Floe Labs

Overview

Payments and metering infrastructure for AI agents. The API meters every call an agent makes and enforces its spend limits before money moves, and an OpenAI-compatible gateway in front of it routes 125 models across 20 providers. I built the API, the console, the gateway, the voice platform, the CLI, and the deploy pipeline.

The problem

An AI agent that calls paid APIs has no way to pay for them and nobody watching what it spends. Every provider bills separately, so a team running agents across twenty vendors reconciles twenty invoices by hand and learns what a run cost only after the money is gone.

What I built

Architected the core payments and metering API in TypeScript, Node, and Postgres: 313 REST endpoints under 2,193 automated tests, with spend limits checked before funds move. In front of it sits an OpenAI-compatible LLM and voice gateway routing 125 models across 20 providers and 6 modalities, which collapses twenty vendor invoices into one real-time bill. A Next.js console adds 39 routes of agent management, spend policy, billing, and webhooks. Signup runs on Privy and provisions embedded wallets behind email or Google login, so nobody handles a seed phrase. Real-time voice runs on a Node WebSocket media gateway that meters each call leg and halts a call breaching budget mid-conversation. Stripe carries the monetization: four subscription tiers, Connect-based client invoicing, and daily usage reporting into Billing Meters.

Results

400 users and 746 wallets onboarded without anyone touching a private key. Four developer packages shipped to npm and PyPI, including a zero-dependency CLI of 33 commands and an MCP server exposing 80 LLM-callable tools, listed in the official MCP Registry. Six CI/CD workflows deploy the production estate with post-deploy verification that blocks a bad release.

Tech stack

• TypeScript
• Node.js
• Next.js
• PostgreSQL
• Stripe
• Privy
• WebSockets
• MCP
• Docker
• GCP