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*   [Providers](/v0.1/docs/integrations/platforms/)
    
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*   [](/v0.1/)
*   [Components](/v0.1/docs/integrations/components/)
*   [Tools](/v0.1/docs/integrations/tools/)
*   Searxng Search tool

Searxng Search tool
===================

The `SearxngSearch` tool connects your agents and chains to the internet.

A wrapper around the SearxNG API, this tool is useful for performing meta-search engine queries using the SearxNG API. It is particularly helpful in answering questions about current events.

Usage[​](#usage "Direct link to Usage")
---------------------------------------

tip

See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install @langchain/openai

    yarn add @langchain/openai

    pnpm add @langchain/openai

    import { ChatOpenAI } from "@langchain/openai";import { AgentExecutor } from "langchain/agents";import { BaseMessageChunk } from "@langchain/core/messages";import { AgentAction, AgentFinish } from "@langchain/core/agents";import { RunnableSequence } from "@langchain/core/runnables";import { ChatPromptTemplate } from "@langchain/core/prompts";import { SearxngSearch } from "@langchain/community/tools/searxng_search";const model = new ChatOpenAI({  maxTokens: 1000,  model: "gpt-4",});// `apiBase` will be automatically parsed from .env file, set "SEARXNG_API_BASE" in .env,const tools = [  new SearxngSearch({    params: {      format: "json", // Do not change this, format other than "json" is will throw error      engines: "google",    },    // Custom Headers to support rapidAPI authentication Or any instance that requires custom headers    headers: {},  }),];const prefix = ChatPromptTemplate.fromMessages([  [    "ai",    "Answer the following questions as best you can. In your final answer, use a bulleted list markdown format.",  ],  ["human", "{input}"],]);// Replace this with your actual output parser.const customOutputParser = (  input: BaseMessageChunk): AgentAction | AgentFinish => ({  log: "test",  returnValues: {    output: input,  },});// Replace this placeholder agent with your actual implementation.const agent = RunnableSequence.from([prefix, model, customOutputParser]);const executor = AgentExecutor.fromAgentAndTools({  agent,  tools,});console.log("Loaded agent.");const input = `What is Langchain? Describe in 50 words`;console.log(`Executing with input "${input}"...`);const result = await executor.invoke({ input });console.log(result);/** * Langchain is a framework for developing applications powered by language models, such as chatbots, Generative Question-Answering, summarization, and more. It provides a standard interface, integrations with other tools, and end-to-end chains for common applications. Langchain enables data-aware and powerful applications. */

#### API Reference:

*   [ChatOpenAI](https://api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai`
*   [AgentExecutor](https://api.js.langchain.com/classes/langchain_agents.AgentExecutor.html) from `langchain/agents`
*   [BaseMessageChunk](https://api.js.langchain.com/classes/langchain_core_messages.BaseMessageChunk.html) from `@langchain/core/messages`
*   [AgentAction](https://api.js.langchain.com/types/langchain_core_agents.AgentAction.html) from `@langchain/core/agents`
*   [AgentFinish](https://api.js.langchain.com/types/langchain_core_agents.AgentFinish.html) from `@langchain/core/agents`
*   [RunnableSequence](https://api.js.langchain.com/classes/langchain_core_runnables.RunnableSequence.html) from `@langchain/core/runnables`
*   [ChatPromptTemplate](https://api.js.langchain.com/classes/langchain_core_prompts.ChatPromptTemplate.html) from `@langchain/core/prompts`
*   [SearxngSearch](https://api.js.langchain.com/classes/langchain_community_tools_searxng_search.SearxngSearch.html) from `@langchain/community/tools/searxng_search`

* * *

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