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Glossary

Genkit Search Plugin

A plugin for Google Genkit (the Firebase AI framework) that adds web search capabilities as a tool, enabling Genkit-based AI agents and flows to query search APIs and incorporate real-time web data into their responses.

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Definition

A plugin for Google Genkit (the Firebase AI framework) that adds web search capabilities as a tool, enabling Genkit-based AI agents and flows to query search APIs and incorporate real-time web data into their responses.

In Depth

Google Genkit is an open-source framework for building AI-powered applications, tightly integrated with Firebase. It provides a flow-based architecture for chaining LLM calls, tools, and data retrieval. Search plugins extend Genkit with web search capabilities that agents can call during flow execution. Plugin architecture: Genkit plugins export tool definitions that the framework registers. A search plugin defines a 'webSearch' tool with input schema (query, platform, num_results) and an execute function that calls the search API. During flow execution, the LLM can invoke this tool when it needs web data. Implementation with Scavio: the plugin wraps Scavio's REST API as a Genkit tool. Configuration requires the API key and default parameters. The tool accepts a query string and optional platform parameter, calls api.scavio.dev/api/v1/search, and returns structured results. Cost: $0.005/query, same as direct API usage. Genkit's advantage over raw API calls: (1) Observability -- Genkit's developer UI shows tool call traces, including search queries, response times, and result counts. (2) Testing -- Genkit flows can be tested with mock tool responses without making real API calls. (3) Deployment -- Genkit flows deploy to Firebase Cloud Functions, scaling automatically. (4) Caching -- Genkit's flow system can cache tool results to avoid duplicate queries. Genkit vs LangChain for search integration: Genkit is simpler (fewer abstractions) and Firebase-native. LangChain has more pre-built integrations but more complexity. For Firebase/GCP teams, Genkit is the natural choice.

Example Usage

Real-World Example

// Genkit search plugin definition (TypeScript) import { defineTool } from '@genkit-ai/ai'; import { z } from 'zod'; const webSearch = defineTool( { name: 'webSearch', description: 'Search the web via Scavio', inputSchema: z.object({ query: z.string(), platform: z.string().default('google') }), outputSchema: z.any() }, async (input) => { const res = await fetch('https://api.scavio.dev/api/v1/search', { method: 'POST', headers: { 'x-api-key': process.env.SCAVIO_KEY!, 'Content-Type': 'application/json' }, body: JSON.stringify(input), }); return res.json(); } );

Platforms

Genkit Search Plugin is relevant across the following platforms, all accessible through Scavio's unified API:

  • Google
  • Amazon
  • YouTube

Related Terms

Agent Tool Dispatch

The mechanism by which an AI agent selects which external tool or API to call based on the user's intent, routes the req...

MCP Search Protocol

The application of Model Context Protocol (MCP) to search functionality, where search providers expose search capabiliti...

Plain Python Agent

An AI agent built using standard Python libraries (requests, json, asyncio) with direct API calls to LLM providers and t...

Frequently Asked Questions

A plugin for Google Genkit (the Firebase AI framework) that adds web search capabilities as a tool, enabling Genkit-based AI agents and flows to query search APIs and incorporate real-time web data into their responses.

// Genkit search plugin definition (TypeScript) import { defineTool } from '@genkit-ai/ai'; import { z } from 'zod'; const webSearch = defineTool( { name: 'webSearch', description: 'Search the web via Scavio', inputSchema: z.object({ query: z.string(), platform: z.string().default('google') }), outputSchema: z.any() }, async (input) => { const res = await fetch('https://api.scavio.dev/api/v1/search', { method: 'POST', headers: { 'x-api-key': process.env.SCAVIO_KEY!, 'Content-Type': 'application/json' }, body: JSON.stringify(input), }); return res.json(); } );

Genkit Search Plugin is relevant to Google, Amazon, YouTube. Scavio provides a unified API to access data from all of these platforms.

Google Genkit is an open-source framework for building AI-powered applications, tightly integrated with Firebase. It provides a flow-based architecture for chaining LLM calls, tools, and data retrieval. Search plugins extend Genkit with web search capabilities that agents can call during flow execution. Plugin architecture: Genkit plugins export tool definitions that the framework registers. A search plugin defines a 'webSearch' tool with input schema (query, platform, num_results) and an execute function that calls the search API. During flow execution, the LLM can invoke this tool when it needs web data. Implementation with Scavio: the plugin wraps Scavio's REST API as a Genkit tool. Configuration requires the API key and default parameters. The tool accepts a query string and optional platform parameter, calls api.scavio.dev/api/v1/search, and returns structured results. Cost: $0.005/query, same as direct API usage. Genkit's advantage over raw API calls: (1) Observability -- Genkit's developer UI shows tool call traces, including search queries, response times, and result counts. (2) Testing -- Genkit flows can be tested with mock tool responses without making real API calls. (3) Deployment -- Genkit flows deploy to Firebase Cloud Functions, scaling automatically. (4) Caching -- Genkit's flow system can cache tool results to avoid duplicate queries. Genkit vs LangChain for search integration: Genkit is simpler (fewer abstractions) and Firebase-native. LangChain has more pre-built integrations but more complexity. For Firebase/GCP teams, Genkit is the natural choice.

Genkit Search Plugin

Start using Scavio to work with genkit search plugin across Google, Amazon, YouTube, Walmart, and Reddit.

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