↓

Research stores: LLM options, Frameworks, Deployment strategy

↓

Coding agent reads the stored information before generating code.

🌐 Step 7: Build the User Interface

Using Streamlit:

import streamlit as st

st.title("Multi-Agent AI Assistant")

task = st.text_area("Describe your task")

if st.button("Run"):

    run_agents(task)

Display: Planner output, Research notes, Generated code, Final report

🚀 Step 8: Deploy the Application

Deploy using: Render, Railway, Hugging Face Spaces

⭐ Features to Add

Beginner:

✅ Planner Agent,

✅ Research Agent,

✅ Coding Agent 

Intermediate:

✅ Reviewer Agent,

✅ Report Generator,

✅ Memory 

Advanced:

✅ Multi-user collaboration,

✅ Human approval workflow,

✅ Long-term memory,

✅ Autonomous task execution,

✅ API integrations

📂 Project Structure

multi-agent-ai-system/

│

├── agents/

│   ├── planner.py

│   ├── researcher.py

│   ├── coder.py

│   ├── reviewer.py

│   └── reporter.py

├── tools/

├── memory/

├── workflows/

├── app.py

├── requirements.txt

├── README.md

└── screenshots/

💼 Resume Project Description

Multi-Agent AI System

Developed a Multi-Agent AI System using Python, LangGraph, LangChain, and Large Language Models. Designed specialized AI agents for planning, research, code generation, review, and reporting, coordinated through an orchestrated workflow with shared memory and tool integrations to automate complex problem-solving.

🎯 Mini Challenge

Enhance your project by adding: 

1. Human approval before critical actions. 

2. Web search integration for live information. 

3. SQL database querying. 

4. PDF generation for reports. 

5. GitHub repository analysis. 

6. Slack or email notifications. 

7. Long-term memory for user preferences. 

8. Autonomous scheduling of recurring tasks.

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