refactor(review): migrate review agents from direct OpenAI to LLMGateway

Replace all direct OpenAI client usage in review agents, orchestrator, learning system, and AI review service with the new LLMGateway abstraction. Agents now call gateway.chatForRole() instead of openai.chat.completions.create(), enabling multi-provider support across all review workflows. Add getAll() method to ToolRegistry for provider capability checking. Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-opencode)
2026-03-27 10:05:50 +00:00 · 2026-03-05 00:32:04 +08:00
parent c6c8e20683
commit 0bb6cf7849
14 changed files with 237 additions and 225 deletions
--- a/src/controllers/feedback.ts
+++ b/src/controllers/feedback.ts
@@ -1,6 +1,5 @@
 import { zValidator } from '@hono/zod-validator';
 import { Hono } from 'hono';
-import OpenAI from 'openai';
 import { z } from 'zod';
 import config from '../config';
 import { LearningSystem } from '../review/learning/learning-system';
@@ -16,13 +15,13 @@ let learningSystem: LearningSystem | null = null;
 let reviewStore: FileReviewStore | null = null;

 // 初始化反馈系统（记忆系统可选）
-export function initializeFeedbackSystem(openaiClient: OpenAI, store: FileReviewStore): void {
+export function initializeFeedbackSystem(store: FileReviewStore): void {
  // 保存store实例以供handlers重用，避免多实例状态不同步
  reviewStore = store;

  // 记忆系统为可选功能
  if (config.review.qdrantUrl && config.review.enableMemory) {
-    memoryStore = new VectorMemoryStore(config.review.qdrantUrl, openaiClient);
+    memoryStore = new VectorMemoryStore(config.review.qdrantUrl);
    learningSystem = new LearningSystem(memoryStore, reviewStore);

    memoryStore.initialize().catch((err) => {
--- a/src/review/agents/correctness-agent.ts
+++ b/src/review/agents/correctness-agent.ts
@@ -1,18 +1,12 @@
-import OpenAI from 'openai';
+import type { LLMGateway } from '../../llm/gateway';
 import type { LearningSystem } from '../learning/learning-system';
 import { ToolRegistry } from '../tools/registry';
 import { SpecialistAgent } from './specialist-agent';

 export class CorrectnessAgent extends SpecialistAgent {
-  constructor(
-    openai: OpenAI,
-    model: string,
-    toolRegistry?: ToolRegistry,
-    learningSystem?: LearningSystem
-  ) {
+  constructor(gateway: LLMGateway, toolRegistry?: ToolRegistry, learningSystem?: LearningSystem) {
    super(
-      openai,
-      model,
+      gateway,
      'correctness',
      'Correctness Agent',
      '业务逻辑正确性、边界条件、空值处理和明显bug',
--- a/src/review/agents/critic-agent.ts
+++ b/src/review/agents/critic-agent.ts
@@ -1,7 +1,8 @@
-import OpenAI from 'openai';
-import { logger } from '../../utils/logger';
-import { withGlobalPrompt } from '../../utils/global-prompt';
 import config from '../../config';
+import type { LLMGateway } from '../../llm/gateway';
+import type { LLMMessage } from '../../llm/types';
+import { withGlobalPrompt } from '../../utils/global-prompt';
+import { logger } from '../../utils/logger';
 import { Finding, ReviewContext } from '../types';

 export interface CritiqueResult {
@@ -19,10 +20,7 @@ export interface CritiqueIssue {
 }

 export class CriticAgent {
-  constructor(
-    private openai: OpenAI,
-    private model: string
-  ) {}
+  constructor(private gateway: LLMGateway) {}

  async critique(
    findings: Omit<Finding, 'id' | 'runId' | 'published'>[],
@@ -71,20 +69,24 @@ ${context.diff.slice(0, 3000)}
 }`;

    try {
-      const response = await this.openai.chat.completions.create({
-        model: this.model,
+      const messages: LLMMessage[] = [
+        {
+          role: 'system',
+          content: withGlobalPrompt(
+            '你是严格的代码审查质量评估专家，以高标准评估findings的质量。',
+            config.review.globalPrompt
+          ),
+        },
+        { role: 'user', content: prompt },
+      ];
+
+      const response = await this.gateway.chatForRole('specialist', {
+        messages,
        temperature: 0.1, // 略高于0以允许批判性思考
-        response_format: { type: 'json_object' },
-        messages: [
-          {
-            role: 'system',
-            content: withGlobalPrompt('你是严格的代码审查质量评估专家，以高标准评估findings的质量。', config.openai.globalPrompt),
-          },
-          { role: 'user', content: prompt },
-        ],
+        responseFormat: 'json',
      });

