fix: v0.6.2 修复工具调用不稳定与会话停止 — 纯 tool_calls 轮丢失 assistant 消息导致 API 400
【根因（main.log 实证）】 19:04 / 19:05 / 19:06 三次会话终止均为同一报错： DeepSeek 400 "Messages with role 'tool' must be a response to a preceding message with 'tool_calls'" 缺陷链：engine 主循环仅在 step.thought 存在（该轮有文本或思考内容）时才 将 assistant 消息加入请求历史。当模型发起纯工具调用（零文本零思考 — DeepSeek 高频行为）时： - assistant(tool_calls) 消息不进 messages - 但 tool 结果消息照常 push → 下一轮请求出现孤立 tool 消息 → 协议 400（不可重试）→ 会话 ERROR 终止 "不稳定" = 模型每轮是否附带文本是概率性行为：带文本正常，纯调用必崩。 DB 持久化侧同源缺陷（if (!step.thought) continue）导致这些步骤的 assistant 与 tool 结果全部不落库 — 重启后工具上下文丢失，模型重复调用。 【修复】 - engine.ts: 有 toolCalls 的轮次必 push assistant（content=null，C-6 规范） - agent.ts: 持久化条件同步修复（无 thought 但有 toolCalls 的步骤落库） - 回归测试: 纯 tool_calls 轮后第二次请求中 tool 消息前必须是带 tool_calls 的 assistant（请求契约断言，engine-toolchain.test.ts） 【纵深防御 — 孤立 tool 消息过滤】 - openai-format.ts（DeepSeek/Agnes/MiMo/OpenAI 四家共享）: 构建请求时 按 tool_call_id 配对过滤孤立 tool 消息（任何来源的历史污染不再 400 死锁） - anthropic.adapter.ts: tool_use/tool_result 同策略配对过滤 - 单测 ×6: 正常配对保留 / 孤立丢弃 / id 不匹配丢弃 / 多轮配对 / includeImages 原位转换 / 非 vision 静默丢弃 【多模态索引对齐收敛】 4 家 adapter 的 images 处理循环原按未过滤的 nonSystemMsgs[i-1] 对齐索引， 孤立 tool 过滤引入后会错位 — 统一收进 buildOpenAICompatibleMessages （includeImages 参数，基于 sanitized 序列原位转换），4 家 adapter 删除 各自的索引对齐循环（DeepSeek vision 判断 / OpenAI 推理模型拒绝保留在 adapter）。 【终止原因可见化】 MAX_ITERATIONS / TIMEOUT 终止此前无任何提示（用户感知"会话直接停止"）— 前端 DONE 事件非 completed 终止原因显示为 system 消息。 【v0.6.1 回归缓解】 web_fetch timeoutMs 120s → 240s：浏览器回退串行化后并发 3 个排队最坏 ~127.5s，旧值让排队末位抓取被工具超时杀掉（表现为抓取不稳定）。 【验证】 lint 0/0；typecheck 双工程 0 错误；test:electron 259/259（+7）； electron-vite build 成功
This commit is contained in:
@@ -10,7 +10,7 @@
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</p>
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<p align="center">
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<img src="https://img.shields.io/badge/version-0.6.1-blue?style=flat-square" alt="Version" />
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<img src="https://img.shields.io/badge/version-0.6.2-blue?style=flat-square" alt="Version" />
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<img src="https://img.shields.io/badge/license-MIT-green?style=flat-square" alt="License" />
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<img src="https://img.shields.io/badge/Electron-35-47848F?style=flat-square&logo=electron" alt="Electron" />
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<img src="https://img.shields.io/badge/React-19-61DAFB?style=flat-square&logo=react" alt="React" />
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@@ -871,7 +871,7 @@ npm run format # Prettier 格式化
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# ─── 测试 ─────────────────────────────────
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npm test # 运行单元测试 (Vitest, 系统 Node — audit 套件因 better-sqlite3 ABI 自动跳过)
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npm run test:electron # 运行全量单元测试 (Electron Node ABI, 252 用例全执行, 含 SQLite 审计链哈希 + 引擎工具链集成)
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npm run test:electron # 运行全量单元测试 (Electron Node ABI, 259 用例全执行, 含 SQLite 审计链哈希 + 引擎工具链集成)
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npm run test:watch # 测试监听模式
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# ─── 构建 ─────────────────────────────────
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@@ -0,0 +1,225 @@
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/**
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* openai-format 孤立 tool 消息过滤测试（v0.6.2 会话停止根因的纵深防御）
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*
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* 背景：OpenAI/DeepSeek 协议要求 role='tool' 消息必须紧跟带 tool_calls 的
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* assistant 消息，违反直接 400 且不可重试。engine 侧已保证配对，此处验证
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* 共享构建函数对任何来源历史污染的兜底过滤。
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*/
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import { describe, it, expect, vi } from 'vitest';
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vi.mock('electron-log', () => ({
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default: { info: vi.fn(), warn: vi.fn(), error: vi.fn(), debug: vi.fn() },
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}));
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import { buildOpenAICompatibleMessages } from '../shared/openai-format';
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import type { MetonaRequest } from '../../types';
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const systemPrompt = { roleDefinition: 'r', outputConstraints: 'o', safetyGuidelines: 's' };
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function makeRequest(messages: MetonaRequest['messages']): MetonaRequest {
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return {
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meta: {
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sessionId: 's',
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iteration: 1,
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requestId: 'r',
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timestamp: Date.now(),
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agentVersion: 't',
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},
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systemPrompt,
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messages,
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params: { stream: false },
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};
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}
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describe('buildOpenAICompatibleMessages — 孤立 tool 过滤', () => {
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it('正常配对序列完整保留（assistant(tool_calls) → tool）', () => {
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const out = buildOpenAICompatibleMessages(
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makeRequest([
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{ role: 'user', content: 'hi', timestamp: Date.now() },
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{
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role: 'assistant',
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content: null,
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toolCalls: [
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{ id: 'tc_1', name: 'read_file', args: {}, iteration: 1, timestamp: Date.now() },
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],
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timestamp: Date.now(),
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},
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{
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role: 'tool',
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content: null,
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toolResult: {
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toolCallId: 'tc_1',
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toolName: 'read_file',
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result: 'data',
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success: true,
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durationMs: 1,
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timestamp: Date.now(),
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},
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timestamp: Date.now(),
