One API to search
every platform

Scavio is a real-time search API built for AI agents and developers. One endpoint searches Google, Amazon, YouTube, and Walmart and returns structured JSON instead of raw HTML. Agents get clean titles, URLs, descriptions, prices, and metadata they can act on immediately. No browser automation, no proxy management, no scraping infrastructure required.

An AI agent sends Scavio a query and a target platform, and gets back structured results it can reason over. For example, a shopping agent can compare prices across Amazon and Walmart in a single workflow. A research agent can pull Google results and YouTube tutorials on the same topic. A content agent can find trending products and generate recommendations with real data. Scavio handles the search infrastructure so the agent focuses on its task.

Most search APIs only return Google results. Scavio searches Google, Amazon, YouTube, and Walmart from one endpoint, so agents can compare data across platforms without managing four different integrations. Responses are structured JSON optimized for AI consumption, not raw SERP HTML. Pricing is 1 credit per request with a free tier of 500 credits/month.

Developers use Scavio to build AI shopping assistants that compare prices across Amazon and Walmart, research agents that pull information from Google and YouTube, RAG pipelines that ground LLM responses in real-time web data, competitive intelligence tools that track products across marketplaces, and content recommendation engines powered by live search results.

Retrieval-Augmented Generation works best when the retrieved data is current and relevant. Scavio provides real-time results from multiple platforms, so your RAG pipeline can ground LLM outputs in live web data rather than a static knowledge base. This is critical for queries about current events, product availability, pricing, or any domain where information changes frequently.

Scavio works with any framework that can make HTTP requests: LangChain, CrewAI, AutoGen, OpenAI Assistants, custom agents, and more. The API is a single POST endpoint that returns JSON, so there are no framework-specific SDKs to install. Most developers connect their agents to Scavio in under 10 minutes.

Google gives you web pages, but many AI agent tasks need specialized data. Product research needs real prices and reviews from Amazon and Walmart. Video discovery needs YouTube metadata like view counts and upload dates. A multi-platform search API lets agents access the right data source for each task instead of trying to extract everything from Google links.

MCP (Model Context Protocol) is an open standard for connecting AI assistants to external tools and data. Scavio runs an MCP server at mcp.scavio.dev/mcp that exposes 9 tools covering Google Search, Amazon, Walmart, and YouTube. Any MCP-compatible client -- including Claude Code, Claude Desktop, Cursor, Windsurf, VS Code, ChatGPT, Cline, and Zed -- can connect with a single JSON config. No code required, setup takes under 2 minutes.

Scavio's MCP server works with Claude Code, Claude Desktop, Cursor, Windsurf, VS Code (GitHub Copilot), ChatGPT, Cline, Zed, and any other tool that supports the Model Context Protocol. The server uses HTTP transport so there is nothing to install locally -- your AI assistant connects directly over the network. Once connected, it gets access to 9 tools for searching Google, Amazon, Walmart, and YouTube.

Yes. Scavio offers a free plan with 500 credits per month and no credit card required. Each search request costs 1 credit regardless of platform. This is enough to build and test an AI agent integration, run a small production project, or evaluate whether multi-platform search adds value to your workflow. Paid plans start at $30/month.

Every Scavio API call returns live results fetched in real time from the target platform. There is no caching layer or stale index. When your agent searches Amazon, it gets current prices and availability. When it searches YouTube, it gets the latest uploads. This makes Scavio ideal for use cases where accuracy depends on recency: price monitoring, trend detection, inventory tracking, and news aggregation.