-      const content = response.choices[0]?.message.content;
+      const content = response.content;
      if (!content) {
        throw new Error('Critic Agent返回空结果');
      }
@@ -160,20 +162,21 @@ ${context.diff.slice(0, 2000)}
 }`;

    try {
-      const response = await this.openai.chat.completions.create({
-        model: this.model,
+      const messages: LLMMessage[] = [
+        {
+          role: 'system',
+          content: withGlobalPrompt('你是代码审查质量评估专家。', config.review.globalPrompt),
+        },
+        { role: 'user', content: prompt },
+      ];
+
+      const response = await this.gateway.chatForRole('specialist', {
+        messages,
        temperature: 0,
-        response_format: { type: 'json_object' },
-        messages: [
-          {
-            role: 'system',
-            content: withGlobalPrompt('你是代码审查质量评估专家。', config.openai.globalPrompt),
-          },
-          { role: 'user', content: prompt },
-        ],
+        responseFormat: 'json',
      });

-      const content = response.choices[0]?.message.content;
+      const content = response.content;
      if (!content) {
        throw new Error('评估失败');
      }
--- a/src/review/agents/debate-orchestrator.ts
+++ b/src/review/agents/debate-orchestrator.ts
@@ -1,7 +1,8 @@
-import OpenAI from 'openai';
-import { logger } from '../../utils/logger';
-import { withGlobalPrompt } from '../../utils/global-prompt';
 import config from '../../config';
+import type { LLMGateway } from '../../llm/gateway';
+import type { LLMMessage } from '../../llm/types';
+import { withGlobalPrompt } from '../../utils/global-prompt';
+import { logger } from '../../utils/logger';
 import { Finding, FindingSeverity } from '../types';
 import { SpecialistAgent } from './specialist-agent';

@@ -14,12 +15,10 @@ interface AgentOpinion {
 }

 export class DebateOrchestrator {
-  private openai: OpenAI;
-  private model: string;
+  private gateway: LLMGateway;

-  constructor(openai: OpenAI, model: string) {
-    this.openai = openai;
-    this.model = model;
+  constructor(gateway: LLMGateway) {
+    this.gateway = gateway;
  }

  async conductDebate(
@@ -103,20 +102,24 @@ export class DebateOrchestrator {
 }`;

    try {
-      const response = await this.openai.chat.completions.create({
-        model: this.model,
+      const messages: LLMMessage[] = [
+        {
+          role: 'system',
+          content: withGlobalPrompt(
+            `你是${agentName}，从你的专业角度独立评估代码问题。`,
+            config.review.globalPrompt
+          ),
+        },
+        { role: 'user', content: prompt },
+      ];
+
+      const response = await this.gateway.chatForRole('specialist', {
+        messages,
        temperature: 0.2,
-        response_format: { type: 'json_object' },
-        messages: [
-          {
-            role: 'system',
-            content: withGlobalPrompt(`你是${agentName}，从你的专业角度独立评估代码问题。`, config.openai.globalPrompt),
-          },
-          { role: 'user', content: prompt },
-        ],
+        responseFormat: 'json',
      });

-      const content = response.choices[0]?.message.content;
+      const content = response.content;
      if (!content) {
        throw new Error('Agent opinion返回空');
      }
@@ -180,20 +183,24 @@ ${otherOpinions
 }`;

    try {
-      const response = await this.openai.chat.completions.create({
-        model: this.model,
+      const messages: LLMMessage[] = [
+        {
+          role: 'system',
+          content: withGlobalPrompt(
+            `你是${agentName}，根据同行意见重新评估，但也要坚持你的专业判断。`,
+            config.review.globalPrompt
+          ),
+        },
+        { role: 'user', content: prompt },
+      ];
+
+      const response = await this.gateway.chatForRole('specialist', {
+        messages,
        temperature: 0.3, // 允许一定灵活性
-        response_format: { type: 'json_object' },
-        messages: [
-          {
-            role: 'system',
-            content: withGlobalPrompt(`你是${agentName}，根据同行意见重新评估，但也要坚持你的专业判断。`, config.openai.globalPrompt),
-          },
-          { role: 'user', content: prompt },
-        ],
+        responseFormat: 'json',
      });

