Deep research agent using Vercel's AI SDK

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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.

Tech stack:

• 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
• Exa API for semantic web search with live crawling
• LibreOffice for PDF generation
• Cloudflare 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.

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GitHub repo

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How the deep research agent works

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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.

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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

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

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}"`,
});

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Relevant code

• 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.

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Learn more about Trigger.dev Realtime

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

• Trigger.dev Realtime - learn more about how to subscribe to runs and get real-time updates
• Realtime streaming - learn more about streaming data from your tasks
• Batch Triggering - learn more about how to trigger tasks in batches
• React hooks - learn more about using React hooks to interact with the Trigger.dev API