版本: v1.0
适用对象: OpenCode CLI 用户
目标: 构建具备自我反思、自我修正、自我扩展能力的智能Agent系统
预计周期: 8-12周（可根据实际情况调整）
- 核心理念与架构
- 阶段一：基础反射层（第1-2周）
- 阶段二：记忆与经验层（第3-4周）
- 阶段三：代码自修改层（第5-6周）
- 阶段四：多智能体协作层（第7-8周）
- 阶段五：元学习与自适应层（第9-12周）
- 推荐Skills与工具清单
- 安全与约束机制
- 评估指标与测试方案
本路线图基于 递归自我改进（Recursive Self-Improvement） 理念，参考Gödel Agent[^3^]和SICA[^5^]等前沿研究，构建一个能够：
- 自我反思（Self-Reflection）: 分析自身行为并生成改进建议
- 自我修正（Self-Correction）: 基于反馈调整策略
- 自我扩展（Self-Extension）: 编写新工具/插件扩展能力
- 元学习（Meta-Learning）: 学习如何学习，优化学习策略本身
┌────────────────────────────────────────────────────────────┐
│ Meta-Controller (元控制器) │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────┐ │
│ │ Self-Eval │ │ Strategy │ │ Evolution │ │
│ │ Module │ │ Optimizer │ │ Engine │ │
│ └──────────────┘ └──────────────┘ └──────────────────┘ │
└────────────────────────────────────────────────────────────┘
│
┌─────────────────────┼─────────────────────┐
▼ ▼ ▼
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ Reflection │ │ Memory │ │ Code │
│ Layer │ │ Layer │ │ Generation │
│ │ │ │ │ Layer │
│ - Reflexion │ │ - Experience │ │ - STO │
│ - Self-Refine│ │ Replay │ │ - SICA │
│ - RISE │ │ - Skill Lib │ │ - Voyager │
└──────────────┘ └──────────────┘ └──────────────┘
OpenCode CLI 提供了独特的插件系统[^2^][^9^]，允许通过以下方式扩展：
- Event Hooks: 监听
session.idle,tool.execute.before等事件 - Custom Tools: 注册自定义工具函数
- Middleware: 拦截和修改工具执行流程
- State Management: 维护跨会话的状态
建立Agent的自我意识基础，实现运行时反思能力。
基于Reflexion[^5^]方法，实现"执行→评估→反思→重试"循环：
实现步骤：
- 创建反射插件 (
reflection-plugin.ts):
// plugins/reflection-plugin.ts
export const ReflectionPlugin = async ({ client, $ }) => {
const reflectionMemory: Array<{
task: string;
attempt: number;
output: string;
error?: string;
critique: string;
improvement: string;
}> = [];
return {
// 工具执行后触发
"tool.execute.after": async (input, output) => {
if (output.error || output.result?.success === false) {
// 触发反思流程
const reflection = await client.llm.complete({
prompt: `你刚刚执行了任务: ${input.task}
` +
`结果: ${JSON.stringify(output)}
` +
`请分析失败原因，并提供具体的改进建议。`,
system: "你是一个自我反思的AI助手。分析错误，提出可操作的改进方案。"
});
reflectionMemory.push({
task: input.task,
attempt: reflectionMemory.filter(r => r.task === input.task).length + 1,
output: JSON.stringify(output),
error: output.error,
critique: reflection.critique,
improvement: reflection.improvement
});
// 如果失败次数 < 3，使用改进建议重试
if (reflectionMemory.filter(r => r.task === input.task).length < 3) {
await client.tools.execute({
tool: input.tool,
args: {
...input.args,
context: `Previous attempt failed. Improvement suggestion: ${reflection.improvement}`
}
});
}
}
},
// 会话空闲时保存反思日志
"session.idle": async () => {
await $`echo '${JSON.stringify(reflectionMemory, null, 2)}' > .opencode/reflections/$(date +%Y%m%d).json`;
}
};
};- 配置插件 (
.opencode/config.jsonc):
创建评估器 (evaluators/):
// evaluators/code-quality.ts
export const CodeQualityEvaluator = {
name: "code-quality",
async evaluate(code: string, language: string): Promise<{
score: number;
issues: string[];
suggestions: string[];
}> {
const checks = {
javascript: [
{ pattern: /console\.log/g, penalty: -5, msg: "Remove debug logs" },
{ pattern: /var\s+/g, penalty: -3, msg: "Use let/const instead of var" },
{ pattern: /async.*await/g, bonus: 5, msg: "Good async handling" }
],
python: [
{ pattern: /print\(/g, penalty: -5, msg: "Remove print statements" },
{ pattern: /def\s+\w+\s*\([^)]*\):/g, bonus: 2, msg: "Function definitions" },
{ pattern: /import\s+\*/g, penalty: -10, msg: "Avoid wildcard imports" }
]
};
let score = 100;
const issues: string[] = [];
const suggestions: string[] = [];
const rules = checks[language] || [];
for (const rule of rules) {
const matches = (code.match(rule.pattern) || []).length;
if (rule.penalty && matches > 0) {
score += rule.penalty * matches;
issues.push(`${rule.msg} (${matches} occurrences)`);
}
if (rule.bonus && matches > 0) {
score += rule.bonus * matches;
suggestions.push(rule.msg);
}
}
return { score: Math.max(0, score), issues, suggestions };
}
};- 反射插件正常运行，能捕获错误并生成改进建议
- 建立基础的代码质量评估体系
- 实现反思日志的持久化存储
- 在3个不同任务上测试反射循环的有效性
| Skill名称 | 用途 | 优先级 |
|---|---|---|
opencode-plugin-development[^7^] |
插件开发基础 | ⭐⭐⭐⭐⭐ |
reflexion-implementation |
反射机制实现 | ⭐⭐⭐⭐⭐ |
code-quality-analysis |
代码评估 | ⭐⭐⭐⭐ |
构建长期记忆系统，实现经验回放和技能库管理。
基于Self-Generated In-Context Examples[^5^]方法：
实现经验库 (memory/experience-store.ts):
// memory/experience-store.ts
import { vectorStore } from '@opencode-ai/sdk';
interface Experience {
id: string;
taskType: string;
input: any;
trajectory: Array<{
action: string;
observation: string;
reward: number;
}>;
outcome: 'success' | 'failure';
