Overview
agent-paylab is a development tool for building and testing the decision logic of AI agents that need to pay, entirely offline. It simulates payment protocols, compares offers, negotiates prices, and generates cryptographically signed receipts without touching a real payment rail. The project is positioned as a tool for the logic that decides before any payment protocol is called, rather than as a competitor to payment protocols.
The tool supports mocked single-transaction protocols including x402, mpp, visatap, mastercardpay, payforcrawl, and ap2, plus per-unit or continuous-streaming protocols lightning_l402 and web_monetization, and an api_key_quota access-check model. Its CLI includes commands such as simulate, auto, compare, negotiate, negotiate-and-choose, stream, and check-access. The negotiation engine is also usable as a library at raw and wrapped levels.
Every simulated payment, approved or rejected, produces a receipt signed with Ed25519, with the private key generated locally on first use. AI-assisted decisions are experimental, use the Gemini API, are non-deterministic, and are not covered by automated tests. The project requires Python 3.11+.
Pricing
Free
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Use Cases
- Teams use agent-paylab to simulate a single payment on a chosen protocol without a real payment rail.
- Teams use agent-paylab to compare offers from different sellers using different payment methods and pick the lowest total cost.
- Teams use agent-paylab to run multi-round negotiations between sellers that maximize expected value each round.
- Teams use agent-paylab to let an LLM choose among top finalists based on natural-language preferences after deterministic negotiation.
- Teams use agent-paylab to simulate per-request or continuous-streaming micropayments.
- Teams use agent-paylab to check API key or quota access under a traditional pre-paid credential model.
- Teams use agent-paylab to produce and verify Ed25519-signed receipts for simulated payments.
- Teams use agent-paylab to use the negotiation engine as a library in their own buyer agents.
Features
Offline payment decision sandbox
Prototype AI agent payment choices
Compare competing payment offers
Simulate offer negotiations
Generate signed receipts
Test without real payment rails
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