cillin
4b5ac86b83
- Add EvaluationHook for post-execution agent evaluation - Add SkillAdaptationHook for dynamic skill adaptation - Add team/ directory with team coordination logic - Add TEAM_PIPELINE.yaml for smoke_fullstack pipeline config - Update RuntimeView, TraderView and RuntimeSettingsPanel UI - Add runtimeApi and websocket services - Add runtime_state.json to smoke_fullstack state Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
498 lines
18 KiB
Python
498 lines
18 KiB
Python
# -*- coding: utf-8 -*-
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"""Agent Factory - Dynamic creation and management of EvoAgents."""
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import logging
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import shutil
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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import yaml
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logger = logging.getLogger(__name__)
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@dataclass
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class ModelConfig:
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"""Model configuration for an agent."""
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model_name: str = "gpt-4o"
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temperature: float = 0.7
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max_tokens: int = 4096
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@dataclass
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class RoleConfig:
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"""Role configuration for an agent."""
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name: str
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description: str = ""
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focus_areas: List[str] = None
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constraints: List[str] = None
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def __post_init__(self):
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if self.focus_areas is None:
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self.focus_areas = []
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if self.constraints is None:
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self.constraints = []
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class EvoAgent:
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"""Represents a configured agent instance."""
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def __init__(
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self,
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agent_id: str,
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agent_type: str,
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workspace_id: str,
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config_path: Path,
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model_config: Optional[ModelConfig] = None,
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role_config: Optional[RoleConfig] = None,
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):
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self.agent_id = agent_id
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self.agent_type = agent_type
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self.workspace_id = workspace_id
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self.config_path = config_path
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self.model_config = model_config or ModelConfig()
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self.role_config = role_config
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self.agent_dir = config_path.parent
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def to_dict(self) -> Dict[str, Any]:
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"""Serialize agent to dictionary."""
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return {
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"agent_id": self.agent_id,
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"agent_type": self.agent_type,
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"workspace_id": self.workspace_id,
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"config_path": str(self.config_path),
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"agent_dir": str(self.agent_dir),
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"model_config": {
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"model_name": self.model_config.model_name,
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"temperature": self.model_config.temperature,
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"max_tokens": self.model_config.max_tokens,
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},
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"role_config": self.role_config.__dict__ if self.role_config else None,
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}
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class AgentFactory:
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"""Factory for creating, cloning, and managing agents."""
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# Default role templates by agent type
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ROLE_TEMPLATES = {
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"technical_analyst": {
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"name": "Technical Analyst",
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"description": "Analyze price patterns, trends, and technical indicators.",
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"focus_areas": [
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"Price action and chart patterns",
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"Support and resistance levels",
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"Technical indicators (RSI, MACD, Moving Averages)",
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"Volume analysis",
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],
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"constraints": [
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"State clear signal, confidence, and invalidation conditions",
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"Use available technical analysis tools",
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],
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},
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"fundamentals_analyst": {
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"name": "Fundamentals Analyst",
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"description": "Analyze company financials, earnings, and business metrics.",
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"focus_areas": [
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"Financial statements analysis",
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"Earnings reports and guidance",
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"Valuation metrics",
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"Business model and competitive position",
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],
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"constraints": [
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"State clear signal, confidence, and invalidation conditions",
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"Use available fundamental analysis tools",
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],
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},
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"sentiment_analyst": {
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"name": "Sentiment Analyst",
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"description": "Analyze market sentiment, news, and social signals.",
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"focus_areas": [
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"News sentiment analysis",
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"Social media sentiment",
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"Analyst ratings and price targets",
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"Insider activity",
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],
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"constraints": [
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"State clear signal, confidence, and invalidation conditions",
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"Use available sentiment analysis tools",
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],
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},
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"valuation_analyst": {
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"name": "Valuation Analyst",
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"description": "Perform valuation analysis and price target calculations.",
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"focus_areas": [
