cillin
45c3996434
Remove deprecated AnalystAgent, PMAgent, and RiskAgent classes. All agent creation now goes through UnifiedAgentFactory creating EvoAgent instances. - Delete backend/agents/analyst.py (169 lines) - Delete backend/agents/portfolio_manager.py (420 lines) - Delete backend/agents/risk_manager.py (139 lines) - Update all imports to use EvoAgent exclusively - Clean up unused imports across 25 files - Update tests to work with simplified agent structure Constraint: EvoAgent is now the single source of truth for all agent roles Constraint: UnifiedAgentFactory handles runtime agent creation Rejected: Keep legacy aliases | creates maintenance burden Confidence: high Scope-risk: moderate (affects agent instantiation paths) Directive: All new agent features must be added to EvoAgent, not legacy classes Not-tested: Kubernetes sandbox executor (marked with TODO)
333 lines
10 KiB
Python
333 lines
10 KiB
Python
# -*- coding: utf-8 -*-
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"""Unified Agent Factory - Centralized agent creation for 大时代.
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This module provides a unified factory for creating all agent types (analysts,
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risk manager, portfolio manager) as EvoAgent instances with consistent
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configuration. It replaces the scattered agent creation logic in main.py,
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pipeline.py, and pipeline_runner.py.
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Key features:
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- Single entry point for all agent creation
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- Creates EvoAgent instances for all agent roles
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- Consistent parameter handling across all agent types
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- Support for workspace-driven configuration
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- Long-term memory integration
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"""
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from __future__ import annotations
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from pathlib import Path
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from typing import Any, Optional, Protocol
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from backend.agents.base.evo_agent import EvoAgent
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class AgentFactoryProtocol(Protocol):
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"""Protocol for agent factory implementations."""
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def create_analyst(
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self,
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analyst_type: str,
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model: Any,
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formatter: Any,
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active_skill_dirs: Optional[list[Path]] = None,
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long_term_memory: Optional[Any] = None,
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) -> EvoAgent: ...
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def create_risk_manager(
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self,
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model: Any,
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formatter: Any,
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active_skill_dirs: Optional[list[Path]] = None,
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long_term_memory: Optional[Any] = None,
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) -> EvoAgent: ...
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def create_portfolio_manager(
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self,
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model: Any,
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formatter: Any,
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initial_cash: float,
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margin_requirement: float,
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active_skill_dirs: Optional[list[Path]] = None,
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long_term_memory: Optional[Any] = None,
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) -> EvoAgent: ...
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class UnifiedAgentFactory:
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"""Unified factory for creating EvoAgent instances with consistent configuration.
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This factory centralizes agent creation logic and creates EvoAgent instances
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for all agent roles (analysts, risk manager, portfolio manager).
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Example:
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factory = UnifiedAgentFactory(
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config_name="smoke_fullstack",
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skills_manager=skills_manager,
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)
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# Create analyst
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analyst = factory.create_analyst(
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analyst_type="fundamentals_analyst",
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model=model,
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formatter=formatter,
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)
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# Create risk manager
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risk_mgr = factory.create_risk_manager(
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model=model,
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formatter=formatter,
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)
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# Create portfolio manager
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pm = factory.create_portfolio_manager(
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model=model,
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formatter=formatter,
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initial_cash=100000.0,
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margin_requirement=0.5,
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)
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"""
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def __init__(
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self,
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config_name: str,
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skills_manager: Any,
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toolkit_factory: Optional[Any] = None,
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):
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"""Initialize the agent factory.
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Args:
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config_name: Run configuration name (e.g., "smoke_fullstack")
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skills_manager: SkillsManager instance for skill/asset management
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toolkit_factory: Optional factory function for creating toolkits
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"""
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self.config_name = config_name
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self.skills_manager = skills_manager
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self.toolkit_factory = toolkit_factory
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def _create_toolkit(
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self,
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agent_type: str,
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active_skill_dirs: Optional[list[Path]] = None,
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owner: Optional[Any] = None,
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) -> Any:
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"""Create toolkit for an agent."""
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if self.toolkit_factory is None:
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from backend.agents.toolkit_factory import create_agent_toolkit
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self.toolkit_factory = create_agent_toolkit
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kwargs: dict[str, Any] = {
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"active_skill_dirs": active_skill_dirs or [],
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}
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if owner is not None:
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kwargs["owner"] = owner
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return self.toolkit_factory(agent_type, self.config_name, **kwargs)
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def _load_agent_config(self, agent_id: str) -> Any:
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"""Load agent configuration from workspace."""
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from backend.agents.agent_workspace import load_agent_workspace_config
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workspace_dir = self.skills_manager.get_agent_asset_dir(
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self.config_name, agent_id
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)
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config_path = workspace_dir / "agent.yaml"
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if config_path.exists():
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return load_agent_workspace_config(config_path)
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# Return default config if no agent.yaml
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return type(
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"AgentConfig",
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(),
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{"prompt_files": ["SOUL.md"]},
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)()
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def _create_evo_agent(
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self,
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agent_id: str,
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model: Any,
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formatter: Any,
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toolkit: Any,
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agent_config: Any,
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long_term_memory: Optional[Any] = None,
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extra_kwargs: Optional[dict[str, Any]] = None,
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) -> EvoAgent:
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"""Create an EvoAgent instance."""