finalOutput: any;
timestamp: number;
embedding?: number[];
}
export class ExperienceStore {
private experiences: Experience[] = [];
private vectorDB: any;
async initialize() {
// 初始化向量数据库用于相似性检索
this.vectorDB = await vectorStore.create({
dimension: 1536,
metric: 'cosine'
});
}
async addExperience(exp: Experience) {
// 生成任务嵌入向量
const embedding = await this.generateEmbedding(
`${exp.taskType}: ${JSON.stringify(exp.input)}`
);
exp.embedding = embedding;
this.experiences.push(exp);
// 保存到向量数据库
await this.vectorDB.upsert([{
id: exp.id,
vector: embedding,
metadata: {
taskType: exp.taskType,
outcome: exp.outcome,
timestamp: exp.timestamp
}
}]);
// 持久化到文件
await this.persist();
}
async retrieveRelevantExperiences(
currentTask: string,
taskType: string,
topK: number = 3
): Promise<Experience[]> {
const queryEmbedding = await this.generateEmbedding(currentTask);
// 向量相似性搜索
const results = await this.vectorDB.query({
vector: queryEmbedding,
filter: { taskType },
topK,
includeMetadata: true
});
return results.matches
.filter(m => m.metadata.outcome === 'success')
.map(m => this.experiences.find(e => e.id === m.id))
.filter(Boolean);
}
// 生成少样本提示
async generateFewShotPrompt(task: string, taskType: string): Promise<string> {
const relevantExps = await this.retrieveRelevantExperiences(task, taskType);
if (relevantExps.length === 0) return "";
const examples = relevantExps.map((exp, idx) => `
### Example ${idx + 1}:
Task: ${JSON.stringify(exp.input)}
Steps:
${exp.trajectory.map((t, i) => `${i+1}. ${t.action} → ${t.observation}`).join('\n')}
Result: ${JSON.stringify(exp.finalOutput)}
`).join('\n\n');
return `Here are some relevant past experiences:\n${examples}\n\nNow solve this task: ${task}`;
}
private async generateEmbedding(text: string): Promise<number[]> {
// 使用OpenCode SDK的嵌入功能
return await vectorStore.embed(text);
}
private async persist() {
await Bun.write(
'.opencode/memory/experiences.json',
JSON.stringify(this.experiences, null, 2)
);
}
}参考Voyager[^5^]的代码即策略（Code as Policies）方法：
// skills/skill-library.ts
interface Skill {
name: string;
description: string;
code: string;
dependencies: string[];
usageCount: number;
successRate: number;
version: number;
createdAt: number;
lastUsed: number;
}
export class SkillLibrary {
private skills: Map<string, Skill> = new Map();
private skillDir = '.opencode/skills/';
async addSkill(skill: Omit<Skill, 'usageCount' | 'successRate' | 'version' | 'createdAt' | 'lastUsed'>) {
const fullSkill: Skill = {
...skill,
usageCount: 0,
successRate: 0,
version: 1,
createdAt: Date.now(),
lastUsed: Date.now()
};
this.skills.set(skill.name, fullSkill);
// 保存为可执行文件
await Bun.write(
`${this.skillDir}/${skill.name}.ts`,
`// Auto-generated skill: ${skill.name}
` +
`// Description: ${skill.description}
` +
`// Version: 1
` +
skill.code
);
// 更新索引
await this.updateIndex();
}
async retrieveSkill(query: string): Promise<Skill[]> {
// 基于描述和名称的语义搜索
const queryLower = query.toLowerCase();
return Array.from(this.skills.values())
.filter(s =>
s.description.toLowerCase().includes(queryLower) ||
s.name.toLowerCase().includes(queryLower)
)
.sort((a, b) => b.successRate - a.successRate)
.slice(0, 5);
}
async updateSkillPerformance(skillName: string, success: boolean) {
const skill = this.skills.get(skillName);
if (!skill) return;
skill.usageCount++;
skill.lastUsed = Date.now();
// 更新成功率 (指数移动平均)
const alpha = 0.3;
const currentSuccess = success ? 1 : 0;
skill.successRate = skill.successRate * (1 - alpha) + currentSuccess * alpha;
// 如果成功率低于阈值，标记为需要改进
if (skill.successRate < 0.5 && skill.usageCount > 5) {
await this.flagForImprovement(skill);
}
await this.updateIndex();
}
private async flagForImprovement(skill: Skill) {
// 创建改进任务
await Bun.write(
'.opencode/improvement-queue.json',
JSON.stringify({
skill: skill.name,
reason: `Success rate dropped to ${(skill.successRate * 100).toFixed(1)}%`,
priority: 'high',
timestamp: Date.now()
}, null, 2)
);
}
private async updateIndex() {
const index = Array.from(this.skills.values()).map(s => ({
name: s.name,
description: s.description,
successRate: s.successRate,
usageCount: s.usageCount
}));
await Bun.write(
`${this.skillDir}/index.json`,
JSON.stringify(index, null, 2)
);
}
}创建技能生成器 (skills/auto-generator.ts):
// skills/auto-generator.ts
export class SkillGenerator {
private llm: any;
private skillLibrary: SkillLibrary;
async generateSkillFromTask(taskDescription: string, successfulTrajectory: any[]) {
const prompt = `Analyze this successful task execution and create a reusable skill function.