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},
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]),
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);
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// system + user + assistant + tool
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expect(out).toHaveLength(4);
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expect(out[2].role).toBe('assistant');
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expect(out[2].tool_calls).toHaveLength(1);
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expect(out[3].role).toBe('tool');
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expect(out[3].tool_call_id).toBe('tc_1');
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});
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it('孤立 tool 消息（前面无 assistant tool_calls）被丢弃，不产生 400 序列', () => {
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const out = buildOpenAICompatibleMessages(
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makeRequest([
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{ role: 'user', content: 'hi', timestamp: Date.now() },
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{
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role: 'tool',
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content: null,
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toolResult: {
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toolCallId: 'tc_orphan',
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toolName: 'read_file',
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result: 'x',
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success: true,
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durationMs: 1,
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timestamp: Date.now(),
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},
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timestamp: Date.now(),
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},
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]),
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);
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// system + user（孤立 tool 被过滤）
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expect(out).toHaveLength(2);
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expect(out.every((m) => m.role !== 'tool')).toBe(true);
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});
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it('tool_call_id 不匹配最近 assistant 的 tool 消息也被过滤', () => {
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const out = buildOpenAICompatibleMessages(
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makeRequest([
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{ role: 'user', content: 'hi', timestamp: Date.now() },
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{
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role: 'assistant',
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content: null,
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toolCalls: [
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{ id: 'tc_a', name: 'read_file', args: {}, iteration: 1, timestamp: Date.now() },
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],
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timestamp: Date.now(),
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},
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{
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role: 'tool',
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content: null,
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// id 不匹配 tc_a
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toolResult: {
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toolCallId: 'tc_other',
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toolName: 'read_file',
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result: 'x',
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success: true,
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durationMs: 1,
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timestamp: Date.now(),
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},
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timestamp: Date.now(),
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},
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]),
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);
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expect(out).toHaveLength(3);
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expect(out.every((m) => m.role !== 'tool')).toBe(true);
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});
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it('多轮工具配对全部保留', () => {
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const out = buildOpenAICompatibleMessages(
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makeRequest([
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{ role: 'user', content: 'hi', timestamp: Date.now() },
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{
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role: 'assistant',
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content: null,
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toolCalls: [
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{ id: 'tc_1', name: 'a', args: {}, iteration: 1, timestamp: Date.now() },
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{ id: 'tc_2', name: 'b', args: {}, iteration: 1, timestamp: Date.now() },
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],
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timestamp: Date.now(),
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},
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{
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role: 'tool',
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content: null,
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toolResult: {
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toolCallId: 'tc_2',
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toolName: 'b',
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result: 'r2',
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success: true,
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durationMs: 1,
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timestamp: Date.now(),
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},
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timestamp: Date.now(),
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},
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{
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role: 'tool',
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content: null,
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toolResult: {
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toolCallId: 'tc_1',
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toolName: 'a',
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result: 'r1',
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success: true,
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durationMs: 1,
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timestamp: Date.now(),
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},
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timestamp: Date.now(),
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},
