cillin
16b54d5ccc
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
485 lines
23 KiB
Python
485 lines
23 KiB
Python
# -*- coding: utf-8 -*-
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"""Initialize run-scoped agent workspace assets under `runs/<run_id>/`."""
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from pathlib import Path
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from typing import Dict, Iterable, Optional
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import yaml
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from .skills_manager import SkillsManager
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from .team_pipeline_config import ensure_team_pipeline_config
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class RunWorkspaceManager:
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"""Create and maintain run-level prompt asset files for each agent."""
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def __init__(self, project_root: Optional[Path] = None):
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self.skills_manager = SkillsManager(project_root=project_root)
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self.project_root = self.skills_manager.project_root
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def get_run_dir(self, config_name: str) -> Path:
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return self.project_root / "runs" / config_name
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def ensure_run_workspace(self, config_name: str) -> Path:
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run_dir = self.get_run_dir(config_name)
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run_dir.mkdir(parents=True, exist_ok=True)
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self.skills_manager.ensure_activation_manifest(config_name)
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ensure_team_pipeline_config(
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project_root=self.project_root,
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config_name=config_name,
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default_analysts=[
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"fundamentals_analyst",
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"technical_analyst",
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"sentiment_analyst",
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"valuation_analyst",
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],
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)
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bootstrap_path = run_dir / "BOOTSTRAP.md"
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if not bootstrap_path.exists():
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bootstrap_path.write_text(
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"---\n"
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"tickers:\n"
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" - AAPL\n"
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" - MSFT\n"
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" - GOOGL\n"
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" - AMZN\n"
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" - NVDA\n"
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" - META\n"
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" - TSLA\n"
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" - AMD\n"
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" - NFLX\n"
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" - AVGO\n"
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" - PLTR\n"
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" - COIN\n"
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"initial_cash: 100000\n"
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"margin_requirement: 0.0\n"
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"enable_memory: false\n"
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"max_comm_cycles: 2\n"
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"agent_overrides: {}\n"
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"---\n\n"
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"# Bootstrap\n\n"
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"Use this file to describe run-specific setup notes, preferred tickers,\n"
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"risk bounds, or strategy constraints before the first execution.\n\n"
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"The YAML front matter above is machine-readable runtime configuration.\n"
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"The markdown body below is injected into agent prompts as run context.\n",
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encoding="utf-8",
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)
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return run_dir
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def bootstrap_path(self, config_name: str) -> Path:
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return self.get_run_dir(config_name) / "BOOTSTRAP.md"
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def ensure_agent_assets(
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self,
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config_name: str,
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agent_id: str,
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file_contents: Optional[Dict[str, str]] = None,
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persona: Optional[Dict[str, object]] = None,
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) -> Path:
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asset_dir = self.skills_manager.get_agent_asset_dir(
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config_name,
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agent_id,
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)
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asset_dir.mkdir(parents=True, exist_ok=True)
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(asset_dir / "skills" / "installed").mkdir(parents=True, exist_ok=True)
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(asset_dir / "skills" / "active").mkdir(parents=True, exist_ok=True)
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(asset_dir / "skills" / "disabled").mkdir(parents=True, exist_ok=True)
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(asset_dir / "skills" / "local").mkdir(parents=True, exist_ok=True)
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file_contents = file_contents or self.build_default_agent_files(agent_id=agent_id)
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for filename, content in file_contents.items():
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legacy_contents = self.build_legacy_agent_file_variants(
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agent_id=agent_id,
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filename=filename,
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persona=persona,
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)
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self._ensure_file(asset_dir / filename, content, legacy_contents=legacy_contents)
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self._ensure_agent_yaml(
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asset_dir / "agent.yaml",
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agent_id=agent_id,
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)
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return asset_dir
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def build_default_agent_files(
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self,
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*,
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agent_id: str,
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persona: Optional[Dict[str, object]] = None,
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) -> Dict[str, str]:
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"""Build default workspace markdown files for one agent."""
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if agent_id.endswith("_analyst"):
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return self._build_analyst_files(agent_id=agent_id, persona=persona or {})
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if agent_id == "portfolio_manager":
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return self._build_portfolio_manager_files()
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if agent_id == "risk_manager":
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return self._build_risk_manager_files()
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return self._build_generic_files(agent_id=agent_id)
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def build_legacy_agent_file_variants(
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self,
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*,
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agent_id: str,
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filename: str,
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persona: Optional[Dict[str, object]] = None,
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) -> list[str]:
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"""Return known generated legacy variants safe to upgrade in-place."""
