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feat: replace per-agent BM25 memory with persistent append-only decision log

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Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Zhigong Liu
2026-04-19 22:13:53 -04:00
parent 8536ccacdd
commit 6abc768c1d
15 changed files with 1046 additions and 340 deletions
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tradingagents/agents/researchers/bull_researcher.py
+2 -10
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@@ -1,6 +1,6 @@
def create_bull_researcher(llm, memory):
def create_bull_researcher(llm):
def bull_node(state) -> dict:
investment_debate_state = state["investment_debate_state"]
history = investment_debate_state.get("history", "")
@@ -12,13 +12,6 @@ def create_bull_researcher(llm, memory):
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
past_memories = memory.get_memories(curr_situation, n_matches=2)
past_memory_str = ""
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
prompt = f"""You are a Bull Analyst advocating for investing in the stock. Your task is to build a strong, evidence-based case emphasizing growth potential, competitive advantages, and positive market indicators. Leverage the provided research and data to address concerns and counter bearish arguments effectively.
Key points to focus on:
@@ -35,8 +28,7 @@ Latest world affairs news: {news_report}
Company fundamentals report: {fundamentals_report}
Conversation history of the debate: {history}
Last bear argument: {current_response}
Reflections from similar situations and lessons learned: {past_memory_str}
Use this information to deliver a compelling bull argument, refute the bear's concerns, and engage in a dynamic debate that demonstrates the strengths of the bull position. You must also address reflections and learn from lessons and mistakes you made in the past.
Use this information to deliver a compelling bull argument, refute the bear's concerns, and engage in a dynamic debate that demonstrates the strengths of the bull position.
"""
response = llm.invoke(prompt)