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"DCF and comparable valuation",
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"Price target derivation",
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"Margin of safety assessment",
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"Risk-adjusted return expectations",
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],
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"constraints": [
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"State clear signal, confidence, and invalidation conditions",
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"Use available valuation tools",
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],
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},
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"risk_manager": {
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"name": "Risk Manager",
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"description": "Quantify concentration, leverage, liquidity, and volatility risk.",
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"focus_areas": [
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"Portfolio concentration risk",
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"Leverage and margin analysis",
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"Liquidity assessment",
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"Volatility and drawdown risk",
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],
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"constraints": [
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"Prioritize highest-severity risk first",
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"State concrete limits and recommendations",
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"Use available risk tools before issuing final memo",
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],
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},
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"portfolio_manager": {
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"name": "Portfolio Manager",
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"description": "Synthesize analyst and risk inputs into portfolio decisions.",
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"focus_areas": [
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"Position sizing and allocation",
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"Risk-adjusted portfolio construction",
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"Trade execution timing",
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"Portfolio rebalancing",
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],
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"constraints": [
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"Be concise, capital-aware, and explicit about sizing rationale",
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"Respect cash, margin, and concentration constraints",
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"Consider all analyst inputs before decisions",
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],
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},
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}
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def __init__(self, project_root: Optional[Path] = None):
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"""Initialize the agent factory.
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Args:
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project_root: Root directory of the project
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"""
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self.project_root = project_root or Path(__file__).parent.parent.parent
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self.workspaces_root = self.project_root / "workspaces"
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self.template_dir = self.project_root / "backend" / "workspaces" / ".template"
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def create_agent(
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self,
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agent_id: str,
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agent_type: str,
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workspace_id: str,
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model_config: Optional[ModelConfig] = None,
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role_config: Optional[RoleConfig] = None,
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clone_from: Optional[str] = None,
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) -> EvoAgent:
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"""Create a new agent.
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Args:
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agent_id: Unique identifier for the agent
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agent_type: Type of agent (e.g., "technical_analyst")
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workspace_id: ID of the workspace to create agent in
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model_config: Model configuration
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role_config: Role configuration (auto-generated if None)
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clone_from: Path to existing agent to clone from (optional)
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Returns:
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EvoAgent instance
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Raises:
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ValueError: If agent already exists or workspace doesn't exist
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"""
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workspace_dir = self.workspaces_root / workspace_id
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if not workspace_dir.exists():
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raise ValueError(f"Workspace '{workspace_id}' does not exist")
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agent_dir = workspace_dir / "agents" / agent_id
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if agent_dir.exists():
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raise ValueError(f"Agent '{agent_id}' already exists in workspace '{workspace_id}'")
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# Create directory structure
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agent_dir.mkdir(parents=True, exist_ok=True)
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(agent_dir / "skills" / "active").mkdir(parents=True, exist_ok=True)
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(agent_dir / "skills" / "local").mkdir(parents=True, exist_ok=True)
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(agent_dir / "skills" / "installed").mkdir(parents=True, exist_ok=True)
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(agent_dir / "skills" / "disabled").mkdir(parents=True, exist_ok=True)
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# Copy template or clone existing agent
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if clone_from:
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self._clone_agent_files(clone_from, agent_dir, agent_id)
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else:
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self._copy_template(agent_dir, agent_id, agent_type)
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# Generate role config if not provided
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if role_config is None:
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role_config = self._generate_role_config(agent_type)
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# Generate ROLE.md
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self._generate_role_md(agent_dir, role_config)
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# Write agent.yaml
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config_path = agent_dir / "agent.yaml"
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self._write_agent_yaml(config_path, agent_id, agent_type, model_config)
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return EvoAgent(
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agent_id=agent_id,
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agent_type=agent_type,
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workspace_id=workspace_id,
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config_path=config_path,
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model_config=model_config,
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role_config=role_config,
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)
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def delete_agent(self, agent_id: str, workspace_id: str) -> bool:
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"""Delete an agent and its workspace.
