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curso-langchain/slides/pages/models/15.md
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2026-05-29 11:50:55 +02:00

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# Ejemplo: Agente con Tavily Search
<div class="text-xs mt-2">
```typescript
import { createAgent } from "langchain";
import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
const searchTool = new TavilySearchResults({ apiKey: process.env.TAVILY_API_KEY, maxResults: 5 });
const agent = createAgent({
model: "openai:gpt-4.1-mini",
tools: [searchTool],
});
const result = await agent.invoke({
messages: [{ role: "user", content: "¿Cuales son las ultimas noticias sobre IA?" }],
});
```
<div class="mt-2 grid grid-cols-2 gap-3">
<div class="bg-blue-50 dark:bg-blue-900/20 p-2 rounded">
**Tavily** - Optimizado para LLMs: extrae contenido relevante, filtra anuncios/ruido, API key gratuita limitada
</div>
<div class="bg-green-50 dark:bg-green-900/20 p-2 rounded">
**Docs**: [langchain.com/integrations/tools](https://docs.langchain.com/oss/javascript/integrations/tools)
</div>
</div>
</div>