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refactor: MoA uses lightweight API references + SDK arbiter as aggregator

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Instead of spawning separate processes or using OpenRouter, MoA now:
- Queries external API models (Kimi, GLM) in parallel for opinions
- Injects opinions into the SDK arbiter's prompt
- The existing subscription-based arbiter aggregates all perspectives

No extra SDK processes, no OpenRouter dependency. Per-model config via
MOA_REF_MODELS + MOA_{NAME}_MODEL/BASE_URL/API_KEY env vars.
This commit is contained in:
Eyejoker
2026-03-31 00:26:24 +09:00
parent f4b04d6c4d
commit f98dd27712
6 changed files with 130 additions and 175 deletions
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16
.env.example
View File

@@ -58,13 +58,17 @@ STATUS_CHANNEL_ID= # Discord channel ID for live status updat
# ARBITER_FALLBACK_ENABLED=true # Fall back to codex on Claude failure (default: true)
# --- Mixture of Agents (MoA) ---
# Queries multiple models in parallel for arbiter verdicts, then aggregates.
# Requires OpenAI-compatible API (OpenRouter recommended for multi-model access).
# Queries external API models in parallel before arbiter runs.
# Their opinions are injected into the arbiter's prompt for better judgment.
# The SDK arbiter (subscription-based, no extra cost) aggregates all perspectives.
# MOA_ENABLED=true
# MOA_BASE_URL=https://openrouter.ai/api/v1
# MOA_API_KEY=sk-or-xxx
# MOA_REFERENCE_MODELS=anthropic/claude-sonnet-4-6,openai/gpt-5.4,deepseek/deepseek-chat
# MOA_AGGREGATOR_MODEL=anthropic/claude-opus-4-6
# MOA_REF_MODELS=kimi,glm # Comma-separated reference model names
# MOA_KIMI_MODEL=kimi-k2.5 # Kimi model
# MOA_KIMI_BASE_URL=https://api.kimi.com/coding # Kimi API endpoint
# MOA_KIMI_API_KEY=sk-kimi-xxx # Kimi API key
# MOA_GLM_MODEL=glm-4-plus # GLM model
# MOA_GLM_BASE_URL=https://open.bigmodel.cn/api/paas/v4 # GLM API endpoint
# MOA_GLM_API_KEY=xxx # GLM API key
# --- Advanced ---
# MAX_CONCURRENT_AGENTS=5 # Max parallel agent processes

46
src/config.ts
View File

@@ -165,39 +165,35 @@ export function getRoleModelConfig(
import type { MoaConfig, MoaModelConfig } from './moa.js';
const MOA_BASE_URL = getEnv('MOA_BASE_URL') || 'https://openrouter.ai/api/v1';
const MOA_API_KEY = getEnv('MOA_API_KEY') || '';
function parseMoaModels(envKey: string): MoaModelConfig[] {
const raw = getEnv(envKey) || '';
return raw
/**
* Parse MOA reference models from env.
* Format: MOA_REF_MODELS=kimi,glm (comma-separated names)
* Each model: MOA_{NAME}_MODEL, MOA_{NAME}_BASE_URL, MOA_{NAME}_API_KEY
*/
function parseMoaReferenceModels(): MoaModelConfig[] {
const names = (getEnv('MOA_REF_MODELS') || '')
.split(',')
.map((s) => s.trim())
.filter(Boolean)
.map((model) => ({
name: model.split('/').pop() || model,
model,
baseUrl: MOA_BASE_URL,
apiKey: MOA_API_KEY,
}));
.filter(Boolean);
return names
.map((name) => {
const prefix = `MOA_${name.toUpperCase()}`;
const model = getEnv(`${prefix}_MODEL`) || '';
const baseUrl = getEnv(`${prefix}_BASE_URL`) || '';
const apiKey = getEnv(`${prefix}_API_KEY`) || '';
if (!model || !baseUrl || !apiKey) return null;
return { name, model, baseUrl, apiKey };
})
.filter((m): m is MoaModelConfig => m !== null);
}
export function getMoaConfig(): MoaConfig {
const referenceModels = parseMoaModels('MOA_REFERENCE_MODELS');
const aggregatorModel = getEnv('MOA_AGGREGATOR_MODEL') || '';
const referenceModels = parseMoaReferenceModels();
return {
enabled:
getEnv('MOA_ENABLED') === 'true' &&
referenceModels.length > 0 &&
!!aggregatorModel &&
!!MOA_API_KEY,
getEnv('MOA_ENABLED') === 'true' && referenceModels.length > 0,
referenceModels,
aggregator: {
name: aggregatorModel.split('/').pop() || aggregatorModel,
model: aggregatorModel,
baseUrl: MOA_BASE_URL,
apiKey: MOA_API_KEY,
},
};
}

6
src/message-agent-executor.test.ts
View File

@@ -25,7 +25,11 @@ vi.mock('./config.js', () => ({
effort: undefined,
fallbackEnabled: true,
})),
getMoaConfig: vi.fn(() => ({ enabled: false, referenceModels: [], aggregator: {} })),
getMoaConfig: vi.fn(() => ({
enabled: false,
referenceModels: [],
aggregator: {},
})),
TIMEZONE: 'Asia/Seoul',
}));

