feat(agent): complete EvoAgent integration for all 6 agent roles
Migrate all agent roles from Legacy to EvoAgent architecture: - fundamentals_analyst, technical_analyst, sentiment_analyst, valuation_analyst - risk_manager, portfolio_manager Key changes: - EvoAgent now supports Portfolio Manager compatibility methods (_make_decision, get_decisions, get_portfolio_state, load_portfolio_state, update_portfolio) - Add UnifiedAgentFactory for centralized agent creation - ToolGuard with batch approval API and WebSocket broadcast - Legacy agents marked deprecated (AnalystAgent, RiskAgent, PMAgent) - Remove backend/agents/compat.py migration shim - Add run_id alongside workspace_id for semantic clarity - Complete integration test coverage (13 tests) - All smoke tests passing for 6 agent roles Constraint: Must maintain backward compatibility with existing run configs Constraint: Memory support must work with EvoAgent (no fallback to Legacy) Rejected: Separate PM implementation for EvoAgent | unified approach cleaner Confidence: high Scope-risk: broad Directive: EVO_AGENT_IDS env var still respected but defaults to all roles Not-tested: Kubernetes sandbox mode for skill execution
This commit is contained in:
457
backend/tools/sandboxed_executor.py
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457
backend/tools/sandboxed_executor.py
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@@ -0,0 +1,457 @@
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# -*- coding: utf-8 -*-
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"""
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多模式技能沙盒执行器
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支持三种模式：
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- none: 直接执行（默认，开发环境）
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- docker: Docker 容器隔离
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- kubernetes: Kubernetes Pod 隔离
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环境变量：
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SKILL_SANDBOX_MODE: 沙盒模式 (none/docker/kubernetes)，默认 none
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SKILL_SANDBOX_IMAGE: Docker 镜像，默认 python:3.11-slim
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SKILL_SANDBOX_MEMORY_LIMIT: 内存限制，默认 512m
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SKILL_SANDBOX_CPU_LIMIT: CPU 限制，默认 1.0
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SKILL_SANDBOX_NETWORK: 网络模式，默认 none
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SKILL_SANDBOX_TIMEOUT: 超时时间（秒），默认 60
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"""
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import json
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import logging
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import os
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import warnings
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from abc import ABC, abstractmethod
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from typing import Any
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logger = logging.getLogger(__name__)
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class SandboxBackend(ABC):
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"""沙盒后端抽象基类"""
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@abstractmethod
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def execute(
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self,
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skill_name: str,
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function_name: str,
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function_args: dict,
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) -> dict:
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"""
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执行技能函数
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Args:
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skill_name: 技能名称，如 "builtin/valuation_review"
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function_name: 要执行的函数名，如 "build_dcf_report"
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function_args: 函数参数字典
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Returns:
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执行结果字典
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"""
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pass
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class NoSandboxBackend(SandboxBackend):
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"""
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无沙盒模式 - 直接执行（默认，仅用于开发环境）
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特性：
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- 直接导入并执行技能模块
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- 零性能开销
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- 无隔离，依赖代码审查保证安全
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"""
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# 函数名到脚本模块名的映射
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FUNCTION_TO_SCRIPT_MAP = {
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# valuation_review 技能
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"build_dcf_report": "dcf_report",
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"build_owner_earnings_report": "owner_earnings_report",
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"build_ev_ebitda_report": "multiple_valuation_report",
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"build_residual_income_report": "multiple_valuation_report",
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}
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def __init__(self):
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self._module_cache = {}
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self._warning_shown = False
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def _get_script_name(self, function_name: str) -> str:
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"""
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根据函数名获取脚本模块名
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优先使用预定义映射，否则尝试自动推断
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"""
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if function_name in self.FUNCTION_TO_SCRIPT_MAP:
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return self.FUNCTION_TO_SCRIPT_MAP[function_name]
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# 自动推断: build_X_report -> X_report
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if function_name.startswith("build_") and function_name.endswith("_report"):
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return function_name[6:] # 去掉 "build_" 前缀
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return function_name
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def execute(
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self,
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skill_name: str,
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function_name: str,
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function_args: dict,
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) -> dict:
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"""直接导入模块并执行函数"""
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# 首次使用时显示安全警告
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if not self._warning_shown:
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warnings.warn(
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"\n" + "=" * 60 + "\n"
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"⚠️ [安全警告] 技能在无沙盒模式下运行 (SKILL_SANDBOX_MODE=none)\n"
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" 技能脚本将直接在当前进程中执行，无隔离保护。\n"
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" 建议：生产环境请设置 SKILL_SANDBOX_MODE=docker\n"
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"=" * 60,
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RuntimeWarning,
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stacklevel=2,
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)
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self._warning_shown = True
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logger.debug(f"[NoSandbox] 执行技能: {skill_name}.{function_name}")
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try:
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# 将技能路径转换为模块路径
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# builtin/valuation_review -> backend.skills.builtin.valuation_review.scripts
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module_path = f"backend.skills.{skill_name.replace('/', '.')}.scripts"
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# 从 function_name 获取脚本模块名
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script_name = self._get_script_name(function_name)
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submodule_path = f"{module_path}.{script_name}"
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logger.debug(f"[NoSandbox] 导入模块: {submodule_path}.{function_name}")
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# 缓存已加载的模块
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if submodule_path not in self._module_cache:
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self._module_cache[submodule_path] = __import__(
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submodule_path,
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fromlist=[function_name],
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)
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module = self._module_cache[submodule_path]
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func = getattr(module, function_name)
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# 执行函数
