Academic Research Paper Generator Setup Guide

What This Agent Does

This powerful workflow automates the creation of comprehensive academic research papers by combining intelligent research aggregation with AI-powered content generation. Simply provide a research topic, and the agent searches multiple academic databases simultaneously, synthesizes the findings, and generates a complete paper with introduction, literature review, methodology, results, discussion, and conclusion sections.

Key benefits include:

• Dramatic time savings: Generate complete research papers in minutes instead of days or weeks
• Comprehensive research: Automatically searches CrossRef, Semantic Scholar, and OpenAlex for the most relevant academic sources
• Professional quality: Uses GPT-4o to produce publication-ready content with proper academic tone and structure
• Consistent formatting: Delivers a fully compiled document ready for submission or further refinement

Ideal use cases:

• Academic researchers conducting literature reviews
• Students writing thesis chapters or research papers
• Content creators developing educational materials
• Organizations synthesizing research on specific topics
• Publishers automating initial draft generation

Who Is It For

This workflow is designed for:

• Academic researchers and scholars who need to accelerate their research and writing process
• Graduate and undergraduate students working on thesis projects, dissertations, or major research assignments
• Content teams at educational institutions, think tanks, and research organizations
• Professionals in regulated industries who need to document research findings
• Anyone seeking to leverage AI to enhance research quality while maintaining academic rigor

No advanced technical knowledge is required—if you can describe a research topic, you can use this agent.

Required Integrations

OpenAI

Why it's needed: OpenAI's GPT-4o model powers the intelligent content generation for all six sections of your research paper. This advanced language model understands academic writing conventions, can synthesize complex information, and produces coherent, well-structured prose that meets scholarly standards.

Setup steps:

1. Create an OpenAI account (if you don't have one)

   • Visit platform.openai.com
   • Click "Sign up" and complete the registration process
   • Verify your email address

2. Access the API keys section

   • Log in to your OpenAI account
   • Navigate to Settings → API Keys
   • Click "Create new secret key"

3. Copy your API key

   • A new key will be generated (starts with sk-)
   • Copy the entire key immediately—you won't be able to see it again
   • Store it securely (never share or commit to version control)

4. Configure in TaskAGI

   • In TaskAGI, go to Integrations → Add Integration
   • Select OpenAI from the available options
   • Paste your API key in the API Key field
   • Name the integration (e.g., "OpenAI-Research-Papers")
   • Click Save and Test to verify the connection
   • You should see a success message confirming authentication

Important notes:

• Ensure your OpenAI account has API credits available (not free trial credits)
• Monitor your usage in the OpenAI dashboard to track costs
• The GPT-4o model is recommended for best results; ensure your account has access to this model

Configuration Steps

Step 1: Webhook Trigger Setup

The workflow begins with a webhook trigger that receives your research request.

Configuration:

• The webhook is automatically created when you deploy the workflow
• You'll receive a unique webhook URL (e.g., https://api.taskagi.com/webhooks/abc123xyz)
• This URL accepts POST requests with your research parameters

Expected input format:

{
  "topic": "Machine Learning Applications in Healthcare",
  "keywords": ["machine learning", "healthcare", "diagnosis"],
  "num_references": 20,
  "academic_level": "graduate"
}

Step 2: Extract Input Data

This node processes the incoming webhook data and prepares it for downstream processing.

Configuration:

• Map the incoming topic field to the variable research_topic
• Extract keywords array for database searches
• Set num_references to control how many sources to retrieve (recommended: 15-25)
• Store academic_level to adjust tone and complexity

Example mapping:

• Input: topic → Output: research_topic
• Input: keywords → Output: search_keywords

Step 3: Parallel Database Searches (Nodes 5821-5823)

Three HTTP request nodes simultaneously search different academic databases. This parallel approach dramatically speeds up research collection.

CrossRef Search Configuration:

• Endpoint: https://api.crossref.org/works
• Method: GET
• Parameters:
  • query: [[nodes.5820.research_topic]]
  • rows: [[nodes.5820.num_references]]
  • sort: relevance
• Expected output: JSON array of academic papers with DOI, title, authors, abstract

Semantic Scholar Search Configuration:

• Endpoint: https://api.semanticscholar.org/graph/v1/paper/search
• Method: GET
• Parameters:
  • query: [[nodes.5820.research_topic]]
  • limit: [[nodes.5820.num_references]]
  • fields: title,authors,abstract,year
• Expected output: Structured paper metadata with relevance scores

OpenAlex Search Configuration:

• Endpoint: https://api.openalex.org/works
• Method: GET
• Parameters:
  • search: [[nodes.5820.research_topic]]
  • per_page: [[nodes.5820.num_references]]
  • sort: cited_by_count:desc
• Expected output: Works data with citation counts and publication details

Step 4: Merge Reference Sources

This node combines results from all three databases, eliminating duplicates and creating a unified reference list.

