Multi_agent_system_part_3

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Multi_agent_system_part_3

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Production-Ready Multi-Agent AI System with LangGraph | Supervisor Agent + Guardrails + Human-in-the-Loop

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.env
.gitattributes
agents.py
config.py
frontend.py
graph.py
mcp_client.py
README.md
requirements.txt
state.py
weather_mcp_server.py

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// repository documentation

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This project extends the Multi-Agent Travel Planning System built in Part 1 and Part 2 by integrating Supervisor Agent + Guardrails + Human-in-the-Loop

## Part 1 of This Project

**GitHub Repository:**  
https://github.com/codewithaarohi/AI-Travel-Planning-System-using-LangGraph

**Video Tutorial:**  
Build a Real-World Multi-Agent AI System using LangGraph | Multi-Agent AI + Memory + APIs  
https://youtu.be/ctHby5vhDqg

## Part 2 of This Project

**GitHub Repository:**  
https://github.com/codewithaarohi/AI-Travel-Planning-App-using-LangGraph-and-MCP

**Video Tutorial:**  
Build a Real-World Multi-Agent AI System using LangGraph | Multi-Agent AI + Memory + MCP  
https://youtu.be/DjMX7o2EeV0

---

# Requirements

## APIs

- Groq API: https://console.groq.com
- Tavily API: https://www.tavily.com/
- AviationStack API: https://aviationstack.com/
- OpenWeatherMap API: https://openweathermap.org/

## Tools

- PostgreSQL: https://www.postgresql.org/download/
- Tavily MCP Server: https://docs.tavily.com/documentation/mcp

---

# Step 1: Create Python Environment

python -m venv langgraph_env3

Activate:

langgraph_env3\Scripts\activate

---

# Step 2: Install Dependencies

pip install langgraph langchain langchain-openai langchain-groq langchain-community langchain-tavily psycopg[binary] psycopg_pool python-dotenv tavily-python requests streamlit

pip install -U "psycopg[binary,pool]" langgraph-checkpoint-postgres

---

# Step 3: Install PostgreSQL

Download PostgreSQL:

https://www.postgresql.org/download/

**Important:** Note your PostgreSQL password and port number during installation.

---

# Step 4: Create Database

CREATE DATABASE langgraph_memory_demo;

---

# Step 5: Setup .env File

Create a `.env` file:

GROQ_API_KEY=your_groq_api_key

TAVILY_API_KEY=your_tavily_api_key

AVIATIONSTACK_API_KEY=your_aviationstack_api_key

DATABASE_URL=postgresql://postgres:postgres@localhost:5433/langgraph_memory_demo

---

# Step 6: Get API Keys

- Groq: https://console.groq.com
- Tavily: https://tavily.com
- AviationStack: https://aviationstack.com
- OpenWeatherMap: https://openweathermap.org/

---

# Setup AviationStack MCP Server (Local MCP Server)

Repository:

https://github.com/Pradumnasaraf/aviationstack-mcp

Open PowerShell:

E:

cd E:\Multi_agent_system_with_MCP

Clone repository:

git clone https://github.com/Pradumnasaraf/aviationstack-mcp.git

cd aviationstack-mcp

## Install UV

Check:

uv --version

Install:

pip install uv

If installation fails:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

## Create .env File

AVIATION_STACK_API_KEY=your_api_key_here

## Install Dependencies

uv sync

This will:

- Create `.venv`
- Install dependencies
- Install AviationStack MCP package

## Activate Environment

.venv\Scripts\activate

## Start MCP Server

uv run -m aviationstack_mcp mcp run

or

python -m aviationstack_mcp mcp run

The server will remain running and wait for MCP requests.

## Stop Server

CTRL + C

---

# Setup Weather MCP Server

Get API key:

https://openweathermap.org/

Add the API key to your `.env` file.

Install dependencies:

pip install mcp requests

---

# Run the Application

## Streamlit Web App
Run:

streamlit run frontend.py

---

# Example Prompt

Plan a complete 7 days Japan trip including flights, hotels and sightseeing under 2 lakhs.

---

# Features

- Multi-Agent Architecture using LangGraph
- PostgreSQL Memory
- Tavily Search Integration
- AviationStack MCP Integration
- Weather MCP Integration
- Streamlit Web App
- Real-Time Travel Planning
- Human in the loop
- Guardrails

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