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{
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role: 'assistant',
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content: null,
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toolCalls: [{ id: 'tc_3', name: 'c', args: {}, iteration: 2, timestamp: Date.now() }],
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timestamp: Date.now(),
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},
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{
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role: 'tool',
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content: null,
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toolResult: {
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toolCallId: 'tc_3',
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toolName: 'c',
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result: 'r3',
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success: true,
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durationMs: 1,
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timestamp: Date.now(),
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},
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timestamp: Date.now(),
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},
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]),
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);
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expect(out).toHaveLength(7);
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expect(out.filter((m) => m.role === 'tool')).toHaveLength(3);
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});
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it('includeImages=true 时 images 转 content parts（原位转换，索引与过滤后序列对齐）', () => {
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const out = buildOpenAICompatibleMessages(
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makeRequest([
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{
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role: 'user',
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content: 'look',
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images: [{ url: 'data:image/png;base64,xxx' }],
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timestamp: Date.now(),
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},
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]),
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true,
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);
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const content = out[1].content as Array<Record<string, unknown>>;
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expect(Array.isArray(content)).toBe(true);
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expect(content[0]).toEqual({ type: 'text', text: 'look' });
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expect(content[1]).toEqual({
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type: 'image_url',
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image_url: { url: 'data:image/png;base64,xxx' },
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});
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});
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it('includeImages=false（DeepSeek 非 vision）时 images 静默丢弃', () => {
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const out = buildOpenAICompatibleMessages(
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makeRequest([
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{
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role: 'user',
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content: 'look',
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images: [{ url: 'data:image/png;base64,xxx' }],
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timestamp: Date.now(),
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},
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]),
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);
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expect(out[1].content).toBe('look');
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});
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});
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@@ -149,34 +149,11 @@ export class AgnesAdapter extends BaseAdapter {
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* - 默认 max_tokens: 65536（1M 上下文，65.5K 最大输出）
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*/
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private toNativeRequest(request: MetonaRequest, stream: boolean): Record<string, unknown> {
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const messages = buildOpenAICompatibleMessages(request);
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// v0.6.2: images 处理收敛至共享层（原索引对齐循环在孤立 tool 过滤后会错位）
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const messages = buildOpenAICompatibleMessages(request, true);
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const tools = buildOpenAICompatibleTools(request.tools);
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// === Agnes 多模态：将 images 转为 OpenAI content 数组 ===
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// buildOpenAICompatibleMessages 不处理图片（各 Provider 自行处理）
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const nonSystemMsgs = request.messages.filter((m) => m.role !== 'system');
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let imageCount = 0;
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for (let i = 0; i < messages.length; i++) {
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// messages[0] 是 system，非 system 消息从 messages[1] 开始
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if (i === 0) continue;
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const origMsg = nonSystemMsgs[i - 1];
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if (!origMsg?.images?.length) continue;
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imageCount += origMsg.images.length;
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const contentParts: Array<Record<string, unknown>> = [];
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if (origMsg.content) {
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contentParts.push({ type: 'text', text: origMsg.content });
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}
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for (const img of origMsg.images) {
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contentParts.push({
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type: 'image_url',
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image_url: { url: img.url },
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});
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}
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messages[i].content = contentParts;
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}
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const imageCount = request.messages.reduce((n, m) => n + (m.images?.length ?? 0), 0);
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if (imageCount > 0) {
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const firstUrl = request.messages.find((m) => m.images?.length)?.images?.[0]?.url ?? '';
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log.info(
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@@ -322,7 +322,10 @@ export class AnthropicAdapter extends BaseAdapter {
