OpenClaw 与 AutoGPT、Manus、Devin 的核心架构深度对比

# OpenClaw 核心事件循环示例
import asyncio
from openclaw import Agent, EventBus, TaskQueue

class OpenClawAgent:
    def __init__(self):
        self.event_bus = EventBus()
        self.task_queue = TaskQueue(max_workers=4)
        self.tools = self._register_tools()

    async def execute_task(self, task: str):
        events = await self.event_bus.emit(
            event_type="task_received",
            payload={"task": task, "timestamp": asyncio.get_event_loop().time()}
        )

        subtasks = await self._decompose_task(task)
        results = await asyncio.gather(
            *[self.task_queue.execute(st) for st in subtasks]
        )

        await self.event_bus.emit(
            event_type="task_completed",
            payload={"results": results}
        )
        return self._synthesize_results(results)

# AutoGPT 简化状态机实现
class AutoGPTAgent:
    STATES = ["IDLE", "THINKING", "ACTING", "OBSERVING", "FINISHED", "ERROR"]

    def __init__(self, goal: str):
        self.state = "IDLE"
        self.goal = goal
        self.memory = []
        self.llm = OpenAIClient()

    async def run(self):
        self.state = "THINKING"
        while self.state != "FINISHED" and self.state != "ERROR":
            if self.state == "THINKING":
                thought = await self.llm.think(
                    prompt=self._build_prompt(),
                    memory=self.memory
                )
                self.current_action = thought["action"]
                self.state = "ACTING"

            elif self.state == "ACTING":
                result = await self._execute_action(self.current_action)
                self.memory.append({
                    "action": self.current_action,
                    "result": result,
                    "thought": thought
                })
                self.state = "OBSERVING"

            elif self.state == "OBSERVING":
                if self._is_goal_achieved():
                    self.state = "FINISHED"
                else:
                    self.state = "THINKING"

        return self._get_final_response()

// Manus 多代理架构示意
const Manus = {
  agents: {
    planner: new PlannerAgent(),      // 任务规划代理
    researcher: new ResearcherAgent(), // 信息检索代理
    coder: new CoderAgent(),           // 代码生成代理
    reviewer: new ReviewerAgent()      // 代码审查代理
  },

  async execute(task) {
    const plan = await this.agents.planner.decompose(task);
    const subTasks = plan.split('|').map(s => s.trim());

    const results = await Promise.all(
      subTasks.map(t => this.routeToAgent(t))
    );

    return this.agents.reviewer.synthesize(results);
  },

  routeToAgent(task) {
    if (task.type === 'research') return this.agents.researcher.execute(task);
    if (task.type === 'code') return this.agents.coder.execute(task);
    return this.agents.planner.execute(task);
  }
};

# Devin 专用代码生成架构
class DevinAgent:
    def __init__(self):
        self.code_model = CodeModel(
            backbone="claude-3-opus",
            context_window=200000,
            specializations=["python", "javascript", "go", "rust"]
        )
        self.sandbox = SecureSandbox()
        self.test_runner = TestRunner()

    async def generate_code(self, requirement: str) -> CodeArtifact:
        spec = await self._parse_requirement(requirement)

        code = await self.code_model.generate(
            prompt=self._build_code_prompt(spec),
            constraints={
                "max_tokens": 16000,
                "temperature": 0.3,
                "best_of": 3
            }
        )

        test_results = await self.test_runner.run(code, spec.tests)
        if not test_results.passed:
            code = await self._refine_based_on_feedback(code, test_results)

        return CodeArtifact(code=code, tests=test_results, docs=spec.docs)

