feat: v0.4.0 四阶段迭代 — 安全加固 + 工程基线 + 架构重构 + 双 Provider 扩展

P0 安全修复: - API Key 加密存储（safeStorage 密钥链，版本化前缀，历史明文平滑兼容） - 间接提示注入防护（SecurityScanHook 工具结果深扫描，网络工具脱敏/本地工具警示分级） - error:report IPC 断链修复（渲染进程错误上报落 electron-log + 审计） - abort 信号贯通工具层（run_command/dev-tools 子进程随会话中断终止） - run_command 沙箱加固（cd 系统目录/敏感文件读取拦截 + chcp 前缀剥离防解析退化） - .env 真实生效（dotenv 回退加载，应用内配置优先） P1 工程基础: - ESLint 9 flat config + 全部 34 条存量 warnings 清零（零容忍基线） - 测试基线 118 用例 11 文件（token/文件防护/权限/沙箱/注入/命令/引擎/注册表/审计链/摘要分层） - test:electron 双模式（ELECTRON_RUN_AS_NODE 跑 Electron ABI，SQLite 套件全执行） - SessionRecorder 多会话隔离 + 9 种 TRACE 事件补全（含最终轮 iteration_end） - Provider 故障转移（重试耗尽/不可重试一次性切换 fallback + 前端通知） - MCP 真就绪（等待全部连接完成再广播 tools:ready） - SLO/HealthChecker 真实接入（60s 巡检 + 托盘状态） - CONFIG_DEFAULTS 单一来源（消除 SEED 双源漂移） P2 架构升级: - handlers.ts 1940 行拆分为 13 个 IPC 域模块（防重入注册 + 多窗口广播） - AgentEngineManager 每会话独立引擎（LRU 30 + adapter 工厂隔离 abort 信号） - TaskOrchestrator EngineProvider 改造 + abortByParent 联动中断 SubAgent - 会话摘要分层上下文（session_summaries 滚动摘要 + 截断游标清理防因果污染） - 消息编辑重发/重新生成（truncateAfter IPC + store 动作 + UI） - Markdown 导出 / WebSearch 并行抓取（并发 3）/ 记忆 TF 缓存 / 版本构建期注入 P3 能力扩展: - OpenAI Adapter（o 系列推理模型 reasoning_effort/max_completion_tokens） - Anthropic Adapter（原生 Messages API：tool_use 块/角色合并/thinking budget/图片 base64/SSE 事件机） - 设置页/Onboarding 六 Provider 全链路接入
2026-08-20 23:17:02 +08:00
parent b9f7ec5118
commit 2230bcec3f
90 changed files with 6581 additions and 2771 deletions
@@ -63,7 +63,7 @@ export class AgnesAdapter extends BaseAdapter {
    }

    const data = await response.json() as Record<string, unknown>;
-    const parsed = parseOpenAICompatibleResponse(data, request.meta.requestId, this.providerId, this.config.defaultModel);
+    const parsed = parseOpenAICompatibleResponse(data);

    return {
      meta: {
@@ -0,0 +1,479 @@
+/**
+ * Anthropic Provider Adapter（P3）
+ *
+ * Anthropic Messages API（/v1/messages）原生协议，支持 Tool Calling、流式输出、
+ * 扩展思考（thinking + budget_tokens）、多模态图片（base64）。
+ *
+ * 与 OpenAI 兼容 API 的关键差异：
+ * - 认证头：x-api-key + anthropic-version（非 Authorization Bearer）
+ * - 消息结构：content 为块数组（text / tool_use / tool_result / image），
+ *   且要求 user/assistant 严格交替（连续同角色需合并）
+ * - 工具定义：input_schema（非 parameters）；工具结果以 user 角色 tool_result 块回传
+ * - SSE 事件：message_start / content_block_start / content_block_delta /
+ *   content_block_stop / message_delta / message_stop（非 OpenAI chunk 格式）
+ * - 图片：仅支持 base64 source（URL 需下载后转换）
+ *
+ * @see https://docs.anthropic.com/en/api/messages
+ */
+
+import { BaseAdapter } from './base-adapter';
+import log from 'electron-log';
+import { nanoid } from 'nanoid';
+import type { MetonaRequest, MetonaResponse, MetonaStreamEvent } from '../types';
+import { MetonaFinishReason, MetonaStreamEventType } from '../types';
+import type { MetonaModelInfo } from '../types/metona-adapter';
+
+export class AnthropicAdapter extends BaseAdapter {
+  override readonly providerId: string = 'anthropic';
+  readonly supportedModels = ['claude-sonnet-4-5', 'claude-opus-4-1', 'claude-haiku-4-5'];
+  readonly supportsToolCalling = true;
+  readonly supportsThinking = true;
+
+  private static readonly MODEL_INFO: Record<string, MetonaModelInfo> = {
+    'claude-sonnet-4-5': {
+      id: 'claude-sonnet-4-5',
+      name: 'Claude Sonnet 4.5',
+      contextWindow: 200_000,
+      maxOutputTokens: 64_000,
+      supportsToolCalling: true,
+      supportsThinking: true,
+      description: 'Anthropic 旗舰模型，200K 上下文，支持扩展思考与工具调用',
+    },
+    'claude-opus-4-1': {
+      id: 'claude-opus-4-1',
+      name: 'Claude Opus 4.1',
+      contextWindow: 200_000,
+      maxOutputTokens: 32_000,
+      supportsToolCalling: true,