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.join('\n\n');
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// 转换消息（非 system）
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const converted: Array<{
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// v0.6.2 纵深防御: 过滤孤立 tool 消息 — Anthropic 协议要求 tool_result 块
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// 必须对应前置 assistant 的 tool_use（违反直接 400）。与 openai-format 同策略。
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const pendingToolUseIds = new Set<string>();
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const convertedRaw: Array<{
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role: 'user' | 'assistant';
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content: Array<Record<string, unknown>>;
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}> = [];
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@@ -330,13 +333,20 @@ export class AnthropicAdapter extends BaseAdapter {
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if (m.role === 'system') continue;
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if (m.role === 'tool' && m.toolResult) {
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if (!pendingToolUseIds.has(m.toolResult.toolCallId)) {
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log.warn(
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`[Anthropic] Dropped orphan tool_result without matching tool_use: ${m.toolResult.toolCallId}`,
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);
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continue;
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}
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pendingToolUseIds.delete(m.toolResult.toolCallId);
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// 工具结果 → user 角色 tool_result 块
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const contentStr = m.toolResult.error
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? m.toolResult.error
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: typeof m.toolResult.result === 'string'
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? m.toolResult.result
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: JSON.stringify(m.toolResult.result);
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converted.push({
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convertedRaw.push({
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role: 'user',
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content: [
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{ type: 'tool_result', tool_use_id: m.toolResult.toolCallId, content: contentStr },
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@@ -349,10 +359,11 @@ export class AnthropicAdapter extends BaseAdapter {
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const content: Array<Record<string, unknown>> = [];
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if (m.content) content.push({ type: 'text', text: m.content });
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for (const tc of m.toolCalls ?? []) {
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pendingToolUseIds.add(tc.id);
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content.push({ type: 'tool_use', id: tc.id, name: tc.name, input: tc.args });
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}
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if (content.length > 0) {
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converted.push({ role: 'assistant', content });
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convertedRaw.push({ role: 'assistant', content });
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}
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continue;
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}
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@@ -365,8 +376,9 @@ export class AnthropicAdapter extends BaseAdapter {
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if (block) content.push(block);
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}
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if (content.length === 0) content.push({ type: 'text', text: '' });
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converted.push({ role: 'user', content });
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convertedRaw.push({ role: 'user', content });
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}
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const converted = convertedRaw;
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// 合并连续同角色消息（Anthropic 要求 user/assistant 交替）
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const merged: Array<{ role: 'user' | 'assistant'; content: Array<Record<string, unknown>> }> =
|
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|
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@@ -262,7 +262,9 @@ export class DeepSeekAdapter extends BaseAdapter {
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* - stream_options: { include_usage: true } — 流式返回 usage
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*/
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private toNativeRequest(request: MetonaRequest, stream: boolean): Record<string, unknown> {
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const messages = buildOpenAICompatibleMessages(request);
|
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// v0.6.2: images 处理收敛至共享层（includeImages = vision 模型才转换，
|
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// 非 vision 静默丢弃——正确行为，见 openai-format.ts #27 记录）
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const messages = buildOpenAICompatibleMessages(request, this.isVisionModel());
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const tools = buildOpenAICompatibleTools(request.tools);
|
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|
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// v0.5.3: max_tokens 按模型上限钳制 — 引擎默认 63488 超过部分模型上限时 API 直接 400
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@@ -278,29 +280,9 @@ export class DeepSeekAdapter extends BaseAdapter {
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stream,
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};
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// v0.5.4: vision 模型的图片处理（OpenAI image_url content parts 格式）
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// 非 vision 模型保持 images 静默丢弃（共享层行为，避免 API 400）
|
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// v0.5.4: vision 模型图片数审计（转换在共享层完成）
|
||||
if (this.isVisionModel()) {
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||||
const nonSystemMsgs = request.messages.filter((m) => m.role !== 'system');
|
||||
let imageCount = 0;
|
||||
// messages[0] 是 system，非 system 消息从 messages[1] 开始（与 nonSystemMsgs 对齐）
|
||||
for (let i = 1; i < messages.length; i++) {
|
||||
const origMsg = nonSystemMsgs[i - 1];
|
||||
if (!origMsg?.images?.length) continue;
|
||||
|
||||
imageCount += origMsg.images.length;
|
||||
const contentParts: Array<Record<string, unknown>> = [];
|
||||
if (origMsg.content) {
|
||||
contentParts.push({ type: 'text', text: origMsg.content });
|
||||
}
|
||||
for (const img of origMsg.images) {
|
||||
contentParts.push({
|
||||
type: 'image_url',
|
||||
image_url: { url: img.url },
|
||||
});
|
||||
}
|
||||
messages[i].content = contentParts;
|
||||
}
|
||||
const imageCount = request.messages.reduce((n, m) => n + (m.images?.length ?? 0), 0);
