feat(i18n): localize researchers, risk debators, research mgr, trader

output_language config now propagates to every user-facing agent.
Previously only the four analysts and portfolio manager respected
the setting, producing partial-localization reports with English
debate text interleaved with non-English analyst sections. Verified
live: 7 agents produce Chinese output when config is set to Chinese.

#575

tradingagents/agents/managers/research_manager.py

@@ -3,7 +3,10 @@
 from __future__ import annotations
 
 from tradingagents.agents.schemas import ResearchPlan, render_research_plan
-from tradingagents.agents.utils.agent_utils import build_instrument_context
+from tradingagents.agents.utils.agent_utils import (
+    build_instrument_context,
+    get_language_instruction,
+)
 from tradingagents.agents.utils.structured import (
     bind_structured,
     invoke_structured_or_freetext,
@@ -37,7 +40,7 @@ Commit to a clear stance whenever the debate's strongest arguments warrant one;
 ---
 
 **Debate History:**
-{history}"""
+{history}""" + get_language_instruction()
 
         investment_plan = invoke_structured_or_freetext(
             structured_llm,

tradingagents/agents/researchers/bear_researcher.py

@@ -1,3 +1,4 @@
+from tradingagents.agents.utils.agent_utils import get_language_instruction
 
 
 def create_bear_researcher(llm):
@@ -31,7 +32,7 @@ Company fundamentals report: {fundamentals_report}
 Conversation history of the debate: {history}
 Last bull argument: {current_response}
 Use this information to deliver a compelling bear argument, refute the bull's claims, and engage in a dynamic debate that demonstrates the risks and weaknesses of investing in the stock.
-"""
+""" + get_language_instruction()
 
         response = llm.invoke(prompt)
 

tradingagents/agents/researchers/bull_researcher.py

@@ -1,3 +1,4 @@
+from tradingagents.agents.utils.agent_utils import get_language_instruction
 
 
 def create_bull_researcher(llm):
@@ -29,7 +30,7 @@ Company fundamentals report: {fundamentals_report}
 Conversation history of the debate: {history}
 Last bear argument: {current_response}
 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.
-"""
+""" + get_language_instruction()
 
         response = llm.invoke(prompt)
 

tradingagents/agents/risk_mgmt/aggressive_debator.py

@@ -1,3 +1,4 @@
+from tradingagents.agents.utils.agent_utils import get_language_instruction
 
 
 def create_aggressive_debator(llm):
@@ -28,7 +29,7 @@ Latest World Affairs Report: {news_report}
 Company Fundamentals Report: {fundamentals_report}
 Here is the current conversation history: {history} Here are the last arguments from the conservative analyst: {current_conservative_response} Here are the last arguments from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints yet, present your own argument based on the available data.
 
-Engage actively by addressing any specific concerns raised, refuting the weaknesses in their logic, and asserting the benefits of risk-taking to outpace market norms. Maintain a focus on debating and persuading, not just presenting data. Challenge each counterpoint to underscore why a high-risk approach is optimal. Output conversationally as if you are speaking without any special formatting."""
+Engage actively by addressing any specific concerns raised, refuting the weaknesses in their logic, and asserting the benefits of risk-taking to outpace market norms. Maintain a focus on debating and persuading, not just presenting data. Challenge each counterpoint to underscore why a high-risk approach is optimal. Output conversationally as if you are speaking without any special formatting.""" + get_language_instruction()
 
         response = llm.invoke(prompt)
 

tradingagents/agents/risk_mgmt/conservative_debator.py

@@ -1,3 +1,4 @@
+from tradingagents.agents.utils.agent_utils import get_language_instruction
 
 
 def create_conservative_debator(llm):
@@ -28,7 +29,7 @@ Latest World Affairs Report: {news_report}
 Company Fundamentals Report: {fundamentals_report}
 Here is the current conversation history: {history} Here is the last response from the aggressive analyst: {current_aggressive_response} Here is the last response from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints yet, present your own argument based on the available data.
 