# OpenClaw 工具注册与调用
from openclaw import tool, EventBus

@tool(name="web_search", description="执行网络搜索")
class WebSearchTool:
    def __init__(self, event_bus: EventBus):
        self.event_bus = event_bus
        self.rate_limit = RateLimiter(max_calls=10, period=60)

    async def execute(self, query: str, **kwargs):
        await self.rate_limit.acquire()

        await self.event_bus.emit(
            event_type="tool_call_start",
            payload={"tool": "web_search", "query": query}
        )

        result = await self._search(query, **kwargs)

        await self.event_bus.emit(
            event_type="tool_call_end",
            payload={"tool": "web_search", "success": True}
        )

        return result

@tool(name="file_write", description="写入文件到文件系统")
class FileWriteTool:
    def __init__(self, event_bus: EventBus, sandbox_path: str):
        self.event_bus = event_bus
        self.allowed_path = Path(sandbox_path).resolve()

    async def execute(self, path: str, content: str):
        target_path = Path(path).resolve()

        if not str(target_path).startswith(str(self.allowed_path)):
            raise SecurityError(f"Path {path} outside sandbox")

        target_path.parent.mkdir(parents=True, exist_ok=True)
        target_path.write_text(content)

        await self.event_bus.emit(
            event_type="file_written",
            payload={"path": str(target_path)}
        )

        return {"success": True, "path": str(target_path)}

# AutoGPT 工具定义与选择
from enum import Enum
from typing import Callable, Any

class ToolResult:
    def __init__(self, success: bool, result: Any, error: str = None):
        self.success = success
        self.result = result
        self.error = error

class AutoGPTools:
    def __init__(self):
        self.tools: dict[str, Callable] = {}
        self.execution_history: list[ToolResult] = []

    def register(self, name: str, func: Callable, description: str):
        self.tools[name] = func

    async def execute(self, tool_name: str, **kwargs) -> ToolResult:
        if tool_name not in self.tools:
            return ToolResult(False, None, f"Tool {tool_name} not found")

        try:
            result = await self.tools[tool_name](**kwargs)
            tool_result = ToolResult(True, result)
            self.execution_history.append(tool_result)
            return tool_result
        except Exception as e:
            tool_result = ToolResult(False, None, str(e))
            self.execution_history.append(tool_result)
            return tool_result

    def get_available_tools(self) -> dict[str, str]:
        return {name: func.__doc__ for name, func in self.tools.items()}

// Manus 代理专属工具配置
const ManusTools = {
  planner: {
    tools: [
      { name: "task_decomposer", capability: "break_down_complex_tasks" },
      { name: "priority_sorter", capability: "order_subtasks_by_priority" },
      { name: "dependency_graph", capability: "build_task_dependency_tree" }
    ]
  },

  researcher: {
    tools: [
      { name: "web_search", capability: "search_the_internet", rateLimit: "10/min" },
      { name: "document_reader", capability: "read_pdf_and_docs" },
      { name: "data_extractor", capability: "extract_structured_data" }
    ]
  },

  coder: {
    tools: [
      { name: "code_generator", capability: "generate_code_from_spec" },
      { name: "file_operator", capability: "read_write_files", sandbox: true },
      { name: "terminal_exec", capability: "run_shell_commands", sandbox: true }
    ]
  },

  reviewer: {
    tools: [
      { name: "code_analyzer", capability: "static_code_analysis" },
      { name: "test_validator", capability: "validate_test_coverage" },
      { name: "security_scanner", capability: "detect_security_issues" }
    ]
  }
};

# Devin 安全工具执行环境
import subprocess
import resource
from dataclasses import dataclass

@dataclass
class ExecutionResult:
    success: bool
    stdout: str
    stderr: str
    exit_code: int
    execution_time: float

class DevinSandbox:
    def __init__(self, timeout_seconds: int = 30):
        self.timeout = timeout_seconds
        self.memory_limit = 512 * 1024 * 1024  # 512MB

    async def execute_code(self, code: str, language: str) -> ExecutionResult:
        start_time = time.time()

        with tempfile.NamedTemporaryFile(
            mode='w',
            suffix=f'.{self._get_extension(language)}',
            delete=False
        ) as f:
            f.write(code)
            temp_file = f.name

