Multi-Agent Research Report Generator

Short Description

Multi-Agent Research Report Generator is an AI workflow that researches a user topic, summarizes the findings, writes a structured report, and reviews the final output.

It solves the problem of turning a broad topic into a clear report by separating the task across specialized AI agents.

Features

✅ Topic-based research workflow
✅ Multi-agent task separation
✅ Research summarization
✅ Structured report generation
✅ Final report review and improvement
✅ Markdown report export
✅ Simple CLI interface
✅ GitHub-friendly project structure
✅ Basic unit test and GitHub Actions workflow

Architecture Diagram

User Topic
   ↓
Python CLI
   ↓
LangGraph Workflow
   ↓
Research Agent
   ↓
Summary Agent
   ↓
Report Writer Agent
   ↓
Review Agent
   ↓
Final Markdown Report

Tech Stack

• Python
• LangGraph
• LangChain
• OpenAI API
• python-dotenv
• pytest
• GitHub Actions
• Markdown

Skills Demonstrated ⭐

• Python
• Software Engineering
• AI Agents
• Multi-Agent Workflows
• LangGraph
• LangChain
• OpenAI API
• Prompt Engineering
• Report Generation
• Workflow Orchestration
• LLM Application Development
• Unit Testing
• GitHub Actions
• CLI Development
• Backend Project Structure
• Documentation

Project Structure

multi-agent-research-report/
│
├── app.py
├── main.py
├── requirements.txt
├── pyproject.toml
├── README.md
├── LICENSE
├── .gitignore
├── .env.example
│
├── docs/
│   ├── architecture.png
│   ├── demo.md
│   ├── evaluation.md
│   └── screenshots/
│       └── terminal-output.png
│
├── src/
│   └── multi_agent_report/
│       ├── __init__.py
│       ├── agents.py
│       ├── config.py
│       ├── workflow.py
│       └── utils.py
│
├── tests/
│   └── test_utils.py
│
└── .github/
    └── workflows/
        └── tests.yml

Installation

1. Clone the repository

git clone https://github.com/ParisaArbab/multi-agent-research-report.git
cd multi-agent-research-report

2. Create a virtual environment

python -m venv .venv

For macOS or Linux:

source .venv/bin/activate

For Windows:

.venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Add your OpenAI API key

cp .env.example .env

Then edit .env:

OPENAI_API_KEY=your_openai_api_key_here

5. Run the project

For macOS or Linux:

PYTHONPATH=src python app.py

For Windows PowerShell:

$env:PYTHONPATH="src"; python app.py

Usage

Enter any research topic when the program asks for input.

Example:

Enter a research topic: Agentic AI in cybersecurity

The project prints the final report in the terminal and saves it here:

outputs/final_report.md

Screenshot

Example Output

Issue:
A broad research topic can be hard to organize into a clear report.

Recommendation:
Use separate AI agents for research, summarization, report writing, and review.

Generated Output:
A structured Markdown report with introduction, main discussion, benefits, challenges, applications, and conclusion.

Confidence:
High, because each agent has one clear responsibility.

How It Works

The user enters a topic. The LangGraph workflow sends the topic to the Research Agent. The research result goes to the Summary Agent. The summary and research go to the Report Writer Agent. The draft report then goes to the Review Agent, which improves clarity, grammar, and structure. The final report is saved as a Markdown file.

Topic
↓
Research Agent
↓
Summary Agent
↓
Report Writer Agent
↓
Review Agent
↓
Markdown Report

Evaluation / Results

Evaluation Area	Current Result
Report structure	Includes title, introduction, discussion, benefits, challenges, applications, and conclusion
Output format	Saves final report as Markdown
Workflow quality	Uses clear multi-agent separation
Test coverage	Includes a basic unit test for report saving
Human evaluation	Can be scored for clarity, usefulness, and completeness
LLM evaluation	Can be added to score the final report automatically

More details are available in docs/evaluation.md.

Challenges

• LLMs can hallucinate information.
• Long topics may exceed token limits.
• The current version does not include live web search.
• The report quality depends on prompt design.
• Source citation support can be improved.

Future Improvements

• Add real web search with citations.
• Add FastAPI endpoints.
• Add Streamlit or React UI.
• Add PDF export.
• Add human approval before final report generation.
• Add memory for previous research topics.
• Add streaming output.
• Add Docker support.
• Add stronger automated evaluation.

Tests

Run tests with:

pytest

This project also includes a GitHub Actions workflow:

.github/workflows/tests.yml

Demo

A 30 second demo can show:

1. Running the project from the terminal
2. Entering a topic
3. Viewing the generated report
4. Opening outputs/final_report.md

Demo notes are available in docs/demo.md.

Project Status

Under Active Development

License

This project is for educational and portfolio purposes. All rights reserved by the author.

Author

Parisa Arbab

GitHub: https://github.com/ParisaArbab
LinkedIn: https://www.linkedin.com/in/parisa-arbab