🤖 Multi-Agent Autonomous Research Assistant

A production-ready AI research system where four specialized CrewAI agents collaborate autonomously to research any topic, verify facts, and generate a comprehensive professional report — powered by Wikipedia and OpenAI.

Architecture

User Input → Research Agent → Analysis Agent → Verification Agent → Writer Agent → PDF Report
                ↓                   ↓                  ↓                  ↓
           Wikipedia API       Trend Analysis      Fact Scoring      Full Report
           Key Facts          Pattern Detection   Confidence %      8 Sections
           Entity Extraction  Gap Identification  Contradiction      PDF Export

Agent Roles

| Agent | Role | Responsibility | |-------|------|----------------| | 🔍 Research Agent | Senior Research Analyst | Searches Wikipedia, extracts facts/dates/entities | | 📊 Analysis Agent | Expert Data Analyst | Identifies trends, patterns, pros/cons, insights | | ✅ Fact Verification Agent | Fact-Check Specialist | Verifies consistency, assigns confidence scores | | ✍️ Report Writer Agent | Professional Writer | Generates full 8-section research report |

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🛠️ Tech Stack

| Layer | Technology | |-------|-----------| | Frontend | React 18 + TypeScript + Vite | | Styling | Tailwind CSS v3 + Dark Mode | | Backend | FastAPI + Python 3.11 | | AI Agents | CrewAI 0.30 + LangChain | | LLM | OpenAI GPT-4o-mini | | Research | Wikipedia API + wikipedia-api | | Database | PostgreSQL 16 | | PDF | ReportLab | | Container | Docker + Docker Compose |

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📁 Project Structure

research-assistant/
├── backend/
│   ├── agents/
│   │   ├── crew_agents.py      # CrewAI agent definitions
│   │   ├── crew_tasks.py       # Task definitions with context
│   │   └── research_crew.py   # Crew orchestration
│   ├── db/
│   │   └── models.py           # SQLAlchemy models + DB setup
│   ├── utils/
│   │   ├── wikipedia_utils.py  # Wikipedia API integration
│   │   └── pdf_utils.py        # ReportLab PDF generation
│   ├── main.py                 # FastAPI application
│   ├── requirements.txt
│   ├── Dockerfile
│   └── .env.example
├── frontend/
│   ├── src/
│   │   ├── components/
│   │   │   ├── Header.tsx       # Top navigation bar
│   │   │   ├── SearchInput.tsx  # Topic input with examples
│   │   │   ├── AgentPanel.tsx   # Real-time agent activity
│   │   │   ├── ReportDisplay.tsx # Accordion report viewer
│   │   │   ├── HistoryPanel.tsx  # Slide-out history drawer
│   │   │   └── LoadingSkeleton.tsx
│   │   ├── hooks/
│   │   │   └── useDarkMode.ts
│   │   ├── utils/
│   │   │   └── api.ts           # API client
│   │   ├── App.tsx
│   │   └── main.tsx
│   ├── package.json
│   ├── vite.config.ts
│   ├── tailwind.config.js
│   └── Dockerfile
└── docker-compose.yml

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🚀 Quick Start

Prerequisites

• Node.js 18+ and npm
• Python 3.11+
• PostgreSQL 16 (or use Docker)
• OpenAI API key

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Option A: Docker Compose (Recommended)

# 1. Clone and enter directory
git clone <repo>
cd research-assistant

# 2. Set your OpenAI API key
echo "OPENAI_API_KEY=sk-your-key-here" > .env

# 3. Start all services
docker-compose up --build

# 4. Open browser
open http://localhost:3000

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Option B: Manual Setup

Step 1 — PostgreSQL

# Using Docker just for the DB
docker run -d \
  --name research_postgres \
  -e POSTGRES_PASSWORD=password \
  -e POSTGRES_DB=research_assistant \
  -p 5432:5432 \
  postgres:16-alpine

