ai-research-assistant-mcp

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🧠 AI Research Assistant Using LangChain + Streamlit (with a Simple MCP Pattern)

This is a minimal AI-powered research assistant built using:


📦 Features
  • 🤖 Planner Agent – Breaks down high-level tasks into subtasks using GPT
  • 🔍 Executor Agent – Uses DuckDuckGo to research each subtask
  • 🧠 Model Context Protocol (MCP) – Shared memory for agents
  • 🖥️ Streamlit UI – Clean web interface to trigger and inspect results

🚀 Getting Started
1. Clone & Setup
git clone https://github.com/your-username/ai-research-assistant-mcp.git
cd ai-research-assistant-mcp
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt
2. Add Your OpenAI API Key

Create a .env file in the root of the project:

OPENAI_API_KEY=sk-...

✅ Your key is already set in .env, so no need to export it manually.

3. Run the App
streamlit run ui.py

🗂 Project Structure
ai-research-assistant-mcp/
├── mcp_context.py         # Shared agent memory (MCP)
├── planner_executor.py    # Agent logic (Planner + Executor)
├── ui.py                  # Streamlit interface
├── requirements.txt       # Python dependencies
├── .env                   # OpenAI API Key (not tracked by Git)

🧠 How It Works
  1. Planner Agent:

    • Reads current_task from mcp_context.py
    • Breaks it into subtasks using GPT
  2. Executor Agent:

    • Searches the web for each subtask
    • Stores results in mcp["agent_memory"]["executor_results"]
  3. Streamlit UI:

    • Displays results in real time with a single button click

✏️ Customize Your Task

Edit the current task in mcp_context.py:

"current_task": "Research LangChain Agents"

🛠 Future Ideas
  • LangGraph or CrewAI integration
  • Memory persistence (Redis, FAISS)
  • CSV/PDF file ingestion with context-aware planning
  • Role-based multi-agent delegation

📄 License

MIT – free to use, improve, and share.


🙋‍♀️ Author

Viplav Fauzdar – viplavfauzdar.com

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