from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder from tradingagents.agents.utils.agent_utils import get_topic_news from tradingagents.agents.utils.tool_runner import run_tool_loop def create_geopolitical_scanner(llm): def geopolitical_scanner_node(state): scan_date = state["scan_date"] tools = [get_topic_news] system_message = ( "You are a geopolitical analyst scanning global news for risks and opportunities affecting financial markets. " "Use get_topic_news to search for news on: geopolitics, trade policy, sanctions, central bank decisions, " "energy markets, and military conflicts. Analyze the results and write a concise report covering: " "(1) Major geopolitical events and their market impact, " "(2) Central bank policy signals, " "(3) Trade/sanctions developments, " "(4) Energy and commodity supply risks. " "Include a risk assessment table at the end." ) prompt = ChatPromptTemplate.from_messages( [ ( "system", "You are a helpful AI assistant, collaborating with other assistants." " Use the provided tools to progress towards answering the question." " If you are unable to fully answer, that's OK; another assistant with different tools" " will help where you left off. Execute what you can to make progress." " You have access to the following tools: {tool_names}.\n{system_message}" " For your reference, the current date is {current_date}.", ), MessagesPlaceholder(variable_name="messages"), ] ) prompt = prompt.partial(system_message=system_message) prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools])) prompt = prompt.partial(current_date=scan_date) chain = prompt | llm.bind_tools(tools) result = run_tool_loop(chain, state["messages"], tools) report = result.content or "" return { "messages": [result], "geopolitical_report": report, "sender": "geopolitical_scanner", } return geopolitical_scanner_node