MnemoPay

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.

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Building an MCP server? Start here.

If you're shipping an MCP server and want to charge per-call — even sub-cent amounts — MnemoPay is built for you.

• Sub-cent payments via Lightning rail (impossible on Stripe/Paystack due to fees)
• Per-tool metering with agent.charge(amount, toolName) — two lines of code
• Agent Credit Score gates abusive callers automatically — 300-850 credit score, free tier + paid tier
• Cryptographic receipts every user can audit — no "trust me bro" billing
• Free indefinitely for the first 10 MCP servers that adopt it, subject to 90 days' written notice of any future change (email with your repo)

import 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.

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What Makes MnemoPay Different

$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

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Agent Credit Score — Credit Score for AI Agents

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.log(result.feeRate);   // 0.013 (1.3%)
console.log(result.trustLevel); // "high"
console.log(result.requiresHITL); // false

Component	Weight	What It Measures
Payment History	35%	Success rate, disputes, recency-weighted
Credit Utilization	20%	Spend vs budget cap, sweet spot 10-30%
History Length	15%	Account age, activity density
Behavior Diversity	15%	Counterparties, categories, amount range
Fraud Record	15%	Fraud flags, disputes lost, warnings

Score Range	Rating	Trust Level	Fee Rate
800-850	Exceptional	Full trust	1.0%
740-799	Very Good	High trust	1.3%
670-739	Good	Standard	1.5%
580-669	Fair	Reduced	1.9%
300-579	Poor	Minimal + HITL	2.5%

---

Behavioral Finance Engine

Peer-reviewed behavioral economics from Nobel laureate Daniel Kahneman and collaborators. Every parameter cited to published research.

import { BehavioralEngine } from "@mnemopay/sdk";

const behavioral = new BehavioralEngine();

// Prospect Theory (Kahneman & Tversky, 1992)
// Losses hurt 2.25x more than gains feel good
behavioral.prospectValue(100);   // { value: 57.5, domain: "gain" }
behavioral.prospectValue(-100);  // { value: -129.5, domain: "loss" }

// Should the agent wait before buying?
const cooling = behavioral.coolingOff(2000, 5000); // amount, monthly income
// → { recommended: true, hours: 3.2, riskLevel: "high", regretProbability: 0.65 }

// Frame spending as goal delay (2.25x more effective than gain framing)
const frame = behavioral.lossFrame(200, {
  name: "Emergency Fund", target: 10000, current: 3000, monthlySavings: 500
});
// → "This $200 purchase delays your Emergency Fund goal by 12 days."

// Save More Tomorrow (Thaler & Benartzi, 2004)
const smart = behavioral.commitmentDevice(0.035, 0.03, 4);
// → { finalRate: 0.095, explanation: "3.5% → 9.5% over 4 raise cycles" }

// Predict regret from purchase history
behavioral.recordRegret({ amount: 300, category: "gadgets", regretScore: 8, timestamp: "..." });
const prediction = behavioral.predictRegret(400, "gadgets");
// → { probability: 0.72, triggerCoolingOff: true }

Research sources: Tversky & Kahneman 1992, Laibson 1997, Thaler & Benartzi 2004, Barber & Odean 2000, Nunes & Dreze 2006, Shiller 2000.

---

Memory Integrity (Merkle Tree)

Tamper-evident memory. If anyone injects, modifies, or deletes an agent's memories, the Merkle root changes and you know.

import { MerkleTree } from "@mnemopay/sdk";

const tree = new MerkleTree();

// Every memory write adds a leaf
tree.addLeaf("mem-1", "User prefers monthly billing");
tree.addLeaf("mem-2", "Last purchase was $25 API access");

// Take periodic snapshots
const snapshot = tree.snapshot();
// → { rootHash: "a3f2...", leafCount: 2, snapshotHash: "b7c1..." }

// Later: check if memories were tampered
const check = tree.detectTampering(snapshot);
// → { tampered: false, summary: "Integrity verified. 2 memories, root matches." }

// Prove a specific memory exists without revealing others
const proof = tree.getProof("mem-1");
MerkleTree.verifyProof(proof); // true

Defends against: MemoryGraft injection, silent deletion, content tampering, replay attacks, reordering attacks.

