langchain-scavio

46 LangChain tools for real-time search across Google, Amazon, Walmart, YouTube, Reddit, TikTok, TikTok Shop, and Instagram -- 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 is a full Tavily alternative built for multi-platform agents — here is Tavily vs Scavio at a glance:

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 and Instagram profile analytics, hashtag tracking, post/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 46 Tools

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, ScavioYouTubeVideo, ScavioYouTubeTranscript,
    ScavioRedditSearch, ScavioRedditPost,
    ScavioTikTokSearchVideos, ScavioTikTokProfile, ScavioTikTokVideo,
)

agent = create_agent(
    "openai:gpt-5.5",
    tools=[
        ScavioSearch(max_results=5),
        ScavioAmazonSearch(max_results=5),
        ScavioAmazonProduct(),
        ScavioWalmartSearch(max_results=5),
        ScavioYouTubeSearch(max_results=5),
        ScavioYouTubeVideo(),
        ScavioYouTubeTranscript(),
        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,                  # deprecated, ignored (v2 always full, 1 credit)
    include_knowledge_graph=True,
    include_questions=True,
    include_related=False,
    country_code="us",
    language="en",
    search_type="classic",               # classic|news|maps
    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, ScavioYouTubeVideo, ScavioYouTubeComments,
    ScavioYouTubeTranscript, ScavioYouTubeChannel,
    ScavioYouTubeChannelVideos, ScavioYouTubeStreams,
)

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
    "features": ["hd", "subtitles"],     # hd|4k|subtitles|creative_commons|live|360|3d|hdr|vr180
})

# Paginate with the previous response's next_cursor
next_page = search.invoke({
    "query": "python tutorial",
    "cursor": result["data"]["next_cursor"],
})

# Full video details (chapters, captions, keywords)
video = ScavioYouTubeVideo()
result = video.invoke({"video_id": "dQw4w9WgXcQ"})  # video ID or watch URL

# Comments (paginate via data.next_cursor)
comments = ScavioYouTubeComments(max_results=10)
result = comments.invoke({"video_id": "dQw4w9WgXcQ"})

# Transcript as plain text or timed SRT (8 credits per call)
transcript = ScavioYouTubeTranscript()
result = transcript.invoke({"video_id": "dQw4w9WgXcQ", "format": "text"})

# Channel details and uploads
channel = ScavioYouTubeChannel()
result = channel.invoke({"channel_id": "@YouTube"})  # ID, @handle, or URL

channel_videos = ScavioYouTubeChannelVideos(max_results=5)
result = channel_videos.invoke({"channel_id": "UC_x5XG1OV2P6uZZ5FSM9Ttw"})

# Playable/downloadable stream URLs (3 credits per call)
streams = ScavioYouTubeStreams()
result = streams.invoke({"video_id": "dQw4w9WgXcQ"})

# ScavioYouTubeMetadata is a deprecated alias of ScavioYouTubeVideo

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})

TikTok Shop

Eight tools over the TikTok Shop catalog. Two things to know before you wire them together:

1. ScavioTikTokShopProduct resolves only about 44% of the product ids that ScavioTikTokShopSearch returns. Upstream has no detail data for the rest, so a not-found result is a normal outcome rather than an error -- skip the product instead of retrying. Search is a listing source, not the first leg of a reliable search-then-detail pipeline.
2. ScavioTikTokShopProduct does not return a price. Upstream masks the digits on the product page, so price.current and price.original come back null. Exact prices are on ScavioTikTokShopSearch, ScavioTikTokShopShopProducts and ScavioTikTokShopCategoryProducts.

from langchain_scavio import (
    ScavioTikTokShopSearch, ScavioTikTokShopSearchSuggestions,
    ScavioTikTokShopProduct, ScavioTikTokShopProductReviews,
    ScavioTikTokShopCategories, ScavioTikTokShopCategoryProducts,
    ScavioTikTokShopShopProducts, ScavioTikTokShopResolve,
)

# Search the US catalog -- this is where exact prices live
search = ScavioTikTokShopSearch(max_results=10)
result = search.invoke({"search": "phone case"})
for product in result["data"]["products"]:
    print(product["title"], product["price"]["current"], product["rating"]["score"])

# Paginate with the opaque cursor; dedupe by product_id across pages
if result["data"]["has_more"]:
    page2 = search.invoke({
        "search": "phone case",
        "cursor": result["data"]["next_cursor"],
    })

# Product detail: rich, but priceless (literally) and only ~44% resolvable
detail = ScavioTikTokShopProduct()
result = detail.invoke({"product_id": "1732293553906094315"})
if result.get("not_found"):
    pass                                     # normal: skip it, do not retry
else:
    result["data"]["variants"]               # stock per SKU
    result["data"]["shop"]["followers_count"]

# Reviews: page with has_more, never with total_reviews (it drifts)
reviews = ScavioTikTokShopProductReviews(max_results=20)
result = reviews.invoke({
    "product_id": "1732293553906094315",
    "page_size": 100,
    "sort": "relevant",                      # "recent" is fresher but text-sparse
    "has_media": True,
})

# Category browse (US and GB only)
categories = ScavioTikTokShopCategories()
tree = categories.invoke({})
category_id = tree["data"]["categories"][0]["category_id"]

listing = ScavioTikTokShopCategoryProducts(max_results=10)
result = listing.invoke({"category_id": category_id})

# A seller's whole catalog, with exact prices
shop = ScavioTikTokShopShopProducts(max_results=10)
result = shop.invoke({"shop_id": "7495514739648989419"})

# Turn any share link into an id
resolve = ScavioTikTokShopResolve()
result = resolve.invoke({"url": "https://vt.tiktok.com/ZT2AHoGsE/"})
result["data"]["product_id"], result["data"]["type"]

