diff --git a/desktop/src/lib/llm-service.ts b/desktop/src/lib/llm-service.ts
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+/**
+ * LLM Service Adapter - Unified LLM interface for L4 self-evolution engines
+ *
+ * Provides a unified interface for:
+ * - ReflectionEngine: Semantic analysis + deep reflection
+ * - ContextCompactor: High-quality summarization
+ * - MemoryExtractor: Semantic importance scoring
+ *
+ * Supports multiple backends:
+ * - OpenAI (GPT-4, GPT-3.5)
+ * - Volcengine (Doubao)
+ * - OpenFang Gateway (passthrough)
+ *
+ * Part of ZCLAW L4 Self-Evolution capability.
+ */
+
+// === Types ===
+
+export type LLMProvider = 'openai' | 'volcengine' | 'gateway' | 'mock';
+
+export interface LLMConfig {
+  provider: LLMProvider;
+  model?: string;
+  apiKey?: string;
+  apiBase?: string;
+  maxTokens?: number;
+  temperature?: number;
+  timeout?: number;
+}
+
+export interface LLMMessage {
+  role: 'system' | 'user' | 'assistant';
+  content: string;
+}
+
+export interface LLMResponse {
+  content: string;
+  tokensUsed?: {
+    input: number;
+    output: number;
+  };
+  model?: string;
+  latencyMs?: number;
+}
+
+export interface LLMServiceAdapter {
+  complete(messages: LLMMessage[], options?: Partial<LLMConfig>): Promise<LLMResponse>;
+  isAvailable(): boolean;
+  getProvider(): LLMProvider;
+}
+
+// === Default Configs ===
+
+const DEFAULT_CONFIGS: Record<LLMProvider, LLMConfig> = {
+  openai: {
+    provider: 'openai',
+    model: 'gpt-4o-mini',
+    apiBase: 'https://api.openai.com/v1',
+    maxTokens: 2000,
+    temperature: 0.7,
+    timeout: 30000,
+  },
+  volcengine: {
+    provider: 'volcengine',
+    model: 'doubao-pro-32k',
+    apiBase: 'https://ark.cn-beijing.volces.com/api/v3',
+    maxTokens: 2000,
+    temperature: 0.7,
+    timeout: 30000,
+  },
+  gateway: {
+    provider: 'gateway',
+    apiBase: '/api/llm',
+    maxTokens: 2000,
+    temperature: 0.7,
+    timeout: 60000,
+  },
+  mock: {
+    provider: 'mock',
+    maxTokens: 100,
+    temperature: 0,
+    timeout: 100,
+  },
+};
+
+// === Storage ===
+
+const LLM_CONFIG_KEY = 'zclaw-llm-config';
+
+// === Mock Adapter (for testing) ===
+
+class MockLLMAdapter implements LLMServiceAdapter {
+  private config: LLMConfig;
+
+  constructor(config: LLMConfig) {
+    this.config = config;
+  }
+
+  async complete(messages: LLMMessage[]): Promise<LLMResponse> {
+    // Simulate latency
+    await new Promise((resolve) => setTimeout(resolve, 50));
+
+    const lastMessage = messages[messages.length - 1];
+    const content = lastMessage?.content || '';
+
+    // Generate mock response based on content type
+    let response = '[Mock LLM Response] ';
+
+    if (content.includes('reflect') || content.includes('反思')) {
+      response += JSON.stringify({
+        patterns: [
+          {
+            observation: '用户经常询问代码优化相关问题',
+            frequency: 5,
+            sentiment: 'positive',
+            evidence: ['多次讨论性能优化', '关注代码质量'],
+          },
+        ],
+        improvements: [
+          {
+            area: '代码解释',
+            suggestion: '可以提供更详细的代码注释',
+            priority: 'medium',
+          },
+        ],
+        identityProposals: [],
+      });
+    } else if (content.includes('summarize') || content.includes('摘要')) {
+      response += '这是一个关于对话内容的摘要，包含了主要讨论的要点和结论。';
+    } else if (content.includes('importance') || content.includes('重要性')) {
+      response += JSON.stringify({
+        memories: [
+          { content: '用户偏好简洁的回答', importance: 7, type: 'preference' },
+        ],
+      });
+    } else {
+      response += 'Processed: ' + content.slice(0, 50);
+    }
+
+    return {
+      content: response,
+      tokensUsed: { input: content.length / 4, output: response.length / 4 },
+      model: 'mock-model',
+      latencyMs: 50,
+    };
+  }
+
+  isAvailable(): boolean {
+    return true;
+  }
+
+  getProvider(): LLMProvider {
+    return 'mock';
+  }
+}
+
+// === OpenAI Adapter ===
+
+class OpenAILLMAdapter implements LLMServiceAdapter {
+  private config: LLMConfig;
+
+  constructor(config: LLMConfig) {
+    this.config = { ...DEFAULT_CONFIGS.openai, ...config };
+  }
