Build an Agentic Event Venue Operator with MongoDB Atlas, Voyage, and LangGraph MongoDB released a tutorial and demo for building an agentic event venue operator using MongoDB Atlas, Voyage AI embeddings, and LangGraph. The fictional scenario involves managing a tennis tournament during a rain delay, with the agent retrieving venue state, prior event memory, and visitor context to make operational decisions. The project includes a FastAPI app, Atlas Vector Search, hybrid retrieval, and optional Langfuse tracing. Introduction This tutorial starts where most agent demos stop: giving the agent persistent memory, operational context, and a place to write back what happened. https://pxllnk.co/twdn5 An event operator does not just need an agent that can summarize a weather report or generate a generic plan. The operator needs an agent that can remember what happened at prior events, retrieve relevant visitor and venue context, respond to live operational changes, and write the outcome back as memory for the next similar situation. We built this event-venue operator demo https://pxllnk.co/twdn5 with MongoDB Atlas, Voyage AI embeddings, LangGraph, and optional Langfuse tracing. The demo scenario is the MongoDB Open, a fictional premium tennis tournament on Day 6 of play. Rain is approaching, covered hospitality capacity is constrained, and the operator has two different visitor journeys to protect: Mikiko, a first-time attendee trying to make the most of the grounds, and Nina, a premier guest with hospitality expectations and a history the agent can retrieve. This is not a customer case study or a production deployment. It is a fictional builder scenario inspired by real event operations economics. Major tennis events show why these decisions matter: the 2025 US Open broke attendance, viewership, and digital reach records https://www.usopen.org/en US/news/articles/2025-09-15/2025 us open celebrates competition milestones and shatters fan records.html and offered $90 million in total player compensation; USTA has also said the three-week US Open drives more than $1.2 billion in annual economic impact https://www.usta.com/en/home/stay-current/national/reimagined-arthur-ashe-stadium-headlines-us-open-transformation.html for New York City. Premium fan expectations are high, too: PwC found https://www.pwc.com/us/en/industries/tmt/library/fan-experience-and-revenue.html that 60% of high-income U.S. sports fans would spend more than $250 for a special event, and 20% would spend more than $1,000. Weather adds another layer of risk, which is why the U.S. Census Bureau now tracks the monetary impact of extreme weather on business sales https://www.census.gov/library/stories/2024/10/business-weather-impact.html through its Business Trends and Outlook Survey. The MongoDB Open demo agent is not just producing a plausible plan. It reads current venue state, retrieves prior event memory, distinguishes between visitor segments, and acts. At the same time, hospitality capacity is still available, and writes the outcome back so the next disruption can be handled with more context. Check out the full repo here https://github.com/mongodb-developer/event-venue-operator . The demo is split into three layers: - A guided, deterministic UI that makes the operator story easy to follow. - A hosted Vercel demo that gives readers a public app link https://event-venue-operator.vercel.app/ . - Live API endpoints and scripts for Atlas Vector Search, vector-plus-lexical retrieval, visual-document RAG, LangGraph execution, and optional Langfuse traces, to demonstrate how the stack all works together. What You Will Build By the end of the tutorial, you will have a FastAPI app backed by MongoDB Atlas that can run locally and deploy to Vercel. The app includes: - A four-tab guided UI for the event-operations story and live backend validation. - Atlas collections for operational state, semantic memory, agent actions, and LangGraph checkpoints. - Voyage multimodal embeddings stored in Atlas. - Atlas Vector Search for memory retrieval. - A hybrid retrieval endpoint that combines vector similarity with lexical scoring. - A Vision RAG endpoint that retrieves visual operational documents and passes them to Claude Vision. - Optional Langfuse tracing for retrieval calls and the live LangGraph run. - A runnable LangGraph script that follows the same rain-delay story. - A Vercel deployment configuration for a hosted demo. The current repo should be treated as a reference demo, not a production platform. There is no production auth, no CI suite, and the full LangGraph agent remains a script-based validation path rather than a public hosted endpoint. Architecture Overview The architecture centers on MongoDB Atlas as both the operational and memory layer. Speed matters in the event venue operator scenario because the useful window for action is short. If rain is 20 minutes away and covered hospitality space is filling up, the operator does not need a post-event dashboard or a batch summary a few minutes later. The agent needs to read the current venue state, retrieve relevant memory, decide what to do, and write back the result while there is still capacity to protect the guest experience. That is why the type of database and how it is used are critical system design choices. Operational records, semantic memory, vector embeddings, visual documents, and agent actions all live in the same data layer. The agent does not need to wait for a separate analytics pipeline, sync data into a second vector database, or reconcile what the memory layer says with what the operational system says. Atlas acts as both the system of record and the retrieval layer for the agent loop: perceive what changed, retrieve the right context, take action, and persist what happened for the next event. This is also why the demo keeps memory in MongoDB rather than treating it as a sidecar. The agent is not just retrieving chunks; it is composing operational context. https://pxllnk.co/twdn5 A useful decision may need visitor history, current venue status, hospitality inventory, prior rain-delay patterns, and relevant visual documents at the same time. With Atlas, those pieces can stay queryable together instead of being scattered across separate systems. The demo uses four main state layers: - Operational records: guests, visits, venue status, weather events, reservations, event metrics, and agent actions. - Semantic memory: memory store, with Voyage embeddings and Atlas Vector Search. - Visual documents: operational images embedded into the same memory store as image-derived multimodal embeddings and document metadata. - Agent state: LangGraph checkpoints and checkpoint writes. Setup Before you begin, make sure you have: - Python 3.12 or later - uv installed - A MongoDB Atlas cluster with Vector Search enabled this can be set up for free - An Anthropic API key or feel free to use an LLM of your choice and reconfigure API keys - A Voyage API key this can be set up for free Clone the repo and install dependencies: GitHub repo https://pxllnk.co/twdn5 git clone https://github.com/mongodb-developer/event-venue-operator.git cd event-venue-operator uv sync If you only want to inspect the app before setting up credentials, start with the live Vercel demo https://event-venue-operator.vercel.app/ . The hosted demo uses the same UI and deployment shape as the repo, while local setup lets you run the full seed, smoke test, Vision RAG, and LangGraph paths yourself. Create your environment file: cp .env.example .env Add the required values: MONGODB URI=mongodb+srv://