-      const content = response.choices[0]?.message.content;
+      const content = response.content;
      if (!content) {
        throw new Error('Revised opinion返回空');
      }
--- a/src/review/agents/maintainability-agent.ts
+++ b/src/review/agents/maintainability-agent.ts
@@ -1,18 +1,12 @@
-import OpenAI from 'openai';
+import type { LLMGateway } from '../../llm/gateway';
 import type { LearningSystem } from '../learning/learning-system';
 import { ToolRegistry } from '../tools/registry';
 import { SpecialistAgent } from './specialist-agent';

 export class MaintainabilityAgent extends SpecialistAgent {
-  constructor(
-    openai: OpenAI,
-    model: string,
-    toolRegistry?: ToolRegistry,
-    learningSystem?: LearningSystem
-  ) {
+  constructor(gateway: LLMGateway, toolRegistry?: ToolRegistry, learningSystem?: LearningSystem) {
    super(
-      openai,
-      model,
+      gateway,
      'maintainability',
      'Maintainability Agent',
      '可维护性、复杂度、接口破坏风险和可测试性不足',
--- a/src/review/agents/reflexion-agent.ts
+++ b/src/review/agents/reflexion-agent.ts
@@ -1,8 +1,9 @@
 import { createHash } from 'node:crypto';
-import OpenAI from 'openai';
-import { logger } from '../../utils/logger';
-import { withGlobalPrompt } from '../../utils/global-prompt';
 import config from '../../config';
+import type { LLMGateway } from '../../llm/gateway';
+import type { LLMMessage } from '../../llm/types';
+import { withGlobalPrompt } from '../../utils/global-prompt';
+import { logger } from '../../utils/logger';
 import { LearningSystem } from '../learning/learning-system';
 import { findingResponseSchema } from '../schema/finding-schema';
 import { ToolRegistry } from '../tools/registry';
@@ -21,16 +22,15 @@ export class ReflexionAgent extends SpecialistAgent {
  private criticAgent: CriticAgent;

  constructor(
-    openai: OpenAI,
-    model: string,
+    gateway: LLMGateway,
    category: FindingCategory,
    agentName: string,
    focusPrompt: string,
    toolRegistry?: ToolRegistry,
    learningSystem?: LearningSystem
  ) {
-    super(openai, model, category, agentName, focusPrompt, toolRegistry, learningSystem);
-    this.criticAgent = new CriticAgent(openai, model);
+    super(gateway, category, agentName, focusPrompt, toolRegistry, learningSystem);
+    this.criticAgent = new CriticAgent(gateway);
  }

  async reviewWithReflection(
@@ -142,20 +142,24 @@ ${context.diff.slice(0, 3000)}
 }`;

    try {
-      const response = await this.openai.chat.completions.create({
-        model: this.model,
+      const messages: LLMMessage[] = [
+        {
+          role: 'system',
+          content: withGlobalPrompt(
+            `你是${this.agentName}，根据批评反馈改进审查结果。`,
+            config.review.globalPrompt
+          ),
+        },
+        { role: 'user', content: prompt },
+      ];
+
+      const response = await this.gateway.chatForRole('specialist', {
+        messages,
        temperature: 0.1,
-        response_format: { type: 'json_object' },
-        messages: [
-          {
-            role: 'system',
-            content: withGlobalPrompt(`你是${this.agentName}，根据批评反馈改进审查结果。`, config.openai.globalPrompt),
-          },
-          { role: 'user', content: prompt },
-        ],
+        responseFormat: 'json',
      });

-      const content = response.choices[0]?.message.content;
+      const content = response.content;
      if (!content) {
        logger.warn(`${this.agentName} Refine返回空结果，使用原findings`);
        return draft;
--- a/src/review/agents/reliability-agent.ts
+++ b/src/review/agents/reliability-agent.ts
@@ -1,18 +1,12 @@
-import OpenAI from 'openai';
+import type { LLMGateway } from '../../llm/gateway';
 import type { LearningSystem } from '../learning/learning-system';
 import { ToolRegistry } from '../tools/registry';
 import { SpecialistAgent } from './specialist-agent';

