video/comments
  +-- ScavioTikTokCommentRepliesAPIWrapper# -> /api/v1/tiktok/video/comments/replies
  +-- ScavioTikTokSearchVideosAPIWrapper  # -> /api/v1/tiktok/search/videos
  +-- ScavioTikTokSearchUsersAPIWrapper   # -> /api/v1/tiktok/search/users
  +-- ScavioTikTokHashtagAPIWrapper       # -> /api/v1/tiktok/hashtag
  +-- ScavioTikTokHashtagVideosAPIWrapper # -> /api/v1/tiktok/hashtag/videos
  +-- ScavioTikTokUserFollowersAPIWrapper # -> /api/v1/tiktok/user/followers
  +-- ScavioTikTokUserFollowingsAPIWrapper# -> /api/v1/tiktok/user/followings

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