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Args:
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agent_id: ID of the agent to delete
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workspace_id: ID of the workspace containing the agent
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Returns:
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True if deleted, False if agent didn't exist
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"""
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agent_dir = self.workspaces_root / workspace_id / "agents" / agent_id
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if not agent_dir.exists():
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return False
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shutil.rmtree(agent_dir)
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return True
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def clone_agent(
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self,
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source_agent_id: str,
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source_workspace_id: str,
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new_agent_id: str,
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target_workspace_id: Optional[str] = None,
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model_config: Optional[ModelConfig] = None,
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) -> EvoAgent:
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"""Clone an existing agent.
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Args:
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source_agent_id: ID of the agent to clone
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source_workspace_id: Workspace containing the source agent
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new_agent_id: ID for the new agent
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target_workspace_id: Target workspace (defaults to source workspace)
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model_config: Optional new model configuration
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Returns:
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EvoAgent instance for the cloned agent
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"""
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target_workspace_id = target_workspace_id or source_workspace_id
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source_dir = self.workspaces_root / source_workspace_id / "agents" / source_agent_id
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if not source_dir.exists():
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raise ValueError(f"Source agent '{source_agent_id}' not found")
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# Load source agent config
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source_config_path = source_dir / "agent.yaml"
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source_config = {}
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if source_config_path.exists():
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with open(source_config_path, "r", encoding="utf-8") as f:
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source_config = yaml.safe_load(f) or {}
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agent_type = source_config.get("agent_type", "generic")
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# Determine source path for cloning
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clone_from = str(source_dir)
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return self.create_agent(
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agent_id=new_agent_id,
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agent_type=agent_type,
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workspace_id=target_workspace_id,
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model_config=model_config,
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clone_from=clone_from,
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)
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def list_agents(self, workspace_id: Optional[str] = None) -> List[Dict[str, Any]]:
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"""List all agents.
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Args:
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workspace_id: Optional workspace to filter by
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Returns:
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List of agent information dictionaries
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"""
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agents = []
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if workspace_id:
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workspaces = [self.workspaces_root / workspace_id]
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else:
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if not self.workspaces_root.exists():
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return agents
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workspaces = [d for d in self.workspaces_root.iterdir() if d.is_dir()]
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for workspace in workspaces:
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agents_dir = workspace / "agents"
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if not agents_dir.exists():
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continue
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for agent_dir in agents_dir.iterdir():
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if not agent_dir.is_dir():
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continue
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config_path = agent_dir / "agent.yaml"
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if config_path.exists():
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try:
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with open(config_path, "r", encoding="utf-8") as f:
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config = yaml.safe_load(f) or {}
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agents.append({
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"agent_id": agent_dir.name,
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"workspace_id": workspace.name,
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"agent_type": config.get("agent_type", "unknown"),
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"config_path": str(config_path),
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})
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except Exception as e:
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logger.warning(f"Failed to load agent config {config_path}: {e}")
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return agents
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def _copy_template(
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self,
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agent_dir: Path,
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agent_id: str,
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agent_type: str,
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) -> None:
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"""Copy template files to agent directory.
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Args:
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agent_dir: Target agent directory
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agent_id: ID of the agent
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agent_type: Type of the agent
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"""
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# Create default markdown files
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default_files = {
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"AGENTS.md": f"# Agent Guide\n\nDocument how {agent_id} should work, collaborate, and choose tools or skills.\n\n",
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"SOUL.md": f"# Soul\n\nDescribe {agent_id}'s temperament, reasoning posture, and voice.\n\n",
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"PROFILE.md": f"# Profile\n\nTrack {agent_id}'s long-lived investment style, preferences, and strengths.\n\n",
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"MEMORY.md": f"# Memory\n\nStore durable lessons, heuristics, and reminders for {agent_id}.\n\n",
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"HEARTBEAT.md": f"# Heartbeat\n\nOptional checklist for periodic review or self-reflection.\n\n",
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"POLICY.md": f"# Policy\n\nOptional run-scoped constraints, limits, or strategy policy.\n\n",
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"STYLE.md": f"# Style\n\nOptional run-scoped communication or reasoning style.\n\n",
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}
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for filename, content in default_files.items():
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filepath = agent_dir / filename
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if not filepath.exists():
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filepath.write_text(content, encoding="utf-8")
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def _clone_agent_files(self, source_path: str, target_dir: Path, new_agent_id: str) -> None:
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"""Clone files from an existing agent.