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persona = persona or {}
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variants: list[dict[str, str]] = [
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self._build_legacy_english_files(agent_id=agent_id),
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self._build_previous_chinese_files(agent_id=agent_id, persona=persona),
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]
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values: list[str] = []
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for item in variants:
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content = item.get(filename)
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if content:
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values.append(content)
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return values
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def load_agent_file(
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self,
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*,
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config_name: str,
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agent_id: str,
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filename: str,
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) -> str:
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"""Load one run-scoped agent workspace file."""
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path = self.skills_manager.get_agent_asset_dir(config_name, agent_id) / filename
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if not path.exists():
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raise FileNotFoundError(f"File not found: {filename}")
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return path.read_text(encoding="utf-8")
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def update_agent_file(
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self,
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*,
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config_name: str,
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agent_id: str,
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filename: str,
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content: str,
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) -> None:
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"""Write one run-scoped agent workspace file."""
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asset_dir = self.skills_manager.get_agent_asset_dir(config_name, agent_id)
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asset_dir.mkdir(parents=True, exist_ok=True)
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path = asset_dir / filename
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path.write_text(content, encoding="utf-8")
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def initialize_default_assets(
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self,
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config_name: str,
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agent_ids: Iterable[str],
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analyst_personas: Optional[Dict[str, Dict]] = None,
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) -> None:
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self.ensure_run_workspace(config_name)
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analyst_personas = analyst_personas or {}
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for agent_id in agent_ids:
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if agent_id.endswith("_analyst"):
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persona = analyst_personas.get(agent_id, {})
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file_contents = self.build_default_agent_files(
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agent_id=agent_id,
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persona=persona,
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)
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else:
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persona = None
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file_contents = self.build_default_agent_files(agent_id=agent_id)
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asset_dir = self.skills_manager.get_agent_asset_dir(config_name, agent_id)
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asset_dir.mkdir(parents=True, exist_ok=True)
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(asset_dir / "skills" / "installed").mkdir(parents=True, exist_ok=True)
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(asset_dir / "skills" / "active").mkdir(parents=True, exist_ok=True)
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(asset_dir / "skills" / "disabled").mkdir(parents=True, exist_ok=True)
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(asset_dir / "skills" / "local").mkdir(parents=True, exist_ok=True)
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for filename, content in file_contents.items():
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self._ensure_file(
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asset_dir / filename,
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content,
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legacy_contents=self.build_legacy_agent_file_variants(
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agent_id=agent_id,
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filename=filename,
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persona=persona,
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),
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)
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self._ensure_agent_yaml(asset_dir / "agent.yaml", agent_id=agent_id)
