Datasets:
roanbrasil
/
devops-gitops-corpus

Dataset card Data Studio Files
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```
- Registers all three agents so I can call them individually or through the supervisor.
- Exposes an HTTP interface using Hono at `http://localhost:3141`.
- Attaches the `observability` instance so trace data persists without extra wiring.
### Running the Agent
Once deployed, the agent handles natural conversations for incoming requests.
![YouTube to Blog Agent running](https://cdn.voltagent.dev/examples/with-youtube-to-blog/start-development.png)
I watch the coordinator delegate work in three steps:
1. `TranscriptFetcher` calls the MCP tool to retrieve the English transcript.
2. `BlogWriter` receives the transcript and formats a structured Markdown article.
3. The supervisor returns the Markdown output without extra commentary.
VoltOps captures each delegation, tool call, and LLM response, so I can inspect the chain step by step.
Prompt example:
```
Extract the transcript of this video: https://www.youtube.com/watch?v=U6s2pdxebSo and write a blog post in English.
```
### Next Steps
Here are the next improvements on my list:
1. Integrate additional MCP providers (for example, keyword research or SEO scoring) before handing the transcript to the writer.
2. Add guardrail agents that fact-check statistics or detect sensitive topics before publication.
3. Persist finished articles to a CMS via webhooks or a platform-specific API.
4. Allow the coordinator to branch into multiple writing styles (technical deep dive, social recap, executive summary) based on user preferences.
5. Introduce human-in-the-loop review stages using VoltAgent workflows and the VoltOps timeline UI.
<|endoftext|>
# source: VoltAgent__voltagent/website/models-docs/overview.md type: docs
---
title: Models
slug: /
sidebar_position: 1
description: Explore 80+ providers and 2193+ models supported by VoltAgent's model registry.
---
<!-- THIS FILE IS AUTO-GENERATED BY website/scripts/generate-model-docs.js. DO NOT EDIT MANUALLY. -->
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
# Models
Explore 80+ providers and 2193+ models using VoltAgent's built-in model registry. Use `provider/model` strings for fast routing, or pass an ai-sdk `LanguageModel` when you need provider-specific control.
The registry is generated from [models.dev](https://models.dev) and bundled with VoltAgent. At runtime, VoltAgent checks required environment variables and reports the exact one that's missing.
## Highlights
- **Zero-import model strings** - Use `provider/model` IDs without adding provider packages.
- **Registry-backed env mapping** - VoltAgent knows which env vars each provider expects.
- **Type-aware model IDs** - `ModelRouterModelId` adds autocomplete and validation.
- **Runtime routing** - Pick models dynamically per request or tenant.
- **Bring your own LanguageModel** - Drop in ai-sdk providers for advanced options.
## Quick start (model strings)
<Tabs>
<TabItem value="OpenAI" label="OpenAI">
```ts
import { Agent } from "@voltagent/core";
const agent = new Agent({
name: "openai-summary",
instructions: "Summarize the update in 2 bullets.",
model: "openai/gpt-4.1-mini",
});
```
</TabItem>
<TabItem value="Anthropic" label="Anthropic">
```ts
import { Agent } from "@voltagent/core";
const agent = new Agent({
name: "claude-notes",
instructions: "Turn notes into action items.",
model: "anthropic/claude-3-5-haiku",
});
```
</TabItem>
<TabItem value="Google" label="Google Gemini">
```ts
import { Agent } from "@voltagent/core";
const agent = new Agent({
name: "gemini-translator",
instructions: "Translate to Turkish and keep tone friendly.",
model: "google/gemini-2.0-flash",
});