115
src/message-agent-executor.ts
View File

@@ -46,8 +46,7 @@ import {
getRoleModelConfig,
getMoaConfig,
} from './config.js';
import { buildArbiterContextPrompt } from './arbiter-context.js';
import { runMoaArbiter } from './moa.js';
import { collectMoaReferences, formatMoaReferencesForPrompt } from './moa.js';
import { readArbiterPrompt } from './platform-prompts.js';
import {
activateCodexFailover,
@@ -183,7 +182,48 @@ export async function runAgentForGroup(
}
}
const effectivePrompt = prompt;
// ── MoA prompt enrichment ─────────────────────────────────────
// When MoA is enabled and we're in arbiter mode, query external API
// models (Kimi, GLM, etc.) in parallel for their opinions, then inject
// those opinions into the arbiter's prompt. The SDK-based arbiter
// agent naturally aggregates all perspectives.
let moaEnrichedPrompt = prompt;
const moaConfig = getMoaConfig();
if (arbiterMode && moaConfig.enabled && pairedExecutionContext) {
logger.info(
{
chatJid,
group: group.name,
runId,
models: moaConfig.referenceModels.map((m) => m.name),
},
'MoA: collecting reference opinions before arbiter',
);
const systemPrompt =
readArbiterPrompt(process.cwd()) || 'You are an arbiter.';
const references = await collectMoaReferences({
config: moaConfig,
systemPrompt,
contextPrompt: prompt,
});
const moaSection = formatMoaReferencesForPrompt(references);
if (moaSection) {
moaEnrichedPrompt = prompt + '\n' + moaSection;
logger.info(
{
chatJid,
successCount: references.filter((r) => !r.error).length,
totalCount: references.length,
},
'MoA: injected reference opinions into arbiter prompt',
);
}
}
const effectivePrompt = moaEnrichedPrompt;
let pairedExecutionStatus: 'succeeded' | 'failed' = 'failed';
let pairedExecutionSummary: string | null = null;
let pairedExecutionCompleted = false;
@@ -328,75 +368,6 @@ export async function runAgentForGroup(
return 'success';
}
// ── MoA arbiter path ────────────────────────────────────────────
// When MoA is enabled and we're in arbiter mode, query multiple
// models in parallel instead of spawning a single agent process.
const moaConfig = getMoaConfig();
if (arbiterMode && moaConfig.enabled && pairedExecutionContext) {
logger.info(
{
chatJid,
group: group.name,
runId,
referenceModels: moaConfig.referenceModels.map((m) => m.model),
aggregator: moaConfig.aggregator.model,
},
'Running MoA arbiter instead of single agent',
);
const systemPrompt =
readArbiterPrompt(process.cwd()) || 'You are an arbiter.';
const contextPrompt = buildArbiterContextPrompt({
chatJid,
taskId: pairedExecutionContext.task.id,
roundTripCount: pairedExecutionContext.task.round_trip_count,
timezone: TIMEZONE,
});
try {
const moaResult = await runMoaArbiter({
config: moaConfig,
systemPrompt,
contextPrompt,
});
pairedExecutionSummary = moaResult.verdict.slice(0, 500);
pairedExecutionStatus = 'succeeded';
// Build display text with reference model opinions
const referenceSection = moaResult.referenceResponses
.filter((r) => !r.error)
.map((r) => `**${r.model}**: ${r.response.split('\n')[0]}`)
.join('\n');
const displayText = referenceSection
? `${moaResult.verdict}\n\n---\n*MoA references: ${moaResult.referenceResponses.filter((r) => !r.error).length} models queried*\n${referenceSection}`
: moaResult.verdict;
await onOutput?.({
status: 'success',
result: null,
output: { visibility: 'public', text: displayText },
phase: 'final',
});
} catch (error) {
logger.error(
{ chatJid, group: group.name, runId, error },
'MoA arbiter failed',
);
pairedExecutionSummary = 'ESCALATE\nMoA arbiter failed';
pairedExecutionStatus = 'failed';
}
completePairedExecutionContext({
taskId: pairedExecutionContext.task.id,
role: 'arbiter',
status: pairedExecutionStatus,
summary: pairedExecutionSummary,
});
pairedExecutionCompleted = true;
return pairedExecutionStatus === 'succeeded' ? 'success' : 'error';
}
const runAttempt = async (
provider: string,
): Promise<{

6
src/message-runtime.test.ts
View File

@@ -23,7 +23,11 @@ vi.mock('./config.js', () => ({
isClaudeService: vi.fn(() => true),
isReviewService: vi.fn(() => false),
isSessionCommandSenderAllowed: vi.fn(() => false),
getMoaConfig: vi.fn(() => ({ enabled: false, referenceModels: [], aggregator: {} })),
getMoaConfig: vi.fn(() => ({
enabled: false,
referenceModels: [],
aggregator: {},
})),
TIMEZONE: 'Asia/Seoul',
}));