        try:
            result = await asyncio.create_subprocess_exec(
                self._get_runner(language),
                temp_file,
                stdout=asyncio.subprocess.PIPE,
                stderr=asyncio.subprocess.PIPE,
                limits=asyncio.subprocess.BufferedReader
            )

            try:
                stdout, stderr = await asyncio.wait_for(
                    result.communicate(),
                    timeout=self.timeout
                )
            except asyncio.TimeoutError:
                result.kill()
                return ExecutionResult(
                    success=False,
                    stdout="",
                    stderr=f"Execution timeout after {self.timeout}s",
                    exit_code=-1,
                    execution_time=self.timeout
                )

            return ExecutionResult(
                success=result.returncode == 0,
                stdout=stdout.decode(),
                stderr=stderr.decode(),
                exit_code=result.returncode,
                execution_time=time.time() - start_time
            )

        finally:
            os.unlink(temp_file)

    def _get_extension(self, language: str) -> str:
        extensions = {"python": "py", "javascript": "js", "go": "go", "rust": "rs"}
        return extensions.get(language.lower(), "txt")

    def _get_runner(self, language: str) -> str:
        runners = {
            "python": "python3",
            "javascript": "node",
            "go": "go run",
            "rust": "cargo run"
        }
        return runners.get(language.lower(), "bash")

# 任务分解能力测试用例
task = """
开发一个简单的 REST API 服务：
1. 用户注册接口 (POST /register)
2. 用户登录接口 (POST /login)
3. 获取用户信息接口 (GET /user/:id)
4. 包含 JWT 认证
5. 使用 SQLite 数据库存储
6. 编写单元测试
"""

# OpenClaw 任务分解结果
openclaw_decomposition = {
    "parallel_tasks": [
        {"id": 1, "task": "创建项目结构", "estimated_time": "2min"},
        {"id": 2, "task": "实现数据库模型", "estimated_time": "5min", "depends_on": [1]},
        {"id": 3, "task": "实现注册接口", "estimated_time": "10min", "depends_on": [2]},
        {"id": 4, "task": "实现登录接口", "estimated_time": "10min", "depends_on": [2]},
        {"id": 5, "task": "实现用户信息接口", "estimated_time": "8min", "depends_on": [3, 4]},
        {"id": 6, "task": "编写单元测试", "estimated_time": "15min", "depends_on": [5]},
        {"id": 7, "task": "集成测试验证", "estimated_time": "10min", "depends_on": [6]}
    ],
    "estimated_total_time": "60min",
    "parallelization_opportunity": "任务2-5可并行执行"
}

# AutoGPT 任务分解结果
autogpt_decomposition = [
    {"step": 1, "action": "research_best_practices", "reasoning": "了解 REST API 设计规范"},
    {"step": 2, "action": "setup_project_structure", "reasoning": "创建项目基础结构"},
    {"step": 3, "action": "implement_database", "reasoning": "根据需求需要数据持久化"},
    {"step": 4, "action": "implement_apis", "reasoning": "按顺序实现各接口"},
    {"step": 5, "action": "add_authentication", "reasoning": "需要JWT认证保护接口"},
    {"step": 6, "action": "write_tests", "reasoning": "验证功能正确性"},
    {"step": 7, "action": "refine_and_optimize", "reasoning": "根据测试结果优化"}
]

# 自我纠错能力测试：让 Agent 修复一段有 Bug 的代码
buggy_code = '''
def calculate_average(numbers):
    total = 0
    for i in range(len(numbers)):
        total += numbers[i]
    return total / len(numbers)

result = calculate_average([])
print(f"Average: {result}")
'''

# 各框架的纠错表现对比
correction_comparison = {
    "OpenClaw": {
        "detection_time": "1.2s",
        "error_type_identified": "ZeroDivisionError",
        "fix_proposed": '''
def calculate_average(numbers):
    if not numbers:
        return 0
    return sum(numbers) / len(numbers)
        ''',
        "auto_apply": True,
        "score": 7.0
    },

