# Source: https://trigger.dev/docs/guides/example-projects/vercel-ai-sdk-deep-research.md

> ## Documentation Index
> Fetch the complete documentation index at: https://trigger.dev/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Deep research agent using Vercel's AI SDK

> Deep research agent which generates comprehensive PDF reports using Vercel's AI SDK.

<Info title="Acknowledgements">
  Acknowledgements: This example project is derived from the brilliant [deep research
  guide](https://aie-feb-25.vercel.app/docs/deep-research) by [Nico
  Albanese](https://x.com/nicoalbanese10).
</Info>

## Overview

This full-stack project is an intelligent deep research agent that autonomously conducts multi-layered web research, generating comprehensive reports which are then converted to PDF and uploaded to storage.

<video controls className="w-full aspect-video" src="https://github.com/user-attachments/assets/aa86d2b2-7aa7-4948-82ff-5e1e80cf8e37" />

**Tech stack:**

* **[Next.js](https://nextjs.org/)** for the web app
* **[Vercel's AI SDK](https://sdk.vercel.ai/)** for AI model integration and structured generation
* **[Trigger.dev](https://trigger.dev)** for task orchestration, execution and real-time progress updates
* **[OpenAI's GPT-4o model](https://openai.com/gpt-4)** for intelligent query generation, content analysis, and report creation
* **[Exa API](https://exa.ai/)** for semantic web search with live crawling
* **[LibreOffice](https://www.libreoffice.org/)** for PDF generation
* **[Cloudflare R2](https://developers.cloudflare.com/r2/)** to store the generated reports

**Features:**

* **Recursive research**: AI generates search queries, evaluates their relevance, asks follow-up questions and searches deeper based on initial findings.
* **Real-time progress**: Live updates are shown on the frontend using Trigger.dev Realtime as research progresses.
* **Intelligent source evaluation**: AI evaluates search result relevance before processing.
* **Research report generation**: The completed research is converted to a structured HTML report using a detailed system prompt.
* **PDF creation and uploading to Cloud storage**: The completed reports are then converted to PDF using LibreOffice and uploaded to Cloudflare R2.

## GitHub repo

<Card title="View the Vercel AI SDK deep research agent repo" icon="GitHub" href="https://github.com/triggerdotdev/examples/tree/main/vercel-ai-sdk-deep-research-agent">
  Click here to view the full code for this project in our examples repository on GitHub. You can
  fork it and use it as a starting point for your own project.
</Card>

## How the deep research agent works

### Trigger.dev orchestration

The research process is orchestrated through three connected Trigger.dev tasks:

1. `deepResearchOrchestrator` - Main task that coordinates the entire research workflow.
2. `generateReport` - Processes research data into a structured HTML report using OpenAI's GPT-4o model
3. `generatePdfAndUpload` - Converts HTML to PDF using LibreOffice and uploads to R2 cloud storage

Each task uses `triggerAndWait()` to create a dependency chain, ensuring proper sequencing while maintaining isolation and error handling.

### The deep research recursive function

The core research logic uses a recursive depth-first search approach. A query is recursively expanded and the results are collected.

**Key parameters:**

* `depth`: Controls recursion levels (default: 2)
* `breadth`: Number of queries per level (default: 2, halved each recursion)

```
Level 0 (Initial Query): "AI safety in autonomous vehicles"
│
├── Level 1 (depth = 1, breadth = 2):
│   ├── Sub-query 1: "Machine learning safety protocols in self-driving cars"
│   │   ├── → Search Web → Evaluate Relevance → Extract Learnings
│   │   └── → Follow-up: "How do neural networks handle edge cases?"
│   │
│   └── Sub-query 2: "Regulatory frameworks for autonomous vehicle testing"
│       ├── → Search Web → Evaluate Relevance → Extract Learnings
│       └── → Follow-up: "What are current safety certification requirements?"
│
└── Level 2 (depth = 2, breadth = 1):
    ├── From Sub-query 1 follow-up:
    │   └── "Neural network edge case handling in autonomous systems"
    │       └── → Search Web → Evaluate → Extract → DEPTH LIMIT REACHED
    │
    └── From Sub-query 2 follow-up:
        └── "Safety certification requirements for self-driving vehicles"
            └── → Search Web → Evaluate → Extract → DEPTH LIMIT REACHED
```

**Process flow:**

1. **Query generation**: OpenAI's GPT-4o generates multiple search queries from the input
2. **Web search**: Each query searches the web via the Exa API with live crawling
3. **Relevance evaluation**: OpenAI's GPT-4o evaluates if results help answer the query
4. **Learning extraction**: Relevant results are analyzed for key insights and follow-up questions
5. **Recursive deepening**: Follow-up questions become new queries for the next depth level
6. **Accumulation**: All learnings, sources, and queries are accumulated across recursion levels

### Using Trigger.dev Realtime to trigger and subscribe to the deep research task

We use the [`useRealtimeTaskTrigger`](/realtime/react-hooks/triggering#userealtimetasktrigger) React hook to trigger the `deep-research` task and subscribe to it's updates.

**Frontend (React Hook)**:

```typescript  theme={"theme":"css-variables"}
const triggerInstance = useRealtimeTaskTrigger<typeof deepResearchOrchestrator>("deep-research", {
  accessToken: triggerToken,
});
const { progress, label } = parseStatus(triggerInstance.run?.metadata);
```

As the research progresses, the metadata is set within the tasks and the frontend is kept updated with every new status:

**Task Metadata**:

```typescript  theme={"theme":"css-variables"}
metadata.set("status", {
  progress: 25,
  label: `Searching the web for: "${query}"`,
});
```

## Relevant code

* **Deep research task**: Core logic in [src/trigger/deepResearch.ts](https://github.com/triggerdotdev/examples/blob/main/vercel-ai-sdk-deep-research-agent/src/trigger/deepResearch.ts) - orchestrates the recursive research process. Here you can change the model, the depth and the breadth of the research.
* **Report generation**: [src/trigger/generateReport.ts](https://github.com/triggerdotdev/examples/blob/main/vercel-ai-sdk-deep-research-agent/src/trigger/generateReport.ts) - creates structured HTML reports from research data. The system prompt is defined in the code - this can be updated to be more or less detailed.
* **PDF generation**: [src/trigger/generatePdfAndUpload.ts](https://github.com/triggerdotdev/examples/blob/main/vercel-ai-sdk-deep-research-agent/src/trigger/generatePdfAndUpload.ts) - converts reports to PDF and uploads to R2. This is a simple example of how to use LibreOffice to convert HTML to PDF.
* **Research agent UI**: [src/components/DeepResearchAgent.tsx](https://github.com/triggerdotdev/examples/blob/main/vercel-ai-sdk-deep-research-agent/src/components/DeepResearchAgent.tsx) - handles form submission and real-time progress display using the `useRealtimeTaskTrigger` hook.
* **Progress component**: [src/components/progress-section.tsx](https://github.com/triggerdotdev/examples/blob/main/deep-research-agent/src/components/progress-section.tsx) - displays live research progress.

## Learn more about Trigger.dev Realtime

To learn more, take a look at the following resources:

* [Trigger.dev Realtime](/realtime) - learn more about how to subscribe to runs and get real-time updates
* [Realtime streaming](/realtime/react-hooks/streams) - learn more about streaming data from your tasks
* [Batch Triggering](/triggering#tasks-batchtrigger) - learn more about how to trigger tasks in batches
* [React hooks](/realtime/react-hooks) - learn more about using React hooks to interact with the Trigger.dev API