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result = func(**function_args)
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return {
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"status": "success",
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"result": result,
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}
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except Exception as e:
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logger.error(f"[NoSandbox] 执行失败: {e}")
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return {
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"status": "error",
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"error": str(e),
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"error_type": type(e).__name__,
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}
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class DockerSandboxBackend(SandboxBackend):
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"""
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Docker 沙盒模式 - 容器隔离
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特性：
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- 使用 Docker 容器隔离执行
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- 支持资源限制（CPU、内存）
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- 支持网络隔离
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- 临时容器，执行后销毁
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依赖：
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pip install agentscope-runtime
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Docker 守护进程运行中
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"""
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# 函数名到脚本模块名的映射
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FUNCTION_TO_SCRIPT_MAP = {
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# valuation_review 技能
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"build_dcf_report": "dcf_report",
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"build_owner_earnings_report": "owner_earnings_report",
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"build_ev_ebitda_report": "multiple_valuation_report",
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"build_residual_income_report": "multiple_valuation_report",
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}
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def __init__(self, config: dict):
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self.config = config
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self._available = None
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def _get_script_name(self, function_name: str) -> str:
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"""
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根据函数名获取脚本模块名
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优先使用预定义映射，否则尝试自动推断
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"""
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if function_name in self.FUNCTION_TO_SCRIPT_MAP:
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return self.FUNCTION_TO_SCRIPT_MAP[function_name]
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# 自动推断: build_X_report -> X_report
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if function_name.startswith("build_") and function_name.endswith("_report"):
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return function_name[6:] # 去掉 "build_" 前缀
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return function_name
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def _check_availability(self) -> bool:
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"""检查 Docker 是否可用"""
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if self._available is not None:
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return self._available
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try:
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from agentscope_runtime.sandbox import BaseSandbox
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self._available = True
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except ImportError:
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logger.error(
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"AgentScope Runtime 未安装，无法使用 Docker 沙盒。"
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"请运行: pip install agentscope-runtime"
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)
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self._available = False
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return self._available
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def execute(
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self,
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skill_name: str,
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function_name: str,
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function_args: dict,
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) -> dict:
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"""在 Docker 容器中执行"""
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if not self._check_availability():
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raise RuntimeError(
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"Docker 沙盒不可用，请安装 agentscope-runtime "
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"或切换到 SKILL_SANDBOX_MODE=none"
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)
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from agentscope_runtime.sandbox import BaseSandbox
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logger.info(f"[DockerSandbox] 执行技能: {skill_name}.{function_name}")
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# 获取脚本模块名
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script_name = self._get_script_name(function_name)
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# 构建执行代码
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code = f"""
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import sys
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import json
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# 挂载路径
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sys.path.insert(0, '/skill/scripts')
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# 导入函数
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from {script_name} import {function_name}
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# 执行
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args = json.loads('{json.dumps(function_args)}')
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result = {function_name}(**args)
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# 输出结果
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print(json.dumps({{"status": "success", "result": result}}))
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"""
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try:
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with BaseSandbox(**self.config) as box:
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# 挂载技能目录（只读）
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host_skill_path = f"backend/skills/{skill_name}"
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box.mount(
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host_path=host_skill_path,
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container_path="/skill",
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read_only=True,
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)
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# 执行代码
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exec_result = box.run_ipython_cell(code=code)
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# 解析结果
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if exec_result.get("exit_code") == 0:
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output = exec_result.get("stdout", "")
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return json.loads(output)
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else:
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return {
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"status": "error",
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"error": exec_result.get("stderr", "Unknown error"),
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"exit_code": exec_result.get("exit_code"),
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}
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except Exception as e:
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logger.error(f"[DockerSandbox] 执行失败: {e}")
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return {
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"status": "error",
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"error": str(e),
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"error_type": type(e).__name__,
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}
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class KubernetesSandboxBackend(SandboxBackend):
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"""
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Kubernetes 沙盒模式 - Pod 隔离（预留接口）
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特性：
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- 使用 Kubernetes Pod 隔离执行
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- 企业级隔离和调度
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- 支持资源配额和命名空间
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TODO: 待实现
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"""
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def __init__(self, config: dict):
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self.config = config
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raise NotImplementedError(
