Memory + wallet for AI agents. Real payment rails, Agent FICO 300-850, Merkle audit, identity.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"io-github-mnemopay-sdk": {
"command": "<see-readme>",
"args": []
}
}
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Memory + wallet for AI agents. Real payment rails, Agent FICO 300-850, Merkle audit, identity.
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The trust & reputation layer for AI agents. Agent Credit Score + Behavioral Finance + Payments + Memory + Identity in one SDK.
Your agent builds a credit score, detects fraud in real time, makes psychologically sound financial decisions, and proves its memory hasn't been tampered with. All on top of persistent memory, real payment rails, and a double-entry ledger that never drifts by a penny.
npm install @mnemopay/sdk
import MnemoPay, { AgentCreditScore, BehavioralEngine, MerkleTree } from "@mnemopay/sdk";
const agent = MnemoPay.quick("my-agent");
await agent.remember("User prefers monthly billing");
const tx = await agent.charge(25, "Monthly API access");
await agent.settle(tx.id);
// Score the agent (portable agent credit score, 300-850 range)
// Not FICO-brand. Not affiliated with Fair Isaac Corporation or any consumer
// credit bureau. Scores agents (software), not humans. Not governed by FCRA.
const score = new AgentCreditScore();
const result = score.compute({ transactions: [tx], createdAt: new Date(), ...});
// → { score: 672, rating: "good", feeRate: 0.015, trustLevel: "standard" }
14 modules. Hash-chained ledger. Replay detection. Reputation streaks. 200K-operation stress tested. Apache 2.0 licensed.
If you're shipping an MCP server and want to charge per-call — even sub-cent amounts — MnemoPay is built for you.
agent.charge(amount, toolName) — two lines of codeimport MnemoPay from "@mnemopay/sdk";
const agent = MnemoPay.quick("my-mcp-server");
// Inside your tool handler:
const tx = await agent.charge(0.002, "embed_document"); // 0.2¢
if (tx.status === "blocked") return { error: "Payment declined" };
await agent.settle(tx.id);
// ... run the tool
Zero-config starter → production Lightning rail → Agent Credit Score gating. Same API.
$87M has been invested across 5 competitors. None have more than 3 of these 10 features:
| Feature | MnemoPay | Mem0 ($24M) | Skyfire ($9.5M) | Kite ($33M) | Payman ($14M) |
|---|---|---|---|---|---|
| Persistent Memory | Yes | Yes | No | No | No |
| Payment Rails (3) | Yes | No | USDC only | Stablecoin | Bank only |
| Agent Identity (KYA) | Yes | No | Building | Passport | No |
| Agent Credit Score (300-850) | Yes | No | No | No | No |
| Behavioral Finance | Yes | No | No | No | No |
| Memory Integrity (Merkle) | Yes | No | No | No | No |
| EWMA Anomaly Detection | Yes | No | No | No | No |
| Double-Entry Ledger | Yes | No | No | No | No |
| Autonomous Commerce | Yes | No | No | No | No |
| Multi-Agent Network | Yes | No | Partial | Partial | No |
| Score | 10/10 | 1/10 | 2/10 | 2/10 | 1/10 |
A novel cross-session credit scoring system for AI agents. Five-component scoring on a 300-850 range (familiar to developers from consumer credit; MnemoPay is not affiliated with Fair Isaac Corporation or any consumer credit bureau):
import { AgentCreditScore } from "@mnemopay/sdk";
const fico = new AgentCreditScore();
const result = fico.compute({
transactions: await agent.history(1000),
createdAt: agentCreationDate,
fraudFlags: 0,
disputeCount: 0,
disputesLost: 0,
warnings: 0,
budgetCap: 5000,
memoriesCount: agent.memories.size,
});
console.log(result.score); // 742
console.log(result.rating); // "very_good"
console.lo
... [View full README on GitHub](https://github.com/mnemopay/mnemopay-sdk#readme)