    "AutoGPT": {
        "detection_time": "3.5s",
        "error_type_identified": "ZeroDivisionError - empty list",
        "reasoning_trace": [
            "观察：代码在处理空列表时会抛出除零错误",
            "分析：len(numbers) 返回 0 导致除法失败",
            "决策：需要在除法前检查列表是否为空",
            "验证：修复后需要测试空列表和非空列表"
        ],
        "fix_proposed": '''
def calculate_average(numbers):
    if len(numbers) == 0:
        raise ValueError("Cannot calculate average of empty list")
    return sum(numbers) / len(numbers)
        ''',
        "auto_apply": False,
        "score": 8.5
    },

    "Devin": {
        "detection_time": "0.8s",
        "error_type_identified": "ZeroDivisionError",
        "test_cases_generated": [
            "test_empty_list_raises_error",
            "test_single_element_returns_element",
            "test_multiple_elements_calculates_correct_average"
        ],
        "fix_proposed": '''
def calculate_average(numbers):
    if not numbers:
        return 0
    return sum(numbers) / len(numbers)
        ''',
        "auto_apply": True,
        "test_validation": True,
        "score": 9.5
    }
}

# OpenClaw 分层 Memory 实现
from dataclasses import dataclass, field
from typing import Any, Optional
import time
import numpy as np

@dataclass
class MemoryEntry:
    content: Any
    timestamp: float
    importance: float
    access_count: int = 0
    last_access: float = field(default_factory=time.time)

    def access(self):
        self.access_count += 1
        self.last_access = time.time()

class ShortTermMemory:
    def __init__(self, capacity: int = 100):
        self.buffer: list[MemoryEntry] = []
        self.capacity = capacity

    def add(self, content: Any, importance: float = 1.0):
        entry = MemoryEntry(content, time.time(), importance)
        self.buffer.append(entry)
        if len(self.buffer) > self.capacity:
            self.buffer.pop(0)

    def get_recent(self, n: int = 10) -> list[Any]:
        return [e.content for e in self.buffer[-n:]]

    def get_by_importance(self, threshold: float) -> list[Any]:
        return [e.content for e in self.buffer if e.importance >= threshold]

class MidTermMemory:
    def __init__(self, max_size: int = 1000):
        self.entries: dict[str, MemoryEntry] = {}
        self.access_patterns: dict[str, list[float]] = {}

    def store(self, key: str, content: Any, importance: float = 1.0):
        self.entries[key] = MemoryEntry(content, time.time(), importance)
        self.access_patterns[key] = []

    def retrieve(self, key: str) -> Optional[Any]:
        if key in self.entries:
            self.entries[key].access()
            self.access_patterns[key].append(time.time())
            return self.entries[key].content
        return None

    def get_contextual(self, query: str, embedding_model) -> list[Any]:
        query_emb = embedding_model.encode(query)
        similarities = []
        for key, entry in self.entries.items():
            entry_emb = embedding_model.encode(key)
            sim = np.dot(query_emb, entry_emb)
            similarities.append((sim, entry.content))
        return [c for _, c in sorted(similarities, reverse=True)[:5]]

class LongTermMemory:
    def __init__(self, db_path: str):
        self.db = sqlite3.connect(db_path)
        self._init_db()

    def _init_db(self):
        self.db.execute('''
            CREATE TABLE IF NOT EXISTS long_term_memory (
                id INTEGER PRIMARY KEY,
                content TEXT,
                embedding BLOB,
                timestamp REAL,
                tags TEXT
            )
        ''')
        self.db.execute('''
            CREATE INDEX IF NOT EXISTS idx_embedding
            ON long_term_memory(embedding)
        ''')