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@staticmethod
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def _ensure_file(path: Path, content: str, *, legacy_contents: Optional[list[str]] = None) -> None:
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if not path.exists():
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path.write_text(content, encoding="utf-8")
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return
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existing = path.read_text(encoding="utf-8")
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normalized_existing = existing.strip()
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candidates = {item.strip() for item in (legacy_contents or []) if item and item.strip()}
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if normalized_existing in candidates:
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path.write_text(content, encoding="utf-8")
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@staticmethod
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def _build_generic_files(agent_id: str) -> Dict[str, str]:
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return {
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"SOUL.md": (
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"# Soul\n\n"
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f"你是 `{agent_id}`，语气冷静、客观、专业。保持清晰推理，优先基于数据而不是情绪下结论。\n"
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),
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"PROFILE.md": (
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"# Profile\n\n"
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"记录这个 agent 长期稳定的分析风格、偏好、优势与盲点。\n"
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),
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"AGENTS.md": (
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"# Agent Guide\n\n"
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"工作要求：\n"
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"- 优先使用已激活的技能和工具\n"
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"- 结论要明确，过程要可追溯\n"
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"- 与其他 agent 协作时保持输入输出简洁\n"
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"- 最终输出必须使用简体中文；如需引用英文术语，仅保留专有名词，解释和结论必须用中文\n"
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),
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"POLICY.md": (
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"# Policy\n\n"
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"- 给出结论时说明核心驱动因素\n"
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"- 明确风险边界和结论失效条件\n"
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"- 出现反例时需要纳入最终判断\n"
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"- 不要输出英文报告标题、英文摘要或整段英文正文\n"
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),
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"MEMORY.md": (
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"# Memory\n\n"
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"记录可复用的经验、失误复盘、有效启发式和需要持续跟踪的提醒。\n"
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),
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}
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@classmethod
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def _build_analyst_files(cls, *, agent_id: str, persona: Dict[str, object]) -> Dict[str, str]:
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role_name = str(persona.get("name") or agent_id)
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focus_items = [
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str(item).strip()
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for item in persona.get("focus", [])
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if str(item).strip()
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]
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focus_md = "\n".join(f"- {item}" for item in focus_items) or "- 根据当前任务选择最相关的分析维度"
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description = str(persona.get("description") or "").strip()
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files = cls._build_generic_files(agent_id)
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files["SOUL.md"] = (
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"# Soul\n\n"
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f"你是一位专业的{role_name}。\n\n"
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"保持谦逊和开放，主动寻找与自己观点相悖的证据，并将其纳入最终评估。"
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"你的分析要体现持续演化的投资哲学，而不是一次性的结论。\n"
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)
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files["PROFILE.md"] = (
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"# Profile\n\n"
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f"角色定位：{role_name}\n\n"
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"你的关注重点：\n"
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f"{focus_md}\n\n"
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"角色说明：\n"
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f"{description or '围绕最关键的基本面、技术面、情绪面或估值因素形成高质量判断。'}\n"
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)
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files["AGENTS.md"] = (
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"# Agent Guide\n\n"
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"分析流程：\n"
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"- 优先识别真正驱动价值或价格变化的核心变量\n"
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"- 使用相关工具和技能补足证据链\n"
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"- 给出可验证、可复查、可执行的分析结果\n"
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"- 在团队讨论中清晰表达你的论点和反论点\n\n"
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"输出要求：\n"