|
||||
if (imageCount > 0) {
|
||||
log.info(`[DeepSeek] Vision model processing ${imageCount} image(s)`);
|
||||
}
|
||||
|
||||
@@ -169,32 +169,10 @@ export class MimoAdapter extends BaseAdapter {
|
||||
* 思考模式下 temperature/top_p 会被 API 强制覆盖，因此不传这两个参数。
|
||||
*/
|
||||
private toNativeRequest(request: MetonaRequest, stream: boolean): Record<string, unknown> {
|
||||
const messages = buildOpenAICompatibleMessages(request);
|
||||
// v0.6.2: images 处理收敛至共享层（原索引对齐循环在孤立 tool 过滤后会错位）
|
||||
const messages = buildOpenAICompatibleMessages(request, true);
|
||||
const tools = buildOpenAICompatibleTools(request.tools);
|
||||
|
||||
// === MiMo 多模态：将 images 转为 OpenAI content 数组 ===
|
||||
// buildOpenAICompatibleMessages 不处理图片（各 Provider 自行处理）
|
||||
// MiMo 是 OpenAI 兼容 API，多模态格式与 Agnes AI 一致
|
||||
const nonSystemMsgs = request.messages.filter((m) => m.role !== 'system');
|
||||
for (let i = 0; i < messages.length; i++) {
|
||||
// messages[0] 是 system，非 system 消息从 messages[1] 开始
|
||||
if (i === 0) continue;
|
||||
const origMsg = nonSystemMsgs[i - 1];
|
||||
if (!origMsg?.images?.length) continue;
|
||||
|
||||
const contentParts: Array<Record<string, unknown>> = [];
|
||||
if (origMsg.content) {
|
||||
contentParts.push({ type: 'text', text: origMsg.content });
|
||||
}
|
||||
for (const img of origMsg.images) {
|
||||
contentParts.push({
|
||||
type: 'image_url',
|
||||
image_url: { url: img.url },
|
||||
});
|
||||
}
|
||||
messages[i].content = contentParts;
|
||||
}
|
||||
|
||||
// MiMo 使用 max_completion_tokens（非 max_tokens）
|
||||
// #41 修复: thinking 模式下未配置时兜底 32768（thinking 占用 token 配额，API 默认值过小会截断输出）
|
||||
// v0.5.3: 按模型上限钳制（pro 131072 / standard 32768）—
|
||||
|
||||
@@ -177,38 +177,22 @@ export class OpenAIAdapter extends BaseAdapter {
|
||||
* - 思考模式下 temperature 被部分推理模型拒绝，不传
|
||||
*/
|
||||
private toNativeRequest(request: MetonaRequest, stream: boolean): Record<string, unknown> {
|
||||
const messages = buildOpenAICompatibleMessages(request);
|
||||
const tools = buildOpenAICompatibleTools(request.tools);
|
||||
|
||||
// 推理模型检测（o 系列使用新参数名）
|
||||
const model = this.config.defaultModel;
|
||||
const isReasoningModel = /^(o\d|gpt-5)/.test(model);
|
||||
|
||||
// === 多模态：将 images 转为 OpenAI content 数组（与 Agnes/MiMo 一致） ===
|
||||
const nonSystemMsgs = request.messages.filter((m) => m.role !== 'system');
|
||||
let imageCount = 0;
|
||||
for (let i = 0; i < messages.length; i++) {
|
||||
if (i === 0) continue; // messages[0] 是 system
|
||||
const origMsg = nonSystemMsgs[i - 1];
|
||||
if (!origMsg?.images?.length) continue;
|
||||
|
||||
imageCount += origMsg.images.length;
|
||||
const contentParts: Array<Record<string, unknown>> = [];
|
||||
if (origMsg.content) {
|
||||
contentParts.push({ type: 'text', text: origMsg.content });
|
||||
}
|
||||
for (const img of origMsg.images) {
|
||||
contentParts.push({ type: 'image_url', image_url: { url: img.url } });
|
||||
}
|
||||
messages[i].content = contentParts;
|
||||
}
|
||||
if (imageCount > 0) {
|
||||
// 推理模型当前不支持图片输入
|
||||
if (isReasoningModel) {
|
||||
// 推理模型不支持图片输入 — 前置校验（转换在共享层，此处仅拦截）
|
||||
if (isReasoningModel) {
|
||||
const hasImages = request.messages.some((m) => m.images?.length);
|
||||
if (hasImages) {
|
||||
throw new Error(`Model "${model}" does not support image inputs`);
|
||||
}
|
||||
}
|
||||
|
||||
// v0.6.2: images 处理收敛至共享层（原索引对齐循环在孤立 tool 过滤后会错位）
|
||||
const messages = buildOpenAICompatibleMessages(request, true);
|
||||
const tools = buildOpenAICompatibleTools(request.tools);
|
||||
|
||||
const body: Record<string, unknown> = {
|
||||
model,
|
||||
messages,
|
||||
|
||||
@@ -10,7 +10,8 @@
|
||||
* @see apis/mimo-api-docs-20260715.html
|
||||
*/
|
||||
|
||||
import type { MetonaRequest, MetonaToolDef } from '../../types';
|
||||
import log from 'electron-log';
|
||||
import type { MetonaMessage, MetonaRequest, MetonaToolDef } from '../../types';
|
||||
|
||||
/**
|
||||
* 构建 OpenAI 兼容的 messages 数组
|
||||
@@ -19,13 +20,15 @@ import type { MetonaRequest, MetonaToolDef } from '../../types';
|
||||
* - System Prompt 拼接（静态区 + 动态区 + 安全准则）
|
||||
* - 工具调用历史保留（reasoning_content + tool_calls）
|
||||
* - 工具结果注入（tool_call_id + content）
|
||||
* - 孤立 tool 消息过滤（纵深防御，见函数内注释）
|
||||
* - 多模态图片（includeImages=true 时转换为 image_url content parts）
|
||||
*
|
||||
* 注意：图片（多模态）处理不属于此共享函数。
|
||||
* 各 Provider 对多模态的支持不同（DeepSeek 不支持，Agnes/Ollama 支持但格式各异），
|
||||
* 应在各自 Adapter 的 toNativeRequest 中处理。
|
||||
* @param includeImages true 时将消息的 images 转为 OpenAI image_url content parts
|
||||
* （Agnes/MiMo/OpenAI 全系、DeepSeek 仅 vision 模型传 true）
|
||||
*/
|
||||
export function buildOpenAICompatibleMessages(
|
||||
request: MetonaRequest,
|
||||
includeImages = false,
|
||||
): Array<Record<string, unknown>> {
|
||||
const systemContent = [
|
||||
request.systemPrompt.roleDefinition,
|
||||
@@ -36,60 +39,97 @@ export function buildOpenAICompatibleMessages(
|
||||
.filter(Boolean)
|
||||
.join('\n\n');
|
||||
|
||||
const nonSystemMessages = request.messages
|
||||
.filter((m) => m.role !== 'system')
|
||||
.map((m) => {
|
||||
// v0.3.0 修复: assistant 消息有 tool_calls 但 content 为空时，content 设为 null
|
||||
// DeepSeek/OpenAI API 要求有 tool_calls 的 assistant 消息 content 必须为 null 而非空字符串
|
||||
const msg: Record<string, unknown> = {
|
||||
role: m.role,
|
||||
content: m.content,
|
||||
};
|
||||
|
||||
// 注意：图片（多模态）处理不在此共享函数中。
|
||||
// DeepSeek 非 vision 模型不支持多模态，images 被静默丢弃是正确行为
|
||||
// （vision 模型在 DeepSeekAdapter.toNativeRequest 中独立处理）。
|
||||
// Agnes/MiMo/OpenAI 各自的 toNativeRequest 中有独立的 images 处理。
|
||||
// 审查修复: #27 曾在此添加 images 处理，但 DeepSeek 非 vision 模型会导致 API 400，已撤销。
|
||||
|
||||
// === Assistant 消息 ===
|
||||
if (m.role === 'assistant') {
|
||||
// 工具调用历史
|
||||
if (m.toolCalls?.length) {
|
||||
msg.tool_calls = m.toolCalls.map((tc) => ({
|
||||
id: tc.id,
|
||||
type: 'function',
|
||||
function: { name: tc.name, arguments: JSON.stringify(tc.args) },
|
||||
}));
|
||||
// 有 tool_calls 时 content 必须为 null（API 规范）
|
||||
if (!m.content) msg.content = null;
|
||||
}
|
||||
// 推理内容（无论是否有工具调用，都保留 reasoning_content）
|
||||
if (m.reasoningContent) {
|
||||
msg.reasoning_content = m.reasoningContent;
|
||||
}
|
||||
// 崩溃修复（纵深防御）: 过滤孤立 tool 消息 — 其 tool_call_id 不属于任何前置
|
||||
// assistant(tool_calls) 消息。OpenAI/DeepSeek 协议要求 tool 消息必须紧跟带
|
||||
// tool_calls 的 assistant，违反直接 400 且不可重试（会话死锁）。正常链路由
|
||||
// engine 保证配对；此处兜底任何来源的历史污染（旧版本数据/导入/边界场景）。
|
||||
const nonSystem = request.messages.filter((m) => m.role !== 'system');
|
||||
const sanitized: MetonaMessage[] = [];
|
||||
/** 已出现且尚未被 tool 结果回应的 tool_call id 集合 */
|
||||
const pendingToolCallIds = new Set<string>();
|
||||
let droppedOrphans = 0;
|
||||
for (const m of nonSystem) {
|
||||
if (m.role === 'assistant' && m.toolCalls?.length) {
|
||||
for (const tc of m.toolCalls) pendingToolCallIds.add(tc.id);
|
||||
sanitized.push(m);
|
||||
continue;
|
||||
}
|
||||
if (m.role === 'tool' && m.toolResult) {
|
||||
if (pendingToolCallIds.has(m.toolResult.toolCallId)) {
|
||||
pendingToolCallIds.delete(m.toolResult.toolCallId);
|
||||
sanitized.push(m);
|
||||
} else {
|
||||
droppedOrphans++;
|
||||
}
|
||||
continue;
|
||||
}
|
||||
sanitized.push(m);
|
||||
}
|
||||
if (droppedOrphans > 0) {
|
||||
log.warn(
|
||||
`[OpenAIFormat] Dropped ${droppedOrphans} orphan tool message(s) without matching assistant tool_calls`,
|
||||
);
|
||||
}
|
||||
|
||||
// === 工具执行结果 ===
|
||||
if (m.role === 'tool' && m.toolResult) {
|
||||
msg.tool_call_id = m.toolResult.toolCallId;
|
||||