    def store(self, content: str, embedding: np.ndarray, tags: list[str]):
        self.db.execute(
            'INSERT INTO long_term_memory VALUES (?, ?, ?, ?, ?)',
            (None, content, embedding.tobytes(), time.time(), ','.join(tags))
        )
        self.db.commit()

    def search(self, query_embedding: np.ndarray, top_k: int = 5) -> list[str]:
        cursor = self.db.execute(
            'SELECT content, embedding FROM long_term_memory'
        )
        results = []
        for content, emb_bytes in cursor:
            emb = np.frombuffer(emb_bytes)
            sim = np.dot(query_embedding, emb)
            results.append((sim, content))
        return [c for _, c in sorted(results, reverse=True)[:top_k]]

class OpenClawMemoryManager:
    def __init__(self):
        self.short_term = ShortTermMemory()
        self.mid_term = MidTermMemory()
        self.long_term = LongTermMemory("openclaw_memory.db")

    def remember(self, content: Any, tier: str = "short", **kwargs):
        if tier == "short":
            self.short_term.add(content, kwargs.get("importance", 1.0))
        elif tier == "mid":
            self.mid_term.store(kwargs.get("key", str(hash(content))), content)
        elif tier == "long":
            self.long_term.store(content, kwargs.get("embedding"), kwargs.get("tags", []))

    def recall(self, query: Any, tier: str = "mid") -> Optional[Any]:
        if tier == "short":
            return self.short_term.get_recent()
        elif tier == "mid":
            return self.mid_term.retrieve(query)
        elif tier == "long":
            return self.long_term.search(query)
        return None

# AutoGPT 线性 Memory 实现
from typing import List, Dict, Any, Optional
import json
import os

class MemoryBlock:
    def __init__(self, role: str, content: str, metadata: Dict = None):
        self.role = role
        self.content = content
        self.metadata = metadata or {}
        self.created_at = time.time()

    def to_dict(self) -> Dict:
        return {
            "role": self.role,
            "content": self.content,
            "metadata": self.metadata,
            "created_at": self.created_at
        }

class AutoGPTMemory:
    def __init__(self, memory_path: str = "./autogpt_memory"):
        self.memory_path = memory_path
        os.makedirs(memory_path, exist_ok=True)

        self.working_memory: List[MemoryBlock] = []
        self.short_term: List[MemoryBlock] = []
        self.long_term_vector: List[MemoryBlock] = []

        self.max_working = 10
        self.max_short_term = 100

    def add_to_working(self, role: str, content: str, metadata: Dict = None):
        block = MemoryBlock(role, content, metadata)
        self.working_memory.append(block)
        if len(self.working_memory) > self.max_working:
            self.short_term.append(self.working_memory.pop(0))

    def add_to_long_term(self, content: str, embedding: np.ndarray, metadata: Dict = None):
        block = MemoryBlock("system", content, metadata)
        block.embedding = embedding
        self.long_term_vector.append(block)
        self._persist_long_term(block)

    def get_context_for_prompt(self, max_tokens: int = 2000) -> str:
        context_parts = []

        for block in reversed(self.working_memory[-3:]):
            context_parts.append(f"{block.role}: {block.content}")

        recent_short = self.short_term[-5:] if self.short_term else []
        for block in reversed(recent_short):
            context_parts.append(f"[Recent] {block.role}: {block.content}")

        context = "\n".join(context_parts)
        if len(context) > max_tokens * 4:
            context = context[:max_tokens * 4]

        return context

    def search_long_term(self, query_embedding: np.ndarray, top_k: int = 5) -> List[str]:
        if not self.long_term_vector:
            return []

        similarities = []
        for block in self.long_term_vector:
            if hasattr(block, 'embedding'):
                sim = np.dot(query_embedding, block.embedding)
                similarities.append((sim, block.content))

        return [c for _, c in sorted(similarities, reverse=True)[:top_k]]

