#42 - Vultr Agent Runtime Hackathon
All right!!!
Meetup #42 will take the shape of a full day 12h Hackathon!!! Bring your laptops and your hacking chops and let's go! (And yes, there will be food, drinks and refreshments throughout the day!)
The hackathon is centred around a single challenge:
Build a web-based agent that performs real work - writing and running code, or operating a real browser - with every action contained inside a sandbox running on Vultr. Your system should act as a centralized control layer that plans a task, dispatches it to an isolated execution environment, and returns verifiable output.
Projects should demonstrate multi-step agentic workflows, real executed results rather than described ones, and a production-style web application - all running on Vultr infrastructure.
Containment-first is required. An agent that only chats is a demo; an agent that executes safely is a product.
There will be a team from Vultr on-site to help guide you through the day! More detailed agenda will be provided. Stay tuned!
Who should participate?
Developers, AI/ML and platform engineers, and technical founders building agentic applications. Participants will work in teams of 2–4. If you show up solo, we'll help you join/form a team!
Pre-reqs
Laptop and GitHub account, working Python (or TypeScript) and API experience, and basic LLM familiarity. No special hardware needed. Vultr will provide credits for cloud compute, and we'll share a short prep guide beforehand.
Prizes
The three most impressive projects formally submitted will all receive cash prizes and additional Vultr credits! (More on this later... Stay tuned!)
Hackathon Infrastructure
Deploy a VM-based backend on Vultr (mandatory)
Agent LLM calls must go through Vultr Serverless Inference (mandatory)
Vultr should be the central system of control and orchestration, not just static hosting
Sandboxes run as containers or throwaway instances on Vultr, never inside your app process
Open Source Sandbox example solutions:
Execution Pattern Integration
Build against one or both patterns:
Pattern A - Sandboxed Code Execution User asks → model writes code → code runs in a container → real output (chart, file, test log) returns → model explains it. On error, stderr is fed back for a retry.
Examples:
CSV Analyst - upload a spreadsheet, ask a question in plain English, get an actually-executed chart plus the code that produced it
Prove-It Code Reviewer - paste a snippet or PR; the agent runs it and its tests in the sandbox and reports what really happened
Chart Anything - natural language in, executed matplotlib or plotly image out
File Fixer - messy CSV or JSON in, cleaned file out, with an executed before/after diff
Self-Healing Runner - the agent runs code, reads the error, patches it, and retries, with the retry loop visible on screen
Pattern B - Sandboxed Browser Use User gives a goal → model plans → Playwright in a container navigates, clicks, types → screenshots return → a vision model on Serverless Inference verifies the result → repeat, with a human approving anything final.
Examples:
Comparison Shopper - two or three product URLs in, screenshot-backed comparison table out
Form Filler - structured data in, multi-step web form filled, with a screenshot trail and an approve-before-submit gate
Site QA Sweep - point it at a web app and get back broken flows and links with screenshots and repro steps
Research with Receipts - a brief where every claim links to a screenshot of its source
Page Watcher - monitors listing or pricing pages, extracts structured data, and diffs against yesterday
Containment Focus
Address real safety and reliability challenges in agent execution:
Process isolation - execution never touches the host or app runtime
Secret hygiene - no API keys or credentials inside the sandbox
Resource limits - time and memory caps on every run
Lifecycle discipline - reset or destroy the environment after each task
Show how isolation makes autonomous execution shippable.
Production-Ready Web Application
Must be accessible via a public web browser
Demonstrate a real product-style experience
Clear user flows, not just a local demo
Key Positioning
Vultr is the central backend powering execution and orchestration
Your application plans, dispatches, and verifies real work
Focus on platform thinking, not just a one-off demo
Developer Expectations
Each team must provide:
GitHub repository with setup and documentation
Vultr VM backend deployment, with agent LLM calls through Vultr Serverless Inference
Public demo URL
Recorded demo video
One containment moment in the video - the sandbox absorbing something unsafe, such as an rm -rf, an infinite loop, or a hostile web page
Clear explanation of architecture and use case
Technology
Required
Vultr VM backend
Web application deployed on Vultr
Vultr Serverless Inference for agent reasoning (OpenAI-compatible; base URL https://api.vultrinference.com/v1)
A sandbox isolated from your application process
Strongly Recommended
Docker container as the sandbox boundary
REST or WebSocket APIs for run status and streaming output
Web dashboards showing the execution loop, retries, and screenshot trails
Approve-before-submit gates on any irreversible browser action
Optional
Vultr API to spin up a throwaway instance per task and destroy it afterward, making Vultr itself the sandbox fabric
Vision-capable models on Serverless Inference for screenshot reasoning
Playwright in Docker for browser automation
Platform & App Acceleration
To help teams ship faster, we recommend:
Vultr Serverless Inference: https://docs.vultr.com/products/serverless/inference/provisioning
Live model list: https://api.vultrinference.com/v1/models
Vultr API (throwaway-instance pattern): https://www.vultr.com/api/
How to Set Up Agent Sandboxing on Vultr Cloud Compute: https://docs.vultr.com/how-to-set-up-agent-sandboxing-on-vultr-cloud-compute
Access Method
$200 in free Vultr credits per participant.
Redemption guide: activate via the Vultr Setup Guide
Redemption walkthrough video: https://del1.vultrobjects.com/tech-talk/redeem-vultr-credits-for-hackathons.mp4
That's it! That's all! Good luck!!!