+      supportsThinking: true,
+      description: 'Anthropic 深度推理模型',
+    },
+    'claude-haiku-4-5': {
+      id: 'claude-haiku-4-5',
+      name: 'Claude Haiku 4.5',
+      contextWindow: 200_000,
+      maxOutputTokens: 32_000,
+      supportsToolCalling: true,
+      supportsThinking: true,
+      description: 'Anthropic 低延迟模型',
+    },
+  };
+
+  private buildHeaders(): Record<string, string> {
+    return {
+      'Content-Type': 'application/json',
+      'x-api-key': this.config.apiKey ?? '',
+      'anthropic-version': '2023-06-01',
+      ...this.config.headers,
+    };
+  }
+
+  // ===== POST /v1/messages（非流式） =====
+
+  async send(request: MetonaRequest): Promise<MetonaResponse> {
+    const body = await this.toNativeRequest(request, false);
+    const response = await this.fetchWithTimeout(`${this.config.baseURL}/v1/messages`, {
+      method: 'POST',
+      headers: this.buildHeaders(),
+      body: JSON.stringify(body),
+    }, this.config.timeoutMs ?? 120_000);
+
+    if (!response.ok) {
+      await this.throwHttpError(response, 'Anthropic API error');
+    }
+
+    const data = await response.json() as Record<string, unknown>;
+    return this.toMetonaResponse(data, request.meta.requestId);
+  }
+
+  // ===== POST /v1/messages（流式） =====
+
+  async *sendStream(request: MetonaRequest): AsyncIterable<MetonaStreamEvent> {
+    const body = await this.toNativeRequest(request, true);
+    const response = await this.fetchWithTimeout(`${this.config.baseURL}/v1/messages`, {
+      method: 'POST',
+      headers: this.buildHeaders(),
+      body: JSON.stringify(body),
+    }, this.config.timeoutMs ?? 300_000);
+
+    if (!response.ok || !response.body) {
+      await this.throwHttpError(response, 'Anthropic stream error');
+    }
+
+    // 非空断言：上方 if 已确保 response.body 不为 null
+    const reader = response.body!.getReader();
+    const decoder = new TextDecoder();
+    let seq = 0;
+    let buffer = '';
+    let eventName = '';
+    let streamEndedNormally = false;
+
+    // 工具调用缓冲：content block index → { id, name, argsBuffer }
+    const toolBlocks = new Map<number, { id: string; name: string; argsBuffer: string }>();
+
+    const base = () => ({
+      requestId: request.meta.requestId,
+      sessionId: request.meta.sessionId,
+      iteration: request.meta.iteration,
+      seq: seq++,
+      timestamp: Date.now(),
+    });
+
+    const processEvent = (name: string, data: Record<string, unknown>): MetonaStreamEvent[] => {
+      const events: MetonaStreamEvent[] = [];
+      switch (name) {
+        case 'content_block_start': {
+          const block = data.content_block as Record<string, unknown> | undefined;
+          const index = (data.index as number) ?? 0;
+          if (block?.type === 'tool_use') {
+            toolBlocks.set(index, {
+              id: (block.id as string) ?? `tc_${nanoid(8)}`,
+              name: (block.name as string) ?? '',
+              argsBuffer: '',
+            });
+          }
+          break;
+        }
+        case 'content_block_delta': {
+          const delta = data.delta as Record<string, unknown> | undefined;
+          const index = (data.index as number) ?? 0;
+          if (delta?.type === 'text_delta' && typeof delta.text === 'string') {
+            events.push({ type: MetonaStreamEventType.TEXT_DELTA, ...base(), delta: delta.text });
+          } else if (delta?.type === 'thinking_delta' && typeof delta.thinking === 'string') {
+            events.push({ type: MetonaStreamEventType.REASONING_DELTA, ...base(), delta: delta.thinking });
+          } else if (delta?.type === 'input_json_delta' && typeof delta.partial_json === 'string') {