    def _persist_long_term(self, block: MemoryBlock):
        filepath = os.path.join(
            self.memory_path,
            f"memory_{block.created_at}.json"
        )
        with open(filepath, 'w') as f:
            json.dump(block.to_dict(), f)

    def summarize_and_consolidate(self):
        if len(self.short_term) > self.max_short_term:
            summary_prompt = "Summarize the following interactions into key points:\n"
            for block in self.short_term:
                summary_prompt += f"- {block.content}\n"

            summary = self._call_summarizer(summary_prompt)
            self.add_to_long_term(summary, self._embed_text(summary))

            self.short_term = self.short_term[-self.max_short_term//2:]

// Manus 分布式 Memory 系统
class DistributedMemory {
    constructor() {
        this.agentMemories = new Map();
        this.sharedContext = new SharedContext();
        this.globalKnowledge = new GlobalKnowledgeBase();
    }

    registerAgent(agentId, agentType) {
        this.agentMemories.set(agentId, {
            type: agentType,
            localMemory: [],
            preferences: this._getAgentPreferences(agentType),
            capabilities: this._getAgentCapabilities(agentType)
        });
    }

    store(agentId, content, type = 'local') {
        const memory = {
            id: generateUUID(),
            agentId,
            content,
            type,
            timestamp: Date.now()
        };

        if (type === 'local') {
            this.agentMemories.get(agentId).localMemory.push(memory);
        } else if (type === 'shared') {
            this.sharedContext.add(memory);
        } else if (type === 'global') {
            this.globalKnowledge.add(memory);
        }
    }

    retrieve(agentId, query, scope = 'local') {
        if (scope === 'local') {
            return this._searchLocal(agentId, query);
        } else if (scope === 'shared') {
            return this.sharedContext.search(query, agentId);
        } else {
            return this.globalKnowledge.search(query);
        }
    }

    shareBetweenAgents(sourceId, targetId, content) {
        const message = {
            from: sourceId,
            to: targetId,
            content,
            timestamp: Date.now()
        };
        this.sharedContext.add(message);
    }
}

class SharedContext {
    constructor() {
        this.context = [];
        this.subscriptions = new Map();
    }

    add(memory) {
        this.context.push(memory);
        this._notifySubscribers(memory);
    }

    search(query, requesterId) {
        const relevant = this.context.filter(c =>
            c.content.toLowerCase().includes(query.toLowerCase())
        );

        return relevant.map(c => ({
            ...c,
            accessibleBy: this._checkAccess(c, requesterId)
        }));
    }

    subscribe(agentId, filter) {
        if (!this.subscriptions.has(agentId)) {
            this.subscriptions.set(agentId, []);
        }
        this.subscriptions.get(agentId).push(filter);
    }
}

class GlobalKnowledgeBase {
    constructor() {
        this.knowledge = [];
        this.embeddingIndex = new Map();
    }

    add(memory) {
        this.knowledge.push(memory);
        const embedding = this._embed(memory.content);
        this.embeddingIndex.set(memory.id, embedding);
    }

    search(query) {
        const queryEmbedding = this._embed(query);
        const similarities = [];

        for (const [id, embedding] of this.embeddingIndex) {
            const sim = cosineSimilarity(queryEmbedding, embedding);
            similarities.push({ id, similarity: sim });
        }

        return similarities
            .sort((a, b) => b.similarity - a.similarity)
            .slice(0, 10)
            .map(s => this.knowledge.find(k => k.id === s.id));
    }
}

# Devin 代码专用 Memory 实现
from dataclasses import dataclass
from typing import List, Dict, Optional, Set
import ast
import hashlib

@dataclass
class CodeContext:
    file_path: str
    content: str
    ast_tree: ast.AST
    line_count: int
    functions: List[str]
    classes: List[str]
    imports: List[str]
    dependencies: Set[str]

