🔬 Multi-Agent Research Assistant

A fully autonomous research pipeline powered by multiple AI agents that search the web, scrape sources, write structured reports, and iteratively refine them based on critic feedback.

Built with LangChain, Groq (LLaMA 3.3 70B), Tavily, and Streamlit.

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🧠 How It Works

The pipeline runs 4 specialized agents and chains in sequence:

• 🔍 Search Agent — finds recent web sources
• 📄 Reader Agent → scrapes the most relevant URL
• ✍️ Writer Chain → drafts a structured report
• 🧐 Critic Chain → scores the report out of 10

score < 8 ──→ revise (max 2 attempts) score ≥ 8 ──→ approved ✅

Agents & Chains

Component	Role	Tool
Search Agent	Finds recent and reliable sources on the topic	Tavily Search API
Reader Agent	Scrapes and extracts clean content from URLs	Custom web scraper
Writer Chain	Drafts a structured report with headings and citations	—
Critic Chain	Scores the report and gives structured feedback	—

Iterative Refinement Loop

The writer and critic work in a quality control loop:

1. Writer produces a first draft
2. Critic scores it out of 10 and lists strengths and weaknesses
3. If score is below 8, the writer revises using the feedback
4. Loop repeats up to 2 times or until score reaches 8 or above

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🗂️ Project Structure

multi-agent-research-assistant/
├── app.py                      # Streamlit UI - call pipeline, display results
├── agents/
│   ├── __init__.py
│   └── agents.py               # Agent and chain definitions
├── pipeline/
│   ├── __init__.py                    
│   └── pipeline.py             # Orchestration — runs agents, manages revision loop
├── tools/
│   ├── __init__.py                    
│   └── tools.py                # Web search and URL scraper tools
├── utils/
│   ├── __init__.py                    
│   └── utils.py                # Logger setup
├── requirements.txt            # Dependencies
└── README.md                   # You are here ! 

⚙️ Tech Stack

Layer	Technology
LLM	LLaMA 3.3 70B via Groq
Agent Framework	LangChain
Web Search	Tavily Search API
Web Scraping	Trafilatura, Readability, BeautifulSoup
UI	Streamlit

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🚀 Run Locally

Prerequisites

• Python 3.10 or higher
• A Groq API key
• A Tavily API key

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Using uv

uv is a fast Python package manager.

Install uv if you do not have it:

curl -Lf https://astral.sh/uv/install.sh | sh

Clone and set up the project:

git clone https://github.com/yourusername/multi-agent-research-assistant.git
cd multi-agent-research-assistant

uv venv
source .venv/bin/activate        # Mac / Linux
.venv\Scripts\activate           # Windows

uv add -r requirements.txt

Open .env and fill in your keys:

GROQ_API_KEY=your_groq_api_key_here
TAVILY_API_KEY=your_tavily_api_key_here

Run the app:

streamlit run app.py

👤 Author Amine Sellami

GitHub: @amijen LinkedIn: Amine Sellami

📄 License This project is licensed under the Apache-2.0 License — see the LICENSE file for details.