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Feat: Add URL verification to Fact Checker

This commit is contained in:
jiwoomap 2026-01-05 00:00:42 +09:00
parent 13b826a31d
commit 693ee70e3b
7 changed files with 244 additions and 53 deletions
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39
tradingagents/agents/__init__.py
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@ -1,40 +1,19 @@
from .utils.agent_utils import create_msg_delete
from .utils.agent_states import AgentState, InvestDebateState, RiskDebateState
from .utils.memory import FinancialSituationMemory
from .analysts.fundamentals_analyst import create_fundamentals_analyst
from .analysts.market_analyst import create_market_analyst
from .analysts.news_analyst import create_news_analyst
from .analysts.fundamentals_analyst import create_fundamentals_analyst
from .analysts.social_media_analyst import create_social_media_analyst
from .researchers.bear_researcher import create_bear_researcher
from .researchers.bull_researcher import create_bull_researcher
from .researchers.bear_researcher import create_bear_researcher
from .managers.research_manager import create_research_manager
from .managers.risk_manager import create_risk_manager
from .managers.fact_checker import create_fact_checker # Added
from .trader.trader import create_trader
from .risk_mgmt.aggresive_debator import create_risky_debator
from .risk_mgmt.conservative_debator import create_safe_debator
from .risk_mgmt.neutral_debator import create_neutral_debator
from .managers.research_manager import create_research_manager
from .managers.risk_manager import create_risk_manager
from .trader.trader import create_trader
__all__ = [
"FinancialSituationMemory",
"AgentState",
"create_msg_delete",
"InvestDebateState",
"RiskDebateState",
"create_bear_researcher",
"create_bull_researcher",
"create_research_manager",
"create_fundamentals_analyst",
"create_market_analyst",
"create_neutral_debator",
"create_news_analyst",
"create_risky_debator",
"create_risk_manager",
"create_safe_debator",
"create_social_media_analyst",
"create_trader",
]
from .utils.agent_utils import create_msg_delete