+            const block = toolBlocks.get(index);
+            if (block) {
+              block.argsBuffer += delta.partial_json;
+              events.push({
+                type: MetonaStreamEventType.TOOL_CALL_DELTA,
+                ...base(),
+                toolCallDelta: { index, name: block.name, argsDelta: delta.partial_json },
+              });
+            }
+          }
+          break;
+        }
+        case 'content_block_stop': {
+          const index = (data.index as number) ?? 0;
+          const block = toolBlocks.get(index);
+          if (block) {
+            let args: Record<string, unknown> = {};
+            try {
+              args = block.argsBuffer ? JSON.parse(block.argsBuffer) : {};
+            } catch {
+              args = {};
+            }
+            events.push({
+              type: MetonaStreamEventType.TOOL_CALL_COMPLETE,
+              ...base(),
+              toolCall: {
+                id: block.id,
+                name: block.name,
+                args,
+                iteration: request.meta.iteration,
+                timestamp: Date.now(),
+              },
+            });
+            toolBlocks.delete(index);
+          }
+          break;
+        }
+        case 'message_delta': {
+          // 结束时的 usage 统计（output_tokens 增量在此事件携带）
+          const usage = data.usage as Record<string, unknown> | undefined;
+          if (usage) {
+            events.push({
+              type: MetonaStreamEventType.USAGE,
+              ...base(),
+              usage: {
+                inputTokens: (this.lastInputTokens as number) ?? 0,
+                outputTokens: (usage.output_tokens as number) ?? 0,
+                totalTokens: ((this.lastInputTokens as number) ?? 0) + ((usage.output_tokens as number) ?? 0),
+              },
+            });
+          }
+          break;
+        }
+        case 'message_stop': {
+          streamEndedNormally = true;
+          events.push({ type: MetonaStreamEventType.DONE, ...base() });
+          break;
+        }
+        case 'error': {
+          const err = data.error as Record<string, unknown> | undefined;
+          events.push({
+            type: MetonaStreamEventType.ERROR,
+            ...base(),
+            error: {
+              code: 'unknown' as never,
+              message: (err?.message as string) ?? 'Anthropic stream error',
+              retryable: false,
+            },
+          });
+          break;
+        }
+      }
+      return events;
+    };
+
+    // message_start 事件携带 input_tokens（记录到 this.lastInputTokens 供 USAGE 汇总）
+    while (true) {
+      const { done, value } = await reader.read();
+      if (done) break;
+
+      buffer += decoder.decode(value, { stream: true });
+      const lines = buffer.split('\n');
+      buffer = lines.pop() ?? '';
+
+      for (const line of lines) {
+        const trimmed = line.trim();
+        if (!trimmed) continue;
+        if (trimmed.startsWith('event:')) {
+          eventName = trimmed.slice(6).trim();
+          continue;
+        }
+        if (!trimmed.startsWith('data:')) continue;
+        const dataStr = trimmed.slice(5).trim();
+        if (dataStr === '[DONE]') continue;
+
+        try {
+          const data = JSON.parse(dataStr) as Record<string, unknown>;
+          // message_start 携带 input_tokens
+          if (eventName === 'message_start') {
+            const msg = data.message as Record<string, unknown> | undefined;
+            const usage = msg?.usage as Record<string, unknown> | undefined;
+            this.lastInputTokens = (usage?.input_tokens as number) ?? 0;
