三大赛道六大自主Agent引擎拆解：编码·科研·深研全链实测

# Codex Agent SDK 调用示例 (2026)
from openai import OpenAI
client = OpenAI()

response = client.responses.create(
    model="codex-5.5",
    input="Fix the authentication bug in auth.py that causes "
          "session tokens to expire prematurely. Write tests.",
    tools=[{
        "type": "codex",
        "sandbox": {"isolated": True, "timeout": 600}
    }]
)

# Codex 返回结构化结果：修改文件列表 + 测试结果 + PR描述
for step in response.output:
    print(f"[{step.type}] {step.action}")
    # [file_edit] auth.py: L42 - L58
    # [test_run] test_auth.py: 4/4 passed
    # [pr_draft] Fix: token expiry calculation...

# Claude Code 终端实战流程
$ claude "重构 payment-service 的事务处理逻辑，
         支持 Saga 模式补偿事务，保持向后兼容"

# Claude Code 实时流式输出：
# → Reading payment-service/src/transaction.ts
# → Analyzing 23 dependents of processPayment()
# → Designing Saga orchestrator pattern...
# → Writing saga-coordinator.ts (new file)
# → Modifying transaction.ts (L67-L142)
# → Running: npm test -- payment-service
# → 47/47 tests passed
# → Git diff ready for review

# Co-Scientist 核心架构（简化）
class CoScientist:
    """基于Gemini的多Agent科学发现系统"""

    def __init__(self):
        self.agents = {
            "generation": GenerationAgent(model="gemini-2.0"),
            "reflection": ReflectionAgent(model="gemini-2.0"),
            "ranking": EloRankingAgent(),
            "evolution": EvolutionAgent(model="gemini-2.0"),
            "proximity": ProximityAgent(),  # 近邻假设聚类
            "meta_review": MetaReviewAgent(model="gemini-2.0"),
            "supervisor": SupervisorAgent()
        }

    async def discover(self, research_goal: str):
        # 1. 生成初始假设池
        hypotheses = await self.agents["generation"].generate(research_goal)

        for tournament_round in range(8):  # 8轮辩论锦标赛
            # 2. 反思Agent质疑每个假设的弱点
            critiques = await self.agents["reflection"].critique(hypotheses)

            # 3. Elo排名Agent对假设两两PK
            rankings = await self.agents["ranking"].rank(hypotheses, critiques)

            # 4. 进化Agent组合高分假设产生新变体
            hypotheses = await self.agents["evolution"].evolve(rankings)

            # 5. 去重和聚类
            hypotheses = await self.agents["proximity"].deduplicate(hypotheses)

        # 6. 元评审生成最终报告
        return await self.agents["meta_review"].synthesize(rankings)

# Robin 多Agent管线架构
class RobinPipeline:
    """FutureHouse Robin: 端到端科学发现"""

    def __init__(self):
        self.pipeline = [
            LiteratureAgent(),      # 文献搜索与综述
            HypothesisAgent(),      # 假设生成
            ExperimentalAgent(),    # 实验方案设计
            DataAnalysisAgent(),    # 数据分析
            InterpretationAgent()   # 结论解释
        ]

    async def research(self, question: str):
        context = {}

        for agent in self.pipeline:
            context = await agent.run(context)

        # Robin 的关键差异：它能连接真实实验平台
        # LiteratureAgent → 搜索PubMed/Semantic Scholar
        # ExperimentalAgent → 调用实验API/机器人平台
        # DataAnalysisAgent → 执行统计分析代码
        return context["conclusion"]

# AlphaEvolve 核心循环
class AlphaEvolve:
    """进化编码Agent：LLM + 遗传算法"""

    async def evolve(self, problem_spec, generations=1000):
        population = [random_program() for _ in range(100)]

        for gen in range(generations):
            # LLM 评估每个程序并建议改进
            scored = []
            for program in population:
                fitness = await self.evaluate(program, problem_spec)
                improvement_prompt = (
                    f"Program: {program}\n"
                    f"Score: {fitness}\n"
                    f"Suggest mutations to improve score."
                )
                mutations = await llm.generate(improvement_prompt)
                scored.append((fitness, program, mutations))

            # 选择 + 变异
            population = self.select_and_mutate(scored)

        return best(population)

# Gemini Deep Research 架构
class DeepResearchMax:
    """三段式自主调研Agent"""

    async def research(self, query: str):
        # 阶段1：规划 - 拆解为多步搜索策略
        plan = await self.plan(query)
        # 输出: [
        #   "搜索: [query] 最新进展 2026",
        #   "搜索: [query] 学术论文 site:arxiv.org",
        #   "搜索: [query] 行业报告 filetype:pdf",
        #   "访问: top 10 结果页面并提取内容",
        #   "交叉验证关键数据和引用"
        # ]