-Engage by questioning their optimism and emphasizing the potential downsides they may have overlooked. Address each of their counterpoints to showcase why a conservative stance is ultimately the safest path for the firm's assets. Focus on debating and critiquing their arguments to demonstrate the strength of a low-risk strategy over their approaches. Output conversationally as if you are speaking without any special formatting."""
+Engage by questioning their optimism and emphasizing the potential downsides they may have overlooked. Address each of their counterpoints to showcase why a conservative stance is ultimately the safest path for the firm's assets. Focus on debating and critiquing their arguments to demonstrate the strength of a low-risk strategy over their approaches. Output conversationally as if you are speaking without any special formatting.""" + get_language_instruction()
 
         response = llm.invoke(prompt)
 

tradingagents/agents/risk_mgmt/neutral_debator.py

@@ -1,3 +1,4 @@
+from tradingagents.agents.utils.agent_utils import get_language_instruction
 
 
 def create_neutral_debator(llm):
@@ -28,7 +29,7 @@ Latest World Affairs Report: {news_report}
 Company Fundamentals Report: {fundamentals_report}
 Here is the current conversation history: {history} Here is the last response from the aggressive analyst: {current_aggressive_response} Here is the last response from the conservative analyst: {current_conservative_response}. If there are no responses from the other viewpoints yet, present your own argument based on the available data.
 
-Engage actively by analyzing both sides critically, addressing weaknesses in the aggressive and conservative arguments to advocate for a more balanced approach. Challenge each of their points to illustrate why a moderate risk strategy might offer the best of both worlds, providing growth potential while safeguarding against extreme volatility. Focus on debating rather than simply presenting data, aiming to show that a balanced view can lead to the most reliable outcomes. Output conversationally as if you are speaking without any special formatting."""
+Engage actively by analyzing both sides critically, addressing weaknesses in the aggressive and conservative arguments to advocate for a more balanced approach. Challenge each of their points to illustrate why a moderate risk strategy might offer the best of both worlds, providing growth potential while safeguarding against extreme volatility. Focus on debating rather than simply presenting data, aiming to show that a balanced view can lead to the most reliable outcomes. Output conversationally as if you are speaking without any special formatting.""" + get_language_instruction()
 
         response = llm.invoke(prompt)
 

tradingagents/agents/trader/trader.py

@@ -7,7 +7,10 @@ import functools
 from langchain_core.messages import AIMessage
 
 from tradingagents.agents.schemas import TraderProposal, render_trader_proposal
-from tradingagents.agents.utils.agent_utils import build_instrument_context
+from tradingagents.agents.utils.agent_utils import (
+    build_instrument_context,
+    get_language_instruction,
+)
 from tradingagents.agents.utils.structured import (
     bind_structured,
     invoke_structured_or_freetext,
@@ -29,6 +32,7 @@ def create_trader(llm):
                     "You are a trading agent analyzing market data to make investment decisions. "
                     "Based on your analysis, provide a specific recommendation to buy, sell, or hold. "
                     "Anchor your reasoning in the analysts' reports and the research plan."
+                    + get_language_instruction()
                 ),
             },
             {

tradingagents/agents/utils/agent_utils.py

@@ -24,8 +24,10 @@ def get_language_instruction() -> str:
     """Return a prompt instruction for the configured output language.
 
     Returns empty string when English (default), so no extra tokens are used.
-    Only applied to user-facing agents (analysts, portfolio manager).
-    Internal debate agents stay in English for reasoning quality.
+    Applied to every agent whose output reaches the saved report —
+    analysts, researchers, debaters, research manager, trader, and
+    portfolio manager — so a non-English run produces a fully localized
+    report rather than a mix of languages.
     """
     from tradingagents.dataflows.config import get_config
     lang = get_config().get("output_language", "English")