// CE-2 修复: 工具失败时 result 为 null，优先用 error 字段作为 content
|
||||
// 否则 LLM 看到 "null" 不知道失败原因，可能重复调用导致死循环
|
||||
msg.content = m.toolResult.error
|
||||
? m.toolResult.error
|
||||
: typeof m.toolResult.result === 'string'
|
||||
? m.toolResult.result
|
||||
: JSON.stringify(m.toolResult.result);
|
||||
// #26 修复: 确保 tool 消息 content 不为 undefined
|
||||
// JSON.stringify(undefined) 返回 undefined（非字符串），会导致 content 字段在序列化后消失
|
||||
// OpenAI/DeepSeek/Agnes API 严格要求 tool 消息必须有 content 字段，缺失会返回 400
|
||||
if (msg.content === undefined) msg.content = '';
|
||||
const messages = sanitized.map((m) => {
|
||||
// v0.3.0 修复: assistant 消息有 tool_calls 但 content 为空时，content 设为 null
|
||||
// DeepSeek/OpenAI API 要求有 tool_calls 的 assistant 消息 content 必须为 null 而非空字符串
|
||||
const msg: Record<string, unknown> = {
|
||||
role: m.role,
|
||||
content: m.content,
|
||||
};
|
||||
|
||||
// === 多模态图片（includeImages=true 时） ===
|
||||
// v0.6.2 收敛: 原先 4 家 adapter 各自按 nonSystemMsgs[i-1] 索引对齐处理 images，
|
||||
// 孤立 tool 过滤引入后索引错位。统一收进共享函数，基于 sanitized 原位转换。
|
||||
// DeepSeek 非 vision 模型传 false（images 静默丢弃是正确行为，见 #27 审查撤销记录）。
|
||||
if (includeImages && m.images?.length) {
|
||||
const contentParts: Array<Record<string, unknown>> = [];
|
||||
if (m.content) contentParts.push({ type: 'text', text: m.content });
|
||||
for (const img of m.images) {
|
||||
contentParts.push({ type: 'image_url', image_url: { url: img.url } });
|
||||
}
|
||||
msg.content = contentParts;
|
||||
}
|
||||
|
||||
return msg;
|
||||
});
|
||||
// === Assistant 消息 ===
|
||||
if (m.role === 'assistant') {
|
||||
// 工具调用历史
|
||||
if (m.toolCalls?.length) {
|
||||
msg.tool_calls = m.toolCalls.map((tc) => ({
|
||||
id: tc.id,
|
||||
type: 'function',
|
||||
function: { name: tc.name, arguments: JSON.stringify(tc.args) },
|
||||
}));
|
||||
// 有 tool_calls 时 content 必须为 null（API 规范）
|
||||
if (!m.content) msg.content = null;
|
||||
}
|
||||
// 推理内容（无论是否有工具调用，都保留 reasoning_content）
|
||||
if (m.reasoningContent) {
|
||||
msg.reasoning_content = m.reasoningContent;
|
||||
}
|
||||
}
|
||||
|
||||
return [{ role: 'system', content: systemContent }, ...nonSystemMessages];
|
||||
// === 工具执行结果 ===
|
||||
if (m.role === 'tool' && m.toolResult) {
|
||||
msg.tool_call_id = m.toolResult.toolCallId;
|
||||
// CE-2 修复: 工具失败时 result 为 null，优先用 error 字段作为 content
|
||||
// 否则 LLM 看到 "null" 不知道失败原因，可能重复调用导致死循环
|
||||
msg.content = m.toolResult.error
|
||||
? m.toolResult.error
|
||||
: typeof m.toolResult.result === 'string'
|
||||
? m.toolResult.result
|
||||
: JSON.stringify(m.toolResult.result);
|
||||
// #26 修复: 确保 tool 消息 content 不为 undefined
|
||||
// JSON.stringify(undefined) 返回 undefined（非字符串），会导致 content 字段在序列化后消失
|
||||
// OpenAI/DeepSeek/Agnes API 严格要求 tool 消息必须有 content 字段，缺失会返回 400
|
||||
if (msg.content === undefined) msg.content = '';
|
||||
}
|
||||
|
||||
return msg;
|
||||
});
|
||||
|
||||
return [{ role: 'system', content: systemContent }, ...messages];
|
||||
}
|
||||
|
||||
/**
|
||||
|
||||
@@ -230,6 +230,37 @@ describe('AgentLoopEngine 工具调用链路（adapter → 引擎 → 真实 Hoo
|
||||
expect(hook.getPendingConfirmations()).toHaveLength(0);
|
||||
});
|
||||
|
||||
it('v0.6.2 回归：纯 tool_calls 轮（零文本零思考）后，下一轮请求的 tool 消息前必须有带 tool_calls 的 assistant', async () => {
|
||||
// 会话停止根因（DeepSeek 400 "Messages with role 'tool' must be a response
|
||||
// to a preceding message with 'tool_calls'"）：模型纯工具调用轮不产生
|
||||
// thought → 原实现跳过 assistant 消息 push → 孤立 tool 消息 → 下一轮 400。
|
||||
// 契约断言：第二次 sendStream 收到的 messages 中，tool 消息的前一条
|
||||
// 必须是带 tool_calls 的 assistant。
|
||||
executedTools.length = 0;
|
||||
recordedRequests.length = 0;
|
||||
const hook = new ConfirmationHook(makeMockWindow(), null);
|
||||
|
||||
const engine = makeEngineWithHooks(
|
||||
[toolCallEvent('read_file', { file_path: 'x.ts' }), textDoneEvent('finished')],
|
||||
hook,
|
||||
);
|
||||
|
||||
await engine.runStream(userMessage, 'sess-orphan-tool', [], systemPrompt);
|
||||
|
||||
expect(recordedRequests.length).toBe(2);
|
||||
const second = recordedRequests[1].messages;
|
||||
// 找到 tool 消息
|
||||
const toolIdx = second.findIndex((m) => m.role === 'tool');
|
||||
expect(toolIdx).toBeGreaterThan(0);
|
||||
// 前一条必须是带 tool_calls 的 assistant（修复前这里是 user — 孤立 tool）
|
||||
const prev = second[toolIdx - 1];
|
||||
expect(prev.role).toBe('assistant');
|
||||
expect(prev.toolCalls?.length).toBeGreaterThan(0);
|
||||
// 且该 assistant 的 toolCalls id 与 tool 消息的 toolCallId 配对
|
||||
const toolMsg = second[toolIdx];
|
||||
expect(prev.toolCalls!.some((tc) => tc.id === toolMsg.toolResult!.toolCallId)).toBe(true);
|
||||
});
|
||||
|
||||
it('HIGH 风险工具经用户批准后执行（pending → approve → 工具运行）', async () => {
|
||||
executedTools.length = 0;
|
||||
const hook = new ConfirmationHook(makeMockWindow(), null);
|
||||
|
||||
@@ -270,11 +270,17 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
this.iterations.push(step);
|
||||
|
||||
// 将 assistant 回复加入消息历史
|
||||
if (step.thought) {
|
||||
// 崩溃修复（会话停止根因）: 原条件 `if (step.thought)` 在模型发起纯工具调用
|
||||
// （零文本、零思考内容 — DeepSeek 高频行为）时跳过 assistant 消息，但下方
|
||||
// tool 结果消息照常 push → 下一轮请求出现孤立 tool 消息 → API 400
|
||||
// "Messages with role 'tool' must be a response to a preceding message
|
||||
// with 'tool_calls'" → 会话 ERROR 终止。有 toolCalls 的轮次必须 push
|
||||
// assistant（content=null，符合 C-6 规范）。
|
||||
if (step.thought || (step.toolCalls && step.toolCalls.length > 0)) {
|
||||
const assistantMsg: MetonaMessage = {
|
||||
role: 'assistant',
|
||||
content: step.thought.content,
|
||||
reasoningContent: step.thought.reasoningContent,
|
||||
content: step.thought?.content ?? null,
|
||||
reasoningContent: step.thought?.reasoningContent,
|
||||
toolCalls: step.toolCalls,
|
||||
timestamp: Date.now(),
|
||||
iteration: this.currentIteration,
|
||||
@@ -299,7 +305,9 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
// 优先使用 error 字段，让 LLM 知道失败原因，避免重复调用导致死循环
|
||||
content: result.error
|
||||
? result.error
|
||||
: (typeof result.result === 'string' ? result.result : JSON.stringify(result.result)),
|
||||
: typeof result.result === 'string'
|
||||
? result.result
|
||||
: JSON.stringify(result.result),
|
||||
toolResult: result,
|
||||
timestamp: Date.now(),
|
||||
iteration: this.currentIteration,
|
||||
@@ -324,7 +332,12 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
// P2-9 修复: toLowerCase 避免大小写敏感导致超时误判为 ERROR
|
||||
// Node fetch 超时错误 "The operation timed out" / abort "Aborted" 都需覆盖
|
||||
const errMsgLower = errMsg.toLowerCase();
|
||||
if (this.aborted || errMsgLower.includes('aborted') || errMsgLower.includes('timed out') || errMsgLower.includes('timeout')) {
|
||||
if (
|
||||
this.aborted ||
|
||||
errMsgLower.includes('aborted') ||
|
||||
errMsgLower.includes('timed out') ||
|
||||
errMsgLower.includes('timeout')
|
||||
) {
|
||||
return this.finish(TerminationReason.USER_INTERRUPT);
|
||||
}
|
||||
// v0.3.0 修复: 不使用 emit('error') — Node EventEmitter 对无监听器的 'error' 事件会同步 throw，
|
||||
@@ -383,10 +396,7 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
});
|
||||
|
||||
try {
|
||||
await Promise.race([
|
||||
this.currentRunPromise.catch(() => {}),
|
||||
timer,
|
||||
]);
|
||||
await Promise.race([this.currentRunPromise.catch(() => {}), timer]);
|
||||
return !timedOut; // 超时返回 false，run 正常结束返回 true
|
||||
} finally {
|
||||
// 审查修复: 无论 race 谁先完成，都清理 timer 防止事件循环残留
|
||||
@@ -443,7 +453,10 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
// 过滤掉 RETRY 类型的 ERROR 事件 — 不转发到前端，避免触发虚假错误 UI
|
||||
// RETRY 事件仅用于 Engine 内部清空缓冲区（见下方 switch 分支）
|
||||