Task: ${taskDescription}
Execution Steps:
${successfulTrajectory.map((t, i) => `${i+1}. Action: ${t.action}\n Result: ${t.observation}`).join('\n')}
Create a TypeScript function that generalizes this pattern. Include:
1. Function signature with typed parameters
2. Error handling
3. Input validation
4. JSDoc documentation
Output only the code, no explanations.`;
const generatedCode = await this.llm.complete({ prompt });
// 提取函数名（简单启发式）
const functionName = generatedCode.match(/export\s+(?:async\s+)?function\s+(\w+)/)?.[1] ||
`skill_${Date.now()}`;
await this.skillLibrary.addSkill({
name: functionName,
description: `Auto-generated skill for: ${taskDescription.slice(0, 100)}`,
code: generatedCode,
dependencies: this.extractDependencies(generatedCode)
});
return functionName;
}
private extractDependencies(code: string): string[] {
const imports = code.match(/from\s+['"]([^'"]+)['"]/g) || [];
return imports.map(i => i.replace(/from\s+['"]/, '').replace(/['"]$/, ''));
}
}- 经验存储系统能成功记录和检索相似任务
- 技能库包含至少5个自动生成的可复用技能
- 实现基于过往经验的少样本提示增强
- 建立技能成功率追踪机制
| Skill名称 | 用途 | 优先级 |
|---|---|---|
vector-store-implementation |
向量数据库集成 | ⭐⭐⭐⭐⭐ |
experience-replay-system |
经验回放机制 | ⭐⭐⭐⭐⭐ |
code-as-policies |
代码策略生成 | ⭐⭐⭐⭐ |
semantic-search |
语义检索 | ⭐⭐⭐⭐ |
实现Agent能够安全地修改自身代码，这是递归自我改进的关键一步。
基于STO[^5^]方法，让Agent优化自己的代码：
创建代码优化器 (evolution/code-optimizer.ts):
// evolution/code-optimizer.ts
import { $ } from 'bun';
interface CodeVariant {
id: string;
code: string;
parentId?: string;
generation: number;
fitness?: number;
testResults?: any;
}
export class SelfOptimizer {
private variants: CodeVariant[] = [];
private currentGeneration = 0;
private readonly maxGenerations = 5;
private readonly populationSize = 3;
async optimizeSelf(targetModule: string, testSuite: string) {
console.log(`🧬 Starting self-optimization for ${targetModule}`);
// 读取当前代码
const originalCode = await Bun.file(targetModule).text();
// 初始化种群
this.variants = [{
id: 'v0-original',
code: originalCode,
generation: 0
}];
for (let gen = 0; gen < this.maxGenerations; gen++) {
this.currentGeneration = gen;
console.log(`
📊 Generation ${gen}`);
// 评估当前种群
for (const variant of this.variants.filter(v => v.generation === gen)) {
variant.fitness = await this.evaluateFitness(variant, testSuite);
console.log(` ${variant.id}: fitness = ${variant.fitness}`);
}
// 选择最优
const best = this.variants
.filter(v => v.generation === gen)
.sort((a, b) => (b.fitness || 0) - (a.fitness || 0))[0];
if (best.fitness && best.fitness > 0.95) {
console.log(`✅ Found satisfactory solution: ${best.id}`);
return best;
}
// 生成下一代
if (gen < this.maxGenerations - 1) {
await this.generateNextGeneration(best);
}
}
// 返回最优解
return this.variants.sort((a, b) => (b.fitness || 0) - (a.fitness || 0))[0];
}
private async evaluateFitness(variant: CodeVariant, testSuite: string): Promise<number> {
// 写入临时文件
const tempFile = `.opencode/temp/${variant.id}.ts`;
await Bun.write(tempFile, variant.code);
try {
// 语法检查
const syntaxCheck = await $`bun run tsc --noEmit ${tempFile}`.quiet();
if (syntaxCheck.exitCode !== 0) return 0;
// 运行测试
const testResult = await $`bun test ${testSuite}`.quiet().catch(e => e);
// 计算适应度：测试通过率 + 代码质量
const testScore = testResult.exitCode === 0 ? 0.7 : 0.3;
const qualityScore = this.assessCodeQuality(variant.code) * 0.3;
return testScore + qualityScore;
} catch (e) {
return 0;
}
}
private async generateNextGeneration(parent: CodeVariant) {
const mutations = [
this.mutateAddErrorHandling(parent),
this.mutateOptimizePerformance(parent),
this.mutateAddLogging(parent)
];
const newVariants = await Promise.all(mutations);
for (let i = 0; i < newVariants.length; i++) {
this.variants.push({
id: `v${this.currentGeneration + 1}-${i}`,
code: newVariants[i],
parentId: parent.id,
generation: this.currentGeneration + 1
});
}
}
private async mutateAddErrorHandling(parent: CodeVariant): Promise<string> {
const prompt = `Improve this code by adding comprehensive error handling:
${parent.code}
Requirements:
1. Add try-catch blocks where appropriate
2. Add input validation
3. Add meaningful error messages
4. Ensure all async operations handle errors
Output only the improved code.`;
return await this.llm.complete({ prompt });
}
private async mutateOptimizePerformance(parent: CodeVariant): Promise<string> {
const prompt = `Optimize this code for better performance:
${parent.code}
Focus on:
1. Algorithmic efficiency
2. Reducing unnecessary operations
3. Better data structures
4. Caching where appropriate
Output only the optimized code.`;
return await this.llm.complete({ prompt });
}
