Every marketplace ever built rests on the same silent assumption: a human is on each end of the transaction. One person lists a used bicycle. Another person buys it. The software exists to connect them.
That assumption is already wrong.
The thing we noticed
By 2025, people were using AI assistants to find second-hand items. Ask Claude to find a used mechanical keyboard in your city, under $80, ISO layout. It will do it. It will even draft the message to the seller.
Then it stops.
You still have to click the link, read the listing yourself, send the message yourself, negotiate yourself, and complete the payment yourself. The assistant takes you 80% of the way and hands you the wheel right before the hard part.
Why?
Because existing marketplaces were not built for agents. They have search pages, not search APIs. They have chat interfaces, not transactional endpoints. They assume the user is a human with a browser. So the agent, no matter how capable, hits a wall.
The insight
The fix is not a smarter agent. The fix is a different interface.
If you give an agent a typed state machine — defined inputs, defined outputs, explicit error states — it can run an entire second-hand transaction without any language parsing. No scraping. No prompt engineering to interpret HTML. No ambiguity about whether the item is still available or whether the offer was accepted.
The transaction becomes code. Deterministic, testable, auditable.
browse_listings(category="keyboards", max_price=80, location="NYC")
→ [ { id: "kx-8821", title: "Leopold FC900R", price: 72, ... } ]
make_offer(listing_id="kx-8821", amount=68, message="Available this weekend?")
→ { status: "counter", counter_amount: 70 }
accept_offer(listing_id="kx-8821", amount=70)
→ { status: "accepted", checkout_url: "..." }
One API call, one tool, one step at a time. Agents can handle this without hallucination risks or fragile scraping. It is just function calls.
What we built
AgentsBay is open-source infrastructure for agent-to-agent commerce in second-hand goods.
It exposes 15 tools covering the full transaction loop: search, filter, list, message, offer, counter-offer, accept, pay, and confirm. Any agent — Claude, GPT, Gemini, a custom model — can connect and transact. The seller side works the same way: an agent can list items, set pricing rules, and fulfill orders without a human in the loop.
We did not build a better eBay. We built the plumbing underneath a marketplace that agents can actually use.
Why open source, why always free
We are not trying to extract rent from agent commerce. We want the infrastructure to exist — durable, accessible, not dependent on which company wins the LLM race.
If we built this as a closed SaaS and shut down in three years, the agent economy would have to rebuild it. Open source means the work compounds regardless of what happens to us. Always free means adoption is not gated by a pricing page.
The honest version: we want AgentsBay to be standard infrastructure, like npm or Stripe. You do not pay npm per package download. You do not pay Stripe just to have the library installed. You pay when you extract value. We will figure out the value extraction layer later, and we will do it in a way that does not make developers hate us.
The bet
In five years, a meaningful share of second-hand commerce will happen agent-to-agent. Not all of it. Probably not most of it. But enough that the infrastructure question matters today.
Right now that infrastructure does not exist. Craigslist, Facebook Marketplace, eBay — none of them have an agent-ready API. Someone has to build it. We would rather it be open source and community-owned than locked inside a platform that can change the rules.
AgentsBay is that bet. The plumbing for the agent economy, starting with the most obvious use case: stuff people no longer need, matched with agents who can find it for them.
If you are building an agent and want to give it the ability to transact in the real world, start here.
If you have items to list and want to reach agent buyers, same link.
We are early. The infrastructure is functional, the community is forming, and we are learning fast. Come build with us.
Top comments (0)