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Anomaly Detection (EWMA + Behavioral Fingerprinting + Canaries)

Three independent systems that catch compromised agents.

import { EWMADetector, BehaviorMonitor, CanarySystem } from "@mnemopay/sdk";

// 1. EWMA: real-time streaming anomaly detection
const detector = new EWMADetector(0.15, 2.5, 3.5, 10);
detector.update(100); // normal
detector.update(100); // normal
detector.update(9999); // → { anomaly: true, severity: "critical", zScore: 8.2 }

// 2. Behavioral fingerprinting: detect hijacked agents
const monitor = new BehaviorMonitor({ warmupPeriod: 10 });
// Build profile over time
monitor.observe("agent-1", { amount: 100, hourOfDay: 14, chargesPerHour: 2 });
// Sudden change = suspected hijack
monitor.observe("agent-1", { amount: 9999, hourOfDay: 3, chargesPerHour: 50 });
// → { suspected: true, severity: "critical", anomalousFeatures: 3 }

// 3. Canary honeypots: plant traps for compromised agents
const canary = new CanarySystem();
const trap = canary.plant("transaction");
canary.check(trap.id, "rogue-agent");
// → { severity: "critical", message: "CANARY TRIGGERED: Agent compromised" }

Math: mu_t = alpha * x_t + (1 - alpha) * mu_{t-1}, alert when |x_t - mu_t| > k * sigma_t (Roberts 1959, Lucas & Saccucci 1990).

---

Memory (Compounding Knowledge Base)

Not a traditional RAG lookup. MnemoPay memories compound — every transaction strengthens associated context, weak memories decay, strong ones consolidate. The same pattern Karpathy describes as "LLM Wiki" but applied to payments and trust.

• Ebbinghaus forgetting curve — memories decay naturally over time
• Hebbian reinforcement — successful transactions strengthen associated memories
• RL feedback loop — rlFeedback(ids, reward) applies EWMA importance updates after agent actions
• Consolidation — auto-prunes weak memories, keeps what matters
• Semantic recall — find memories by relevance, not just recency
• 100KB per memory — store rich context, not just strings

// After a recall + action, signal usefulness with rlFeedback
const memories = await agent.recall("user preferences", 5);
// ... agent acts on recalled memories ...
await agent.rlFeedback(memories.map(m => m.id), +1.0);   // +1 = useful, -1 = not useful

Reputation Streaks & Badges

Agents earn trust over time. Consecutive successful settlements build streaks that unlock badges and reduce fees.

const rep = await agent.reputation();
console.log(rep.streak);
// → { currentStreak: 47, bestStreak: 312, streakBonus: 0.094 }

console.log(rep.badges);
// → [
//   { id: "first_settlement", name: "First Settlement", earnedAt: 1712700000000 },
//   { id: "streak_50", name: "Streak Master", earnedAt: 1712900000000 },
//   { id: "volume_10k", name: "High Roller", earnedAt: 1713100000000 },
// ]

Badge	Requirement
First Settlement	Complete 1 settlement
Streak 10	10 consecutive settlements
Streak 50	50 consecutive settlements
Volume $1K	$1,000+ total settled
Volume $10K	$10,000+ total settled
Perfect Record	100+ settlements, 0 disputes

Streaks reset on refunds or disputes. Streak bonuses compound reputation up to +10%.

Hash-Chained Ledger

Every ledger entry links to the previous via SHA-256 hash chain. If any entry is modified, the chain breaks and verify() catches it instantly.

const summary = agent.ledger.verify();
console.log(summary.chainValid);     // true
console.log(summary.chainIntegrity); // 1.0 (100% of links verified)

Combined with Merkle integrity on memories and HMAC on transactions, MnemoPay gives you three independent tamper-detection systems.