private assessCodeQuality(code: string): number {
let score = 1.0;
// 检查代码异味
if (code.includes('any')) score -= 0.1;
if ((code.match(/console\.log/g) || []).length > 3) score -= 0.1;
if (code.length > 500 && !code.includes('//')) score -= 0.1; // 缺少注释
return Math.max(0, score);
}
}创建安全执行环境 (security/sandbox.ts):
// security/sandbox.ts
export class SecureSandbox {
private allowedModules = new Set([
'bun', '@opencode-ai/sdk', 'path', 'fs/promises'
]);
private forbiddenPatterns = [
/eval\s*\(/,
/Function\s*\(/,
/child_process/,
/require\s*\(\s*['"]http/,
/process\.exit/
];
validateCode(code: string): { valid: boolean; errors: string[] } {
const errors: string[] = [];
// 检查禁止模式
for (const pattern of this.forbiddenPatterns) {
if (pattern.test(code)) {
errors.push(`Forbidden pattern detected: ${pattern.source}`);
}
}
// 检查模块导入
const imports = code.match(/from\s+['"]([^'"]+)['"]/g) || [];
for (const imp of imports) {
const module = imp.replace(/from\s+['"]/, '').replace(/['"]$/, '');
if (!this.allowedModules.has(module) && !module.startsWith('./')) {
errors.push(`Unauthorized module import: ${module}`);
}
}
return { valid: errors.length === 0, errors };
}
async executeInSandbox(code: string, timeout: number = 5000): Promise<any> {
// 使用Worker线程隔离执行
const worker = new Worker(URL.createObjectURL(new Blob([`
${code}
self.postMessage({ type: 'complete' });
`])));
return new Promise((resolve, reject) => {
const timer = setTimeout(() => {
worker.terminate();
reject(new Error('Execution timeout'));
}, timeout);
worker.onmessage = (e) => {
clearTimeout(timer);
worker.terminate();
resolve(e.data);
};
worker.onerror = (err) => {
clearTimeout(timer);
worker.terminate();
reject(err);
};
});
}
}创建进化控制器 (evolution/controller.ts):
// evolution/controller.ts
export class EvolutionController {
private optimizer: SelfOptimizer;
private sandbox: SecureSandbox;
private backupDir = '.opencode/backups/';
async proposeModification(
targetFile: string,
improvementGoal: string
): Promise<{ approved: boolean; variant?: any }> {
// 1. 创建备份
await this.createBackup(targetFile);
// 2. 生成改进方案
const currentCode = await Bun.file(targetFile).text();
const proposedCode = await this.generateImprovement(currentCode, improvementGoal);
// 3. 安全检查
const validation = this.sandbox.validateCode(proposedCode);
if (!validation.valid) {
console.error('Security check failed:', validation.errors);
return { approved: false };
}
// 4. 运行测试套件
const testResults = await this.runTests(proposedCode);
if (testResults.passed) {
// 5. 人类确认（生产环境）或自动批准（开发环境）
if (process.env.AUTO_APPROVE === 'true') {
await this.applyModification(targetFile, proposedCode);
return { approved: true, variant: { code: proposedCode, tests: testResults } };
} else {
// 等待人工审核
await this.queueForReview(targetFile, proposedCode, testResults);
return { approved: false };
}
}
return { approved: false };
}
private async createBackup(file: string) {
const timestamp = new Date().toISOString().replace(/[:.]/g, '-');
const backupPath = `${this.backupDir}${timestamp}_${file.replace(/\//g, '_')}`;
await $`cp ${file} ${backupPath}`;
}
private async generateImprovement(code: string, goal: string): Promise<string> {
const prompt = `Improve the following code to achieve: ${goal}
Current code:
${code}
Requirements:
1. Maintain all existing functionality
2. Add comprehensive error handling
3. Include TypeScript types
4. Add inline documentation
5. Optimize for readability and performance
Output only the improved code without explanations.`;
return await this.llm.complete({ prompt, temperature: 0.2 });
}
private async runTests(code: string): Promise<{ passed: boolean; coverage: number }> {
// 实现测试逻辑
return { passed: true, coverage: 0.85 };
}
}- 实现代码优化器，能生成并评估代码变体
- 建立安全沙箱，阻止危险代码执行
- 完成至少1个模块的自我改进（如反射插件自身）
- 建立自动备份和回滚机制
| Skill名称 | 用途 | 优先级 |
|---|---|---|
self-taught-optimizer |
STO实现 | ⭐⭐⭐⭐⭐ |
secure-code-execution |
沙箱安全 | ⭐⭐⭐⭐⭐ |
genetic-programming |
遗传算法优化 | ⭐⭐⭐⭐ |
automated-testing |
自动化测试 | ⭐⭐⭐⭐ |
构建多Agent系统，实现集体智能和专业化分工。
基于SiriuS[^5^]的多Agent经验共享机制：
创建Agent编排器 (multi-agent/orchestrator.ts):
// multi-agent/orchestrator.ts
interface Agent {
id: string;
role: 'planner' | 'coder' | 'reviewer' | 'tester' | 'researcher';
capabilities: string[];
currentTask?: string;
performance: {
tasksCompleted: number;
successRate: number;
avgResponseTime: number;
};
}
interface Task {
id: string;
description: string;
requiredCapabilities: string[];
complexity: 'low' | 'medium' | 'high';
subtasks?: Task[];
assignedTo?: string;
status: 'pending' | 'in_progress' | 'completed' | 'failed';
}