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Args:
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source_path: Path to source agent directory
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target_dir: Target agent directory
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new_agent_id: ID for the new agent
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"""
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source_dir = Path(source_path)
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if not source_dir.exists():
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raise ValueError(f"Source path '{source_path}' does not exist")
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# Copy markdown files
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for md_file in source_dir.glob("*.md"):
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target_file = target_dir / md_file.name
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content = md_file.read_text(encoding="utf-8")
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# Update agent references in content
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source_name = source_dir.name
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content = content.replace(source_name, new_agent_id)
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target_file.write_text(content, encoding="utf-8")
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# Copy skills directory structure (but not contents)
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for skill_subdir in ["active", "local", "installed", "disabled"]:
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source_skills = source_dir / "skills" / skill_subdir
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if source_skills.exists():
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target_skills = target_dir / "skills" / skill_subdir
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target_skills.mkdir(parents=True, exist_ok=True)
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# Copy skill files
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for skill_file in source_skills.iterdir():
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if skill_file.is_file():
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shutil.copy2(skill_file, target_skills / skill_file.name)
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def _generate_role_config(self, agent_type: str) -> RoleConfig:
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"""Generate role configuration for an agent type.
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Args:
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agent_type: Type of agent
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Returns:
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RoleConfig instance
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"""
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template = self.ROLE_TEMPLATES.get(agent_type, {})
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return RoleConfig(
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name=template.get("name", agent_type.replace("_", " ").title()),
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description=template.get("description", ""),
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focus_areas=template.get("focus_areas", []),
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constraints=template.get("constraints", []),
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)
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def _generate_role_md(self, agent_dir: Path, role_config: RoleConfig) -> None:
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"""Generate ROLE.md file.
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Args:
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agent_dir: Agent directory
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role_config: Role configuration
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"""
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lines = [f"# {role_config.name}", ""]
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if role_config.description:
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lines.extend([role_config.description, ""])
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if role_config.focus_areas:
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lines.extend(["## Focus Areas", ""])
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for area in role_config.focus_areas:
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lines.append(f"- {area}")
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lines.append("")
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if role_config.constraints:
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lines.extend(["## Constraints", ""])
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for constraint in role_config.constraints:
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lines.append(f"- {constraint}")
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lines.append("")
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content = "\n".join(lines)
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(agent_dir / "ROLE.md").write_text(content, encoding="utf-8")
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def _write_agent_yaml(
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self,
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config_path: Path,
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agent_id: str,
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agent_type: str,
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model_config: Optional[ModelConfig] = None,
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) -> None:
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"""Write agent.yaml configuration file.
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Args:
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config_path: Path to write configuration
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agent_id: Agent ID
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agent_type: Agent type
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model_config: Optional model configuration
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"""
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config = {
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"agent_id": agent_id,
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"agent_type": agent_type,
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"prompt_files": [
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"SOUL.md",
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"PROFILE.md",
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"AGENTS.md",
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"POLICY.md",
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"MEMORY.md",
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],
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"enabled_skills": [],
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"disabled_skills": [],
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"active_tool_groups": [],
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"disabled_tool_groups": [],
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}
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if model_config:
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config["model"] = {
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"name": model_config.model_name,
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"temperature": model_config.temperature,
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"max_tokens": model_config.max_tokens,
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}
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with open(config_path, "w", encoding="utf-8") as f:
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yaml.safe_dump(config, f, allow_unicode=True, sort_keys=False)
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