// H-11 修复: 使用 MetonaErrorCode.RETRY 替代 'as string' 强制转换，确保类型安全
|
||||
if (event.type === MetonaStreamEventType.ERROR && event.error?.code === MetonaErrorCode.RETRY) {
|
||||
if (
|
||||
event.type === MetonaStreamEventType.ERROR &&
|
||||
event.error?.code === MetonaErrorCode.RETRY
|
||||
) {
|
||||
// 内部处理：清空已累积的内容和缓冲区（重试会从头开始接收）
|
||||
fullContent = '';
|
||||
reasoningContent = '';
|
||||
@@ -535,7 +548,9 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
// 确保第3轮重复调用的副作用不会产生（工具尚未执行）
|
||||
if (step.toolCalls && step.toolCalls.length > 0) {
|
||||
if (this.detectDeadLoop(step.toolCalls)) {
|
||||
log.warn(`[AgentLoop] Dead loop detected at iteration ${this.currentIteration} (before tool execution)`);
|
||||
log.warn(
|
||||
`[AgentLoop] Dead loop detected at iteration ${this.currentIteration} (before tool execution)`,
|
||||
);
|
||||
this.emit('deadLoop', {
|
||||
iteration: this.currentIteration,
|
||||
runId: this.runId,
|
||||
@@ -555,7 +570,11 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
|
||||
// L-19 修复: 提取 executeToolCallsParallel 子方法（EXECUTING 阶段）
|
||||
// 返回 null 表示被 abort 中断
|
||||
const raceResult = await this.executeToolCallsParallel(step.toolCalls, request.meta.requestId, sessionId);
|
||||
const raceResult = await this.executeToolCallsParallel(
|
||||
step.toolCalls,
|
||||
request.meta.requestId,
|
||||
sessionId,
|
||||
);
|
||||
|
||||
if (raceResult === null) {
|
||||
// 被 abort 中断，标记步骤并退出
|
||||
@@ -609,7 +628,8 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
// === 上下文压缩（基于 token 使用率触发） ===
|
||||
// 有效上下文窗口：Ollama 使用 contextLength (numCtx)，其他 Provider 使用 contextWindow
|
||||
// v0.3.18 修复: 加默认值 128_000 保护，避免 config 都为 undefined 时 compressionThreshold 变 NaN 导致压缩永不触发
|
||||
const effectiveContextWindow = this.config.contextLength ?? this.config.contextWindow ?? 128_000;
|
||||
const effectiveContextWindow =
|
||||
this.config.contextLength ?? this.config.contextWindow ?? 128_000;
|
||||
const estimatedTokens = this.estimateMessagesTokens(request.messages);
|
||||
// v0.3.18 修复: 取 max(估算值, 真实值) 作为实际占用，避免估算偏低导致不压缩但 API 413
|
||||
// 估算值用于 LLM 尚未返回 usage 时的早期判断（首轮或重试场景）
|
||||
@@ -749,10 +769,13 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
|
||||
if (!this.toolRegistry) {
|
||||
return {
|
||||
toolCallId: toolCall.id, toolName: toolCall.name,
|
||||
result: null, success: false,
|
||||
toolCallId: toolCall.id,
|
||||
toolName: toolCall.name,
|
||||
result: null,
|
||||
success: false,
|
||||
error: `Tool '${toolCall.name}' not available: no ToolRegistry configured`,
|
||||
durationMs: Date.now() - startTs, timestamp: Date.now(),
|
||||
durationMs: Date.now() - startTs,
|
||||
timestamp: Date.now(),
|
||||
};
|
||||
}
|
||||
|
||||
@@ -761,9 +784,13 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
const result = await hook.beforeExecute(toolCall, this.currentSessionId);
|
||||
if (result.blocked) {
|
||||
return {
|
||||
toolCallId: toolCall.id, toolName: toolCall.name,
|
||||
result: null, success: false, error: `Blocked: ${result.reason}`,
|
||||
durationMs: Date.now() - startTs, timestamp: Date.now(),
|
||||
toolCallId: toolCall.id,
|
||||
toolName: toolCall.name,
|
||||
result: null,
|
||||
success: false,
|
||||
error: `Blocked: ${result.reason}`,
|
||||
durationMs: Date.now() - startTs,
|
||||
timestamp: Date.now(),
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -777,10 +804,13 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
if (['read_file', 'write_file', 'file_editor'].includes(toolCall.name)) {
|
||||
if (this.isTargetingRootMemoryMd(toolCall)) {
|
||||
return {
|
||||
toolCallId: toolCall.id, toolName: toolCall.name,
|
||||
result: null, success: false,
|
||||
toolCallId: toolCall.id,
|
||||
toolName: toolCall.name,
|
||||
result: null,
|
||||
success: false,
|
||||
error: 'Access to workspace root MEMORY.md is protected by security policy',
|
||||
durationMs: Date.now() - startTs, timestamp: Date.now(),
|
||||
durationMs: Date.now() - startTs,
|
||||
timestamp: Date.now(),
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -860,9 +890,13 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
|
||||
// 提取工具参数中的路径（不同工具使用不同的参数名）
|
||||
const args = toolCall.args;
|
||||
const pathStr = (args.path as string) || (args.file_path as string) ||
|
||||
(args.filePath as string) || (args.file as string) ||
|
||||
(args.target as string) || (args.destination as string);
|
||||
const pathStr =
|
||||
(args.path as string) ||
|
||||
(args.file_path as string) ||
|
||||
(args.filePath as string) ||
|
||||
(args.file as string) ||
|
||||
(args.target as string) ||
|
||||
(args.destination as string);
|
||||
|
||||
if (!pathStr || typeof pathStr !== 'string') return false;
|
||||
|
||||
@@ -1003,7 +1037,8 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
// 5xx 服务器错误 — 可重试
|
||||
if (err.status && err.status >= 500 && err.status < 600) return true;
|
||||
// 网络超时/连接错误 — 可重试
|
||||
if (err.code === 'ECONNRESET' || err.code === 'ETIMEDOUT' || err.code === 'ENOTFOUND') return true;
|
||||
if (err.code === 'ECONNRESET' || err.code === 'ETIMEDOUT' || err.code === 'ENOTFOUND')
|
||||
return true;
|
||||
// P2-9 一致性修复: toLowerCase 避免大小写敏感漏判
|
||||
// SSE 流中断 — 可重试（注意：用户主动 abort 已在 chatStreamWithRetry 入口由 this.aborted 提前拦截）
|
||||
const msg = err.message?.toLowerCase() ?? '';
|
||||
@@ -1057,22 +1092,25 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
// 将当前轮次的工具调用序列化为签名
|
||||
// v0.3.0 修复：使用 stable stringify，对对象键排序，确保相同内容不同键顺序产生相同签名
|
||||
// v0.3.0 修复：添加 visited Set 防循环引用，深度上限防过度递归
|
||||
const stableStringify = (obj: unknown, visited: Set<unknown> = new Set(), depth = 0): string => {
|
||||
const stableStringify = (
|
||||
obj: unknown,
|
||||
visited: Set<unknown> = new Set(),
|
||||
depth = 0,
|
||||
): string => {
|
||||
if (depth > 10) return '...'; // 深度上限防止过度递归
|
||||
if (obj === null || typeof obj !== 'object') return JSON.stringify(obj);
|
||||
if (visited.has(obj)) return '"[Circular]"'; // 循环引用防护
|
||||
visited.add(obj);
|
||||
try {
|
||||
if (Array.isArray(obj)) return `[${obj.map((v) => stableStringify(v, visited, depth + 1)).join(',')}]`;
|
||||
if (Array.isArray(obj))
|
||||
return `[${obj.map((v) => stableStringify(v, visited, depth + 1)).join(',')}]`;
|
||||
const keys = Object.keys(obj as Record<string, unknown>).sort();
|
||||
return `{${keys.map((k) => `${JSON.stringify(k)}:${stableStringify((obj as Record<string, unknown>)[k], visited, depth + 1)}`).join(',')}}`;
|
||||
} finally {
|
||||
visited.delete(obj);
|
||||
}
|
||||
};
|
||||
const signature = toolCalls
|
||||
.map((tc) => `${tc.name}(${stableStringify(tc.args)})`)
|
||||
.join('|');
|
||||
const signature = toolCalls.map((tc) => `${tc.name}(${stableStringify(tc.args)})`).join('|');
|
||||
|
||||
this.toolCallHistory.push(signature);
|
||||
|
||||
@@ -1114,7 +1152,8 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
private async compressMessages(messages: MetonaMessage[]): Promise<MetonaMessage[] | null> {
|
||||
// v0.3.18 修复: 动态计算保留预算，避免固定 10 条在超长消息场景仍超限
|
||||
// 加默认值 128_000 保护，避免 config 都为 undefined 时 keepBudget 变 NaN
|
||||
const effectiveContextWindow = this.config.contextLength ?? this.config.contextWindow ?? 128_000;
|
||||
const effectiveContextWindow =
|
||||
this.config.contextLength ?? this.config.contextWindow ?? 128_000;
|
||||
const keepBudget = Math.floor(effectiveContextWindow * 0.5); // 保留区占上下文窗口 50%
|
||||
const minKeepCount = 2; // 至少保留最后 2 条（user + assistant），保证有可推理上下文
|
||||
|
||||
@@ -1165,10 +1204,12 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
|
||||