205
tradingagents/agents/managers/fact_checker.py Normal file
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@ -0,0 +1,205 @@
from langchain_core.messages import AIMessage
import json
import re
import requests
import warnings
from requests.packages.urllib3.exceptions import InsecureRequestWarning
from concurrent.futures import ThreadPoolExecutor
# Suppress only the single warning from urllib3 needed.
warnings.simplefilter('ignore', InsecureRequestWarning)
def verify_url(url):
try:
# Use requests library instead of curl for better portability across OS
# mimicking the headers we found effective
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',
'Accept-Language': 'en-US,en;q=0.9',
'Referer': 'https://www.google.com/',
'Upgrade-Insecure-Requests': '1',
'Sec-Fetch-Dest': 'document',
'Sec-Fetch-Mode': 'navigate',
'Sec-Fetch-Site': 'cross-site',
'Sec-Fetch-User': '?1',
'Cache-Control': 'max-age=0'
}
# Use GET with stream=True to check headers/status without downloading full body
# verify=False is equivalent to curl -k (insecure)
response = requests.get(url, headers=headers, timeout=15, stream=True, verify=False)
status_code = response.status_code
# Treat 403 as potentially accessible but blocked by WAF
if 200 <= status_code < 400:
return url, "VALID"
elif status_code == 404:
return url, "NOT FOUND (404)"
elif status_code == 403:
return url, "VALID (Protected/403)"
else:
return url, f"ACCESSIBLE (Status: {status_code})"
except Exception as e:
# Fallback error handling
return url, f"ERROR (Could not access: {str(e)})"
def get_unique_urls(text):
if not text:
return []
# Simple regex to find URLs
urls = re.findall(r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', text)
# Remove duplicates and clean trailing punctuation
clean_urls = []
for url in urls:
# Strip common trailing punctuation that might be captured
url = url.rstrip(').,;]')
clean_urls.append(url)
return list(set(clean_urls))
def check_urls_and_get_data(text, source_label):
unique_urls = get_unique_urls(text)
if not unique_urls:
return []
results = []
# Limit max workers to avoid being flagged as DoS
with ThreadPoolExecutor(max_workers=5) as executor:
futures = [executor.submit(verify_url, url) for url in unique_urls]
for future in futures:
url, status = future.result()
results.append({
"url": url,
"status": status,
"source": source_label
})
return results
def create_fact_checker(llm):
def fact_checker_node(state) -> dict:
investment_debate_state = state["investment_debate_state"]
current_response = investment_debate_state.get("current_response", "")
# Reports to verify against
market_research_report = state["market_report"]
sentiment_report = state["sentiment_report"]
news_report = state["news_report"]
fundamentals_report = state["fundamentals_report"]
# If there's no response to check, pass
if not current_response:
return {}
# Verify URLs - collect structured data
verified_data = []
# 1. Check URLs in News Report
news_verified = check_urls_and_get_data(news_report, "News Analyst")
verified_data.extend(news_verified)
# 2. Check URLs in Current Response (Debate)
response_verified = check_urls_and_get_data(current_response, "Debate Speaker")
verified_data.extend(response_verified)
# Deduplicate by URL (keep first occurrence or merge sources)
unique_verified_map = {}
for item in verified_data:
if item["url"] not in unique_verified_map:
unique_verified_map[item["url"]] = item
else:
# If already exists, append source if different
if item["source"] not in unique_verified_map[item["url"]]["source"]:
unique_verified_map[item["url"]]["source"] += f", {item['source']}"
final_verified_list = list(unique_verified_map.values())
# Generate text report for LLM prompt
url_check_report = ""
if final_verified_list:
url_check_report = "\n[URL Verification Report]\n"
for item in final_verified_list:
url_check_report += f"- {item['url']}: {item['status']} (Source: {item['source']})\n"
print(f"DEBUG: URL Verification completed.\n{url_check_report}")
prompt = f"""You are a strict Fact Checker for a financial analysis team.
Your job is to verify the claims made in the following statement against the provided source reports AND verify the validity of the sources.
Statement to Verify:
"{current_response}"
Source Reports:
1. Market Research: {market_research_report}
2. Sentiment Report: {sentiment_report}
3. News Report: {news_report}
4. Fundamentals Report: {fundamentals_report}
URL Verification Status (Physical Check of Links):
{url_check_report}
Instructions:
1. Extract every factual claim (numbers, dates, specific news events) from the statement.
2. Check if each claim exists in the Source Reports.
3. **CRITICAL**: Check the "URL Verification Status" section. If the statement relies on a news article whose URL is marked as "NOT FOUND", "ERROR", or "INVALID", you MUST flag this as a potential invalid source or deleted article.
4. If a claim is NOT found or contradicts the reports, flag it as a HALLUCINATION.
5. If the statement is mostly opinion/analysis, it is acceptable.
6. **ALWAYS** list the Verified Sources (URLs) at the end of your response, even if verified.
Output Format:
If all facts are supported and sources are valid:
"VERIFIED: The statement is consistent with the provided data and sources appear valid.
[Verified Sources]
- [URL1] (Status: VALID)
...
"
If errors are found:
"CORRECTION NEEDED:
- Claim: [Claim] -> Error: [Not found / Contradiction / Source URL Invalid or Deleted]
...
[Verified Sources]
- [URL1] (Status: ...)
...
"
Be extremely strict about numbers. If revenue is 10B in reports but statement says 12B, flag it.
Be strict about source validity. If a key argument is based on a broken link, flag it.
"""
response = llm.invoke(prompt)
check_result = response.content
# Log the fact check result
print(f"\n\n[Fact Checker]:\n{check_result}\n")
# Prepare state update
new_state = investment_debate_state.copy()
# Merge existing verified urls with new ones
existing_verified = new_state.get("verified_urls", [])
# We want to accumulate unique URLs seen so far
existing_url_map = {item['url']: item for item in existing_verified}
for item in final_verified_list:
existing_url_map[item['url']] = item
new_state["verified_urls"] = list(existing_url_map.values())
if "CORRECTION NEEDED" in check_result:
updated_response = f"{current_response}\n\n[SYSTEM NOTE: Fact Check Warning]\n{check_result}"
new_state["current_response"] = updated_response
new_state["history"] += f"\n\n[Fact Check Warning]: {check_result}"
return {"investment_debate_state": new_state}
elif "VERIFIED" in check_result and url_check_report:
new_state["history"] += f"\n\n[Fact Checker]: {check_result}"
return {"investment_debate_state": new_state}
# Even if no text update, we return state to save verified_urls
return {"investment_debate_state": new_state}
return fact_checker_node

14
tradingagents/agents/managers/research_manager.py
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@ -31,21 +31,21 @@ Strategic Actions: Concrete steps for implementing the recommendation.
Take into account your past mistakes on similar situations. Use these insights to refine your decision-making and ensure you are learning and improving. Present your analysis conversationally, as if speaking naturally, without special formatting.
Here are your past reflections on mistakes:
\"{past_memory_str}\"
"{past_memory_str}"
Here is the debate:
Debate History:
{history}"""
response = llm.invoke(prompt)
# Log the manager's decision
print(f"\n\n[Research Manager Decision]\n{response.content}\n")
new_investment_debate_state = {
new_investment_debate_state = investment_debate_state.copy()
new_investment_debate_state.update({
"judge_decision": response.content,
"history": investment_debate_state.get("history", ""),
"bear_history": investment_debate_state.get("bear_history", ""),
"bull_history": investment_debate_state.get("bull_history", ""),
"current_response": response.content,
"count": investment_debate_state["count"],
}
})
return {
"investment_debate_state": new_investment_debate_state,

