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chore: import upstream snapshot with attribution
2026-07-13 11:58:51 +08:00

157 lines
6.1 KiB
Python

# TradingAgents/graph/setup.py
from typing import Any
from langgraph.graph import END, START, StateGraph
from langgraph.prebuilt import ToolNode
from tradingagents.agents import (
create_aggressive_debator,
create_bear_researcher,
create_bull_researcher,
create_conservative_debator,
create_fundamentals_analyst,
create_market_analyst,
create_msg_delete,
create_neutral_debator,
create_news_analyst,
create_portfolio_manager,
create_research_manager,
create_sentiment_analyst,
create_trader,
)
from tradingagents.agents.utils.agent_states import AgentState
from .analyst_execution import build_analyst_execution_plan
from .conditional_logic import ConditionalLogic
# Every target a shared conditional router can return. Each edge driven by the
# router maps all of them, so a fall-through return (e.g. under prompt/i18n/
# refactor drift in the speaker labels) can never hit a missing path_map entry
# and crash LangGraph mid-run (#1088).
DEBATE_PATH_MAP = {
"Bull Researcher": "Bull Researcher",
"Bear Researcher": "Bear Researcher",
"Research Manager": "Research Manager",
}
RISK_ANALYSIS_PATH_MAP = {
"Aggressive Analyst": "Aggressive Analyst",
"Conservative Analyst": "Conservative Analyst",
"Neutral Analyst": "Neutral Analyst",
"Portfolio Manager": "Portfolio Manager",
}
class GraphSetup:
"""Handles the setup and configuration of the agent graph."""
def __init__(
self,
quick_thinking_llm: Any,
deep_thinking_llm: Any,
tool_nodes: dict[str, ToolNode],
conditional_logic: ConditionalLogic,
):
"""Initialize with required components."""
self.quick_thinking_llm = quick_thinking_llm
self.deep_thinking_llm = deep_thinking_llm
self.tool_nodes = tool_nodes
self.conditional_logic = conditional_logic
def setup_graph(
self, selected_analysts=("market", "social", "news", "fundamentals")
):
"""Set up and compile the agent workflow graph.
Args:
selected_analysts (list): List of analyst types to include. Options are:
- "market": Market analyst
- "social": Social media analyst
- "news": News analyst
- "fundamentals": Fundamentals analyst
"""
plan = build_analyst_execution_plan(selected_analysts)
analyst_factories = {
"market": lambda: create_market_analyst(self.quick_thinking_llm),
"social": lambda: create_sentiment_analyst(self.quick_thinking_llm),
"news": lambda: create_news_analyst(self.quick_thinking_llm),
"fundamentals": lambda: create_fundamentals_analyst(self.quick_thinking_llm),
}
# Create researcher and manager nodes
bull_researcher_node = create_bull_researcher(self.quick_thinking_llm)
bear_researcher_node = create_bear_researcher(self.quick_thinking_llm)
research_manager_node = create_research_manager(self.deep_thinking_llm)
trader_node = create_trader(self.quick_thinking_llm)
# Create risk analysis nodes
aggressive_analyst = create_aggressive_debator(self.quick_thinking_llm)
neutral_analyst = create_neutral_debator(self.quick_thinking_llm)
conservative_analyst = create_conservative_debator(self.quick_thinking_llm)
portfolio_manager_node = create_portfolio_manager(self.deep_thinking_llm)
# Create workflow
workflow = StateGraph(AgentState)
# Add analyst nodes to the graph
for spec in plan.specs:
workflow.add_node(spec.agent_node, analyst_factories[spec.key]())
workflow.add_node(spec.clear_node, create_msg_delete())
workflow.add_node(spec.tool_node, self.tool_nodes[spec.key])
# Add other nodes
workflow.add_node("Bull Researcher", bull_researcher_node)
workflow.add_node("Bear Researcher", bear_researcher_node)
workflow.add_node("Research Manager", research_manager_node)
workflow.add_node("Trader", trader_node)
workflow.add_node("Aggressive Analyst", aggressive_analyst)
workflow.add_node("Neutral Analyst", neutral_analyst)
workflow.add_node("Conservative Analyst", conservative_analyst)
workflow.add_node("Portfolio Manager", portfolio_manager_node)
# Define edges
# Start with the first analyst
workflow.add_edge(START, plan.specs[0].agent_node)
# Connect analysts in sequence
for i, spec in enumerate(plan.specs):
current_analyst = spec.agent_node
current_tools = spec.tool_node
current_clear = spec.clear_node
# Add conditional edges for current analyst
workflow.add_conditional_edges(
current_analyst,
getattr(self.conditional_logic, f"should_continue_{spec.key}"),
[current_tools, current_clear],
)
workflow.add_edge(current_tools, current_analyst)
# Connect to next analyst or to Bull Researcher if this is the last analyst
if i < len(plan.specs) - 1:
workflow.add_edge(current_clear, plan.specs[i + 1].agent_node)
else:
workflow.add_edge(current_clear, "Bull Researcher")
# Both research-debate edges share the complete DEBATE_PATH_MAP (#1088).
for debate_node in ("Bull Researcher", "Bear Researcher"):
workflow.add_conditional_edges(
debate_node,
self.conditional_logic.should_continue_debate,
DEBATE_PATH_MAP,
)
workflow.add_edge("Research Manager", "Trader")
workflow.add_edge("Trader", "Aggressive Analyst")
# All three risk edges share the complete RISK_ANALYSIS_PATH_MAP (#1088).
for risk_node in ("Aggressive Analyst", "Conservative Analyst", "Neutral Analyst"):
workflow.add_conditional_edges(
risk_node,
self.conditional_logic.should_continue_risk_analysis,
RISK_ANALYSIS_PATH_MAP,
)
workflow.add_edge("Portfolio Manager", END)
return workflow