 export class ReliabilityAgent extends SpecialistAgent {
-  constructor(
-    openai: OpenAI,
-    model: string,
-    toolRegistry?: ToolRegistry,
-    learningSystem?: LearningSystem
-  ) {
+  constructor(gateway: LLMGateway, toolRegistry?: ToolRegistry, learningSystem?: LearningSystem) {
    super(
-      openai,
-      model,
+      gateway,
      'reliability',
      'Reliability Agent',
      '错误处理、重试策略、幂等性、并发一致性和资源释放',
--- a/src/review/agents/security-agent.ts
+++ b/src/review/agents/security-agent.ts
@@ -1,18 +1,12 @@
-import OpenAI from 'openai';
+import type { LLMGateway } from '../../llm/gateway';
 import type { LearningSystem } from '../learning/learning-system';
 import { ToolRegistry } from '../tools/registry';
 import { SpecialistAgent } from './specialist-agent';

 export class SecurityAgent extends SpecialistAgent {
-  constructor(
-    openai: OpenAI,
-    model: string,
-    toolRegistry?: ToolRegistry,
-    learningSystem?: LearningSystem
-  ) {
+  constructor(gateway: LLMGateway, toolRegistry?: ToolRegistry, learningSystem?: LearningSystem) {
    super(
-      openai,
-      model,
+      gateway,
      'security',
      'Security Agent',
      '注入漏洞、权限绕过、敏感信息泄露、反序列化和输入校验缺失',
--- a/src/review/agents/specialist-agent.ts
+++ b/src/review/agents/specialist-agent.ts
@@ -1,8 +1,9 @@
 import { createHash } from 'node:crypto';
-import OpenAI from 'openai';
-import { logger } from '../../utils/logger';
-import { withGlobalPrompt } from '../../utils/global-prompt';
 import config from '../../config';
+import type { LLMGateway } from '../../llm/gateway';
+import type { LLMMessage, LLMToolCall } from '../../llm/types';
+import { withGlobalPrompt } from '../../utils/global-prompt';
+import { logger } from '../../utils/logger';
 import type { LearningSystem } from '../learning/learning-system';
 import { findingResponseSchema } from '../schema/finding-schema';
 import { ToolRegistry } from '../tools/registry';
@@ -90,8 +91,7 @@ function toCompactContext(context: ReviewContext): string {

 export class SpecialistAgent {
  constructor(
-    protected readonly openai: OpenAI,
-    protected readonly model: string,
+    protected readonly gateway: LLMGateway,
    protected readonly category: FindingCategory,
    protected readonly agentName: string,
    protected readonly focusPrompt: string,
@@ -123,21 +123,24 @@ export class SpecialistAgent {
 ${toCompactContext(context)}`;

    try {
-      const response = await this.openai.chat.completions.create({
-        model: this.model,
+      const messages: LLMMessage[] = [
+        {
+          role: 'system',
+          content: withGlobalPrompt(
+            '你是严格的代码审查专家。返回结构化JSON，不输出额外文字。confidence取值范围0到1。line必须是正整数且引用新增行。',
+            config.review.globalPrompt
+          ),
+        },
+        { role: 'user', content: prompt },
+      ];
+
+      const response = await this.gateway.chatForRole('specialist', {
+        messages,
        temperature: 0,
-        response_format: { type: 'json_object' },
-        messages: [
-          {
-            role: 'system',
-            content:
-              withGlobalPrompt('你是严格的代码审查专家。返回结构化JSON，不输出额外文字。confidence取值范围0到1。line必须是正整数且引用新增行。', config.openai.globalPrompt),
-          },
-          { role: 'user', content: prompt },
-        ],
+        responseFormat: 'json',
      });

-      const content = response.choices[0]?.message.content;
+      const content = response.content;
      if (!content) {
        return { agentName: this.agentName, findings: [] };
      }
@@ -166,10 +169,11 @@ ${toCompactContext(context)}`;
  private async reviewWithReAct(run: ReviewRun, context: ReviewContext): Promise<AgentResult> {
    const maxIterations = 5;
    const findingsMap = new Map<string, Omit<Finding, 'id' | 'runId' | 'published'>>();
-    const messages: OpenAI.Chat.ChatCompletionMessageParam[] = [
+    const messages: LLMMessage[] = [
      {
        role: 'system',
-        content: withGlobalPrompt(`你是${this.agentName}，专注于${this.focusPrompt}。
+        content: withGlobalPrompt(
+          `你是${this.agentName}，专注于${this.focusPrompt}。

 你可以使用以下工具进行深入调查：
 ${this.toolRegistry!.getAll()
@@ -198,7 +202,9 @@ ${this.toolRegistry!.getAll()
  ],
  "need_more_investigation": false
 }
-每个 finding 对象的所有字段都是必填的。无问题时返回空数组 {"findings": [], "need_more_investigation": false}。`, config.openai.globalPrompt),
+每个 finding 对象的所有字段都是必填的。无问题时返回空数组 {"findings": [], "need_more_investigation": false}。`,
+          config.review.globalPrompt
+        ),
      },
    ];