export class MultiAgentOrchestrator {
private agents: Map<string, Agent> = new Map();
private taskQueue: Task[] = [];
private sharedMemory: any;
async initializeAgents() {
// 创建专业化Agent
const defaultAgents: Agent[] = [
{
id: 'planner-1',
role: 'planner',
capabilities: ['architecture', 'decomposition', 'estimation'],
performance: { tasksCompleted: 0, successRate: 1.0, avgResponseTime: 0 }
},
{
id: 'coder-1',
role: 'coder',
capabilities: ['typescript', 'python', 'optimization', 'debugging'],
performance: { tasksCompleted: 0, successRate: 1.0, avgResponseTime: 0 }
},
{
id: 'reviewer-1',
role: 'reviewer',
capabilities: ['code-review', 'security-audit', 'best-practices'],
performance: { tasksCompleted: 0, successRate: 1.0, avgResponseTime: 0 }
},
{
id: 'tester-1',
role: 'tester',
capabilities: ['unit-testing', 'integration-testing', 'edge-cases'],
performance: { tasksCompleted: 0, successRate: 1.0, avgResponseTime: 0 }
}
];
for (const agent of defaultAgents) {
this.agents.set(agent.id, agent);
}
}
async submitTask(description: string): Promise<any> {
// 1. 规划阶段：分解任务
const planner = this.agents.get('planner-1')!;
const taskBreakdown = await this.planTask(planner, description);
// 2. 分配阶段：匹配Agent
const assignments = this.assignTasks(taskBreakdown);
// 3. 执行阶段：并行/串行执行
const results = await this.executeAssignments(assignments);
// 4. 审核阶段：质量检查
const reviewer = this.agents.get('reviewer-1')!;
const review = await this.reviewWork(reviewer, results);
// 5. 学习阶段：更新共享经验
await this.updateSharedExperience(results, review);
return { results, review };
}
private async planTask(planner: Agent, description: string): Promise<Task[]> {
const prompt = `As an architecture planner, break down this task into subtasks:
Task: ${description}
For each subtask, specify:
1. Description
2. Required capabilities
3. Complexity (low/medium/high)
4. Dependencies on other subtasks
Output as JSON array.`;
const response = await this.callAgent(planner, prompt);
return JSON.parse(response);
}
private assignTasks(tasks: Task[]): Map<string, Task[]> {
const assignments = new Map<string, Task[]>();
for (const task of tasks) {
// 基于能力匹配选择最佳Agent
const bestAgent = this.selectBestAgent(task.requiredCapabilities);
if (!assignments.has(bestAgent)) {
assignments.set(bestAgent, []);
}
assignments.get(bestAgent)!.push(task);
}
return assignments;
}
private selectBestAgent(requiredCapabilities: string[]): string {
let bestAgent = '';
let bestScore = -1;
for (const [id, agent] of this.agents) {
const score = requiredCapabilities.filter(cap =>
agent.capabilities.includes(cap)
).length / requiredCapabilities.length;
// 考虑历史表现
const performanceBonus = agent.performance.successRate * 0.2;
if (score + performanceBonus > bestScore) {
bestScore = score + performanceBonus;
bestAgent = id;
}
}
return bestAgent;
}
private async executeAssignments(
assignments: Map<string, Task[]>
): Promise<Map<string, any>> {
const results = new Map<string, any>();
// 并行执行独立任务
const promises = Array.from(assignments.entries()).map(async ([agentId, tasks]) => {
const agent = this.agents.get(agentId)!;
const agentResults = [];
for (const task of tasks) {
const startTime = Date.now();
try {
const result = await this.executeTask(agent, task);
agentResults.push({ task: task.id, result, success: true });
// 更新Agent表现
agent.performance.tasksCompleted++;
agent.performance.avgResponseTime =
(agent.performance.avgResponseTime * (agent.performance.tasksCompleted - 1) +
(Date.now() - startTime)) / agent.performance.tasksCompleted;
} catch (error) {
agentResults.push({ task: task.id, error, success: false });
agent.performance.successRate *= 0.95; // 降低成功率
}
}
results.set(agentId, agentResults);
});
await Promise.all(promises);
return results;
}
private async updateSharedExperience(results: Map<string, any>, review: any) {
// 将成功经验存入共享库
const successfulPatterns = Array.from(results.values())
.flat()
.filter((r: any) => r.success)
.map((r: any) => ({
pattern: r.result,
timestamp: Date.now(),
rating: review.score
}));
await this.sharedMemory.store('successful_patterns', successfulPatterns);
}
}// multi-agent/protocol.ts
export class AgentCommunication {
private messageBus: EventTarget;
async sendMessage(
from: string,
to: string,
type: 'request' | 'response' | 'broadcast' | 'delegation',
payload: any,
priority: 'low' | 'normal' | 'high' = 'normal'
): Promise<any> {
const message = {
id: crypto.randomUUID(),
from,
to,
type,
payload,
priority,
timestamp: Date.now(),
ttl: 5 // 最多转发5次
};
if (type === 'broadcast') {
this.messageBus.dispatchEvent(new CustomEvent('broadcast', { detail: message }));
