mcp-trip-orchestrator
Enables multi-agent travel planning by orchestrating specialized agents for transport, accommodation, and experiences, using live web search and structured data extraction to generate complete trip plans.
README
🚀 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
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