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"Kubernetes 沙盒模式尚未实现，"
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"请使用 SKILL_SANDBOX_MODE=docker 或 none"
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)
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def execute(
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self,
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skill_name: str,
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function_name: str,
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function_args: dict,
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) -> dict:
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raise NotImplementedError()
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class SkillSandbox:
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"""
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技能沙盒执行器
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统一接口，根据配置自动选择后端。
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默认使用 none 模式（无沙盒）。
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示例：
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>>> sandbox = SkillSandbox()
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>>> result = sandbox.execute_skill(
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... skill_name="builtin/valuation_review",
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... function_name="build_dcf_report",
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... function_args={"rows": [...], "current_date": "2024-01-01"}
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... )
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>>> print(result)
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{"status": "success", "result": "..."}
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"""
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_instance = None
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_mode = None
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def __new__(cls):
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"""单例模式"""
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if cls._instance is None:
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cls._instance = super().__new__(cls)
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cls._instance._initialized = False
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return cls._instance
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def __init__(self):
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if self._initialized:
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return
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self.mode = os.getenv("SKILL_SANDBOX_MODE", "none").lower()
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self._backend = self._create_backend()
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self._initialized = True
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logger.info(f"SkillSandbox 初始化完成，模式: {self.mode}")
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def _create_backend(self) -> SandboxBackend:
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"""根据模式创建对应后端"""
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if self.mode == "none":
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logger.info("使用无沙盒模式（直接执行）")
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return NoSandboxBackend()
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elif self.mode == "docker":
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config = {
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"image": os.getenv(
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"SKILL_SANDBOX_IMAGE", "python:3.11-slim"
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),
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"memory_limit": os.getenv(
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"SKILL_SANDBOX_MEMORY_LIMIT", "512m"
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),
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"cpu_limit": float(
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os.getenv("SKILL_SANDBOX_CPU_LIMIT", "1.0")
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),
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"network": os.getenv("SKILL_SANDBOX_NETWORK", "none"),
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"timeout": int(os.getenv("SKILL_SANDBOX_TIMEOUT", "60")),
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}
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logger.info(f"使用 Docker 沙盒模式，配置: {config}")
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return DockerSandboxBackend(config)
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elif self.mode == "kubernetes":
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config = {
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"namespace": os.getenv(
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"SKILL_SANDBOX_NAMESPACE", "agentscope"
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),
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"memory_limit": os.getenv(
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"SKILL_SANDBOX_MEMORY_LIMIT", "512Mi"
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),
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"cpu_limit": os.getenv("SKILL_SANDBOX_CPU_LIMIT", "1000m"),
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"timeout": int(os.getenv("SKILL_SANDBOX_TIMEOUT", "60")),
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}
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logger.info(f"使用 Kubernetes 沙盒模式，配置: {config}")
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return KubernetesSandboxBackend(config)
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else:
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raise ValueError(
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f"未知的沙盒模式: {self.mode}，"
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f"请设置 SKILL_SANDBOX_MODE=none/docker/kubernetes"
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)
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def execute_skill(
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self,
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skill_name: str,
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function_name: str,
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function_args: dict | None = None,
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) -> Any:
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"""
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执行技能函数
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Args:
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skill_name: 技能名称，如 "builtin/valuation_review"
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function_name: 函数名，如 "build_dcf_report"
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function_args: 函数参数，默认 None
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Returns:
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函数执行结果（成功时返回 result 字段，失败时抛出异常）
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Raises:
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RuntimeError: 执行失败
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"""
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if function_args is None:
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function_args = {}
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logger.debug(
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f"执行技能: {skill_name}.{function_name} "
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f"(模式: {self.mode})"
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)
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result = self._backend.execute(
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skill_name=skill_name,
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function_name=function_name,
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function_args=function_args,
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)
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if result.get("status") == "error":
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error_msg = result.get("error", "Unknown error")
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error_type = result.get("error_type", "Exception")
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raise RuntimeError(f"[{error_type}] {error_msg}")
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return result.get("result")
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@property
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def current_mode(self) -> str:
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"""获取当前沙盒模式"""
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return self.mode
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def get_sandbox() -> SkillSandbox:
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"""
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获取 SkillSandbox 单例实例
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Returns:
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SkillSandbox 实例
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"""
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return SkillSandbox()
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def reset_sandbox():
|
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"""
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重置沙盒实例（用于测试）
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"""
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SkillSandbox._instance = None
|
||||
SkillSandbox._mode = None
|
||||
Reference in New Issue
Block a user