@@ -211,7 +217,22 @@ ${this.toolRegistry!.getAll()
          run.repo
        );
        if (fewShotExamples.length > 0) {
-          messages.push(...fewShotExamples);
+          const llmFewShotExamples = fewShotExamples
+            .map((msg) => {
+              if (
+                (msg.role === 'system' || msg.role === 'user' || msg.role === 'assistant') &&
+                typeof msg.content === 'string'
+              ) {
+                return { role: msg.role, content: msg.content } as const;
+              }
+              return null;
+            })
+            .filter(
+              (msg): msg is { role: 'system' | 'user' | 'assistant'; content: string } =>
+                msg !== null
+            );
+
+          messages.push(...llmFewShotExamples);
          logger.debug(`${this.agentName} 添加了 ${fewShotExamples.length} 条Few-shot示例`, {
            runId: run.id,
          });
@@ -239,24 +260,24 @@ ${this.toolRegistry!.getAll()
        // 仅在最后一轮迭代强制 JSON 输出（无工具调用时解析结果）
        // 避免 response_format: json_object 与 tools 参数冲突导致工具不被调用
        const isLastIteration = iteration === maxIterations - 1;
-        const response = await this.openai.chat.completions.create({
-          model: this.model,
-          temperature: 0,
-          ...(isLastIteration ? { response_format: { type: 'json_object' as const } } : {}),
+        const response = await this.gateway.chatForRole('specialist', {
          messages,
-          tools: this.toolRegistry!.toOpenAIFunctions(),
-          tool_choice: isLastIteration ? 'none' : 'auto',
+          temperature: 0,
+          tools: this.toolRegistry!.toToolDefinitions(),
+          providerOptions: { tool_choice: isLastIteration ? 'none' : 'auto' },
+          responseFormat: isLastIteration ? 'json' : undefined,
        });

-        const choice = response.choices[0];
-        if (!choice) break;
-
        // 处理工具调用
-        if (choice.message.tool_calls && choice.message.tool_calls.length > 0) {
-          messages.push(choice.message as OpenAI.Chat.ChatCompletionMessageParam);
+        if (response.toolCalls.length > 0) {
+          messages.push({
+            role: 'assistant',
+            content: response.content || '',
+            toolCalls: response.toolCalls,
+          });

          // 执行所有工具调用
-          const toolResults = await this.executeTools(choice.message.tool_calls, {
+          const toolResults = await this.executeTools(response.toolCalls, {
            workspacePath: context.workspacePath,
            mirrorPath: context.mirrorPath,
            runId: run.id,
@@ -266,7 +287,7 @@ ${this.toolRegistry!.getAll()
          for (const toolResult of toolResults) {
            messages.push({
              role: 'tool',
-              tool_call_id: toolResult.toolCallId,
+              toolCallId: toolResult.toolCallId,
              content: JSON.stringify(toolResult.result || { error: toolResult.error }),
            });
          }
@@ -275,9 +296,9 @@ ${this.toolRegistry!.getAll()
        }

        // 解析findings（模型选择返回内容而非调用工具）
-        if (choice.message.content) {
+        if (response.content) {
          try {
-            const parsed = JSON.parse(choice.message.content);
+            const parsed = JSON.parse(response.content);

            if (parsed.findings && parsed.findings.length > 0) {
              // 使用schema验证findings，防止畸形数据流入发布系统
@@ -301,7 +322,10 @@ ${this.toolRegistry!.getAll()
            }

            // 模型要求继续调查但没有调用工具：注入 user 消息打破潜在的自我重复
-            messages.push(choice.message as OpenAI.Chat.ChatCompletionMessageParam);
+            messages.push({
+              role: 'assistant',
+              content: response.content,
+            });
            messages.push({
              role: 'user',
              content:
@@ -314,7 +338,10 @@ ${this.toolRegistry!.getAll()
              runId: run.id,
              error: parseError instanceof Error ? parseError.message : String(parseError),
            });
-            messages.push(choice.message as OpenAI.Chat.ChatCompletionMessageParam);
+            messages.push({
+              role: 'assistant',
+              content: response.content,
+            });
            messages.push({
              role: 'user',
              content:
@@ -338,28 +365,28 @@ ${this.toolRegistry!.getAll()
  }

