🧠 Agentic AI Research Assistant

An agent-based AI research assistant built with LangChain, Groq LLMs, and custom tools to automatically research a topic, validate facts, analyze trends, generate citations, and optionally save results to a file — all through a single prompt-driven workflow.

---

🚀 Features

• 🔍 Wikipedia-first research (primary source validation)
• ✅ Fact checking to reduce hallucinations
• 📈 Trend & popularity analysis for topics
• 📚 Automatic citation generation
• 💾 Save research output to a file
• 🧩 Agent-based tool calling using LangChain
• 📦 Structured JSON output using Pydantic models

---

Architecture Overview

User Prompt
↓
Agent (LangChain)
↓
Tool Selection & Reasoning Loop
├── Wikipedia Search
├── Fact Check
├── Trend Analysis
├── Citation Generation
└── Save Output
↓
Structured JSON Response

---

🛠️ Tech Stack

Layer	Technology
Language	Python
LLM	Groq – llama-3.3-70b-versatile
Agent Framework	LangChain
Output Validation	Pydantic
Secrets Management	dotenv

---

🧠 How It Works

1. User inputs a research topic.

2. The AI agent:

   • Searches Wikipedia first
   • Cross-checks facts using a fact-checking tool
   • Analyzes popularity or trends
   • Generates citations
   • Optionally saves the output

3. The final response is always returned in structured JSON format:

   {
     "topic": "...",
     "summary": "...",
     "sources": ["..."],
     "tools_used": ["..."]
   }

---

🧩 Agent Tools Used

Tool	Description
wiki_tool	Fetches and verifies information from Wikipedia
fact_check	Validates claims against known sources
analyze_trends	Analyzes topic popularity or relevance
generate_citation	Produces citations for sources
save_tool	Saves generated research to a file

---

📂 Project Structure

.
├── main.py
├── tools.py
├── .env.example
├── requirements.txt
└── README.md

---

⚙️ Setup Instructions

1️⃣ Clone the Repository

git clone https://github.com/Vinay-15/Technical_analyst_Agentic_AI.git
cd Technical_analyst_Agentic_AI

2️⃣ Create a Virtual Environment (Recommended)

python -m venv venv
source venv/bin/activate  # macOS/Linux
venv\Scripts\activate     # Windows

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Configure Environment Variables

Create a .env file (do not commit this):

GROQ_API_KEY=your_groq_api_key_here

You can refer to .env.example for the format.

---

▶️ Running the Application

python main.py

You’ll be prompted with:

What can I help you Research?

Example input:

Impact of AI on supply chain management

---

📤 Sample Output

{
  "topic": "AI in Supply Chain Management",
  "summary": "Artificial intelligence is transforming supply chains by...",
  "sources": ["Wikipedia", "Industry Reports"],
  "tools_used": ["wiki_tool", "fact_check", "analyze_trends"]
}

---

🔐 Security Notes

• API keys are managed using .env
• .env is excluded via .gitignore
• Always rotate keys if exposed

---

🌱 Future Enhancements

• Web UI or Streamlit interface
• Multi-source research beyond Wikipedia
• Confidence scoring for facts
• Async tool execution
• Vector-based memory for long-term research

---

🤝 Contributing

Contributions, suggestions, and improvements are welcome! Feel free to fork the repo or open an issue.

---

🌱 Hackathon Extensions (Easy Wins)

• 🌐 Web UI (Streamlit / Next.js)
• 🧠 Multi-agent collaboration
• 🔗 External APIs (news, finance, healthcare)
• 🧾 Confidence scoring per claim
• 🗂️ Vector memory for long-term research

---

👥 Ideal For

• Hackathons (LA Hacks, HackMIT, etc.)
• AI agent demos
• Research automation
• Tool-augmented LLM experiments

---

📜 License

MIT License