# TradingAgents/graph/propagation.py from typing import Dict, Any, List, Optional from tradingagents.agents.utils.agent_states import ( AgentState, InvestDebateState, RiskDebateState, ) class Propagator: """Handles state initialization and propagation through the graph.""" def __init__(self, max_recur_limit=100): """Initialize with configuration parameters.""" self.max_recur_limit = max_recur_limit def create_initial_state( self, company_name: str, trade_date: str ) -> Dict[str, Any]: """Create the initial state for the agent graph.""" return { "messages": [("human", company_name)], "company_of_interest": company_name, "trade_date": str(trade_date), "investment_debate_state": InvestDebateState( { "bull_history": "", "bear_history": "", "history": "", "current_response": "", "judge_decision": "", "count": 0, } ), "risk_debate_state": RiskDebateState( { "aggressive_history": "", "conservative_history": "", "neutral_history": "", "history": "", "latest_speaker": "", "current_aggressive_response": "", "current_conservative_response": "", "current_neutral_response": "", "judge_decision": "", "count": 0, } ), "market_report": "", "fundamentals_report": "", "sentiment_report": "", "news_report": "", } def get_graph_args( self, callbacks: Optional[List] = None, thread_id: Optional[str] = None, ) -> Dict[str, Any]: """Get arguments for the graph invocation. Args: callbacks: Optional list of callback handlers for tool execution tracking. Note: LLM callbacks are handled separately via LLM constructor. thread_id: LangGraph checkpoint thread (required when graph uses a checkpointer). """ config: Dict[str, Any] = {"recursion_limit": self.max_recur_limit} if callbacks: config["callbacks"] = callbacks if thread_id is not None: config["configurable"] = {"thread_id": thread_id} return { "stream_mode": "values", "config": config, }