+            continue;
+          }
+          for (const ev of processEvent(eventName, data)) {
+            yield ev;
+          }
+        } catch (parseErr) {
+          log.warn(`[Anthropic] Failed to parse SSE line: ${(parseErr as Error).message}`, trimmed.slice(0, 200));
+        }
+      }
+    }
+
+    // 流中断（连接断开等）补发 DONE，防止 Agent Loop 挂起（与 Ollama 行为一致）
+    if (!streamEndedNormally) {
+      yield { type: MetonaStreamEventType.DONE, ...base() };
+    }
+  }
+
+  /** message_start 捕获的 input_tokens（供 message_delta 汇总 usage） */
+  private lastInputTokens = 0;
+
+  // ===== 模型与上下文窗口 =====
+
+  override async listModels(): Promise<MetonaModelInfo[]> {
+    // Anthropic 无公开 /models 列表端点，返回本地元数据
+    return this.supportedModels.map((id) => AnthropicAdapter.MODEL_INFO[id] ?? { id });
+  }
+
+  override getContextWindow(): number {
+    if (typeof this.config.contextWindow === 'number' && this.config.contextWindow > 0) {
+      return this.config.contextWindow;
+    }
+    const modelInfo = AnthropicAdapter.MODEL_INFO[this.config.defaultModel];
+    return modelInfo?.contextWindow ?? 200_000;
+  }
+
+  // ========== 私有方法 ==========
+
+  /**
+   * 构建 Anthropic 原生请求体
+   *
+   * 转换要点：
+   * 1. MetonaMessage → Anthropic 消息（content 块数组）
+   * 2. tool 消息 → user 角色 tool_result 块
+   * 3. assistant 工具调用 → tool_use 块
+   * 4. 连续同角色消息合并（API 要求严格交替）
+   * 5. 首条消息必须为 user（历史以 assistant 开头时补占位）
+   */
+  private async toNativeRequest(request: MetonaRequest, stream: boolean): Promise<Record<string, unknown>> {
+    // System Prompt 拼接（Anthropic 使用顶层 system 字段）
+    const system = [
+      request.systemPrompt.roleDefinition,
+      request.systemPrompt.outputConstraints,
+      request.systemPrompt.safetyGuidelines,
+      request.systemPrompt.dynamicReminders,
+    ]
+      .filter(Boolean)
+      .join('\n\n');
+
+    // 转换消息（非 system）
+    const converted: Array<{ role: 'user' | 'assistant'; content: Array<Record<string, unknown>> }> = [];
+    for (const m of request.messages) {
+      if (m.role === 'system') continue;
+
+      if (m.role === 'tool' && m.toolResult) {
+        // 工具结果 → user 角色 tool_result 块
+        const contentStr = m.toolResult.error
+          ? m.toolResult.error
+          : typeof m.toolResult.result === 'string'
+            ? m.toolResult.result
+            : JSON.stringify(m.toolResult.result);
+        converted.push({
+          role: 'user',
+          content: [{ type: 'tool_result', tool_use_id: m.toolResult.toolCallId, content: contentStr }],
+        });
+        continue;
+      }
+
+      if (m.role === 'assistant') {
+        const content: Array<Record<string, unknown>> = [];
+        if (m.content) content.push({ type: 'text', text: m.content });
+        for (const tc of m.toolCalls ?? []) {
+          content.push({ type: 'tool_use', id: tc.id, name: tc.name, input: tc.args });
+        }
+        if (content.length > 0) {
+          converted.push({ role: 'assistant', content });
+        }
+        continue;
+      }
+
+      // user 消息（含多模态图片）
+      const content: Array<Record<string, unknown>> = [];
+      if (m.content) content.push({ type: 'text', text: m.content });
+      for (const img of m.images ?? []) {
+        const block = await this.toImageBlock(img.url);
+        if (block) content.push(block);
+      }
+      if (content.length === 0) content.push({ type: 'text', text: '' });
+      converted.push({ role: 'user', content });
+    }
+
+    // 合并连续同角色消息（Anthropic 要求 user/assistant 交替）
+    const merged: Array<{ role: 'user' | 'assistant'; content: Array<Record<string, unknown>> }> = [];
+    for (const msg of converted) {
