Create Python Virtual Env from your root directory python -m venv venv
Activate Virtual Env a) Windows: venv\Scripts\activate.bat b) Unix: source venv/bin/activate
Installing Dependencies pip install -r requirements.txt

Install GTK-for-Windows (needed to generate the PDF) 1.Download the installer and install: Go to the GTK-for-Windows runtime project on GitHub (https://github.com/tschoonj/GTK-for-Windows-Runtime-Environment-Installer/releases) 2.Ensure you check the box during installation that says "Add to the PATH environment variable".

Disable Smart App Control (SAC) on Windows 1.Open the Start menu, type Windows Security, and press Enter. 2.Click on App & browser control from the left navigation panel or home menu. 3.Click the Smart App Control settings link. 4.Select Off to disable the feature

Setup OPENAI_API_KEY, LANGCHAIN_API_KEY, and SERPAPI_API_KEY from terminal set LANGCHAIN_API_KEY= set OPENAI_API_KEY= sett SERPAPI_API_KEY=
Running the Streamlit Application python -m streamlit run app.py

capstone_msagi

Course-end Project: Product Strategy Simulation

Multi-Agent Market Research and GTM Planning (n8n, MCP, and CrewAI)

Description

Overview In this project, you will design and implement a multi-agent workflow system that automates market research and go-to-market (GTM) planning. The systems are implemented in n8n and CrewAI separately, but with similar functionality. The solution integrates four agents:

• Head Planner (orchestrator and documenter) • Research Agent (finder) • Analyst Agent (sense-maker) • Strategy Agent (planner)

The workflow must orchestrate desk research, competitor analysis, and GTM plan drafting before exporting structured strategy documents to Google Docs.

Instructions

• Review the lessons and supporting materials on n8n workflows, MCP, and CrewAI • Set up the required environment on the Ubuntu VM, including Node.js, n8n, and Python for CrewAI • Execute the following steps:

Build the multi-agent orchestration with a Head Planner and three specialist agents with tools Start the MCP server Configure and connect the n8n workflow with the MCP
Implement CrewAI with defined agents, flows, and tools (MCP, SerpAPI) • Test and debug each component individually (MCP tools with curl, n8n nodes with Execute Node, and CrewAI tasks end-to-end) • Document the architecture, configuration steps, test runs, and error resolutions • Submit the following:

Exported n8n workflow JSON file CrewAI project files (UV structure) Sample Google Doc output CrewAI chatbot screenshots Documentation (README file) covering the architecture, setup, and testing notes