---
title: "Deep Researcher"
description: "A multi-model collaborative workflow that generates structured research reports in minutes"
enableComments: true
author: "AdamPlatin123"
github_username: "AdamPlatin123"
---

⬇️ Try it out before reading

<iframe
 src="https://instance.hellodify.com/chatbot/NKZ90sIADQ1PFxOz"
 style={{
   width: '100%',
   minHeight: '600px'
 }}
 frameBorder="0"
 allow="microphone">
</iframe>

---

## 📖 Overview

Deep Researcher is a workflow built on the Dify platform that reproduces the core functionality of Deep Research. By integrating multi-source retrieval (local knowledge base + web search) with multi-model collaboration, it can generate structured research reports of tens of thousands of words in 5 minutes. The system adopts a modular design and supports flexible replacement of underlying models and data sources.

## ✨ Core Features

### Intelligent Topic Analysis
Using Gemini 2.0 Flash model for multi-level topic decomposition, supporting 4-dimensional in-depth analysis

### Hybrid Retrieval Engine
`Local Knowledge Base + Wikipedia/Google/Bing API` multi-channel retrieval

### Dynamic Rhythm Control
Using a 2>1 model cascade architecture, implementing processing rhythm optimization through conditional branches and dialogue round markers

### Efficient Report Generation
Integrating models like deepseek-r1-distill for paragraph-level content generation, supporting Markdown structured output

## 🛠️ Technical Architecture

The workflow adopts the following architecture:
<Mermaid
  chart="
graph TD
    A[User Question] --> B{Topic Analysis}
    B --> F[Question Generation]
    F --> H[User Answer]
    H --> C
    B --> I[Topic Analysis]
    I --> C{Secondary Topic Extraction}
    C --> D[Hybrid Iterative Retrieval Engine]
    D --> E[Multi-model Collaborative Generation]
    E --> G[Structured Report]
    
    style B fill:#4CAF50,stroke:#388E3C
    style D fill:#2196F3,stroke:#1565C0
  "
/>

## ⚠️ Notes

### Performance Optimization Suggestions

In principle, the workflow supports any model. When the request pressure of using local models is high, it may trigger the `Timepouterror` of the LLM node. You can consider switching to an online API service or modifying the timeout time in the Dify configuration file.

If you are a Google free API user, you can insert a local model node to limit the RPM (Google's default rate limit is 15 RPM, and errors will be reported if there are too many requests in a short time)

---

<Cards>
  <Card 
    icon={<i className="ri-flow-chart" />} 
    href="https://discord.gg/PwZDHH4mv3" 
    title="Get the workflow"
  >
  <p>Join FirstLab to get the DSL file</p>
    <img src="https://s2.loli.net/2025/05/15/HFT2SE3uIyMflG4.png" alt="Deep Researcher" />
  </Card>
</Cards> 