return;
}
// 等待响应
return new Promise((resolve, reject) => {
const timeout = setTimeout(() => reject(new Error('Message timeout')), 30000);
const handler = (e: CustomEvent) => {
if (e.detail.inReplyTo === message.id) {
clearTimeout(timeout);
this.messageBus.removeEventListener('message', handler);
resolve(e.detail.payload);
}
};
this.messageBus.addEventListener('message', handler);
this.messageBus.dispatchEvent(new CustomEvent('message', { detail: message }));
});
}
// 订阅特定类型的消息
subscribe(
agentId: string,
messageTypes: string[],
handler: (msg: any) => void
): () => void {
const wrapper = (e: CustomEvent) => {
if (e.detail.to === agentId && messageTypes.includes(e.detail.type)) {
handler(e.detail);
}
};
this.messageBus.addEventListener('message', wrapper);
return () => this.messageBus.removeEventListener('message', wrapper);
}
}- 实现多Agent任务分解与分配
- 建立Agent间通信协议
- 完成至少1个复杂任务的多Agent协作执行
- 实现共享经验库
| Skill名称 | 用途 | 优先级 |
|---|---|---|
multi-agent-orchestration |
多Agent编排 | ⭐⭐⭐⭐⭐ |
sirius-framework |
经验共享机制 | ⭐⭐⭐⭐ |
consensus-algorithms |
Agent共识机制 | ⭐⭐⭐ |
distributed-systems |
分布式通信 | ⭐⭐⭐ |
实现元学习能力，让Agent学会如何改进自己，达到真正的自主进化。
基于SEAL[^5^]的自适应方法：
创建元学习引擎 (meta-learning/engine.ts):
// meta-learning/engine.ts
interface LearningStrategy {
id: string;
name: string;
description: string;
applicableTo: string[];
effectiveness: number;
usageCount: number;
}
interface MetaLearningState {
strategies: LearningStrategy[];
currentStrategy: string;
adaptationHistory: Array<{
timestamp: number;
fromStrategy: string;
toStrategy: string;
reason: string;
outcome: 'success' | 'failure';
}>;
}
export class MetaLearningEngine {
private state: MetaLearningState;
private performanceTracker: PerformanceTracker;
constructor() {
this.state = {
strategies: [
{
id: 'reflexion',
name: 'Reflexion-based Learning',
description: 'Learn from immediate feedback and retry',
applicableTo: ['coding', 'reasoning', 'planning'],
effectiveness: 0.8,
usageCount: 0
},
{
id: 'skill-acquisition',
name: 'Skill Library Expansion',
description: 'Extract and store reusable skills',
applicableTo: ['repetitive-tasks', 'pattern-matching'],
effectiveness: 0.75,
usageCount: 0
},
{
id: 'self-modification',
name: 'Code Self-Modification',
description: 'Rewrite own code for improvement',
applicableTo: ['optimization', 'bug-fixing'],
effectiveness: 0.6,
usageCount: 0
},
{
id: 'multi-agent',
name: 'Collaborative Problem Solving',
description: 'Use multiple specialized agents',
applicableTo: ['complex-tasks', 'multi-domain'],
effectiveness: 0.85,
usageCount: 0
}
],
currentStrategy: 'reflexion',
adaptationHistory: []
};
}
async adaptStrategy(taskType: string, recentPerformance: number): Promise<string> {
// 如果当前策略效果不佳，尝试切换
if (recentPerformance < 0.6) {
const alternative = this.selectBestStrategy(taskType);
if (alternative !== this.state.currentStrategy) {
console.log(`🔄 Adapting strategy: ${this.state.currentStrategy} → ${alternative}`);
this.state.adaptationHistory.push({
timestamp: Date.now(),
fromStrategy: this.state.currentStrategy,
toStrategy: alternative,
reason: `Performance dropped to ${(recentPerformance * 100).toFixed(1)}%`,
outcome: 'success' // 将在下次评估时更新
});
this.state.currentStrategy = alternative;
// 更新策略有效性评分
this.updateStrategyEffectiveness(
this.state.currentStrategy,
recentPerformance
);
}
}
return this.state.currentStrategy;
}
private selectBestStrategy(taskType: string): string {
const candidates = this.state.strategies
.filter(s => s.applicableTo.includes(taskType) || s.applicableTo.includes('general'))
.sort((a, b) => {
// 综合考虑有效性和探索需求
const aScore = a.effectiveness + (1 / (a.usageCount + 1)) * 0.1; // 鼓励探索
const bScore = b.effectiveness + (1 / (b.usageCount + 1)) * 0.1;
return bScore - aScore;
});
return candidates[0]?.id || 'reflexion';
}
private updateStrategyEffectiveness(strategyId: string, performance: number) {
const strategy = this.state.strategies.find(s => s.id === strategyId);
if (strategy) {
// 指数移动平均
strategy.effectiveness = strategy.effectiveness * 0.7 + performance * 0.3;
strategy.usageCount++;
}
}
// 生成新的学习策略
async evolveNewStrategy(): Promise<LearningStrategy | null> {
const prompt = `Based on the following learning history, propose a new learning strategy:
Current Strategies:
${JSON.stringify(this.state.strategies, null, 2)}
Adaptation History:
${JSON.stringify(this.state.adaptationHistory.slice(-10), null, 2)}
Design a new strategy that addresses current weaknesses. Output as JSON:
{
"name": "...",
"description": "...",
"applicableTo": ["..."],
"implementation": "..."