// 构建摘要请求
|
||||
// 不截断单条消息——摘要请求是独立 API 调用，不共享主对话上下文窗口
|
||||
const conversationText = toCompress.map((m) => {
|
||||
const role = m.role.toUpperCase();
|
||||
return `[${role}] ${m.content ?? ''}`;
|
||||
}).join('\n\n');
|
||||
const conversationText = toCompress
|
||||
.map((m) => {
|
||||
const role = m.role.toUpperCase();
|
||||
return `[${role}] ${m.content ?? ''}`;
|
||||
})
|
||||
.join('\n\n');
|
||||
|
||||
const summaryRequest: MetonaRequest = {
|
||||
meta: {
|
||||
@@ -1180,14 +1221,18 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
},
|
||||
systemPrompt: {
|
||||
roleDefinition: 'You are a conversation summarizer.',
|
||||
outputConstraints: 'Summarize the following conversation history concisely. Preserve key facts, decisions, tool results, and context needed for future reasoning. Output in the same language as the conversation. Maximum 300 words.',
|
||||
safetyGuidelines: 'Do not include sensitive data like passwords or API keys in the summary.',
|
||||
outputConstraints:
|
||||
'Summarize the following conversation history concisely. Preserve key facts, decisions, tool results, and context needed for future reasoning. Output in the same language as the conversation. Maximum 300 words.',
|
||||
safetyGuidelines:
|
||||
'Do not include sensitive data like passwords or API keys in the summary.',
|
||||
},
|
||||
messages: [{
|
||||
role: 'user',
|
||||
content: `Please summarize the following conversation history:\n\n${conversationText}`,
|
||||
timestamp: Date.now(),
|
||||
}],
|
||||
messages: [
|
||||
{
|
||||
role: 'user',
|
||||
content: `Please summarize the following conversation history:\n\n${conversationText}`,
|
||||
timestamp: Date.now(),
|
||||
},
|
||||
],
|
||||
params: {
|
||||
maxTokens: 2048,
|
||||
temperature: 0.0,
|
||||
@@ -1241,7 +1286,9 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
return msg;
|
||||
});
|
||||
|
||||
log.info(`[AgentLoop] Context compressed: ${toCompress.length} messages → 1 summary, kept ${finalKeep.length} recent (${keepTokens} tokens budget)`);
|
||||
log.info(
|
||||
`[AgentLoop] Context compressed: ${toCompress.length} messages → 1 summary, kept ${finalKeep.length} recent (${keepTokens} tokens budget)`,
|
||||
);
|
||||
|
||||
// 审查修复: #30 修复将摘要改为 assistant 角色，可能导致连续两个 assistant 消息
|
||||
// (summary + 带 tool_calls 的 assistant)，部分 Provider 会返回 400。
|
||||
@@ -1253,7 +1300,10 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
...finalKeep,
|
||||
];
|
||||
} catch (error) {
|
||||
log.warn('[AgentLoop] Context compression failed, keeping original messages:', (error as Error).message);
|
||||
log.warn(
|
||||
'[AgentLoop] Context compression failed, keeping original messages:',
|
||||
(error as Error).message,
|
||||
);
|
||||
return null;
|
||||
}
|
||||
}
|
||||
@@ -1264,11 +1314,7 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
this.totalTokens.totalTokens += usage.totalTokens;
|
||||
}
|
||||
|
||||
private finish(
|
||||
reason: TerminationReason,
|
||||
answer?: string,
|
||||
error?: Error,
|
||||
): AgentLoopOutput {
|
||||
private finish(reason: TerminationReason, answer?: string, error?: Error): AgentLoopOutput {
|
||||
// P0-1 修复: ERROR/DEAD_LOOP 终止时先发 ERROR 流式事件，让前端能看到错误
|
||||
// v0.3.0 删除了 emit('error') 导致所有 adapter 错误对前端不可见
|
||||
// 此处用 emit('streamEvent', { type: ERROR }) 不会触发 EventEmitter 的同步 throw
|
||||
|
||||
@@ -64,7 +64,10 @@ export class WebFetchTool implements IMetonaTool {
|
||||
category: MetonaToolCategory.NETWORK,
|
||||
riskLevel: MetonaRiskLevel.LOW,
|
||||
requiresPermission: false,
|
||||
timeoutMs: 120_000,
|
||||
// v0.6.2: 120s → 240s — v0.6.1 浏览器回退串行化后，web_search 并发 3 个回退
|
||||
// 排队最坏 ~127.5s（每个 30s load + 2.5s 渲染 + 10s eval），旧值 120s 会让
|
||||
// 排队末位的抓取在队列等待中被工具超时杀掉（表现为抓取不稳定）。
|
||||
timeoutMs: 240_000,
|
||||
};
|
||||
|
||||
async execute(args: Record<string, unknown>, _context: ToolExecutionContext): Promise<unknown> {
|
||||
|
||||
+12
-5
@@ -549,8 +549,13 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
}
|
||||
|
||||
// 保存每轮迭代的 assistant 消息到数据库（含思考内容和工具调用）
|
||||
// 崩溃修复（会话停止根因的持久化侧）: 原 `if (!step.thought) continue;` 把
|
||||
// 纯工具调用轮（零文本零思考）整个跳过 — assistant 与 tool 结果都不落库。
|
||||
// 后果：① 重启后历史缺失工具上下文（模型"忘记"做过什么，重复调用 →
|
||||
// 表现为工具调用不稳定）；② 与 engine 运行时缺陷同源。现改为：
|
||||
// 有 thought 或有 toolCalls 的步骤都保存。
|
||||
for (const step of output.iterations) {
|
||||
if (!step.thought) continue;
|
||||
if (!step.thought && !(step.toolCalls && step.toolCalls.length > 0)) continue;
|
||||
|
||||
const toolCallsWithResults = step.toolCalls?.map((tc) => {
|
||||
const result = step.toolResults?.find((r) => r.toolCallId === tc.id);
|
||||
@@ -567,18 +572,20 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
|
||||
// 只有当有内容、思考内容或工具调用时才保存
|
||||
if (
|
||||
step.thought.content ||
|
||||
step.thought.reasoningContent ||
|
||||
step.thought?.content ||
|
||||
step.thought?.reasoningContent ||
|
||||
toolCallsWithResults?.length
|
||||
) {
|
||||
// C-6 修复: assistant 消息仅有 tool_calls 时 content 必须为 null（而非空字符串）
|
||||
const assistantContent =
|
||||
toolCallsWithResults?.length && !step.thought.content ? null : step.thought.content;
|
||||
toolCallsWithResults?.length && !step.thought?.content
|
||||
? null
|
||||
: (step.thought?.content ?? null);
|
||||
sessionService.saveMessage({
|
||||
sessionId,
|
||||
role: 'assistant',
|
||||
content: assistantContent,
|
||||
reasoningContent: step.thought.reasoningContent || undefined,
|
||||
reasoningContent: step.thought?.reasoningContent || undefined,
|
||||
toolCalls: toolCallsWithResults,
|
||||
iteration: step.iteration,
|
||||
});
|
||||
|
||||
+1
-1
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "metona-ai-desktop",
|
||||
"version": "0.6.1",
|
||||
"version": "0.6.2",
|
||||
"description": "MetonaAI Desktop — 生产级通用 AI Agent 智能体桌面应用",
|
||||
"main": "dist-electron/main/main.js",
|
||||
"author": "Metona Team",
|
||||
|
||||
@@ -124,6 +124,8 @@ export function useAgentStream(): void {
|
||||
score: number;
|
||||
issues: Array<{ severity: string; type: string; message: string }>;
|
||||
};
|
||||
/** DONE 事件的终止原因（completed / max_iterations / timeout / user_interrupt / dead_loop / error） */
|
||||
terminationReason?: string;
|
||||
error?: { code: string; message: string };
|
||||
state?: string;
|
||||
};
|
||||
@@ -415,7 +417,29 @@ export function useAgentStream(): void {
|
||||
}
|
||||
|
||||
// 流结束
|
||||
case 'done':
|
||||
case 'done': {
|
||||
// F-可见性修复: 非 completed 的终止原因此前静默结束（MAX_ITERATIONS/
|
||||
// TIMEOUT 无任何提示 — 用户感知为"会话直接停止"）。此处显示 system 消息。
|
||||
// USER_INTERRUPT 不提示（用户主动触发已有感知）；DEAD_LOOP 已有专属 toast。
|
||||
const reason = data.terminationReason;
|
||||
if (
|
||||
reason &&
|
||||
reason !== 'completed' &&
|
||||
reason !== 'user_interrupt' &&
|
||||
reason !== 'dead_loop'
|
||||
) {
|
||||
const reasonLabels: Record<string, string> = {
|
||||
max_iterations: '已达到最大迭代次数',
|
||||
timeout: '总执行超时',
|
||||
error: '执行出错',
|
||||
};
|
||||
getStore().addMessage({
|
||||
id: genMsgId('system'),
|
||||
role: 'system',
|
||||
content: `⏹ 会话已停止：${reasonLabels[reason] ?? reason}`,
|
||||
timestamp: Date.now(),
|
||||
});
|
||||
}
|
||||
// F5: 流结束前立即 flush 缓冲区，避免最后一段 delta 丢失
|
||||
if (textRafId !== null) {
|
||||
cancelAnimationFrame(textRafId);
|
||||
@@ -436,6 +460,7 @@ export function useAgentStream(): void {
|
||||
getStore().updateLastTraceStep({ completedAt: Date.now() });
|
||||
getStore().saveTraceData();
|
||||
break;
|
||||
}
|
||||
|
||||
// 错误
|
||||
case 'error':
|
||||
|
||||
Reference in New Issue
Block a user