3
tradingagents/agents/utils/agent_states.py
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@ -1,4 +1,4 @@
from typing import Annotated, Sequence
from typing import Annotated, Sequence, List, Dict
from datetime import date, timedelta, datetime
from typing_extensions import TypedDict, Optional
from langchain_openai import ChatOpenAI
@ -19,6 +19,7 @@ class InvestDebateState(TypedDict):
current_response: Annotated[str, "Latest response"] # Last response
judge_decision: Annotated[str, "Final judge decision"] # Last response
count: Annotated[int, "Length of the current conversation"] # Conversation length
verified_urls: Annotated[List[Dict], "List of verified URLs with status and source"]
# Risk management team state

4
tradingagents/dataflows/google.py
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@ -21,7 +21,9 @@ def get_google_news(
for news in news_results:
news_str += (
f"### {news['title']} (source: {news['source']}) \n\n{news['snippet']}\n\n"
f"### {news['title']} (source: {news['source']}) \n"
f"URL: {news['link']}\n"
f"{news['snippet']}\n\n"
)
if len(news_results) == 0:

12
tradingagents/dataflows/local.py
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@ -110,7 +110,9 @@ def get_finnhub_news(
continue
for entry in data:
current_news = (
"### " + entry["headline"] + f" ({day})" + "\n" + entry["summary"]
f"### {entry['headline']} ({day})\n"
f"URL: {entry.get('url', 'N/A')}\n"
f"{entry['summary']}"
)
combined_result += current_news + "\n\n"
@ -411,9 +413,9 @@ def get_reddit_global_news(
news_str = ""
for post in posts:
if post["content"] == "":
news_str += f"### {post['title']}\n\n"
news_str += f"### {post['title']}\nURL: {post.get('url', 'N/A')}\n\n"
else:
news_str += f"### {post['title']}\n\n{post['content']}\n\n"
news_str += f"### {post['title']}\nURL: {post.get('url', 'N/A')}\n\n{post['content']}\n\n"
return f"## Global News Reddit, from {before} to {curr_date}:\n{news_str}"
@ -468,8 +470,8 @@ def get_reddit_company_news(
news_str = ""
for post in posts:
if post["content"] == "":
news_str += f"### {post['title']}\n\n"
news_str += f"### {post['title']}\nURL: {post.get('url', 'N/A')}\n\n"
else:
news_str += f"### {post['title']}\n\n{post['content']}\n\n"
news_str += f"### {post['title']}\nURL: {post.get('url', 'N/A')}\n\n{post['content']}\n\n"
return f"##{query} News Reddit, from {start_date} to {end_date}:\n\n{news_str}"

20
tradingagents/graph/setup.py
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@ -92,6 +92,8 @@ class GraphSetup:
bear_researcher_node = create_bear_researcher(
self.quick_thinking_llm, self.bear_memory
)
fact_checker_node = create_fact_checker(self.quick_thinking_llm)
research_manager_node = create_research_manager(
self.deep_thinking_llm, self.invest_judge_memory
)
@ -119,6 +121,7 @@ class GraphSetup:
# Add other nodes
workflow.add_node("Bull Researcher", bull_researcher_node)
workflow.add_node("Bear Researcher", bear_researcher_node)
workflow.add_node("Fact Checker", fact_checker_node)
workflow.add_node("Research Manager", research_manager_node)
workflow.add_node("Trader", trader_node)
workflow.add_node("Risky Analyst", risky_analyst)
@ -153,22 +156,21 @@ class GraphSetup:
workflow.add_edge(current_clear, "Bull Researcher")
# Add remaining edges
# Bull/Bear Researchers now go to Fact Checker instead of conditional edge
workflow.add_edge("Bull Researcher", "Fact Checker")
workflow.add_edge("Bear Researcher", "Fact Checker")
# Fact Checker decides where to go next
workflow.add_conditional_edges(
"Bull Researcher",
"Fact Checker",
self.conditional_logic.should_continue_debate,
{
"Bull Researcher": "Bull Researcher",
"Bear Researcher": "Bear Researcher",
"Research Manager": "Research Manager",
},
)
workflow.add_conditional_edges(
"Bear Researcher",
self.conditional_logic.should_continue_debate,
{
"Bull Researcher": "Bull Researcher",
"Research Manager": "Research Manager",
},
)
workflow.add_edge("Research Manager", "Trader")
workflow.add_edge("Trader", "Risky Analyst")
workflow.add_conditional_edges(