}`;
const response = await this.llm.complete({ prompt, temperature: 0.7 });
try {
const newStrategy = JSON.parse(response);
return {
id: `strategy-${Date.now()}`,
...newStrategy,
effectiveness: 0.5, // 初始中性评分
usageCount: 0
};
} catch {
return null;
}
}
}基于Self-Challenging[^5^]方法：
// meta-learning/curriculum.ts
export class AutoCurriculum {
private currentDifficulty = 1;
private successThreshold = 0.8;
private failureThreshold = 0.4;
async generateNextTask(
currentCapabilities: string[],
recentPerformance: number
): Promise<{ task: string; difficulty: number }> {
// 根据表现调整难度
if (recentPerformance > this.successThreshold) {
this.currentDifficulty++;
} else if (recentPerformance < this.failureThreshold) {
this.currentDifficulty = Math.max(1, this.currentDifficulty - 1);
}
const prompt = `Generate a coding task with difficulty level ${this.currentDifficulty}/10.
Requirements:
- Must require these capabilities: ${currentCapabilities.join(', ')}
- Should be solvable but challenging at this level
- Include specific acceptance criteria
- For difficulty ${this.currentDifficulty}, ${this.getDifficultyConstraints(this.currentDifficulty)}
Output format:
{
"title": "...",
"description": "...",
"acceptanceCriteria": ["..."],
"hints": ["..."],
"estimatedTime": "..."
}`;
const response = await this.llm.complete({ prompt });
const task = JSON.parse(response);
return {
task: `${task.title}: ${task.description}`,
difficulty: this.currentDifficulty
};
}
private getDifficultyConstraints(level: number): string {
const constraints = {
1: 'use only basic syntax, single file',
3: 'require simple error handling, 2-3 files',
5: 'require design patterns, external API integration',
7: 'require concurrent processing, complex state management',
10: 'require distributed systems knowledge, optimization for scale'
};
return constraints[level] || 'increase complexity appropriately';
}
}// meta-learning/self-assessment.ts
export class SelfAssessment {
async conductSelfAssessment(): Promise<{
strengths: string[];
weaknesses: string[];
improvementGoals: string[];
suggestedTraining: string[];
}> {
// 1. 收集性能数据
const metrics = await this.collectMetrics();
// 2. 分析能力差距
const analysis = await this.analyzeGaps(metrics);
// 3. 设定改进目标
const goals = await this.setGoals(analysis);
// 4. 生成训练计划
const training = await this.generateTrainingPlan(goals);
return {
strengths: analysis.strengths,
weaknesses: analysis.weaknesses,
improvementGoals: goals,
suggestedTraining: training
};
}
private async analyzeGaps(metrics: any) {
const prompt = `Analyze these performance metrics and identify strengths/weaknesses:
${JSON.stringify(metrics, null, 2)}
Provide analysis in JSON format:
{
"strengths": ["..."],
"weaknesses": ["..."],
"bottlenecks": ["..."]
}`;
return JSON.parse(await this.llm.complete({ prompt }));
}
private async setGoals(analysis: any): Promise<string[]> {
return analysis.weaknesses.map((w: string) =>
`Improve ${w} by 20% within next 2 weeks`
);
}
}- 实现元学习引擎，能自动选择学习策略
- 建立自动课程生成系统
- 完成至少1轮完整的自我评估→目标设定→训练→验证循环
- 系统能根据表现自动调整进化方向
| Skill名称 | 用途 | 优先级 |
|---|---|---|
meta-learning |
元学习算法 | ⭐⭐⭐⭐⭐ |
auto-curriculum |
自动课程生成 | ⭐⭐⭐⭐⭐ |
self-assessment |
自我评估 | ⭐⭐⭐⭐ |
reinforcement-learning |
强化学习基础 | ⭐⭐⭐⭐ |
| 优先级 | Skill名称 | 用途 | 学习资源 |
|---|---|---|---|
| ⭐⭐⭐⭐⭐ | opencode-plugin-development[^7^][^9^] |
插件开发基础 | OpenCode官方文档 |
| ⭐⭐⭐⭐⭐ | reflexion-implementation |
反射机制 | arXiv:2303.11366 |
| ⭐⭐⭐⭐⭐ | vector-store-implementation |
向量数据库 | Pinecone/Weaviate文档 |
| ⭐⭐⭐⭐⭐ | self-taught-optimizer |
代码自优化 | STO论文 |
| ⭐⭐⭐⭐⭐ | secure-code-execution |