Configuration:

• Merge type: Combine arrays
• Deduplication: Enable (matches by DOI or title)
• Output: Single array of unique references with source attribution

Step 5: Process References

A custom function node formats and enriches the reference data.

Function purpose:

• Extract key information from each reference
• Create structured citation objects
• Generate relevance scores based on citation counts
• Sort by relevance to the research topic

Output structure:

{
  "references": [
    {
      "title": "Paper Title",
      "authors": ["Author 1", "Author 2"],
      "year": 2023,
      "abstract": "...",
      "relevance_score": 0.95,
      "source": "CrossRef"
    }
  ]
}

Step 6: Prepare AI Context

This node formats the research data into a context document for the AI writing nodes.

Configuration:

• Compile top references (sorted by relevance)
• Create a summary of key themes across sources
• Format citations in academic style
• Include research topic and academic level information

Steps 7-12: AI Content Generation Nodes

Six sequential AI completion nodes generate each section of your paper. Each uses GPT-4o with specialized prompts.

Common configuration for all AI nodes:

• Model: gpt-4o
• Temperature: 0.7 (balances creativity with consistency)
• Max tokens: 2000 (adjust based on desired section length)
• System prompt: Specialized for each section (e.g., "You are an expert academic writer specializing in [field]...")

Section-specific guidance:

Prompt template example:

You are an expert academic writer specializing in [field]. 
Write a comprehensive [section name] for a research paper on: [topic]
Use the following references as your foundation: [references]
Maintain an academic tone appropriate for [academic_level] level.
Include citations in the format: (Author, Year)
Target length: approximately 500-800 words.

Step 13: Compile Document

This function node assembles all six sections into a complete, formatted research paper.

Configuration:

• Combine sections in proper order
• Add title page with research topic and metadata
• Insert table of contents
• Format references in APA or Chicago style
• Add page numbers and headers

Output format: Complete document ready for export (PDF, DOCX, or markdown)

Step 14: Respond to Webhook

The final node sends results back to the requesting system.

Response structure:

{
  "success": true,
  "title": "Research Paper Title",
  "document": "Full paper content",
  "references_count": 20,
  "generation_time_seconds": 145,
  "sections": {
    "introduction": "...",
    "literature_review": "...",
    "methodology": "...",
    "results": "...",
    "discussion": "...",
    "conclusion": "..."
  }
}

Testing Your Agent

Test Execution

1. Deploy the workflow

   • Click Deploy in TaskAGI
   • Wait for confirmation that all nodes are active
   • Copy the webhook URL provided

2. Send a test request

   • Use Postman, cURL, or TaskAGI's built-in webhook tester
   • Example cURL command:

   curl -X POST https://your-webhook-url \
     -H "Content-Type: application/json" \
     -d '{
       "topic": "Artificial Intelligence in Education",
       "keywords": ["AI", "education", "learning"],
       "num_references": 15,
       "academic_level": "graduate"
     }'
   

3. Monitor execution

   • Watch the workflow execution in real-time
   • Check each node's output in the TaskAGI dashboard
   • Note execution time and any errors

Verification Checklist

• ✅ Webhook trigger: Receives request successfully
• ✅ Database searches: All three sources return results
• ✅ Reference merging: Duplicates removed, unified list created
• ✅ AI generation: Each section generates without errors
• ✅ Document compilation: All sections present and properly formatted
• ✅ Webhook response: Returns complete paper within 5 minutes

Expected Results

A successful execution produces a complete, publication-ready research paper with:

• Professional introduction establishing context
• Comprehensive literature review citing 15+ sources
• Clear methodology section
• Well-organized results
• Thoughtful discussion connecting findings
• Strong conclusion with future directions
• Properly formatted reference list

Success indicators:

• Response time: 2-5 minutes depending on complexity
• Document length: 3,000-5,000 words
• All sections present and coherent
• Proper citations throughout
• No generation errors in logs

Your academic research paper generator is now ready to accelerate your research workflow!