+
+  async complete(messages: LLMMessage[], options?: Partial<LLMConfig>): Promise<LLMResponse> {
+    const config = { ...this.config, ...options };
+    const startTime = Date.now();
+
+    if (!config.apiKey) {
+      throw new Error('[OpenAI] API key not configured');
+    }
+
+    const response = await fetch(`${config.apiBase}/chat/completions`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+        Authorization: `Bearer ${config.apiKey}`,
+      },
+      body: JSON.stringify({
+        model: config.model,
+        messages,
+        max_tokens: config.maxTokens,
+        temperature: config.temperature,
+      }),
+      signal: AbortSignal.timeout(config.timeout || 30000),
+    });
+
+    if (!response.ok) {
+      const error = await response.text();
+      throw new Error(`[OpenAI] API error: ${response.status} - ${error}`);
+    }
+
+    const data = await response.json();
+    const latencyMs = Date.now() - startTime;
+
+    return {
+      content: data.choices[0]?.message?.content || '',
+      tokensUsed: {
+        input: data.usage?.prompt_tokens || 0,
+        output: data.usage?.completion_tokens || 0,
+      },
+      model: data.model,
+      latencyMs,
+    };
+  }
+
+  isAvailable(): boolean {
+    return !!this.config.apiKey;
+  }
+
+  getProvider(): LLMProvider {
+    return 'openai';
+  }
+}
+
+// === Volcengine Adapter ===
+
+class VolcengineLLMAdapter implements LLMServiceAdapter {
+  private config: LLMConfig;
+
+  constructor(config: LLMConfig) {
+    this.config = { ...DEFAULT_CONFIGS.volcengine, ...config };
+  }
+
+  async complete(messages: LLMMessage[], options?: Partial<LLMConfig>): Promise<LLMResponse> {
+    const config = { ...this.config, ...options };
+    const startTime = Date.now();
+
+    if (!config.apiKey) {
+      throw new Error('[Volcengine] API key not configured');
+    }
+
+    const response = await fetch(`${config.apiBase}/chat/completions`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+        Authorization: `Bearer ${config.apiKey}`,
+      },
+      body: JSON.stringify({
+        model: config.model,
+        messages,
+        max_tokens: config.maxTokens,
+        temperature: config.temperature,
+      }),
+      signal: AbortSignal.timeout(config.timeout || 30000),
+    });
+
+    if (!response.ok) {
+      const error = await response.text();
+      throw new Error(`[Volcengine] API error: ${response.status} - ${error}`);
+    }
+
+    const data = await response.json();
+    const latencyMs = Date.now() - startTime;
+
+    return {
+      content: data.choices[0]?.message?.content || '',
+      tokensUsed: {
+        input: data.usage?.prompt_tokens || 0,
+        output: data.usage?.completion_tokens || 0,
+      },
+      model: data.model,
+      latencyMs,
+    };
+  }
+
+  isAvailable(): boolean {
+    return !!this.config.apiKey;
+  }
+
+  getProvider(): LLMProvider {
+    return 'volcengine';
+  }
+}
+
+// === Gateway Adapter (pass through to OpenFang) ===
+
+class GatewayLLMAdapter implements LLMServiceAdapter {
+  private config: LLMConfig;
+
+  constructor(config: LLMConfig) {
+    this.config = { ...DEFAULT_CONFIGS.gateway, ...config };
+  }
+
+  async complete(messages: LLMMessage[], options?: Partial<LLMConfig>): Promise<LLMResponse> {
+    const config = { ...this.config, ...options };
+    const startTime = Date.now();
+
+    const response = await fetch(`${config.apiBase}/complete`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+      },
+      body: JSON.stringify({
+        messages,
+        max_tokens: config.maxTokens,
+        temperature: config.temperature,
+      }),
+      signal: AbortSignal.timeout(config.timeout || 60000),
+    });
+
+    if (!response.ok) {
+      const error = await response.text();
+      throw new Error(`[Gateway] API error: ${response.status} - ${error}`);
+    }
+
+    const data = await response.json();
+    const latencyMs = Date.now() - startTime;
+
+    return {
+      content: data.content || data.choices?.[0]?.message?.content || '',
+      tokensUsed: data.tokensUsed || { input: 0, output: 0 },
+      model: data.model,
+      latencyMs,
+    };