# Or create the DB manually
createdb research_assistant

Step 2 — Backend

cd backend

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env and set your OPENAI_API_KEY

# Start the API server
uvicorn main:app --reload --port 8000

Step 3 — Frontend

cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

# Open http://localhost:3000

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🔌 API Endpoints

| Method | Endpoint | Description | |--------|----------|-------------| | POST | /api/research/start | Start a new research session | | GET | /api/research/status/{id} | Poll session status + logs | | GET | /api/research/report/{id} | Get the completed report | | GET | /api/research/download/{id} | Download PDF report | | GET | /api/research/history | List all past sessions | | DELETE | /api/research/{id} | Delete a session | | GET | /health | Health check |

API Documentation

Interactive docs available at: http://localhost:8000/docs

Example: Start Research

curl -X POST http://localhost:8000/api/research/start \
  -H "Content-Type: application/json" \
  -d '{"topic": "Quantum Computing"}'

Response:

{
  "session_id": "uuid-here",
  "topic": "Quantum Computing",
  "status": "started",
  "message": "Research started for topic: Quantum Computing"
}

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📊 Database Schema

research_sessions   -- Session tracking (id, topic, status, progress)
research_reports    -- Full reports (8 sections + pdf_path)
agent_logs          -- Per-agent activity logs
wikipedia_cache     -- Cached Wikipedia responses

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Report Structure

Every generated report includes:

1. Executive Summary — High-level overview of findings
2. Introduction — Context and research objectives
3. Background Information — Historical context and origins
4. Key Findings — Bullet-pointed most important facts
5. Detailed Analysis — In-depth multi-section analysis
6. Verified Facts — Numbered list with confidence scores
7. Conclusion — Summary, implications, future outlook
8. References — All Wikipedia sources used

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✨ Features

• Real-time agent tracking — Watch each agent work live
• Progress visualization — Animated progress bar per agent
• Dark mode — System preference + manual toggle
• Research history — Search and revisit past reports
• PDF export — Professional formatted PDF download
• Agent activity logs — Terminal-style live log viewer
• Example topics — Quick-start buttons for common topics
• Responsive design — Mobile-friendly layout

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⚙️ Configuration

Backend .env

OPENAI_API_KEY=sk-...         # Required: your OpenAI key
DATABASE_URL=postgresql://... # PostgreSQL connection string
ENVIRONMENT=development       # development | production

Frontend environment

Create frontend/.env.local:

VITE_API_URL=http://localhost:8000

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🔧 Customization

Change the LLM model

In backend/agents/crew_agents.py:

def get_llm():
    return ChatOpenAI(
        model="gpt-4o",          # or "gpt-3.5-turbo" for cheaper
        temperature=0.3,
    )

Add more Wikipedia sources

In backend/utils/wikipedia_utils.py, increase results:

search_results = search_wikipedia(topic, num_results=8)  # default 5

Customize report sections

Edit task prompts in backend/agents/crew_tasks.py.

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🐛 Troubleshooting

| Problem | Solution | |---------|----------| | OpenAI API error | Check OPENAI_API_KEY in .env | | Database connection failed | Ensure PostgreSQL is running | | Wikipedia no results | Try a broader topic name | | PDF generation fails | Check write permissions on generated_pdfs/ | | Frontend can't reach backend | Verify VITE_API_URL or Vite proxy config |

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📈 Sample Output

Topic: Artificial Intelligence

The system produces ~2,000–4,000 word reports covering:

• Origins (1950s Turing, Dartmouth Conference)
• Key milestones (Expert Systems, Deep Learning, LLMs)
• Applications, benefits, risks
• Verified facts with confidence scores (HIGH/MEDIUM/LOW)
• Future outlook and ethical considerations

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🤝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/my-feature
3. Commit changes: git commit -m 'Add my feature'
4. Push: git push origin feature/my-feature
5. Open a Pull Request

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📝 License

MIT License — free to use, modify, and distribute.

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Built with CrewAI, FastAPI, React, and ❤️