Acknowledgements: This example project is derived from the brilliant deep research guide by Nico Albanese.

ℹ️ Note: This is a v4 project. If you are using v3 and want to upgrade, please refer to our v4 upgrade guide.

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.

This project uses the following technologies:

• Next.js for the web app
• Vercel's AI SDK for AI model integration and structured generation
• Trigger.dev for task orchestration, execution and real-time progress updates
• OpenAI's GPT-4o model for intelligent query generation, content analysis, and report creation (this can be modified to use any other model available on the AI SDK)
• Exa API for semantic web search with live crawling
• LibreOffice for PDF generation
• Cloudflare R2 to store the generated reports. This can also be adapted to work with any other s3 compatible storage.

• 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 upload to Cloud storage: The completed reports are then converted to PDF using LibreOffice and uploaded to Cloudflare R2.

1. After cloning the repo, run npm install to install the dependencies.
2. Copy the example.env.local file to .env and fill in the required environment variables:
   • OpenAI API key. Create a free account at OpenAI.
   • Sign up for a free Trigger.dev account here and create a new project.
   • Exa API key for web search. Create a free account at Exa.
   • Cloudflare R2 bucket for PDF storage. Create a free account at Cloudflare.
3. Copy your project ref from your Trigger.dev dashboard and add it to the trigger.config.ts file.
4. Run the Next.js server with npm run dev.
5. In a separate terminal, run the Trigger.dev dev CLI command with npx trigger@v4-beta dev (it may ask you to authorize the CLI if you haven't already).
6. To test your deep research agent, go to http://localhost:3000 and start researching any topic.

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 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 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

We use the useRealtimeTaskTrigger React hook to trigger the deep-research task and subscribe to it's updates.

Frontend (React Hook):

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:

metadata.set("status", {
  progress: 25,
  label: `Searching the web for: "${query}"`,
});

• Deep research task: Core logic in 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 - 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 - 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 - handles form submission and real-time progress display using the useRealtimeTaskTrigger hook.
• Progress component: src/components/progress-section.tsx - displays live research progress.

• Trigger.dev Documentation - Task orchestration and real-time features
• Trigger.dev Realtime - Live progress and streaming to frontend
• Trigger.dev React Hooks - Frontend integration patterns
• OpenAI API - GPT-4 integration and prompt engineering
• Exa API - Semantic web search capabilities
• Cloudflare R2 - Cloud storage provider