+      const last = merged[merged.length - 1];
+      if (last && last.role === msg.role) {
+        last.content.push(...msg.content);
+      } else {
+        merged.push({ ...msg });
+      }
+    }
+
+    // 首条消息必须为 user
+    if (merged.length === 0 || merged[0].role !== 'user') {
+      merged.unshift({ role: 'user', content: [{ type: 'text', text: '[Conversation history follows]' }] });
+    }
+
+    const body: Record<string, unknown> = {
+      model: this.config.defaultModel,
+      max_tokens: request.params.maxTokens ?? 8192,
+      system,
+      messages: merged,
+      stream,
+    };
+
+    // 工具定义（input_schema 命名）
+    if (request.tools?.length) {
+      body.tools = request.tools.map((t) => ({
+        name: t.name,
+        description: t.description,
+        input_schema: t.parameters,
+      }));
+    }
+
+    // Thinking 模式：budget_tokens（必须小于 max_tokens，此处钳制到一半）
+    if (request.params.thinkingEnabled) {
+      const budgetMap: Record<string, number> = { low: 1024, medium: 4096, high: 16384, max: 32768 };
+      const budget = Math.min(
+        budgetMap[request.params.thinkingEffort ?? 'high'] ?? 16384,
+        Math.floor((body.max_tokens as number) / 2),
+      );
+      body.thinking = { type: 'enabled', budget_tokens: budget };
+    } else {
+      body.temperature = request.params.temperature;
+    }
+
+    // 停止序列
+    if (request.params.stopSequences?.length) {
+      body.stop_sequences = request.params.stopSequences;
+    }
+
+    return body;
+  }
+
+  /**
+   * 图片 URL → Anthropic image 块
+   * data URI 直接解析；http(s) URL 下载后转 base64（Anthropic 不支持 URL 引用）
+   */
+  private async toImageBlock(url: string): Promise<Record<string, unknown> | null> {
+    try {
+      if (url.startsWith('data:')) {
+        // data:image/png;base64,xxx → { media_type, data }
+        const match = url.match(/^data:([^;]+);base64,(.*)$/s);
+        if (!match) return null;
+        return { type: 'image', source: { type: 'base64', media_type: match[1], data: match[2] } };
+      }
+      if (url.startsWith('http://') || url.startsWith('https://')) {
+        const res = await this.fetchWithTimeout(url, {}, 30_000);
+        if (!res.ok) throw new Error(`HTTP ${res.status}`);
+        const contentType = res.headers.get('content-type') ?? 'image/png';
+        const buf = Buffer.from(await res.arrayBuffer());
+        return {
+          type: 'image',
+          source: { type: 'base64', media_type: contentType, data: buf.toString('base64') },
+        };
+      }
+      return null;
+    } catch (err) {
+      log.warn(`[Anthropic] Failed to load image: ${(err as Error).message}`);
+      return null;
+    }
+  }
+
+  /** 非流式响应 → MetonaResponse */
+  private toMetonaResponse(data: Record<string, unknown>, requestId: string): MetonaResponse {
+    const contentBlocks = (data.content as Array<Record<string, unknown>>) ?? [];
+    let text = '';
+    let reasoningContent: string | undefined;
+    const toolCalls: MetonaResponse['toolCalls'] = [];
+
+    for (const block of contentBlocks) {
+      if (block.type === 'text') text += (block.text as string) ?? '';
+      else if (block.type === 'thinking') reasoningContent = (block.thinking as string) ?? undefined;
+      else if (block.type === 'tool_use') {
+        let args: Record<string, unknown> = {};
+        const rawInput = block.input;
+        if (rawInput && typeof rawInput === 'object') args = rawInput as Record<string, unknown>;
+        toolCalls?.push({
+          id: (block.id as string) ?? `tc_${nanoid(8)}`,
+          name: (block.name as string) ?? '',
+          args,
+          iteration: 0,