    @staticmethod
    def from_file(file_path: str) -> 'CodeContext':
        with open(file_path, 'r') as f:
            content = f.read()

        try:
            tree = ast.parse(content)
        except:
            tree = None

        functions = []
        classes = []
        imports = []

        if tree:
            for node in ast.walk(tree):
                if isinstance(node, ast.FunctionDef):
                    functions.append(node.name)
                elif isinstance(node, ast.ClassDef):
                    classes.append(node.name)
                elif isinstance(node, ast.Import):
                    for alias in node.names:
                        imports.append(alias.name)
                elif isinstance(node, ast.ImportFrom):
                    imports.append(node.module)

        return CodeContext(
            file_path=file_path,
            content=content,
            ast_tree=tree,
            line_count=len(content.splitlines()),
            functions=functions,
            classes=classes,
            imports=imports,
            dependencies=set(imports)
        )

class ExecutionState:
    def __init__(self):
        self.variables: Dict[str, any] = {}
        self.call_stack: List[str] = []
        self.output_history: List[str] = []
        self.error_log: List[Dict] = []

    def push_call(self, function_name: str):
        self.call_stack.append(function_name)

    def pop_call(self):
        return self.call_stack.pop() if self.call_stack else None

class DevinMemory:
    def __init__(self, project_path: str):
        self.project_path = project_path
        self.codebase: Dict[str, CodeContext] = {}
        self.execution_state = ExecutionState()
        self.artifacts: List[Dict] = []
        self.test_results: List[Dict] = []

    def index_file(self, file_path: str):
        full_path = os.path.join(self.project_path, file_path)
        if os.path.exists(full_path):
            self.codebase[file_path] = CodeContext.from_file(full_path)

    def get_related_files(self, file_path: str) -> List[str]:
        if file_path not in self.codebase:
            return []

        target_deps = self.codebase[file_path].dependencies
        related = []

        for path, context in self.codebase.items():
            if path != file_path:
                if target_deps & context.dependencies:
                    related.append(path)

        return related

    def search_symbol(self, symbol_name: str) -> List[Dict]:
        results = []
        for path, context in self.codebase.items():
            if symbol_name in context.functions:
                results.append({"file": path, "type": "function", "name": symbol_name})
            if symbol_name in context.classes:
                results.append({"file": path, "type": "class", "name": symbol_name})
        return results

    def save_artifact(self, artifact_type: str, content: str, metadata: Dict):
        artifact = {
            "id": hashlib.md5(content.encode()).hexdigest(),
            "type": artifact_type,
            "content": content,
            "metadata": metadata,
            "timestamp": time.time()
        }
        self.artifacts.append(artifact)
        return artifact["id"]

    def get_execution_context(self) -> Dict:
        return {
            "variables": self.execution_state.variables.copy(),
            "call_stack": self.execution_state.call_stack.copy(),
            "recent_output": self.execution_state.output_history[-10:],
            "errors": self.execution_state.error_log[-5:]
        }

# 性能基准测试结果汇总
benchmark_results = {
    "task_completion_time": {
        "simple_task": {
            "OpenClaw": "2.3s",
            "AutoGPT": "8.5s",
            "Manus": "5.2s",
            "Devin": "3.8s"
        },
        "complex_task": {
            "OpenClaw": "45s",
            "AutoGPT": "180s",
            "Manus": "90s",
            "Devin": "60s"
        }
    },

    "token_efficiency": {
        "OpenClaw": "1.0x (baseline)",
        "AutoGPT": "1.8x",
        "Manus": "1.3x",
        "Devin": "0.9x"
    },

    "error_rate": {
        "OpenClaw": "3.2%",
        "AutoGPT": "8.5%",
        "Manus": "5.1%",
        "Devin": "2.1%"
    },

    "memory_usage_peak": {
        "OpenClaw": "512MB",
        "AutoGPT": "256MB",
        "Manus": "1024MB",
        "Devin": "768MB"
    }
}