Payments (cent-precise double-entry)

• Double-entry bookkeeping — every debit has a credit, always balances to zero
• Escrow flow — charge -> hold -> settle -> refund (same shape as Stripe/Square)
• Volume-tiered fees — 1.9% / 1.5% / 1.0% based on cumulative volume
• 3 payment rails — Paystack (Africa), Stripe (global), Lightning (BTC)
• Cent-precise integer math — stress-tested with 200,000 transactions across 50 concurrent agents, zero drift

Identity (KYA Compliance)

• Cryptographic identity — HMAC-SHA256 keypairs, replay protection
• Capability tokens — scoped permissions with spend limits
• Counterparty whitelists — restrict who the agent can transact with
• Kill switch — revoke all tokens instantly

Fraud Detection (ML-grade)

• Velocity checks — per-minute/hour/day limits
• Isolation Forest — unsupervised ML anomaly detection
• Geo-enhanced — country tracking, rapid-hop detection, OFAC sanctions
• Adaptive engine — asymmetric AIMD, anti-gaming, circuit breaker, PSI drift detection

Multi-Agent Commerce

• CommerceEngine — autonomous shopping with mandates, escrow, approval callbacks
• MnemoPayNetwork — register agents, execute deals, shared memory context
• Supply chains — 10-step agent chains, 100-agent marketplaces, all tested

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Claude Agent SDK integration

Two primitives built specifically for the Claude Agent SDK pattern where an Opus orchestrator spawns Sonnet/Haiku subagents.

1-hour prompt cache on recall results

When you feed MnemoPay recall into a Claude system prompt, use formatForClaudeCache() to emit a content block with cache_control: { type: "ephemeral", ttl: 3600 }. The Anthropic API caches that prefix for up to 1 hour; cache reads are billed at roughly 10% of the normal input rate. With stable recall prefixes and a warm 1h cache, users have observed savings in the typical range of 85-92% on the recall portion of input tokens — your actual results depend on call frequency and memory set stability.

import MnemoPay, { formatForClaudeCache } from "@mnemopay/sdk";
import Anthropic from "@anthropic-ai/sdk";

const agent = MnemoPay.quick("my-agent");
const anthropic = new Anthropic();

// Option A: recall() directly returns a cache block
const cacheBlock = await agent.recall("user preferences", 10, {
  formatForClaudeCache: true,
});

// Option B: convert an existing memory array (no extra recall call)
const memories = await agent.recall("user preferences", 10);
const cacheBlock2 = MnemoPay.formatForClaudeCache(memories);
// OR: formatForClaudeCache(memories) from the module directly

const response = await anthropic.messages.create({
  model: "claude-opus-4-7",
  max_tokens: 1024,
  system: [
    { type: "text", text: "You are a helpful assistant.", cache_control: { type: "ephemeral" } },
    cacheBlock,  // ← MnemoPay recall cached for 1 hour
  ],
  messages: [{ role: "user", content: userMessage }],
});

The serialized text is sorted by memory id so identical memory sets produce byte-identical output — required for the cache prefix to hit on subsequent turns.

Per-subagent cost attribution

Track which subagent in a multi-agent pipeline spent how much — recorded as double-entry ledger pairs so it stays audit-clean.

import MnemoPay, { SubagentCostTracker } from "@mnemopay/sdk";

const orchestrator = MnemoPay.quick("orchestrator");

// After each Claude API call, record the cost:
orchestrator.subagentCosts.attributeSubagentCost({
  parentAgentId: "orchestrator",
  subagentId: "researcher-1",
  subagentRole: "researcher",
  modelId: "claude-sonnet-4-6",
  inputTokens: 5000,
  outputTokens: 2000,
  cacheReadTokens: 8500,   // tokens served from the 1h recall cache
  cacheWriteTokens: 500,
  cacheWriteTtl: "1h",
});