# Keyword expansion, the only endpoint with genuine 8-region coverage
suggestions = ScavioTikTokShopSearchSuggestions()
result = suggestions.invoke({"search": "wireless", "region": "GB"})
result["data"]["suggestions"]                # plain strings, no volume or score

Instagram

from langchain_scavio import (
    ScavioInstagramProfile, ScavioInstagramUserPosts, ScavioInstagramUserReels,
    ScavioInstagramTaggedPosts, ScavioInstagramStories,
    ScavioInstagramPost, ScavioInstagramPostComments,
    ScavioInstagramCommentReplies, ScavioInstagramSearchUsers,
    ScavioInstagramSearchHashtags,
    ScavioInstagramUserFollowers, ScavioInstagramUserFollowings,
)

# Look up a user profile (returns user_id usable by other tools)
profile = ScavioInstagramProfile()
result = profile.invoke({"username": "instagram"})
user_id = result["data"]["user"]["id"]

# Fetch their recent posts and reels
posts = ScavioInstagramUserPosts(max_results=5)
result = posts.invoke({"username": "instagram"})

reels = ScavioInstagramUserReels(max_results=5)
result = reels.invoke({"username": "instagram"})

# Get a single post's details and comments
post = ScavioInstagramPost()
result = post.invoke({"shortcode": "C1a2b3c4d5e"})

comments = ScavioInstagramPostComments(max_results=10)
result = comments.invoke({
    "shortcode": "C1a2b3c4d5e",
    "sort_order": "newest",                  # popular (default) or newest
})

# Search users and hashtags
search_users = ScavioInstagramSearchUsers(max_results=5)
result = search_users.invoke({"keyword": "cooking"})

search_hashtags = ScavioInstagramSearchHashtags(max_results=5)
result = search_hashtags.invoke({"keyword": "travel"})

Agent-Controllable Parameters

ScavioSearch

ScavioAmazonSearch

ScavioWalmartSearch

ScavioYouTubeSearch

ScavioYouTubeVideo / ScavioYouTubeMetadata

ScavioYouTubeComments

ScavioYouTubeTranscript

ScavioYouTubeChannel

ScavioYouTubeChannelVideos

ScavioYouTubeStreams

ScavioRedditSearch

ScavioRedditPost

ScavioTikTokProfile

ScavioTikTokUserPosts

ScavioTikTokVideo

ScavioTikTokVideoComments

ScavioTikTokCommentReplies

ScavioTikTokSearchVideos

ScavioTikTokSearchUsers

ScavioTikTokHashtag

ScavioTikTokHashtagVideos

ScavioTikTokUserFollowers / ScavioTikTokUserFollowings

ScavioInstagramProfile / ScavioInstagramStories

ScavioInstagramUserPosts / ScavioInstagramUserReels / ScavioInstagramTaggedPosts / ScavioInstagramUserFollowers / ScavioInstagramUserFollowings

ScavioInstagramPost

ScavioInstagramPostComments

ScavioInstagramCommentReplies

ScavioInstagramSearchUsers / ScavioInstagramSearchHashtags

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/v2/google (+ news, maps)
  +-- 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
  +-- ScavioYouTubeVideoAPIWrapper        # -> /api/v1/youtube/video
  +-- ScavioYouTubeMetadataAPIWrapper     # -> /api/v1/youtube/video (deprecated alias)
  +-- ScavioYouTubeCommentsAPIWrapper     # -> /api/v1/youtube/comments
  +-- ScavioYouTubeTranscriptAPIWrapper   # -> /api/v1/youtube/transcript
  +-- ScavioYouTubeChannelAPIWrapper      # -> /api/v1/youtube/channel
  +-- ScavioYouTubeChannelVideosAPIWrapper# -> /api/v1/youtube/channel/videos
  +-- ScavioYouTubeStreamsAPIWrapper      # -> /api/v1/youtube/streams
  +-- 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
  +-- ScavioInstagramProfileAPIWrapper        # -> /api/v1/instagram/profile
  +-- ScavioInstagramUserPostsAPIWrapper      # -> /api/v1/instagram/user/posts
  +-- ScavioInstagramUserReelsAPIWrapper      # -> /api/v1/instagram/user/reels
  +-- ScavioInstagramTaggedPostsAPIWrapper    # -> /api/v1/instagram/user/tagged
  +-- ScavioInstagramStoriesAPIWrapper        # -> /api/v1/instagram/user/stories
  +-- ScavioInstagramPostAPIWrapper           # -> /api/v1/instagram/post
  +-- ScavioInstagramPostCommentsAPIWrapper   # -> /api/v1/instagram/post/comments
  +-- ScavioInstagramCommentRepliesAPIWrapper # -> /api/v1/instagram/post/comments/replies
  +-- ScavioInstagramSearchUsersAPIWrapper    # -> /api/v1/instagram/search/users
  +-- ScavioInstagramSearchHashtagsAPIWrapper # -> /api/v1/instagram/search/hashtags
  +-- ScavioInstagramUserFollowersAPIWrapper  # -> /api/v1/instagram/user/followers
  +-- ScavioInstagramUserFollowingsAPIWrapper # -> /api/v1/instagram/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, or read more on migrating from Tavily.

License

MIT

About Scavio

Scavio is a unified search API for AI agents — one API key, structured JSON, no scraping or proxies. A real-time Tavily alternative and SerpAPI alternative with data from:

• Google Search API — SERP results, news, images, maps, and knowledge graph
• Amazon Product API and Walmart Product API — product search and details
• YouTube API, TikTok API, and Instagram API — video and social media data
• Reddit API — posts and threaded comments

Get a free API key and explore the documentation.