+          timestamp: Date.now(),
+        });
+      }
+    }
+
+    const usage = (data.usage as Record<string, number>) ?? {};
+    const stopReason = (data.stop_reason as string) ?? 'end_turn';
+    const finishReason: MetonaFinishReason =
+      stopReason === 'tool_use' ? MetonaFinishReason.TOOL_CALLS
+      : stopReason === 'max_tokens' ? MetonaFinishReason.LENGTH
+      : MetonaFinishReason.STOP;
+
+    return {
+      meta: {
+        requestId,
+        provider: this.providerId,
+        model: (data.model as string) ?? this.config.defaultModel,
+        latencyMs: 0,
+        timestamp: Date.now(),
+      },
+      content: text,
+      reasoningContent,
+      toolCalls,
+      usage: {
+        inputTokens: usage.input_tokens ?? 0,
+        outputTokens: usage.output_tokens ?? 0,
+        totalTokens: (usage.input_tokens ?? 0) + (usage.output_tokens ?? 0),
+      },
+      finishReason,
+    };
+  }
+}
@@ -12,7 +12,7 @@

 import { BaseAdapter } from './base-adapter';
 import type { MetonaRequest, MetonaResponse, MetonaStreamEvent } from '../types';
-import { MetonaFinishReason, MetonaErrorCode } from '../types';
+import { MetonaFinishReason } from '../types';
 import type { MetonaModelInfo } from '../types/metona-adapter';
 import { buildOpenAICompatibleMessages, buildOpenAICompatibleTools } from './shared/openai-format';
 import { parseSSEStream, parseOpenAICompatibleResponse } from './shared/sse-stream';
@@ -68,7 +68,7 @@ export class DeepSeekAdapter extends BaseAdapter {
    }

    const data = await response.json() as Record<string, unknown>;
-    const parsed = parseOpenAICompatibleResponse(data, request.meta.requestId, this.providerId, this.config.defaultModel);
+    const parsed = parseOpenAICompatibleResponse(data);

    return {
      meta: {
@@ -1,11 +1,13 @@
 /**
 * Provider Adapter 导出
 *
- * 四种 Provider 各自独立继承 BaseAdapter，无耦合关系：
+ * 六种 Provider 各自独立继承 BaseAdapter，无耦合关系：
 * - DeepSeekAdapter  — OpenAI 兼容 + DeepSeek 特有参数
 * - AgnesAdapter     — OpenAI 兼容 + Agnes 特有参数
 * - MimoAdapter      — OpenAI 兼容 + MiMo 特有参数
 * - OllamaAdapter    — Ollama 原生 API
+ * - OpenAIAdapter    — OpenAI 原生（P3，o 系列推理模型支持）
+ * - AnthropicAdapter — Anthropic Messages API 原生（P3，扩展思考支持）
 *
 * 共享工具（仅供 OpenAI 兼容 Adapter 使用）：
 * - shared/openai-format — 消息/工具格式构建
@@ -17,3 +19,5 @@ export { DeepSeekAdapter } from './deepseek.adapter';
 export { AgnesAdapter } from './agnes-ai.adapter';
 export { MimoAdapter } from './mimo.adapter';
 export { OllamaAdapter } from './ollama.adapter';
+export { OpenAIAdapter } from './openai.adapter';
+export { AnthropicAdapter } from './anthropic.adapter';
@@ -74,7 +74,7 @@ export class MimoAdapter extends BaseAdapter {
    }

    const data = await response.json() as Record<string, unknown>;
-    const parsed = parseOpenAICompatibleResponse(data, request.meta.requestId, this.providerId, this.config.defaultModel);
+    const parsed = parseOpenAICompatibleResponse(data);

    return {
      meta: {
@@ -168,15 +168,12 @@ export class MimoAdapter extends BaseAdapter {
    // 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');
-    let imageCount = 0;
    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;

-      imageCount += origMsg.images.length;
-
      const contentParts: Array<Record<string, unknown>> = [];
      if (origMsg.content) {
        contentParts.push({ type: 'text', text: origMsg.content });
@@ -551,7 +551,7 @@ export class OllamaAdapter extends BaseAdapter {
      },
      content: (message?.content as string) ?? '',
      reasoningContent: message?.thinking as string | undefined,
-      toolCalls: toolCalls?.map((tc, i) => {
