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What is UCP? UCP is an open standard that enables AI agents and platforms to complete purchases on any UCP-enabled merchant without custom integrations.
What the demo shows:
Discovery via /.well-known/ucp - how platforms find merchant capabilities Checkout Sessions API - create, update, and complete purchases Toggle "Debug Mode" to see the actual API calls in real-time
What's implemented vs mocked:
- Discovery endpoint with capabilities and payment handlers - Full checkout flow with line items, buyer info, payment selection - Payment processing uses test tokens (no real charges) - In-memory storage (resets on each session)
Links:
Demo: https://ucp-demo.web.app
UCP Spec: https://ucp.dev
Source: https://github.com/hemanth/ucp-demo
Would love feedback on the developer experience. Is the protocol discoverable enough? What's missing?
I built agentlearn after noticing that most AI agent tutorials focus on frameworks (LangChain, CrewAI) rather than fundamentals. The result is developers who can copy-paste code but struggle when things break.
This is a free, interactive course covering:
The Agent Loop - Why loops matter (think vs. act vs. observe) Context Engineering - The real skill behind "prompt engineering" Tools & Function Calling - Bridging text generation to real actions Memory Systems - Short-term vs. long-term, vector DBs Protocols - MCP, A2A, and the emerging standards Production Patterns - Error handling, cost optimization, observability Each concept has runnable code sandboxes you can step through. The design is intentionally "hand-drawn" to feel less intimidating than typical technical docs.
Tech stack: Vanilla JS + Vite, no framework.
Why no framework? Because understanding fundamentals means understanding what frameworks abstract away. Once you get the core loop, you can use any framework—or build your own.
Feedback welcome! Especially interested in what topics are missing.
So I built agentu.
The core is simple: → >> chains steps sequentially → & runs them in parallel
But it's grown into a full stack:
- Sessions: stateful conversations with automatic context - Evaluation: test your agents before production - Observability: real-time dashboard, metrics, tracing - Skills: domain expertise that loads on-demand - Tool Search: scale to 100s of tools without context bloat - MCP Integration: Model Context Protocol support
`pip install agentu`