  private async executeTools(
-    toolCalls: OpenAI.Chat.ChatCompletionMessageToolCall[],
+    toolCalls: LLMToolCall[],
    context: ToolExecutionContext
  ): Promise<ToolResult[]> {
    const results: ToolResult[] = [];

    for (const toolCall of toolCalls) {
-      const tool = this.toolRegistry!.get(toolCall.function.name);
+      const tool = this.toolRegistry!.get(toolCall.name);

      if (!tool) {
        results.push({
          toolCallId: toolCall.id,
          success: false,
-          error: `工具 ${toolCall.function.name} 未找到`,
+          error: `工具 ${toolCall.name} 未找到`,
        });
        continue;
      }

      try {
-        const params = JSON.parse(toolCall.function.arguments);
+        const params = JSON.parse(toolCall.arguments);
        const result = await tool.execute(params, context);

-        logger.info(`工具调用成功: ${toolCall.function.name}`, {
+        logger.info(`工具调用成功: ${toolCall.name}`, {
          runId: context.runId,
          params,
        });
@@ -370,7 +397,7 @@ ${this.toolRegistry!.getAll()
          result,
        });
      } catch (error) {
-        logger.error(`工具调用失败: ${toolCall.function.name}`, {
+        logger.error(`工具调用失败: ${toolCall.name}`, {
          runId: context.runId,
          error: error instanceof Error ? error.message : String(error),
        });
--- a/src/review/learning/learning-system.ts
+++ b/src/review/learning/learning-system.ts
@@ -1,4 +1,4 @@
-import OpenAI from 'openai';
+import type { LLMMessage } from '../../llm/types';
 import config from '../../config';
 import { logger } from '../../utils/logger';
 import { VectorMemoryStore } from '../memory/vector-store';
@@ -59,7 +59,7 @@ export class LearningSystem {
    category: FindingCategory,
    owner?: string,
    repo?: string
-  ): Promise<OpenAI.Chat.ChatCompletionMessageParam[]> {
+  ): Promise<LLMMessage[]> {
    const targetCount = config.review.fewShotExamplesCount;

    // 提前检查：如果few-shot被禁用（targetCount=0），直接返回，避免无意义的向量查询
@@ -105,7 +105,7 @@ export class LearningSystem {
      );
    }

-    const examples: OpenAI.Chat.ChatCompletionMessageParam[] = [];
+    const examples: LLMMessage[] = [];

    const negativeCount = Math.floor(targetCount * 0.4);

--- a/src/review/memory/vector-store.ts
+++ b/src/review/memory/vector-store.ts
@@ -1,18 +1,16 @@
 import { QdrantClient } from '@qdrant/js-client-rest';
-import OpenAI from 'openai';
+import { llmGateway } from '../../llm/gateway';
 import { logger } from '../../utils/logger';
 import { Finding } from '../types';
 import { MemoryEntry, MemorySearchResult } from './types';

 export class VectorMemoryStore {
  private client: QdrantClient;
-  private openai: OpenAI;
  private collectionName = 'code_review_memory';
  private initialized = false;

-  constructor(qdrantUrl: string, openaiClient: OpenAI) {
+  constructor(qdrantUrl: string) {
    this.client = new QdrantClient({ url: qdrantUrl });
-    this.openai = openaiClient;
  }

  async initialize(): Promise<void> {
@@ -47,7 +45,7 @@ export class VectorMemoryStore {
  async storeMemory(entry: MemoryEntry): Promise<void> {
    await this.initialize();

-    const embedding = await this.getEmbedding(entry.content);
+    const [embedding] = await this.getEmbedding([entry.content]);

    await this.client.upsert(this.collectionName, {
      points: [
@@ -73,7 +71,7 @@ export class VectorMemoryStore {
  async searchSimilar(query: string, limit = 5, filter?: any): Promise<MemorySearchResult[]> {
    await this.initialize();

-    const queryEmbedding = await this.getEmbedding(query);
+    const [queryEmbedding] = await this.getEmbedding([query]);

    const results = await this.client.search(this.collectionName, {
      vector: queryEmbedding,
@@ -101,14 +99,9 @@ export class VectorMemoryStore {
    }));
  }

