LangChain Integration

Scavio ships as a ready-made tool package for LangChain, the most widely used framework for building LLM applications. Install langchain-scavio and hand any tool to a LangChain agent to give it real-time search across Google, Amazon, Walmart, YouTube, Reddit, TikTok, TikTok Shop, and Instagram — a cost-effective Tavily and SerpAPI alternative, with one package, one API key, and no custom HTTP code.

Introduction

The langchain-scavio package exposes 46 tools across 8 providers, all following the Scavio<Provider><Action> naming convention. Each tool subclasses LangChain's BaseTool and declares an args_schema, so the model gets accurate argument hints and LangChain validates every call before it runs.

You need Python 3.10 or later and a Scavio API key from dashboard.scavio.dev. The package depends only on langchain-core, so it works with LangChain, LangGraph, or any framework that speaks the BaseTool interface — you do not need the full langchain package unless you use create_agent.

Step-by-Step Integration Guide

Step 1: Install

pip install langchain-scavio

This pulls in langchain-core. Install langchain as well if you want the create_agent helper shown below. Requires Python 3.10 or later.

Step 2: Set your API key

Get a key at dashboard.scavio.dev (50 free credits to start, no card), then set it as an environment variable:

export SCAVIO_API_KEY=sk_live_your_key

Every tool reads SCAVIO_API_KEY from the environment. You can also pass it explicitly per tool with the scavio_api_key keyword argument: ScavioSearch(scavio_api_key="sk_live_...").

Step 3: Invoke a tool directly

Every tool works standalone, which is the fastest way to check your key and see the response shape before wiring up an agent:

from langchain_scavio import ScavioSearch

tool = ScavioSearch(max_results=5)

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

Step 4: Use with an agent

Scavio tools plug into the current create_agent API from langchain.agents. Pass as many or as few tools as the agent needs:

from langchain.agents import create_agent
from langchain_scavio import ScavioSearch, ScavioRedditSearch

agent = create_agent(
    "openai:gpt-5.5",
    tools=[
        ScavioSearch(max_results=5),
        ScavioRedditSearch(max_results=5),
    ],
    system_prompt="You are a research assistant. Cite your sources.",
)

response = agent.invoke(
    {"messages": [{"role": "user", "content": "What are people saying about Bun in 2026?"}]}
)
print(response["messages"][-1].content)

Available Tools

46 tools across 8 providers. All follow the Scavio<Provider><Action> naming convention — note there is no Tool suffix. Import only the tools an agent needs:

from langchain_scavio import (
    ScavioSearch,             # Google web search
    ScavioAmazonSearch,       # Amazon product search
    ScavioYouTubeSearch,      # YouTube video search
    ScavioRedditSearch,       # Reddit post search
)

Configuring a tool

Tool options split into two groups, which is what keeps agent behaviour predictable:

• Instantiation-only options are fixed by you when you construct the tool and the model cannot change them. These control cost and payload size — max_results, include_knowledge_graph, include_questions, include_related, include_ai_overviews, and the other include_* flags.
• Model-controlled arguments live in the tool's args_schema and are chosen per call — query, search_type, country_code, language, device, and page. Set any of them at construction time to pin a default the model can still override.

from langchain_scavio import ScavioSearch

tool = ScavioSearch(
    max_results=10,
    include_knowledge_graph=True,
    include_questions=True,
    country_code="gb",      # default the model may still override
)

Async

Every tool supports async invocation, so agents can fan out across platforms concurrently:

import asyncio
from langchain_scavio import ScavioSearch, ScavioRedditSearch

async def main():
    google, reddit = ScavioSearch(), ScavioRedditSearch()
    web, threads = await asyncio.gather(
        google.ainvoke({"query": "vector database benchmarks 2026"}),
        reddit.ainvoke({"query": "vector database benchmarks"}),
    )
    print(web, threads)

asyncio.run(main())

Advanced Example

A single agent that cross-checks retail listings against real community sentiment — the kind of multi-platform research a Google-only search tool cannot do:

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

agent = create_agent(
    "openai:gpt-5.5",
    tools=[
        ScavioAmazonSearch(max_results=5),
        ScavioAmazonProduct(),
        ScavioRedditSearch(max_results=5),
        ScavioYouTubeSearch(max_results=3),
        ScavioSearch(max_results=5),
    ],
    system_prompt=(
        "You are a product research analyst. Compare listings, then check what "
        "real users say on Reddit and YouTube before recommending anything."
    ),
)

response = agent.invoke(
    {
        "messages": [
            {
                "role": "user",
                "content": (
                    "Compare the top mechanical keyboards on Amazon and tell me "
                    "what r/MechanicalKeyboards actually thinks of them."
                ),
            }
        ]
    }
)
print(response["messages"][-1].content)

How it works

Each tool is a LangChain BaseTool with a Pydantic args_schema, so the model gets accurate argument hints and LangChain validates calls before they run. Requests go to the Scavio API over HTTPS with your key as a bearer token; the shared API wrapper handles auth, headers, client-side rate limiting, and sync and async transport. Errors are raised as ToolException with a remediation hint, and because handle_tool_error is enabled the agent sees the message and can retry rather than crashing the run.

Credit costs

Most calls cost 1 credit, including every Google, Amazon, Walmart, YouTube, Reddit, TikTok, and TikTok Shop tool. Instagram is priced per endpoint because upstream costs differ: 10 credits for profile, reels, tagged posts, stories, post comments, follower and following, and user and hashtag search; 8 credits for ScavioInstagramPost and ScavioInstagramCommentReplies; and 2 credits for ScavioInstagramUserPosts. See the rate limits reference for plan limits and the errors reference for retry guidance.

Benefits of Scavio + LangChain

• 46 tools, one package: hand any Scavio<Provider><Action> tool to a LangChain or LangGraph agent.
• Typed argument schemas: every tool is a BaseTool with a Pydantic args_schema, so calls are validated before they run.
• Eight platforms, one key: Google, Amazon, Walmart, YouTube, Reddit, TikTok, TikTok Shop, and Instagram behind a single API key.
• Sync and async: invoke and ainvoke on every tool, so agents can fan out across platforms concurrently.
• Cost-effective: most calls cost a single credit — a Tavily and SerpAPI alternative with far broader platform coverage.

Next Steps

• Scavio API quickstart — keys, credits, and your first request
• Google Search API reference — the endpoint behind ScavioSearch
• MCP Integration — every Scavio endpoint as a tool
• langchain-scavio on PyPI
• langchain-scavio on GitHub
• LangChain documentation