+  }
+
+  isAvailable(): boolean {
+    // Gateway is available if we're connected to OpenFang
+    return typeof window !== 'undefined';
+  }
+
+  getProvider(): LLMProvider {
+    return 'gateway';
+  }
+}
+
+// === Factory ===
+
+let cachedAdapter: LLMServiceAdapter | null = null;
+
+export function createLLMAdapter(config?: Partial<LLMConfig>): LLMServiceAdapter {
+  const savedConfig = loadConfig();
+  const finalConfig = { ...savedConfig, ...config };
+
+  switch (finalConfig.provider) {
+    case 'openai':
+      return new OpenAILLMAdapter(finalConfig);
+    case 'volcengine':
+      return new VolcengineLLMAdapter(finalConfig);
+    case 'gateway':
+      return new GatewayLLMAdapter(finalConfig);
+    case 'mock':
+    default:
+      return new MockLLMAdapter(finalConfig);
+  }
+}
+
+export function getLLMAdapter(): LLMServiceAdapter {
+  if (!cachedAdapter) {
+    cachedAdapter = createLLMAdapter();
+  }
+  return cachedAdapter;
+}
+
+export function resetLLMAdapter(): void {
+  cachedAdapter = null;
+}
+
+// === Config Management ===
+
+export function loadConfig(): LLMConfig {
+  if (typeof window === 'undefined') {
+    return DEFAULT_CONFIGS.mock;
+  }
+
+  try {
+    const saved = localStorage.getItem(LLM_CONFIG_KEY);
+    if (saved) {
+      return JSON.parse(saved);
+    }
+  } catch {
+    // Ignore parse errors
+  }
+
+  // Default to mock for safety
+  return DEFAULT_CONFIGS.mock;
+}
+
+export function saveConfig(config: LLMConfig): void {
+  if (typeof window === 'undefined') return;
+
+  // Don't save API key to localStorage for security
+  const safeConfig = { ...config };
+  delete safeConfig.apiKey;
+
+  localStorage.setItem(LLM_CONFIG_KEY, JSON.stringify(safeConfig));
+  resetLLMAdapter();
+}
+
+// === Prompt Templates ===
+
+export const LLM_PROMPTS = {
+  reflection: {
+    system: `你是一个 AI Agent 的自我反思引擎。分析最近的对话历史，识别行为模式，并生成改进建议。
+
+输出 JSON 格式：
+{
+  "patterns": [
+    {
+      "observation": "观察到的模式描述",
+      "frequency": 数字,
+      "sentiment": "positive/negative/neutral",
+      "evidence": ["证据1", "证据2"]
+    }
+  ],
+  "improvements": [
+    {
+      "area": "改进领域",
+      "suggestion": "具体建议",
+      "priority": "high/medium/low"
+    }
+  ],
+  "identityProposals": []
+}`,
+    user: (context: string) => `分析以下对话历史，进行自我反思：
+
+${context}
+
+请识别行为模式（积极和消极），并提供具体的改进建议。`,
+  },
+
+  compaction: {
+    system: `你是一个对话摘要专家。将长对话压缩为简洁的摘要，保留关键信息。
+
+要求：
+1. 保留所有重要决策和结论
+2. 保留用户偏好和约束
+3. 保留未完成的任务
+4. 保持时间顺序
+5. 摘要应能在后续对话中替代原始内容`,
+    user: (messages: string) => `请将以下对话压缩为简洁摘要，保留关键信息：
+
+${messages}`,
+  },
+
+  extraction: {
+    system: `你是一个记忆提取专家。从对话中提取值得长期记住的信息。
+
+提取类型：
+- fact: 用户告知的事实（如"我的公司叫XXX"）
+- preference: 用户的偏好（如"我喜欢简洁的回答"）
+- lesson: 本次对话的经验教训
+- task: 未完成的任务或承诺
+
+输出 JSON 数组：
+[
+  {
+    "content": "记忆内容",
+    "type": "fact/preference/lesson/task",
+    "importance": 1-10,
+    "tags": ["标签1", "标签2"]
+  }
+]`,
+    user: (conversation: string) => `从以下对话中提取值得长期记住的信息：
+
+${conversation}
+
+如果没有值得记忆的内容，返回空数组 []。`,
+  },
+};
+
+// === Helper Functions ===
+
+export async function llmReflect(context: string, adapter?: LLMServiceAdapter): Promise<string> {
+  const llm = adapter || getLLMAdapter();
+
+  const response = await llm.complete([
+    { role: 'system', content: LLM_PROMPTS.reflection.system },
+    { role: 'user', content: LLM_PROMPTS.reflection.user(context) },
+  ]);
+
+  return response.content;
+}
+
+export async function llmCompact(messages: string, adapter?: LLMServiceAdapter): Promise<string> {
+  const llm = adapter || getLLMAdapter();
+
+  const response = await llm.complete([
+    { role: 'system', content: LLM_PROMPTS.compaction.system },
+    { role: 'user', content: LLM_PROMPTS.compaction.user(messages) },
+  ]);
+
+  return response.content;
+}
+
+export async function llmExtract(
+  conversation: string,
+  adapter?: LLMServiceAdapter
+): Promise<string> {
+  const llm = adapter || getLLMAdapter();
+
+  const response = await llm.complete([
+    { role: 'system', content: LLM_PROMPTS.extraction.system },
+    { role: 'user', content: LLM_PROMPTS.extraction.user(conversation) },
+  ]);
+
+  return response.content;
+}