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workspace_dir = self.skills_manager.get_agent_asset_dir(
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self.config_name, agent_id
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)
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kwargs: dict[str, Any] = {
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"agent_id": agent_id,
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"config_name": self.config_name,
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"workspace_dir": workspace_dir,
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"model": model,
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"formatter": formatter,
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"skills_manager": self.skills_manager,
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"prompt_files": getattr(agent_config, "prompt_files", ["SOUL.md"]),
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"long_term_memory": long_term_memory,
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}
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if extra_kwargs:
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kwargs.update(extra_kwargs)
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agent = EvoAgent(**kwargs)
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agent.toolkit = toolkit
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setattr(agent, "run_id", self.config_name)
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# Keep workspace_id for backward compatibility
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setattr(agent, "workspace_id", self.config_name)
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return agent
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def create_analyst(
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self,
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analyst_type: str,
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model: Any,
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formatter: Any,
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active_skill_dirs: Optional[list[Path]] = None,
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long_term_memory: Optional[Any] = None,
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) -> EvoAgent:
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"""Create an analyst agent.
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Args:
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analyst_type: Type of analyst (fundamentals, technical, sentiment, valuation)
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model: LLM model instance
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formatter: Message formatter instance
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active_skill_dirs: Optional list of active skill directories
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long_term_memory: Optional long-term memory instance
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Returns:
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EvoAgent instance
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"""
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toolkit = self._create_toolkit(analyst_type, active_skill_dirs)
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agent_config = self._load_agent_config(analyst_type)
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return self._create_evo_agent(
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agent_id=analyst_type,
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model=model,
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formatter=formatter,
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toolkit=toolkit,
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agent_config=agent_config,
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long_term_memory=long_term_memory,
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)
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def create_risk_manager(
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self,
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model: Any,
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formatter: Any,
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active_skill_dirs: Optional[list[Path]] = None,
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long_term_memory: Optional[Any] = None,
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) -> EvoAgent:
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"""Create a risk manager agent.
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Args:
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model: LLM model instance
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formatter: Message formatter instance
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active_skill_dirs: Optional list of active skill directories
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long_term_memory: Optional long-term memory instance
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Returns:
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EvoAgent instance
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"""
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toolkit = self._create_toolkit("risk_manager", active_skill_dirs)
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agent_config = self._load_agent_config("risk_manager")
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return self._create_evo_agent(
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agent_id="risk_manager",
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model=model,
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formatter=formatter,
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toolkit=toolkit,
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agent_config=agent_config,
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long_term_memory=long_term_memory,
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)
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def create_portfolio_manager(
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self,
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model: Any,
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formatter: Any,
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initial_cash: float,
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margin_requirement: float,
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active_skill_dirs: Optional[list[Path]] = None,
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long_term_memory: Optional[Any] = None,
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) -> EvoAgent:
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"""Create a portfolio manager agent.
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Args:
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model: LLM model instance
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formatter: Message formatter instance
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initial_cash: Initial cash allocation
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margin_requirement: Margin requirement ratio
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active_skill_dirs: Optional list of active skill directories
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long_term_memory: Optional long-term memory instance
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Returns:
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EvoAgent instance
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"""
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agent_config = self._load_agent_config("portfolio_manager")
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# For PM, toolkit is created after agent (needs owner reference)
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workspace_dir = self.skills_manager.get_agent_asset_dir(
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self.config_name, "portfolio_manager"
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)
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agent = EvoAgent(
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agent_id="portfolio_manager",
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config_name=self.config_name,
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workspace_dir=workspace_dir,
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model=model,
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formatter=formatter,
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skills_manager=self.skills_manager,
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prompt_files=getattr(agent_config, "prompt_files", ["SOUL.md"]),
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initial_cash=initial_cash,
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margin_requirement=margin_requirement,
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long_term_memory=long_term_memory,
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)
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agent.toolkit = self._create_toolkit(
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"portfolio_manager", active_skill_dirs, owner=agent
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)
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setattr(agent, "run_id", self.config_name)
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# Keep workspace_id for backward compatibility
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setattr(agent, "workspace_id", self.config_name)
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return agent
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# Singleton factory instance cache
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_factory_cache: dict[str, UnifiedAgentFactory] = {}
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def get_agent_factory(
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config_name: str,
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skills_manager: Any,
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toolkit_factory: Optional[Any] = None,
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) -> UnifiedAgentFactory:
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"""Get or create a cached agent factory instance.
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Args:
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config_name: Run configuration name
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skills_manager: SkillsManager instance
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toolkit_factory: Optional toolkit factory function
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Returns:
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UnifiedAgentFactory instance (cached per config_name)
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"""
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cache_key = f"{config_name}:{id(skills_manager)}"
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if cache_key not in _factory_cache:
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_factory_cache[cache_key] = UnifiedAgentFactory(
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config_name=config_name,
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skills_manager=skills_manager,
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toolkit_factory=toolkit_factory,
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)
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return _factory_cache[cache_key]
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def clear_factory_cache() -> None:
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"""Clear the factory cache. Useful for testing."""
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_factory_cache.clear()
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__all__ = [
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"UnifiedAgentFactory",
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"AgentFactoryProtocol",
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"get_agent_factory",
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"clear_factory_cache",
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] |