feat: add multi-provider LLM support with thinking configurations

Models added:
- OpenAI: GPT-5.2, GPT-5.1, GPT-5, GPT-5 Mini, GPT-5 Nano, GPT-4.1
- Anthropic: Claude Opus 4.5/4.1, Claude Sonnet 4.5/4, Claude Haiku 4.5
- Google: Gemini 3 Pro/Flash, Gemini 2.5 Flash/Flash Lite
- xAI: Grok 4, Grok 4.1 Fast (Reasoning/Non-Reasoning)

Configs updated:
- Add unified thinking_level for Gemini (maps to thinking_level for Gemini 3,
  thinking_budget for Gemini 2.5; handles Pro's lack of "minimal" support)
- Add OpenAI reasoning_effort configuration
- Add NormalizedChatGoogleGenerativeAI for consistent response handling

Fixes:
- Fix Bull/Bear researcher display truncation
- Replace ChromaDB with BM25 for memory retrieval

cli/main.py

@@ -484,6 +484,28 @@ def get_user_selections():
     selected_shallow_thinker = select_shallow_thinking_agent(selected_llm_provider)
     selected_deep_thinker = select_deep_thinking_agent(selected_llm_provider)
 
+    # Step 7: Provider-specific thinking configuration
+    thinking_level = None
+    reasoning_effort = None
+
+    provider_lower = selected_llm_provider.lower()
+    if provider_lower == "google":
+        console.print(
+            create_question_box(
+                "Step 7: Thinking Mode",
+                "Configure Gemini thinking mode"
+            )
+        )
+        thinking_level = ask_gemini_thinking_config()
+    elif provider_lower == "openai":
+        console.print(
+            create_question_box(
+                "Step 7: Reasoning Effort",
+                "Configure OpenAI reasoning effort level"
+            )
+        )
+        reasoning_effort = ask_openai_reasoning_effort()
+
     return {
         "ticker": selected_ticker,
         "analysis_date": analysis_date,
@@ -493,6 +515,8 @@ def get_user_selections():
         "backend_url": backend_url,
         "shallow_thinker": selected_shallow_thinker,
         "deep_thinker": selected_deep_thinker,
+        "google_thinking_level": thinking_level,
+        "openai_reasoning_effort": reasoning_effort,
     }
 
 
@@ -717,23 +741,45 @@ def update_research_team_status(status):
         message_buffer.update_agent_status(agent, status)
 
 def extract_content_string(content):
-    """Extract string content from various message formats."""
+    """Extract string content from various message formats.
+    Returns None if no meaningful text content is found.
+    """
+    import ast
+
+    def is_empty(val):
+        """Check if value is empty using Python's truthiness."""
+        if val is None or val == '':
+            return True
+        if isinstance(val, str):
+            s = val.strip()
+            if not s:
+                return True
+            try:
+                return not bool(ast.literal_eval(s))
+            except (ValueError, SyntaxError):
+                return False  # Can't parse = real text
+        return not bool(val)
+
+    if is_empty(content):
+        return None
+
     if isinstance(content, str):
-        return content
-    elif isinstance(content, list):
-        # Handle Anthropic's list format
-        text_parts = []
-        for item in content:
-            if isinstance(item, dict):
-                if item.get('type') == 'text':
-                    text_parts.append(item.get('text', ''))
-                elif item.get('type') == 'tool_use':
-                    text_parts.append(f"[Tool: {item.get('name', 'unknown')}]")
-            else:
-                text_parts.append(str(item))
-        return ' '.join(text_parts)
-    else:
-        return str(content)
+        return content.strip()
+
+    if isinstance(content, dict):
+        text = content.get('text', '')
+        return text.strip() if not is_empty(text) else None
+
+    if isinstance(content, list):
+        text_parts = [
+            item.get('text', '').strip() if isinstance(item, dict) and item.get('type') == 'text'
+            else (item.strip() if isinstance(item, str) else '')
+            for item in content
+        ]
+        result = ' '.join(t for t in text_parts if t and not is_empty(t))
+        return result if result else None
+
+    return str(content).strip() if not is_empty(content) else None
 
 def run_analysis():
     # First get all user selections
@@ -747,6 +793,9 @@ def run_analysis():
     config["deep_think_llm"] = selections["deep_thinker"]
     config["backend_url"] = selections["backend_url"]
     config["llm_provider"] = selections["llm_provider"].lower()
+    # Provider-specific thinking configuration
+    config["google_thinking_level"] = selections.get("google_thinking_level")
+    config["openai_reasoning_effort"] = selections.get("openai_reasoning_effort")
 
     # Initialize the graph
     graph = TradingAgentsGraph(
@@ -853,18 +902,23 @@ def run_analysis():
                 last_message = chunk["messages"][-1]
 
                 # Extract message content and type
-                if hasattr(last_message, "content"):
-                    content = extract_content_string(last_message.content)  # Use the helper function
+                content = None
                 msg_type = "Reasoning"
-                else:
-                    content = str(last_message)
+
+                if hasattr(last_message, "content"):
+                    content = extract_content_string(last_message.content)
+                elif last_message is not None:
+                    raw = str(last_message).strip()
+                    if raw and raw != '{}':
+                        content = raw
                         msg_type = "System"
 
