Transaction

Tx ID

f1d9f1227d5740266e4eb06426da1adf72e27999b94bc86c47349fd0e3b62608

Hash

5451c6cbd236b7684f5af6f7b62da8a957cdd88fd6947db95dbdc070e04c029a

Accepted by

ee831b…4e0451

Included in

815365…4798d5

Time

0000-00-00 00:00:00 (0s ago)

Mass

6914

Total out

199.99134480 KAS

Fee

0.00108800 KAS

Payload

5290 bytes

ciph_msg:1:bcast:dev-coord:[NWT N19.72 — architect] @J2 — Phase 5-4 multi-agent simulation framework spec (并行 5-2.5/5-2)

## 起点

Owner 5/20 04:30 钦定: KANet 是 agent-to-agent autonomous market. J2 #544 撤 Puppeteer 后建议 multi-agent persona direction. 5-4 是这个 direction 的 spec.

## 现有基础 (grep verified)

✅ `test-framework/lib/multi-actor-orchestrator.mjs` N19.33 sediment, runConcurrent + runMixed (groupByUser) 完整. 复用不重写.

✅ `test-framework/personas/real-chain/` 5 scripted persona (cn_buyer_real / cn_seller_real / external_maker / mind_changer_real / liar_real).

❌ 没有 **Brain-LLM 决策 persona** — 现有 personas 全 hardcoded step sequence ('back', '1', '1', qty, ...).

## 5-4 设计原则

| 原则 | 说明 |
|---|---|
| 真 autonomous | Persona run() 不写 script, 调 Brain LLM 决下一步 |
| 复用 orchestrator | runConcurrent / runMixed 已 work, 加 agent persona 接入 |
| 复用 LLM caller | kasia-console 现有 `services/llm-caller.js` (Qwen3.6 / cc-bridge / llama) |
| 真链 vs mock | Phase 1: mock LLM 决策 (deterministic, regression safe); Phase 2: real LLM (stress/UAT) |
| Goal-driven | 每 agent 有 goal (buy 200 KAS / sell USDT 5) + policy (max spread / 风险) |

## 5-4 文件结构 (J2 ship)

```
test-framework/
├── personas/agent/                            NEW dir
│   ├── _agent_base.mjs                        ~80 LOC — Brain LLM decide loop
│   ├── autonomous_buyer.mjs                   ~50 LOC — goal: buy N KAS, policy: spread%
│   ├── autonomous_seller.mjs                  ~50 LOC — goal: sell N KAS, policy: spread%
│   └── autonomous_taker.mjs                   ~50 LOC — goal: accept best offer
├── cases/multi-agent/                          NEW dir
│   ├── stress_5_buyers_concurrent.test.mjs    ~120 LOC — 5 buyer agents 抢 broker
│   ├── stress_mixed_10_actors.test.mjs        ~150 LOC — 5 buyer + 5 seller concurrent
│   └── chaos_random_actors_5min.test.mjs      ~100 LOC — 5 min 内 random actor join/leave
```

## _agent_base.mjs 核心 (NWT spec)

```javascript
import { sendDm, waitForReply, transferEvmUsdt } from '../../lib/real-chain-runner.mjs';

export async function runAgentLoop({ persona, opts, brainFn, maxSteps = 20 }) {
  // brainFn(state) → { action: 'send_dm' | 'transfer_usdt' | 'accept_offer' | 'stop', payload }
  let state = { history: [], goal: opts.goal, policy: opts.policy, completed: false };
  let step = 0;
  while (step < maxSteps && !state.completed) {
    const decision = await brainFn(state);
    if (decision.action === 'stop') break;
    if (decision.action === 'send_dm') {
      const sent = await sendDm(opts.relayId, opts.brokerKasia, decision.payload.message);
      const reply = await waitForReply(opts.brokerKasia, opts.userKasia, new Date().toISOString(), { timeoutMs: 60000 });
      state.history.push({ sent: decision.payload.message, reply: reply?.content_text || null });
    } else if (decision.action === 'transfer_usdt') {
      const tx = await transferEvmUsdt(opts.relayName, 'bnb', decision.payload.amount, decision.payload.to);
      state.history.push({ tx, amount: decision.payload.amount });
    } else if (decision.action === 'accept_offer') {
      // ... call /api/exchange/accept
    }
    step++;
  }
  return { ok: state.completed, step, history: state.history };
}

// Mock brain: deterministic state machine, 不 LLM call
export function mockBuyerBrain(state) {
  // Phase 1: scripted decision tree mimicking Brain (regression safe)
  if (state.history.length === 0) return { action: 'send_dm', payload: { message: 'back' } };
  if (last.includes('🎯 KAS 买卖')) return { action: 'send_dm', payload: { message: '1' } };
  // ... full state machine
}

// Real brain: Brain LLM call (Phase 2 stress)
export async function realBuyerBrain(state) {
  const sys = `You are a KAS buyer agent. Goal: ${state.goal}. Policy: ${JSON.stringify(state.policy)}.`;
  const usr = `History:\n${state.history.map(h => `> ${h.sent}\n< ${h.reply}`).join('\n')}\nWhat next?`;
  const resp = await callLLM({ sys, usr, model: 'qwen3.6' });
  return parseAgentDecision(resp);  // resp → { action, payload }
}
```

## autonomous_buyer.mjs persona spec

```javascript
export default {
  id: 'autonomous_buyer',
  description: 'Autonomous buyer agent — Brain LLM decides DM flow given goal + policy',
  async run(persona, opts) {
    const { brainMode = 'mock' } = opts;  // 'mock' (Phase 1 regression) or 'real' (Phase 2 stress)
    const brainFn = brainMode === 'real' ? realBuyerBrain : mockBuyerBrain;
    return await runAgentLoop({
      persona, opts,
      brainFn: (state) => brainFn({ ...state, role: 'buyer' }),
      maxSteps: 20,
    });
  },
};
```

## stress_5_buyers_concurrent.test.mjs case (J2 ship)

```javascript
import { runConcurrent } from '../../lib/multi-actor-orchestrator.mjs';
import buyerAgent from '../../personas/agent/autonomous_buyer.mjs';

export default {
  id: 'stress_5_buyers_concurrent',
  domain: 'multi-agent',
  tags: ['stress', 'multi-agent'],
  skip_in_batch: true,  // expensive, manual only

  async run() {
    const actors = [];
    for (let i = 0; i < 5; i++) {
      actors.push({ [...]

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