databricks-solutions/agents-with-mcp-on-databricks-apps
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A hands-on lab for building AI agents on Databricks using , , , , and . You'll iteratively create a Swiss travel assistant that queries live train schedules, weather forecasts, and activity...
概要
A hands-on lab for building AI agents on Databricks using , , , , and . You'll iteratively create a Swiss travel assistant that queries live train schedules, weather forecasts, and activity recommendations — with tools governed by Unity Catalog, the LLM governed by a Unity AI Gateway (routing, rate limits, guardrails, and usage logging defined once and inherited by every lab), and durable per-user memory governed by Unity Catalog. - Building an AI Agent for Transportation Applications with Databricks - Advancing Your Agentic AI with MCP — A Travel Planning Use Case on Databricks Original code: genseral/ai_agent_transport A travel planning agent that evolves across four labs: A ReAct-style LangGraph agent that calls Unity Catalog functions to query the Swiss Transport API for live train connections and station departures.
README
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Swiss Travel Agent Lab
A hands-on lab for building AI agents on Databricks using LangGraph, Unity Catalog functions, MCP (Model Context Protocol), Unity AI Gateway, and Managed Memory. You’ll iteratively create a Swiss travel assistant that queries live train schedules, weather forecasts, and activity recommendations — with tools governed by Unity Catalog, the LLM governed by a Unity AI Gateway model service (routing, rate limits, guardrails, and usage logging defined once and inherited by every lab), and durable per-user memory governed by Unity Catalog.
The labs are designed for a 2–3 hour hands-on session.
Based on the blog series by Alexander Genser:
- Building an AI Agent for Transportation Applications with Databricks
- Advancing Your Agentic AI with MCP — A Travel Planning Use Case on Databricks
Original code: genseral/ai_agent_transport
What You’ll Build
A travel planning agent that evolves across four labs:
| Lab | What You Build | Tools & Data Sources | Duration |
|---|---|---|---|
| Lab 1 — Agent Foundations | A LangGraph ChatAgent with UC function tools, its LLM governed by a Unity AI Gateway model service |
Swiss Transport API (real-time train schedules) | ~45 min |
| Lab 2 — MCP Extension | Extend the agent as a ResponsesAgent with three types of MCP servers |
Managed MCP (UC functions), Custom MCP (Open-Meteo via Databricks App), External MCP (web search via Tavily) | ~60 min |
| Lab 3 — Agent on Databricks Apps | Package the agent as a Databricks App with custom React chat UI | OpenAI Agents SDK + all 3 MCP servers from Labs 1–2 | ~30 min |
| Lab 4 — Durable Memory | Give the agent durable, user-scoped memory — facts & rules it learns and reuses, plus the managed-conversation thread primitive | Databricks Managed Memory (conversations + entries) — zero extra infra | ~30 min |
Lab 1 Architecture
A ReAct-style LangGraph agent that calls Unity Catalog functions to query the Swiss Transport API for live train connections and station departures.
Lab 2 Architecture
インストール
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