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"- 给出明确投资信号：看涨、看跌或中性\n"
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"- 包含置信度（0-100）\n"
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"- 如果你确定要分享最终分析，请先给出结论，再给出推理依据\n"
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"- 最终输出必须使用简体中文，不要生成英文版 analysis report\n"
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)
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files["POLICY.md"] = (
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"# Policy\n\n"
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"- 深化你的投资逻辑，确保每项建议都有清晰、可追溯、可重复的依据\n"
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"- 明确风险边界：在什么具体情况下当前结论会失效\n"
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"- 做逆向测试：说明市场主流共识与你的不同点\n"
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"- 每次分析后反思这次案例如何验证或挑战你现有的信念\n"
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"- 即使输入新闻或财报原文是英文，最终表达也必须用中文\n"
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)
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return files
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@classmethod
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def _build_portfolio_manager_files(cls) -> Dict[str, str]:
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files = cls._build_generic_files("portfolio_manager")
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files["SOUL.md"] = (
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"# Soul\n\n"
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"你是一位负责做出投资决策的投资组合经理。你需要综合多个分析视角，"
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"做出保守、明确、资本约束下可执行的组合决策。\n"
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)
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files["PROFILE.md"] = (
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"# Profile\n\n"
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"核心职责：\n"
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"- 分析分析师和风险管理经理的输入\n"
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"- 基于信号和市场情境做出投资决策\n"
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"- 使用可用工具记录每个 ticker 的决策\n"
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)
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files["AGENTS.md"] = (
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"# Agent Guide\n\n"
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"决策框架：\n"
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"- 审阅分析以理解市场观点\n"
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"- 在做决策前先考虑风险警告\n"
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"- 评估当前投资组合持仓、现金与保证金占用\n"
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"- 决策必须与整体投资目标和风险约束一致\n\n"
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"决策类型：\n"
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'- `long`：看涨，建议买入\n'
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'- `short`：看跌，建议卖出或做空\n'
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'- `hold`：中性，维持当前持仓\n\n'
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"输出要求：\n"
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"- 使用 `make_decision` 工具记录每个股票的最终决策\n"
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"- 记录完成后给出投资逻辑总结\n"
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"- 最终总结必须使用简体中文\n"
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)
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files["POLICY.md"] = (
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"# Policy\n\n"
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"- 在决定数量时考虑可用现金，不要超出现金允许范围\n"
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"- 考虑做空头寸的保证金要求\n"
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"- 仓位规模相对于组合总资产保持保守\n"
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"- 始终为决策提供清晰理由\n"
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"- 不要输出英文投资报告或英文结论\n"
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)
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return files
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@classmethod
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def _build_risk_manager_files(cls) -> Dict[str, str]:
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files = cls._build_generic_files("risk_manager")
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files["SOUL.md"] = (
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"# Soul\n\n"
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"你是一位专业的风险管理经理，负责监控投资组合风险并提供风险警告。"
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"你的目标不是输出空泛的谨慎，而是给出量化、可执行、可优先级排序的风险意见。\n"
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)
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files["PROFILE.md"] = (
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"# Profile\n\n"
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"核心职责：\n"
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"- 监控投资组合敞口和集中度风险\n"
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"- 评估仓位规模相对于波动性是否合理\n"
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"- 评估保证金使用和杠杆水平\n"
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"- 识别潜在风险因素并提供警告\n"
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"- 基于市场条件建议仓位限制\n"
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)
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files["AGENTS.md"] = (
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"# Agent Guide\n\n"
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"决策流程：\n"
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"- 优先使用可用的风险工具量化集中度、波动率和保证金压力\n"
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"- 结合工具结果与当前市场上下文做判断\n"
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"- 生成可操作的风险警告和仓位限制建议\n"
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"- 为风险评估提供清晰理由\n\n"
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"输出要求：\n"
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"- 风险评估要简洁但全面\n"