-  private async getEmbedding(text: string): Promise<number[]> {
+  private async getEmbedding(texts: string[]): Promise<number[][]> {
    try {
-      const response = await this.openai.embeddings.create({
-        model: 'text-embedding-3-small',
-        input: text.slice(0, 8000), // 限制长度防止超出token限制
-      });
-
-      return response.data[0].embedding;
+      return llmGateway.embedForRole(texts.map((text) => text.slice(0, 8000))); // 限制长度防止超出token限制
    } catch (error) {
      logger.error('生成embedding失败', {
        error: error instanceof Error ? error.message : String(error),
--- a/src/review/orchestrator.ts
+++ b/src/review/orchestrator.ts
@@ -1,6 +1,6 @@
 import { randomUUID } from 'node:crypto';
-import OpenAI from 'openai';
 import config from '../config';
+import { LLMGateway, llmGateway } from '../llm/gateway';
 import { giteaService } from '../services/gitea';
 import { logger } from '../utils/logger';
 import { DebateOrchestrator } from './agents/debate-orchestrator';
@@ -42,7 +42,7 @@ function summarizeGatedCount(gatedCount: number): string {
 }

 export class ReviewOrchestrator {
-  private readonly openai: OpenAI;
+  private readonly gateway: LLMGateway;
  private readonly toolRegistry: ToolRegistry;
  private readonly correctnessAgent: ReflexionAgent;
  private readonly securityAgent: ReflexionAgent;
@@ -58,10 +58,7 @@ export class ReviewOrchestrator {
    private readonly localRepoManager: LocalRepoManager,
    private readonly diffExtractor: DiffExtractor
  ) {
-    this.openai = new OpenAI({
-      baseURL: config.openai.baseUrl,
-      apiKey: config.openai.apiKey,
-    });
+    this.gateway = llmGateway;

    // 初始化工具注册表
    this.toolRegistry = new ToolRegistry();
@@ -75,7 +72,7 @@ export class ReviewOrchestrator {

    // 初始化记忆和学习系统（可选）
    if (config.review.qdrantUrl && config.review.enableMemory) {
-      this.memoryStore = new VectorMemoryStore(config.review.qdrantUrl, this.openai);
+      this.memoryStore = new VectorMemoryStore(config.review.qdrantUrl);
      this.learningSystem = new LearningSystem(this.memoryStore, this.store);

      this.memoryStore.initialize().catch((err) => {
@@ -87,8 +84,7 @@ export class ReviewOrchestrator {

    // 创建Reflexion-wrapped agents并传递工具注册表和学习系统
    this.correctnessAgent = new ReflexionAgent(
-      this.openai,
-      config.review.modelSpecialist,
+      this.gateway,
      'correctness',
      'Correctness Agent',
      '业务逻辑正确性、边界条件、空值处理和明显bug',
@@ -97,8 +93,7 @@ export class ReviewOrchestrator {
    );

    this.securityAgent = new ReflexionAgent(
-      this.openai,
-      config.review.modelSpecialist,
+      this.gateway,
      'security',
      'Security Agent',
      '注入漏洞、权限绕过、敏感信息泄露、反序列化和输入校验缺失',
@@ -107,8 +102,7 @@ export class ReviewOrchestrator {
    );

    this.reliabilityAgent = new ReflexionAgent(
-      this.openai,
-      config.review.modelSpecialist,
+      this.gateway,
      'reliability',
      'Reliability Agent',
      '错误处理、重试策略、幂等性、并发一致性和资源释放',
@@ -117,8 +111,7 @@ export class ReviewOrchestrator {
    );

    this.maintainabilityAgent = new ReflexionAgent(
-      this.openai,
-      config.review.modelSpecialist,
+      this.gateway,
      'maintainability',
      'Maintainability Agent',
      '可维护性、复杂度、接口破坏风险和可测试性不足',
@@ -127,7 +120,7 @@ export class ReviewOrchestrator {
    );

    this.judgeAgent = new JudgeAgent();
-    this.debateOrchestrator = new DebateOrchestrator(this.openai, config.review.modelSpecialist);
+    this.debateOrchestrator = new DebateOrchestrator(this.gateway);
  }

  async execute(run: ReviewRun): Promise<void> {
--- a/src/review/tools/registry.ts
+++ b/src/review/tools/registry.ts
@@ -1,6 +1,7 @@
 import { z } from 'zod';
 import zodToJsonSchema from 'zod-to-json-schema';
 import type { JsonSchema7Type } from 'zod-to-json-schema';
+import type { LLMToolDefinition } from '../../llm/types';
 import { Tool } from './types';

 export class ToolRegistry {
@@ -30,6 +31,14 @@ export class ToolRegistry {
    }));
  }