+      toolCalls: toolCalls?.map((tc) => {
        const fn = tc.function as Record<string, unknown>;
        const rawArgs = fn?.arguments;
        let args: Record<string, unknown> = {};
@@ -0,0 +1,246 @@
+/**
+ * OpenAI Provider Adapter（P3）
+ *
+ * OpenAI Chat Completions API（/v1/chat/completions），支持 Tool Calling、
+ * 流式输出、多模态图片、o 系列推理模型的 reasoning_effort 参数。
+ *
+ * 与 DeepSeek 适配器的关键差异：
+ * - o 系列 / gpt-5 系列模型使用 max_completion_tokens（非 max_tokens）
+ * - Thinking 模式通过顶层 reasoning_effort 参数（o 系列模型）
+ * - 模型列表从 /v1/models 动态获取
+ *
+ * @see apis 官方文档 https://platform.openai.com/docs/api-reference/chat
+ */
+
+import { BaseAdapter } from './base-adapter';
+import type { MetonaRequest, MetonaResponse, MetonaStreamEvent } from '../types';
+import { MetonaFinishReason } from '../types';
+import type { MetonaModelInfo } from '../types/metona-adapter';
+import { buildOpenAICompatibleMessages, buildOpenAICompatibleTools } from './shared/openai-format';
+import { parseSSEStream, parseOpenAICompatibleResponse } from './shared/sse-stream';
+
+export class OpenAIAdapter extends BaseAdapter {
+  override readonly providerId: string = 'openai';
+  readonly supportedModels = ['gpt-4o', 'gpt-4o-mini', 'gpt-4.1', 'o3-mini'];
+  readonly supportsToolCalling = true;
+  readonly supportsThinking = true;
+
+  private static readonly MODEL_INFO: Record<string, MetonaModelInfo> = {
+    'gpt-4o': {
+      id: 'gpt-4o',
+      name: 'GPT-4o',
+      contextWindow: 128_000,
+      maxOutputTokens: 16_384,
+      supportsToolCalling: true,
+      supportsThinking: false,
+      description: 'OpenAI 旗舰多模态模型，128K 上下文',
+    },
+    'gpt-4o-mini': {
+      id: 'gpt-4o-mini',
+      name: 'GPT-4o mini',
+      contextWindow: 128_000,
+      maxOutputTokens: 16_384,
+      supportsToolCalling: true,
+      supportsThinking: false,
+      description: 'OpenAI 高性价比模型，128K 上下文',
+    },
+    'gpt-4.1': {
+      id: 'gpt-4.1',
+      name: 'GPT-4.1',
+      contextWindow: 1_000_000,
+      maxOutputTokens: 32_768,
+      supportsToolCalling: true,
+      supportsThinking: false,
+      description: 'OpenAI 长上下文模型，1M 上下文',
+    },
+    'o3-mini': {
+      id: 'o3-mini',
+      name: 'o3-mini',
+      contextWindow: 200_000,
+      maxOutputTokens: 100_000,
+      supportsToolCalling: true,
+      supportsThinking: true,
+      description: 'OpenAI 推理模型，支持 reasoning_effort',
+    },
+  };
+
+  // ===== POST /v1/chat/completions（非流式） =====
+
+  async send(request: MetonaRequest): Promise<MetonaResponse> {
+    const body = this.toNativeRequest(request, false);
+
+    const response = await this.fetchWithTimeout(`${this.config.baseURL}/chat/completions`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+        Authorization: `Bearer ${this.config.apiKey}`,
+        ...this.config.headers,
+      },
+      body: JSON.stringify(body),
+    }, this.config.timeoutMs ?? 120_000);
+
+    if (!response.ok) {
+      await this.throwHttpError(response, 'OpenAI API error');
+    }
+
+    const data = await response.json() as Record<string, unknown>;
+    const parsed = parseOpenAICompatibleResponse(data);
+
+    return {
+      meta: {
+        requestId: request.meta.requestId,
+        provider: this.providerId,
+        model: (data.model as string) ?? this.config.defaultModel,
+        latencyMs: 0,
+        timestamp: Date.now(),
+      },
+      content: parsed.content,
+      reasoningContent: parsed.reasoningContent,
+      toolCalls: parsed.toolCalls,
+      usage: parsed.usage,
+      finishReason: parsed.finishReason as MetonaFinishReason,
+    };
+  }
+
+  // ===== POST /v1/chat/completions（流式） =====
+