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"- 按严重程度优先排序警告\n"
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"- 提供具体、可操作的建议\n"
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"- 尽可能包含量化指标\n"
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"- 最终风险结论必须使用简体中文\n"
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)
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files["POLICY.md"] = (
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"# Policy\n\n"
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"- 先量化，再判断，不要只给抽象风险表述\n"
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"- 高严重度风险必须先说\n"
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"- 最终结论需要明确仓位限制或调整建议\n"
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"- 不要输出英文风险报告或英文摘要\n"
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)
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return files
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@staticmethod
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def _build_legacy_english_files(agent_id: str) -> Dict[str, str]:
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policy_tail = "Optional run-scoped constraints, limits, or strategy policy.\n\n"
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if agent_id == "portfolio_manager":
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policy_tail += "Respect cash, margin, and portfolio concentration constraints before recording decisions.\n"
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elif agent_id == "risk_manager":
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policy_tail += "Use available risk tools before issuing the final risk memo.\n"
|
||
elif agent_id.endswith("_analyst"):
|
||
policy_tail += "State a clear signal, confidence, and the conditions that would invalidate the thesis.\n"
|
||
return {
|
||
"SOUL.md": "# Soul\n\nDescribe the agent's temperament, reasoning posture, and voice.\n\n",
|
||
"PROFILE.md": "# Profile\n\nTrack this agent's long-lived investment style, preferences, and strengths.\n\n",
|
||
"AGENTS.md": "# Agent Guide\n\nDocument how this agent should work, collaborate, and choose tools or skills.\n\n",
|
||
"POLICY.md": "# Policy\n\n" + policy_tail,
|
||
"MEMORY.md": "# Memory\n\nStore durable lessons, heuristics, and reminders for this agent.\n\n",
|
||
}
|
||
|
||
@classmethod
|
||
def _build_previous_chinese_files(cls, *, agent_id: str, persona: Dict[str, object]) -> Dict[str, str]:
|
||
if agent_id.endswith("_analyst"):
|
||
role_name = str(persona.get("name") or agent_id)
|
||
focus_items = [
|
||
str(item).strip()
|
||
for item in persona.get("focus", [])
|
||
if str(item).strip()
|
||
]
|
||
focus_md = "\n".join(f"- {item}" for item in focus_items) or "- 根据当前任务选择最相关的分析维度"
|
||
description = str(persona.get("description") or "").strip()
|
||
return {
|
||
"SOUL.md": (
|
||
"# Soul\n\n"
|
||
f"你是一位专业的{role_name}。\n\n"
|
||
"保持谦逊和开放，主动寻找与自己观点相悖的证据，并将其纳入最终评估。"
|
||
"你的分析要体现持续演化的投资哲学，而不是一次性的结论。\n"
|
||
),
|
||
"PROFILE.md": (
|
||
"# Profile\n\n"
|
||
f"角色定位：{role_name}\n\n"
|
||
"你的关注重点：\n"
|
||
f"{focus_md}\n\n"
|
||
"角色说明：\n"
|
||
f"{description or '围绕最关键的基本面、技术面、情绪面或估值因素形成高质量判断。'}\n"
|
||
),
|
||
"AGENTS.md": (
|
||
"# Agent Guide\n\n"
|
||
"分析流程：\n"
|
||
"- 优先识别真正驱动价值或价格变化的核心变量\n"
|
||
"- 使用相关工具和技能补足证据链\n"
|
||
"- 给出可验证、可复查、可执行的分析结果\n"
|
||
"- 在团队讨论中清晰表达你的论点和反论点\n\n"
|
||
"输出要求：\n"
|
||
"- 给出明确投资信号：看涨、看跌或中性\n"
|
||
"- 包含置信度（0-100）\n"
|
||
"- 如果你确定要分享最终分析，请先给出结论，再给出推理依据\n"
|
||
),
|
||
"POLICY.md": (
|
||
"# Policy\n\n"
|
||
"- 深化你的投资逻辑，确保每项建议都有清晰、可追溯、可重复的依据\n"
|
||
"- 明确风险边界：在什么具体情况下当前结论会失效\n"
|
||
"- 做逆向测试：说明市场主流共识与你的不同点\n"
|
||
"- 每次分析后反思这次案例如何验证或挑战你现有的信念\n"
|
||
),
|
||
"MEMORY.md": "# Memory\n\n记录可复用的经验、失误复盘、有效启发式和需要持续跟踪的提醒。\n",
|
||
}
|
||
if agent_id == "portfolio_manager":
|
||
return {
|
||
"SOUL.md": "# Soul\n\n你是一位负责做出投资决策的投资组合经理。你需要综合多个分析视角，做出保守、明确、资本约束下可执行的组合决策。\n",
|
||
"PROFILE.md": "# Profile\n\n核心职责：\n- 分析分析师和风险管理经理的输入\n- 基于信号和市场情境做出投资决策\n- 使用可用工具记录每个 ticker 的决策\n",
|
||
"AGENTS.md": "# Agent Guide\n\n决策框架：\n- 审阅分析以理解市场观点\n- 在做决策前先考虑风险警告\n- 评估当前投资组合持仓、现金与保证金占用\n- 决策必须与整体投资目标和风险约束一致\n\n决策类型：\n- `long`：看涨，建议买入\n- `short`：看跌，建议卖出或做空\n- `hold`：中性，维持当前持仓\n\n输出要求：\n- 使用 `make_decision` 工具记录每个股票的最终决策\n- 记录完成后给出投资逻辑总结\n",
|
||
"POLICY.md": "# Policy\n\n- 在决定数量时考虑可用现金，不要超出现金允许范围\n- 考虑做空头寸的保证金要求\n- 仓位规模相对于组合总资产保持保守\n- 始终为决策提供清晰理由\n",
|
||
"MEMORY.md": "# Memory\n\n记录可复用的经验、失误复盘、有效启发式和需要持续跟踪的提醒。\n",
|
||
}
|
||
if agent_id == "risk_manager":
|
||
return {
|
||
"SOUL.md": "# Soul\n\n你是一位专业的风险管理经理，负责监控投资组合风险并提供风险警告。你的目标不是输出空泛的谨慎，而是给出量化、可执行、可优先级排序的风险意见。\n",
|
||
"PROFILE.md": "# Profile\n\n核心职责：\n- 监控投资组合敞口和集中度风险\n- 评估仓位规模相对于波动性是否合理\n- 评估保证金使用和杠杆水平\n- 识别潜在风险因素并提供警告\n- 基于市场条件建议仓位限制\n",
|
||
"AGENTS.md": "# Agent Guide\n\n决策流程：\n- 优先使用可用的风险工具量化集中度、波动率和保证金压力\n- 结合工具结果与当前市场上下文做判断\n- 生成可操作的风险警告和仓位限制建议\n- 为风险评估提供清晰理由\n\n输出要求：\n- 风险评估要简洁但全面\n- 按严重程度优先排序警告\n- 提供具体、可操作的建议\n- 尽可能包含量化指标\n",
|
||
"POLICY.md": "# Policy\n\n- 先量化，再判断，不要只给抽象风险表述\n- 高严重度风险必须先说\n- 最终结论需要明确仓位限制或调整建议\n",
|
||
"MEMORY.md": "# Memory\n\n记录可复用的经验、失误复盘、有效启发式和需要持续跟踪的提醒。\n",
|
||
}
|
||
return cls._build_legacy_english_files(agent_id)
|
||
|
||
@staticmethod
|
||
def _ensure_agent_yaml(path: Path, agent_id: str) -> None:
|
||
if path.exists():
|
||
return
|
||
|
||
payload = {
|
||
"agent_id": agent_id,
|
||
"prompt_files": [
|
||
"SOUL.md",
|
||
"PROFILE.md",
|
||
"AGENTS.md",
|
||
"POLICY.md",
|
||
"MEMORY.md",
|
||
],
|
||
"enabled_skills": [],
|
||
"disabled_skills": [],
|
||
"active_tool_groups": [],
|
||
"disabled_tool_groups": [],
|
||
}
|
||
path.write_text(
|
||
yaml.safe_dump(payload, allow_unicode=True, sort_keys=False),
|
||
encoding="utf-8",
|
||
)
|
||
|
||
|
||
# Backward-compatible alias: many runtime paths still import WorkspaceManager
|
||
# from this module when they mean the run-scoped manager.
|
||
WorkspaceManager = RunWorkspaceManager
|