🚀 **Built an MCP-Powered Multi-Agent Travel Planner**

I’ve been exploring how to design practical **Agentic AI systems**, so I built **MCP Trip Orchestrator** using Python, Gemini, LangChain, Tavily MCP, and Pydantic.

The project implements 3 core agentic patterns:

🔹 **Supervisor / Orchestrator**
Coordinates the workflow and manages budget allocation across tasks.

🔹 **Multi-Agent Collaboration**
Specialized workers independently handle:
🚆 Transport
🏨 Accommodation
🏰 Experiences & Dining

🔹 **Tool Use + Structured Extraction**
Agents use **Tavily MCP** for live web search and **Gemini + Pydantic** to transform unstructured results into structured booking data.

### Architecture

```text
User Request
     ↓
Supervisor / Orchestrator
     ├── Transport Agent
     ├── Stay Agent
     └── Experience Agent
             ↓
        Tavily MCP
             ↓
          Gemini
             ↓
    Structured Output
             ↓
       Final Trip Plan
```

What I’m focusing on is not just using an LLM, but understanding **how to build reliable agentic workflows around LLMs using tools, specialization, orchestration, and structured outputs.**

🛠️ Python | Gemini | LangChain | Tavily MCP | Pydantic

Next step: making the orchestrator fully adaptive so it can **re-plan when constraints change or a worker fails.**

#AgenticAI #AIAgents #MCP #ModelContextProtocol #GenerativeAI #Gemini #LangChain #Python #MultiAgentSystems #AIEngineering #MachineLearning #OpenToWork