Keryx v0.1.0 — citation-toll reading agent (Lepton Hackathon)
Keryx is a citation-toll reading agent: ask it a question with a budget, and it autonomously decides which paid content sources to buy over x402, reads enough to answer, writes a grounded answer with citations, and then settles a weighted nanopayment to every source it actually cited — in USDC on Arc.
Built for the Lepton Agents Hackathon (Canteen × Circle, on Arc).
What's in this release
- The agent brain (
lib/agent/) — decompose → discover → decide → fetch → sufficiency → synthesize → attribute → settle, emitting a human-readable rationale for every buy / skip / cache / stop decision. - Real x402 settlement on Arc testnet — fetch tolls and weighted citation rewards both settle via Circle Gateway batching (
@circle-fin/x402-batching). No mocked settlement in the demo path. - Two-tier creator economy — a small access toll on fetch + a citation pool distributed by LLM-assigned contribution weight; multi-author works split the reward automatically.
- One-click creator onboarding —
/register→ paste an RSS URL → wallet + x402 endpoint created. - Live web app at keryx.cc (Next.js 16 · React 19 · Tailwind 4 · shadcn/ui), with a streaming reasoning UI, metrics, and a creator leaderboard.
- Agent-to-agent mode — Keryx exposed as a paid x402 endpoint.
- CLI tooling —
npm run ask(one query + full reasoning trace),npm run seed(autonomous volume engine),npm run metrics.
Verified traction (real, on-chain)
- 15 settled payments (11 weighted citations + 4 access tolls) on Arc testnet
- $0.16 USDC to creators — 100% to creator wallets, 0% platform fee
- 4 creators earning · 100% reader→payer conversion · avg ~$0.0107 (true nanopayments)
Run it
npm install
npm run ask -- "How do x402 and stablecoins enable autonomous AI agent commerce?" --budget 0.05
npm run dev # http://localhost:3939Runs offline with zero keys (heuristic reasoning + simulated payments). Add an LLM key, fund the agent wallet, and set KERYX_FORCE_OFFLINE=0 for real on-chain settlement.
See README.md, DECISIONS.md, and TRACTION.md for details.