The Problem

In today’s fast-paced environment, gathering accurate and structured information from multiple sources is time-consuming and inefficient. Users often struggle with:

  • Fragmented information across web and research papers
  • Lack of structured research outputs
  • Difficulty distinguishing between simple queries and deep research needs
  • Manual effort required to synthesize insights

There is a need for an intelligent system that can automatically analyze queries, perform research, and generate structured responses.

Our Solution

This AI-powered Research Assistant using LangChain and LangGraph can:

  • Understands user queries intelligently
  • Classifies them into:
    • General queries
    • Clarification-needed queries
    • Research-intensive queries
  • Automatically creates a research plan
  • Uses tools like web search and ArXiv retrieval
  • Synthesizes results into a structured research report
  • Provides an interactive Streamlit-based chat interface

Solution Architecture

Deliverables

  • Fully functional AI Research Assistant
  • Streamlit-based interactive UI
  • LangGraph-based agent workflow
  • Integration with:
    • Tavily Web Search
    • ArXiv Research Papers
  • End-to-end research automation pipeline

Tech Stack

  • AI / LLM
    • LangChain
    • LangGraph
    • Groq API (LLaMA 3.1 8b model)
  • Tools & APIs
    • Tavily Search API
    • ArXiv Retriever
  • Frontend
    • Streamlit
  • Language
    • Python
  • Other Libraries
    • JSON
    • Typing (TypedDict)

Business Impact

  • Increased Productivity: Reduces manual research time.
  • Provides structured and reliable insights
  • Combines web + academic sources
  • Automates research tasks → reduces human effort
  • Enhanced User Experience
  • Conversational interface
  • Intelligent query handling

    Demo Video

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