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

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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
This commit is contained in:
Yijia-Xiao
2026-05-11 05:41:42 +00:00
parent 384fe1a3d2
commit 6b384f74f9
8 changed files with 24 additions and 10 deletions
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7
tradingagents/agents/managers/research_manager.py
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@@ -3,7 +3,10 @@
from __future__ import annotations from __future__ import annotations
from tradingagents.agents.schemas import ResearchPlan, render_research_plan 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 ( from tradingagents.agents.utils.structured import (
bind_structured, bind_structured,
invoke_structured_or_freetext, invoke_structured_or_freetext,
@@ -37,7 +40,7 @@ Commit to a clear stance whenever the debate's strongest arguments warrant one;
--- ---
**Debate History:** **Debate History:**
{history}""" {history}""" + get_language_instruction()
investment_plan = invoke_structured_or_freetext( investment_plan = invoke_structured_or_freetext(
structured_llm, structured_llm,

3
tradingagents/agents/researchers/bear_researcher.py
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@@ -1,3 +1,4 @@
from tradingagents.agents.utils.agent_utils import get_language_instruction
def create_bear_researcher(llm): def create_bear_researcher(llm):
@@ -31,7 +32,7 @@ Company fundamentals report: {fundamentals_report}
Conversation history of the debate: {history} Conversation history of the debate: {history}
Last bull argument: {current_response} 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. 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) response = llm.invoke(prompt)

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

3
tradingagents/agents/risk_mgmt/aggressive_debator.py
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@@ -1,3 +1,4 @@
from tradingagents.agents.utils.agent_utils import get_language_instruction
def create_aggressive_debator(llm): def create_aggressive_debator(llm):
@@ -28,7 +29,7 @@ Latest World Affairs Report: {news_report}
Company Fundamentals Report: {fundamentals_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. 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) response = llm.invoke(prompt)

3
tradingagents/agents/risk_mgmt/conservative_debator.py
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@@ -1,3 +1,4 @@
from tradingagents.agents.utils.agent_utils import get_language_instruction
def create_conservative_debator(llm): def create_conservative_debator(llm):
@@ -28,7 +29,7 @@ Latest World Affairs Report: {news_report}
Company Fundamentals Report: {fundamentals_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. 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) response = llm.invoke(prompt)

3
tradingagents/agents/risk_mgmt/neutral_debator.py
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@@ -1,3 +1,4 @@
from tradingagents.agents.utils.agent_utils import get_language_instruction
def create_neutral_debator(llm): def create_neutral_debator(llm):
@@ -28,7 +29,7 @@ Latest World Affairs Report: {news_report}
Company Fundamentals Report: {fundamentals_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. 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) response = llm.invoke(prompt)

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

6
tradingagents/agents/utils/agent_utils.py
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@@ -24,8 +24,10 @@ def get_language_instruction() -> str:
"""Return a prompt instruction for the configured output language. """Return a prompt instruction for the configured output language.
Returns empty string when English (default), so no extra tokens are used. Returns empty string when English (default), so no extra tokens are used.
Only applied to user-facing agents (analysts, portfolio manager). Applied to every agent whose output reaches the saved report —
Internal debate agents stay in English for reasoning quality. 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 from tradingagents.dataflows.config import get_config
lang = get_config().get("output_language", "English") lang = get_config().get("output_language", "English")