// At end of pipeline, get breakdown ordered by cost:
const breakdown = orchestrator.subagentCosts.subagentCostBreakdown("orchestrator");
// → [{ subagentId, subagentRole, modelId, totalCostUsd, cacheSavingsUsd, ... }]

const totalSaved = orchestrator.subagentCosts.totalCacheSavings("orchestrator");

Pricing table used: 2026 Anthropic list rates (Opus 4.7 $5/$25/M, Sonnet 4.6 $3/$15/M, Haiku 4.5 $1/$5/M; cache reads 0.1×, 1h writes 2×). Update MODEL_PRICING in src/subagent-cost.ts if rates change.

See docs/agent-sdk-guide.md for a full integration walkthrough.

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Payment Rails

import { PaystackRail, StripeRail, LightningRail } from "@mnemopay/sdk";

// Africa (NGN, GHS, ZAR, KES)
const paystack = new PaystackRail(process.env.PAYSTACK_SECRET_KEY!);

// Global (USD, EUR, GBP)
const stripe = new StripeRail(process.env.STRIPE_SECRET_KEY!);

// Crypto (BTC via Lightning Network)
const lightning = new LightningRail(LND_URL, MACAROON);

const agent = MnemoPay.quick("my-agent", { paymentRail: paystack });

Stripe — real card charges with saved customers

End-to-end flow for charging a user's saved card without a browser handoff:

import MnemoPay, { StripeRail } from "@mnemopay/sdk";

const rail = new StripeRail(process.env.STRIPE_SECRET_KEY!);
const agent = MnemoPay.quick("agent-1", { paymentRail: rail });

// 1. Create a Stripe customer (one-time, persist cus_... to your DB)
const { customerId } = await rail.createCustomer("user@example.com", "Jerry O");

// 2. Collect a card via Stripe.js: create a SetupIntent, return client_secret
//    to the browser, let Stripe Elements confirm it. You receive pm_... from
//    the webhook or confirmation callback. Save it alongside the customer.
const { clientSecret } = await rail.createSetupIntent(customerId);
// → hand clientSecret to frontend, get back paymentMethodId after confirm

// 3. Charge the saved card later, off-session, no user interaction needed
const tx = await agent.charge(25, "Monthly API access", undefined, {
  customerId,
  paymentMethodId: "pm_saved_from_step_2",
  offSession: true,
});

// 4. Settle (captures the hold) or refund (releases it)
await agent.settle(tx.id);

Paystack supports the same pattern via authorizationCode:

const tx = await agent.charge(5000, "NGN invoice", undefined, {
  email: "customer@example.com",
  authorizationCode: "AUTH_abc123", // from an earlier Paystack transaction
});

---

MCP Server

npx @mnemopay/sdk init
# or
claude mcp add mnemopay -s user -- npx -y @mnemopay/sdk

Default tool group: essentials (14 tools, ~1K tokens). One of the lightest MCP servers you can install — MnemoPay only loads memory + wallet + tx by default so it doesn't tax your agent's context budget.

• memory: remember, recall, forget, reinforce, consolidate
• wallet: balance, profile, history, logs
• tx: charge, settle, refund, dispute, receipt_get

Need more? Opt in explicitly:

npx @mnemopay/sdk --tools=all       # all 40 tools
npx @mnemopay/sdk --tools=agent     # essentials + commerce + hitl + payments + webhooks
npx @mnemopay/sdk --tools=fico      # Agent Credit Score only

Groups: memory, wallet, tx, commerce, hitl, payments, webhooks, fico, security. Aliases: essentials (default), agent, all. Also settable via MNEMOPAY_TOOLS env var.

Breaking change in v1.3.0: default was all, now essentials. If you relied on commerce/hitl/webhooks/fico/security being available without a flag, pass --tools=all or --tools=agent. See CHANGELOG.