        # 阶段2：执行 - 并行搜索 + 页面提取
        results = []
        for step in plan:
            result = await self.execute(step)
            results.append(result)
            # 动态调整：根据结果补充搜索
            if result.needs_followup:
                plan.extend(self.generate_followups(result))

        # 阶段3：综合 - 生成结构化报告
        report = await self.synthesize(results)
        return report  # 带引用的结构化报告

graph TB
    subgraph "赛道一：编码自动化"
        direction TB
        C1[Codex GPT-5.5] --> C1A[沙箱隔离执行]
        C1 --> C1B[多任务并行]
        C1 --> C1C[异步PR提交]

        C2[Claude Code Opus 4.7] --> C2A[终端原生交互]
        C2 --> C2B[流式同步推理]
        C2 --> C2C[全上下文访问]
    end

    subgraph "赛道二：科学发现"
        direction TB
        S1[Co-Scientist] --> S1A[多Agent辩论锦标赛]
        S1 --> S1B[Elo假设排名]
        S1 --> S1C[进化假设组合]

        S2[Robin] --> S2A[顺序管线闭环]
        S2 --> S2B[实验平台集成]
        S2 --> S2C[端到端验证]

        S3[AlphaEvolve] --> S3A[LLM+遗传算法]
        S3 --> S3B[程序自动变异]
        S3 --> S3C[适应度评估]
    end

    subgraph "赛道三：深度调研"
        direction TB
        R1[Gemini Deep Research Max] --> R1A[规划-执行-综合]
        R1 --> R1B[MCP协议数据源]
        R1 --> R1C[Google生态集成]

        R2[OpenAI Deep Research] --> R2A[推理模型+RL]
        R2 --> R2B[深度趋势推导]
        R2 --> R2C[Bing搜索支撑]
    end

    style C1 fill:#4CAF50,color:white
    style C2 fill:#7C4DFF,color:white
    style S1 fill:#2196F3,color:white
    style S2 fill:#FF9800,color:white
    style S3 fill:#00BCD4,color:white
    style R1 fill:#F44336,color:white
    style R2 fill:#9C27B0,color:white

# Codex 自纠循环（伪代码还原）
async def codex_self_correct(task, max_retries=3):
    for attempt in range(max_retries):
        # 1. 执行修改
        changes = await generate_code(task)
        apply_changes(changes)

        # 2. 运行测试套件
        test_result = await run_tests()

        if test_result.all_passed:
            return Success(changes)

        # 3. 分析失败原因
        failure_analysis = await analyze_failures(
            test_result.failures,
            changes
        )

        # 4. 将失败信息注入下一轮prompt
        task = f"""
        Previous attempt failed:
        {failure_analysis}
        Original task: {task}
        Fix the issues while maintaining the original intent.
        """

    # 超过重试次数，标记需要人工介入
    return EscalateToHuman(changes, failure_analysis)

Generation Agent: "假设药物X可通过抑制Y通路治疗疾病Z"
    ↓
Reflection Agent: "质疑：1) Y通路在Z中的因果性尚未确立
                        2) X的脱靶效应可能混淆结果
                        3) 现有3项研究中有2项不支持该通路"
    ↓
Elo Ranking:  假设得分从 0.78 降至 0.62
    ↓
Evolution Agent: 将"脱靶效应控制"纳入改进假设
    ↓
改进假设: "假设药物X通过选择性抑制Y通路中的Z2亚基...
          需通过CRISPR敲除验证特异性"

class QualityGatedPipeline:
    """Robin 的质量门控管线"""

    async def run(self, research_question):
        # Stage 1: 文献综述
        lit_review = await self.literature_agent.search(research_question)
        if not self.validate_coverage(lit_review, min_papers=20):
            lit_review = await self.literature_agent.expand_search(lit_review)

        # Stage 2: 假设生成（依赖Stage 1的质量）
        hypothesis = await self.hypothesis_agent.generate(lit_review)
        if not self.validate_novelty(hypothesis, lit_review):
            hypothesis = await self.hypothesis_agent.refine(hypothesis)

        # Stage 3: 实验设计（依赖Stage 2的假设质量）
        experiment = await self.experiment_agent.design(hypothesis)
        if not self.validate_feasibility(experiment):
            experiment = await self.experiment_agent.simplify(experiment)

        return experiment