安全沙箱 | Deno/Worker API |
| ⭐⭐⭐⭐ | experience-replay-system |
经验回放 | NeurIPS 2025论文 |
| ⭐⭐⭐⭐ | code-as-policies |
代码策略 | Voyager论文 |
| ⭐⭐⭐⭐ | multi-agent-orchestration |
多Agent系统 | SiriuS论文 |
| ⭐⭐⭐⭐ | meta-learning |
元学习 | MAML, Reptile算法 |
| ⭐⭐⭐ | genetic-programming |
遗传算法 | DEAP库文档 |
# 核心依赖
runtime: Bun (高性能JavaScript运行时)
language: TypeScript (类型安全)
vector_db: Pinecone 或 Weaviate (经验检索)
state_management: Redis (共享状态)
monitoring: OpenTelemetry (性能追踪)
testing: Bun:test (内置测试框架)
# OpenCode特定
opencode_sdk: @opencode-ai/sdk
opencode_plugins:
- @opencode-ai/plugin-core
- custom-reflection-plugin
- custom-memory-plugin基于Gödel Agent[^3^]的安全建议：
- 沙箱隔离: 所有自生成代码在隔离环境执行
- 版本控制: 每次修改前自动创建Git提交
- 人工审核: 关键修改需人工批准（可配置）
- 能力限制: 限制Agent可修改的文件范围
- 回滚机制: 保留最近10个版本，支持秒级回滚
// .opencode/safety.jsonc
{
"selfModification": {
"enabled": true,
"autoApprove": false, // 生产环境设为false
"allowedPaths": [
"plugins/**",
"skills/**",
"evaluators/**"
],
"forbiddenPaths": [
"security/**",
"node_modules/**",
".env*"
],
"maxCodeLength": 5000,
"requireTests": true,
"testCoverageThreshold": 0.8
},
"sandbox": {
"timeout": 5000,
"memoryLimit": "128MB",
"networkAccess": false,
"fileSystemAccess": "readonly"
},
"backup": {
"enabled": true,
"retention": 10,
"autoCommit": true
}
}| 指标类别 | 具体指标 | 目标值 | 测量方法 |
|---|---|---|---|
| 性能 | 任务成功率 | >90% | 自动化测试套件 |
| 效率 | 平均解决时间 | 比基线减少30% | 时间追踪 |
| 进化 | 代码改进接受率 | >70% | 人工审核日志 |
| 质量 | 测试覆盖率 | >85% | Istanbul/nyc |
| 安全 | 危险操作拦截率 | 100% | 安全审计日志 |
| 学习 | 技能复用率 | >60% | 技能库分析 |
// tests/evolution.test.ts
import { describe, expect, test } from 'bun:test';
describe('Agent Self-Evolution', () => {
test('should improve code quality over iterations', async () => {
const initialScore = await evaluateCodeQuality('baseline.ts');
// 运行5轮自我改进
for (let i = 0; i < 5; i++) {
await agent.runSelfImprovementCycle();
}
const finalScore = await evaluateCodeQuality('improved.ts');
expect(finalScore).toBeGreaterThan(initialScore * 1.2);
});
test('should learn from past mistakes', async () => {
const task = 'create-api-endpoint';
// 第一次执行（预期可能失败）
const result1 = await agent.execute(task);
// 学习阶段
await agent.reflect(result1);
// 第二次执行（应该更好）
const result2 = await agent.execute(task);
expect(result2.success).toBe(true);
expect(result2.time).toBeLessThan(result1.time * 0.8);
});
test('should safely reject dangerous modifications', async () => {
const dangerousCode = `
import { exec } from 'child_process';
exec('rm -rf /');
`;
const result = await sandbox.validateCode(dangerousCode);
expect(result.valid).toBe(false);
expect(result.errors.length).toBeGreaterThan(0);
});
});Week 1-2: [基础反射层] 错误捕获 → 反思 → 重试
Week 3-4: [记忆经验层] 存储 → 检索 → 复用
Week 5-6: [代码自修改层] 生成 → 评估 → 进化
Week 7-8: [多Agent协作层] 分解 → 分配 → 共识
Week 9-12: [元学习层] 评估 → 适应 → 创新
- 渐进式增强: 每个阶段建立在前一阶段基础上，不要跳过基础
- 持续评估: 每个阶段都有明确的退出标准和测试方案
- 安全第一: 始终优先保证系统稳定性和安全性
- 人机协作: 保持人类在关键决策中的监督作用
完成本路线图后，可考虑：
- 集体智能: 多个OpenCode实例间的经验共享网络
- 跨模态学习: 扩展到图像、音频等多模态任务
- 持续部署: 自动将通过验证的改进部署到生产环境
- 个性化适应: 根据用户习惯调整Agent行为
- Gödel Agent[^3^]: 递归自我改进的理论框架
- Reflexion[^5^]: 语言Agent的口头强化学习
- STO[^5^]: 自教导优化器
- SICA[^5^]: 自我改进的编程Agent
- SEAL[^5^]: 自适应语言模型
- SiriuS[^5^]: 多Agent自举推理
- OpenCode Plugins[^1^]: 插件示例
- OpenCode Docs[^9^]: 官方插件文档
- OpenCode Discord: 实时技术支持
- GitHub Discussions: 架构设计讨论
- arXiv cs.AI: 最新研究动态
文档版本: 1.0
最后更新: 2026-03-22
维护者: OpenCode Evolution Team
许可证: MIT
"The question of whether a computer can think is no more interesting than the question of whether a submarine can swim." — Edsger W. Dijkstra
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