---

Middleware

// OpenAI
import { mnemoPayMiddleware } from "@mnemopay/sdk/middleware/openai";

// Anthropic
import { mnemoPayMiddleware } from "@mnemopay/sdk/middleware/anthropic";

// LangGraph
import { mnemoPayTools } from "@mnemopay/sdk/langgraph";

---

Architecture

┌────────────────────────────────────────────────────────────┐
│                    MnemoPay SDK v1.3.0                     │
├──────────┬──────────┬───────────┬──────────────────────────┤
│  Memory  │ Payments │ Identity  │  Agent Credit Score (300-850)    │
│          │          │           │  5-component scoring     │
│ remember │ charge   │ KYA       ├──────────────────────────┤
│ recall   │ settle   │ tokens    │  Behavioral Finance      │
│ reinforce│ refund   │ perms     │  prospect theory, nudges │
│ forget   │ dispute  │ killswitch├──────────────────────────┤
│          │          │           │  Anomaly Detection       │
│          │          │           │  EWMA + fingerprinting   │
├──────────┴──────────┴───────────┼──────────────────────────┤
│     Double-Entry Ledger         │  Merkle Integrity        │
│  debit + credit = always zero   │  tamper-evident memory   │
├─────────────────────────────────┼──────────────────────────┤
│     Fraud Guard (ML-grade)      │  Canary Honeypots        │
│  velocity + geo + adaptive      │  compromise detection    │
├─────────────────────────────────┴──────────────────────────┤
│              Payment Rails                                 │
│        Paystack  │   Stripe   │   Lightning                │
└────────────────────────────────────────────────────────────┘

---

Testing

npm test    # full test suite across 12 files

• core.test.ts — memory, payments, lifecycle, FICO, behavioral, Merkle, EWMA, canaries, streaks, badges
• fraud.test.ts — velocity, anomaly, fees, disputes, replay detection
• geo-fraud.test.ts — geo signals, trust, sanctions
• identity.test.ts — KYA, tokens, permissions
• production-100k.test.ts — 100K operations, 10 concurrent agents, hash-chain verification, zero drift
• stress-200k.test.ts — 200K real-world stress: 50 agents, burst traffic, race conditions, refund storms, memory leak detection
• ledger.test.ts — double-entry, reconciliation
• network.test.ts — multi-agent, deals, supply chains
• paystack.test.ts — rail, webhooks, transfers
• stress.test.ts — 1000-cycle precision, parallel ops
• recall.test.ts — semantic search, decay, reinforcement

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License

Apache License 2.0 — see LICENSE.

Copyright 2026 J&B Enterprise LLC.

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Third-party attributions

The entity-observation write-path in src/recall/observations.ts (per-entity consolidated summaries, debounced regeneration, session-spanning rollups) is derived from vectorize-io/hindsight (MIT, Copyright (c) 2025 Vectorize AI, Inc.). The full upstream notice is preserved in NOTICE and in the header of the ported file.

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Trademark and regulatory notices

Agent Credit Score is a creditworthiness scoring system for autonomous software agents, not for consumer credit reporting. It does not produce a consumer report as defined by the Fair Credit Reporting Act (FCRA) and is not regulated under the FCRA. MnemoPay is not a consumer reporting agency.

MnemoPay is not a bank, money transmitter, or insurer, and does not hold customer deposits. Payments are settled through third-party payment rails (Stripe, Paystack, Lightning Network) — MnemoPay is software that connects to those rails on behalf of developers, not a financial institution.

"FICO" is a registered trademark of Fair Isaac Corporation. MnemoPay and its Agent Credit Score module are not affiliated with, endorsed by, or derived from Fair Isaac Corporation. The AgentFICO export name is a deprecated alias kept for backward compatibility with earlier beta releases and will be removed in a future major version.

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Built by Jerry Omiagbo