116
src/moa.ts
View File

@@ -1,9 +1,12 @@
/**
* Mixture of Agents (MoA) for arbiter verdicts.
* Mixture of Agents (MoA) — lightweight reference opinions.
*
* Queries multiple LLM models in parallel, then aggregates their
* opinions into a single binding verdict. Uses OpenAI-compatible
* chat completions API (works with OpenRouter, direct providers, etc.)
* Queries external API models (Kimi, GLM, etc.) in parallel for their
* opinions on the deadlock. These opinions are then injected into the
* SDK-based arbiter's prompt so it can aggregate all perspectives.
*
* No extra SDK processes. The existing arbiter (Claude/Codex subscription)
* naturally becomes the aggregator.
*/
import { logger } from './logger.js';
@@ -17,7 +20,12 @@ export interface MoaModelConfig {
export interface MoaConfig {
enabled: boolean;
referenceModels: MoaModelConfig[];
aggregator: MoaModelConfig;
}
export interface MoaReferenceResult {
model: string;
response: string;
error?: string;
}
async function queryModel(
@@ -52,7 +60,9 @@ async function queryModel(
if (!response.ok) {
const body = await response.text().catch(() => '');
throw new Error(`${response.status} ${response.statusText}: ${body.slice(0, 200)}`);
throw new Error(
`${response.status} ${response.statusText}: ${body.slice(0, 200)}`,
);
}
const data = (await response.json()) as {
@@ -66,20 +76,23 @@ async function queryModel(
}
}
export async function runMoaArbiter(args: {
/**
* Query all reference models in parallel and return their opinions.
* These are injected into the SDK arbiter's prompt — the arbiter
* aggregates them into a final verdict.
*/
export async function collectMoaReferences(args: {
config: MoaConfig;
systemPrompt: string;
contextPrompt: string;
}): Promise<{
verdict: string;
referenceResponses: { model: string; response: string; error?: string }[];
}> {
}): Promise<MoaReferenceResult[]> {
const { config, systemPrompt, contextPrompt } = args;
// Phase 1: Query reference models in parallel
logger.info(
{ modelCount: config.referenceModels.length },
'MoA: querying reference models',
{
models: config.referenceModels.map((m) => m.name),
},
'MoA: querying reference models for opinions',
);
const results = await Promise.allSettled(
@@ -88,7 +101,7 @@ export async function runMoaArbiter(args: {
),
);
const referenceResponses = results.map((result, i) => {
return results.map((result, i) => {
const model = config.referenceModels[i].name;
if (result.status === 'fulfilled') {
logger.info(
@@ -104,68 +117,31 @@ export async function runMoaArbiter(args: {
logger.warn({ model, error }, 'MoA: reference model failed');
return { model, response: '', error };
});
const successfulResponses = referenceResponses.filter((r) => !r.error);
if (successfulResponses.length === 0) {
logger.error('MoA: all reference models failed, using ESCALATE');
return {
verdict:
'ESCALATE\n\nAll reference models failed to respond. Human judgment required.',
referenceResponses,
};
}
// Phase 2: Aggregate via aggregator model
const opinions = successfulResponses
.map((r, i) => `### Opinion ${i + 1} (${r.model}):\n${r.response}`)
/**
* Format reference opinions into a section that gets appended
* to the arbiter's prompt.
*/
export function formatMoaReferencesForPrompt(
references: MoaReferenceResult[],
): string | null {
const successful = references.filter((r) => !r.error && r.response);
if (successful.length === 0) return null;
const opinions = successful
.map((r) => `### ${r.model}:\n${r.response}`)
.join('\n\n---\n\n');
const aggregatorPrompt = [
contextPrompt,
return [
'',
'---',
'',
`The following ${successfulResponses.length} independent AI models have each reviewed the deadlock and provided their analysis:`,
`<moa-references count="${successful.length}">`,
`The following ${successful.length} independent AI models have also reviewed this deadlock:`,
'',
opinions,
'',
'---',
'',
'Consider all perspectives above. Where they agree, that strengthens the case.',
'Where they disagree, weigh the evidence each side presents.',
'Render your final verdict. Start your first line with: PROCEED, REVISE, RESET, or ESCALATE.',
'Consider these perspectives alongside the conversation. Where they agree, that strengthens the case.',
'Where they disagree, weigh the evidence. Your verdict is final.',
'</moa-references>',
].join('\n');
logger.info(
{ aggregator: config.aggregator.name },
'MoA: running aggregator',
);
try {
const verdict = await queryModel(
config.aggregator,
systemPrompt,
aggregatorPrompt,
90_000,
);
logger.info(
{
aggregator: config.aggregator.name,
verdictPreview: verdict.slice(0, 100),
},
'MoA: aggregator verdict rendered',
);
return { verdict, referenceResponses };
} catch (error) {
// Aggregator failed — fall back to majority vote from reference models
logger.warn(
{ error },
'MoA: aggregator failed, falling back to first successful reference',
);
return {
verdict: successfulResponses[0].response,
referenceResponses,
};
}
}