# Project export: Aipify - Your Agentic to API platform

This document was generated by HackStack to give an AI agent context about a hackathon project. Sections are labeled with their provenance; content marked as truncated was cut to keep this document small.

## Project metadata

- Hackathon: Cal Hacks 12.0
- Tagline: Aipify - Your Agentic to API platform
- Devpost: https://devpost.com/software/aipify-your-agentic-to-api-platform
- GitHub: not linked
- Video: https://www.youtube.com/embed/GNZHGg0zuv4?enablejsapi=1&hl=en_US&rel=0&start=&version=3&wmode=transparent
- Team: contributor stats unavailable

## Devpost submission (written by the team)

### Inspiration

I wanted to book a Waymo ride programmatically, but quickly realized there was no public API. I set that aside until CalHacks, where I was trying to query event details and prizes — and again, found myself needing heavy infrastructure for a small gain. I started noticing this pattern everywhere: whenever humans interacted with interfaces, it required complex setups to automate or access data. Meanwhile, AI agents have become smart enough to reason, plan, and act like us — but there’s still no easy way to query them programmatically. That’s when I thought, why not fix this?

### What it does

Aipify turns agentic workflows into simple, queryable REST APIs — allowing you to expose complex AI-driven actions as endpoints in seconds.

### How we built it

I built Aipify using: 🧠 Groq – for high-speed inference ⚙️ Lava Labs – for agent orchestration 🤖 Fetch.ai – for autonomous agent logic 🐳 Docker – to isolate and manage AI containers 🌐 Browserbase – to give agents real browser access 💬 Claude – for reasoning and planning 🧩 Chroma – for vector memory and context recall 🚀 FastAPI – for the backend and API layer 💻 Next.js – for the web interface Together, they create a full-stack system that spins up intelligent agents, executes tasks, and exposes those capabilities as REST endpoints.

### Challenges we ran into

Spinning up and managing live AI-powered Docker instances was surprisingly difficult. Keeping them stable, responsive, and secure while running browsers and agents in real-time required deep debugging and experimentation.

### Accomplishments we're proud of

I got it working end-to-end 🎉 — an agent can think, act, and respond through an exposed REST API. Seeing that first successful flow felt like magic.

### What we learned

Deep insights into system design and container orchestration How to connect multiple AI and infrastructure tools cohesively The importance of persistence and iteration when hacking end-to-end systems

### What's next

for Aipify Next, I plan to: 🚀 Launch Aipify publicly as a developer platform 🧩 Allow users to create and deploy AI-driven APIs with one click 🌍 Explore integrations with existing automation tools (Zapier, Retool, etc.) The potential is huge — every human-interaction workflow could become an API call.

## README (from the GitHub repository)

No README available.

## Detected evidence (automated analysis)

No repository was indexed for this project. Claimed technologies below could not be checked against code.
- Docker (technology) — claimed on Devpost, not found in the code

## Codebase structure

No repository index available.

## Key source files

No repository index available; no source files included.