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
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scripts/test_sandbox.py
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scripts/test_sandbox.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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沙盒执行器测试脚本
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测试多模式技能沙盒执行器的基本功能。
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默认使用 none 模式（无沙盒）。
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"""
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import os
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import sys
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# 确保后端目录在路径中
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "backend"))
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def test_sandbox_initialization():
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"""测试沙盒初始化"""
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print("=" * 60)
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print("测试 1: 沙盒初始化")
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print("=" * 60)
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from backend.tools.sandboxed_executor import get_sandbox, SkillSandbox
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# 重置单例（确保测试干净）
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SkillSandbox._instance = None
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# 默认应该使用 none 模式
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sandbox = get_sandbox()
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assert sandbox.current_mode == "none", f"期望模式 'none'，实际 '{sandbox.current_mode}'"
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print(f"✓ 沙盒模式: {sandbox.current_mode}")
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print(f"✓ 后端类型: {type(sandbox._backend).__name__}")
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return sandbox
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def test_no_sandbox_warning():
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"""测试无沙盒模式的安全警告"""
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print("\n" + "=" * 60)
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print("测试 2: 无沙盒模式安全警告")
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print("=" * 60)
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import warnings
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from backend.tools.sandboxed_executor import NoSandboxBackend
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backend = NoSandboxBackend()
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# 捕获警告
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with warnings.catch_warnings(record=True) as w:
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warnings.simplefilter("always")
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# 执行会触发警告
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try:
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backend.execute(
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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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except Exception:
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pass # 我们不关心执行结果，只关心警告
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# 检查是否产生了警告
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runtime_warnings = [x for x in w if issubclass(x.category, RuntimeWarning)]
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if runtime_warnings:
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print("✓ 安全警告已触发")
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print(f" 警告内容: {runtime_warnings[0].message}")
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else:
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print("⚠ 未触发安全警告（可能已缓存）")
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def test_docker_config():
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"""测试 Docker 模式配置解析"""
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print("\n" + "=" * 60)
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print("测试 3: Docker 模式配置解析")
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print("=" * 60)
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# 设置环境变量
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os.environ["SKILL_SANDBOX_MODE"] = "docker"
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os.environ["SKILL_SANDBOX_MEMORY_LIMIT"] = "1g"
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os.environ["SKILL_SANDBOX_CPU_LIMIT"] = "2.0"
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from backend.tools.sandboxed_executor import SkillSandbox
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# 重置单例
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SkillSandbox._instance = None
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try:
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sandbox = SkillSandbox()
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print(f"✓ 沙盒模式: {sandbox.current_mode}")
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print(f"✓ 后端类型: {type(sandbox._backend).__name__}")
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# 检查配置
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backend = sandbox._backend
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assert backend.config["memory_limit"] == "1g"
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assert backend.config["cpu_limit"] == 2.0
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print(f"✓ 内存限制: {backend.config['memory_limit']}")
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print(f"✓ CPU 限制: {backend.config['cpu_limit']}")
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except Exception as e:
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print(f"⚠ Docker 后端创建失败（预期，可能未安装 agentscope-runtime）: {e}")
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# 恢复环境变量
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os.environ["SKILL_SANDBOX_MODE"] = "none"
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SkillSandbox._instance = None
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def test_analysis_tools_import():
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"""测试分析工具导入"""
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print("\n" + "=" * 60)
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print("测试 4: 分析工具导入")
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print("=" * 60)
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try:
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from backend.tools.analysis_tools import (
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TOOL_REGISTRY,
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_sandbox,
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dcf_valuation_analysis,
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)
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print(f"✓ TOOL_REGISTRY 包含 {len(TOOL_REGISTRY)} 个工具")
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print(f"✓ _sandbox 实例模式: {_sandbox.current_mode}")
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print(f"✓ dcf_valuation_analysis 函数可用")
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# 检查估值分析工具是否都在
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valuation_tools = [
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"dcf_valuation_analysis",
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"owner_earnings_valuation_analysis",
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"ev_ebitda_valuation_analysis",
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"residual_income_valuation_analysis",
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]
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for tool in valuation_tools:
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if tool in TOOL_REGISTRY:
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print(f" ✓ {tool}")
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else:
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print(f" ✗ {tool} 缺失")
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except Exception as e:
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print(f"✗ 导入失败: {e}")
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import traceback
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traceback.print_exc()
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def test_skill_execution_mock():
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"""测试技能执行（模拟）"""
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print("\n" + "=" * 60)
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print("测试 5: 技能执行（无沙盒模式）")
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print("=" * 60)
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from backend.tools.sandboxed_executor import get_sandbox
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sandbox = get_sandbox()
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# 使用模拟参数调用
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try:
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# 注意：这需要实际的技能模块存在
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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={
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"rows": [{"ticker": "AAPL", "current_fcf": 100000000}],
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"current_date": "2024-01-01",
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},
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)
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print(f"✓ 技能执行成功")
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print(f" 结果类型: {type(result)}")
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print(f" 结果预览: {str(result)[:100]}...")
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except Exception as e:
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print(f"⚠ 技能执行失败（可能缺少依赖或数据）: {e}")
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def main():
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"""主测试函数"""
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print("\n" + "=" * 60)
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print("技能沙盒执行器测试")
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print("=" * 60)
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print(f"当前 SKILL_SANDBOX_MODE: {os.getenv('SKILL_SANDBOX_MODE', '未设置（默认 none）')}")
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# 确保使用默认模式测试
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os.environ["SKILL_SANDBOX_MODE"] = "none"
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try:
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test_sandbox_initialization()
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test_no_sandbox_warning()
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test_docker_config()
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test_analysis_tools_import()
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test_skill_execution_mock()
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print("\n" + "=" * 60)
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print("测试完成")
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print("=" * 60)
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except Exception as e:
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print(f"\n✗ 测试失败: {e}")
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import traceback
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traceback.print_exc()
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sys.exit(1)
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if __name__ == "__main__":
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main()
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