+  async *sendStream(request: MetonaRequest): AsyncIterable<MetonaStreamEvent> {
+    const body = this.toNativeRequest(request, true);
+
+    const response = await this.fetchWithTimeout(`${this.config.baseURL}/chat/completions`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+        Authorization: `Bearer ${this.config.apiKey}`,
+        ...this.config.headers,
+      },
+      body: JSON.stringify(body),
+    }, this.config.timeoutMs ?? 300_000);
+
+    if (!response.ok || !response.body) {
+      await this.throwHttpError(response, 'OpenAI stream error');
+    }
+
+    yield* parseSSEStream(
+      // 非空断言：上方 if 已确保 response.body 不为 null
+      response.body!,
+      request.meta.requestId,
+      request.meta.sessionId,
+      request.meta.iteration,
+    );
+  }
+
+  // ===== GET /v1/models =====
+
+  override async listModels(): Promise<MetonaModelInfo[]> {
+    try {
+      const response = await fetch(`${this.config.baseURL}/models`, {
+        headers: { Authorization: `Bearer ${this.config.apiKey}` },
+        signal: AbortSignal.timeout(10_000),
+      });
+      if (response.ok) {
+        const data = await response.json() as { data?: Array<{ id: string }> };
+        if (data.data?.length) {
+          return data.data.map((m) => OpenAIAdapter.MODEL_INFO[m.id] ?? { id: m.id });
+        }
+      }
+    } catch {
+      // API 不可用时降级
+    }
+    return this.supportedModels.map((id) => OpenAIAdapter.MODEL_INFO[id] ?? { id });
+  }
+
+  override getContextWindow(): number {
+    if (typeof this.config.contextWindow === 'number' && this.config.contextWindow > 0) {
+      return this.config.contextWindow;
+    }
+    const modelInfo = OpenAIAdapter.MODEL_INFO[this.config.defaultModel];
+    return modelInfo?.contextWindow ?? 128_000;
+  }
+
+  // ========== 私有方法 ==========
+
+  /**
+   * 构建 OpenAI 原生请求体
+   *
+   * OpenAI 特有处理：
+   * - 多模态图片：user 消息 images[] → content 数组
+   * - o 系列（o1/o3/o4）与 gpt-5 系列使用 max_completion_tokens + reasoning_effort
+   * - 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) {
+        throw new Error(`Model "${model}" does not support image inputs`);
+      }
+    }
+
+    const body: Record<string, unknown> = {
+      model,
+      messages,
+      stream,
+    };
+
+    // Token 上限参数：o 系列/gpt-5 使用 max_completion_tokens
+    if (request.params.maxTokens) {
+      if (isReasoningModel) {
+        body.max_completion_tokens = request.params.maxTokens;
+      } else {
+        body.max_tokens = request.params.maxTokens;
+      }
+    } else if (isReasoningModel) {
+      // 推理模型未配置时使用兜底值（thinking 占用 token 配额，默认值过小会被截断）
+      body.max_completion_tokens = 32_768;
+    }
+
+    if (stream) {
+      body.stream_options = { include_usage: true };
+    }
+
+    if (tools) {
+      body.tools = tools;
+    }
+
+    // Thinking 模式：推理模型映射 reasoning_effort；非推理模型忽略
+    if (request.params.thinkingEnabled && isReasoningModel) {
+      const effortMap: Record<string, string> = { low: 'low', medium: 'medium', high: 'high', max: 'high' };
+      body.reasoning_effort = effortMap[request.params.thinkingEffort ?? 'high'] ?? 'high';
+    } else if (!isReasoningModel) {
+      // 非推理模型使用温度控制
+      body.temperature = request.params.temperature;
+    }
+
+    // 停止序列
+    if (request.params.stopSequences?.length) {
+      body.stop = request.params.stopSequences;
+    }
+
+    return body;
+  }
+}
@@ -218,9 +218,6 @@ export async function* parseSSEStream(
 */
 export function parseOpenAICompatibleResponse(
  data: Record<string, unknown>,
-  requestId: string,
-  provider: string,
-  defaultModel: string,
 ): {
  content: string;
  reasoningContent?: string;