mcp-trip-orchestrator

🚀 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

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