+  toToolDefinitions(): LLMToolDefinition[] {
+    return this.getAll().map((tool) => ({
+      name: tool.name,
+      description: tool.description,
+      parameters: this.zodToJsonSchema(tool.parameters) as Record<string, unknown>,
+    }));
+  }
+
  private zodToJsonSchema(schema: z.ZodTypeAny): JsonSchema7Type {
    /**
     * 使用zod-to-json-schema库转换Zod schema为JSON Schema。
--- a/src/services/ai-review.ts
+++ b/src/services/ai-review.ts
@@ -1,15 +1,10 @@
-import OpenAI from 'openai';
 import config from '../config';
-import { logger } from '../utils/logger';
+import { llmGateway } from '../llm/gateway';
+import { LLMMessage } from '../llm/types';
 import { withGlobalPrompt } from '../utils/global-prompt';
+import { logger } from '../utils/logger';
 import { PullRequestFile, giteaService } from './gitea';

-// 创建OpenAI客户端
-const openai = new OpenAI({
-  baseURL: config.openai.baseUrl,
-  apiKey: config.openai.apiKey,
-});
-
 // 代码审查结果接口
 export interface CodeReviewResult {
  summary: string;
@@ -198,23 +193,26 @@ export const aiReviewService = {

      请根据以上信息，特别是考虑每个文件的完整内容和上下文，提供代码审查评价。如果没有发现明显问题，请简短说明代码质量良好即可。`;

-      const summaryPrompt = config.openai.customSummaryPrompt || defaultSummaryPrompt;
+      const summaryPrompt = config.review.customSummaryPrompt || defaultSummaryPrompt;

      // 获取总体评价
-      const summaryResponse = await openai.chat.completions.create({
-        model: config.openai.model,
-        messages: [
-          {
-            role: 'system',
-            content:
-              withGlobalPrompt('你是一个专业的代码审查助手，擅长识别代码中的严重问题和bug。你会查看代码的完整上下文，而不是为了评论而评论。如无明显问题，应给予简短肯定。', config.openai.globalPrompt),
-          },
-          { role: 'user', content: summaryPrompt },
-        ],
+      const messages: LLMMessage[] = [
+        {
+          role: 'system',
+          content: withGlobalPrompt(
+            '你是一个专业的代码审查助手，擅长识别代码中的严重问题和bug。你会查看代码的完整上下文，而不是为了评论而评论。如无明显问题，应给予简短肯定。',
+            config.review.globalPrompt
+          ),
+        },
+        { role: 'user', content: summaryPrompt },
+      ];
+
+      const summaryResponse = await llmGateway.chatForRole('legacy', {
+        messages,
        temperature: 0.1,
      });

-      const summary = summaryResponse.choices[0]?.message.content || '无法生成代码审查摘要';
+      const summary = summaryResponse.content || '无法生成代码审查摘要';
      return summary;
    } catch (error: any) {
      logger.error('生成总体评价失败:', error);
@@ -270,24 +268,27 @@ export const aiReviewService = {
        ]
        只返回JSON数组，不要有其他文本。`;

-        const filePrompt = config.openai.customLineCommentPrompt || defaultFilePrompt;
+        const filePrompt = config.review.customLineCommentPrompt || defaultFilePrompt;

        // 获取行级评论
-        const lineResponse = await openai.chat.completions.create({
-          model: config.openai.model,
-          messages: [
-            {
-              role: 'system',
-              content:
-                withGlobalPrompt('你是一个谨慎的代码审查助手，只对有明显bug或严重问题的代码行提供评论。大多数情况下，如果代码没有严重问题，你应该返回空数组。请以JSON格式返回结果。', config.openai.globalPrompt),
-            },
-            { role: 'user', content: filePrompt },
-          ],
+        const messages: LLMMessage[] = [
+          {
+            role: 'system',
+            content: withGlobalPrompt(
+              '你是一个谨慎的代码审查助手，只对有明显bug或严重问题的代码行提供评论。大多数情况下，如果代码没有严重问题，你应该返回空数组。请以JSON格式返回结果。',
+              config.review.globalPrompt
+            ),
+          },
+          { role: 'user', content: filePrompt },
+        ];
+
+        const lineResponse = await llmGateway.chatForRole('legacy', {
+          messages,
          temperature: 0.1,
-          response_format: { type: 'json_object' },
+          responseFormat: 'json',
        });

-        const content = lineResponse.choices[0]?.message.content;
+        const content = lineResponse.content;
        if (!content) continue;

        try {