-                # Add message to buffer
+                # Only add message to buffer if there's actual content
+                if content:
                     message_buffer.add_message(msg_type, content)
 
-                # If it's a tool call, add it to tool calls
-                if hasattr(last_message, "tool_calls"):
+                # Handle tool calls separately
+                if hasattr(last_message, "tool_calls") and last_message.tool_calls:
                     for tool_call in last_message.tool_calls:
                         # Handle both dictionary and object tool calls
                         if isinstance(tool_call, dict):
@@ -928,32 +982,18 @@ def run_analysis():
 
                     # Update Bull Researcher status and report
                     if "bull_history" in debate_state and debate_state["bull_history"]:
-                        # Keep all research team members in progress
                         update_research_team_status("in_progress")
-                        # Extract latest bull response
-                        bull_responses = debate_state["bull_history"].split("\n")
-                        latest_bull = bull_responses[-1] if bull_responses else ""
-                        if latest_bull:
-                            message_buffer.add_message("Reasoning", latest_bull)
-                            # Update research report with bull's latest analysis
                         message_buffer.update_report_section(
                             "investment_plan",
-                                f"### Bull Researcher Analysis\n{latest_bull}",
+                            f"### Bull Researcher Analysis\n{debate_state['bull_history']}",
                         )
 
                     # Update Bear Researcher status and report
                     if "bear_history" in debate_state and debate_state["bear_history"]:
-                        # Keep all research team members in progress
                         update_research_team_status("in_progress")
-                        # Extract latest bear response
-                        bear_responses = debate_state["bear_history"].split("\n")
-                        latest_bear = bear_responses[-1] if bear_responses else ""
-                        if latest_bear:
-                            message_buffer.add_message("Reasoning", latest_bear)
-                            # Update research report with bear's latest analysis
                         message_buffer.update_report_section(
                             "investment_plan",
-                                f"{message_buffer.report_sections['investment_plan']}\n\n### Bear Researcher Analysis\n{latest_bear}",
+                            f"### Bear Researcher Analysis\n{debate_state['bear_history']}",
                         )
 
                     # Update Research Manager status and final decision
@@ -961,18 +1001,11 @@ def run_analysis():
                         "judge_decision" in debate_state
                         and debate_state["judge_decision"]
                     ):
-                        # Keep all research team members in progress until final decision
                         update_research_team_status("in_progress")
-                        message_buffer.add_message(
-                            "Reasoning",
-                            f"Research Manager: {debate_state['judge_decision']}",
-                        )
-                        # Update research report with final decision
                         message_buffer.update_report_section(
                             "investment_plan",
-                            f"{message_buffer.report_sections['investment_plan']}\n\n### Research Manager Decision\n{debate_state['judge_decision']}",
+                            f"### Research Manager Decision\n{debate_state['judge_decision']}",
                         )
-                        # Mark all research team members as completed
                         update_research_team_status("completed")
                         # Set first risk analyst to in_progress
                         message_buffer.update_agent_status(

cli/utils.py

@@ -128,21 +128,28 @@ def select_shallow_thinking_agent(provider) -> str:
     # Define shallow thinking llm engine options with their corresponding model names
     SHALLOW_AGENT_OPTIONS = {
         "openai": [
-            ("GPT-4o-mini - Fast and efficient for quick tasks", "gpt-4o-mini"),
-            ("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"),
-            ("GPT-4.1-mini - Compact model with good performance", "gpt-4.1-mini"),
-            ("GPT-4o - Standard model with solid capabilities", "gpt-4o"),
+            ("GPT-5 Mini - Cost-optimized reasoning", "gpt-5-mini"),
+            ("GPT-5 Nano - Ultra-fast, high-throughput", "gpt-5-nano"),
+            ("GPT-5.2 - Latest flagship", "gpt-5.2"),
+            ("GPT-5.1 - Flexible reasoning", "gpt-5.1"),
+            ("GPT-4.1 - Smartest non-reasoning, 1M context", "gpt-4.1"),
         ],
         "anthropic": [
-            ("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"),
-            ("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"),
-            ("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"),
-            ("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"),
+            ("Claude Haiku 4.5 - Fast + extended thinking", "claude-haiku-4-5"),
+            ("Claude Sonnet 4.5 - Best for agents/coding", "claude-sonnet-4-5"),
+            ("Claude Sonnet 4 - High-performance", "claude-sonnet-4-20250514"),
         ],
         "google": [
-            ("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"),
-            ("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"),
-            ("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"),
+            ("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
+            ("Gemini 2.5 Flash - Balanced, recommended", "gemini-2.5-flash"),
+            ("Gemini 3 Pro - Reasoning-first", "gemini-3-pro-preview"),
+            ("Gemini 2.5 Flash Lite - Fast, low-cost", "gemini-2.5-flash-lite"),
+        ],
+        "xai": [
+            ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
+            ("Grok 4 Fast (Non-Reasoning) - Speed optimized", "grok-4-fast-non-reasoning"),
+            ("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
+            ("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"),
         ],
         "openrouter": [
             ("Meta: Llama 4 Scout", "meta-llama/llama-4-scout:free"),
@@ -186,26 +193,31 @@ def select_deep_thinking_agent(provider) -> str:
     # Define deep thinking llm engine options with their corresponding model names
     DEEP_AGENT_OPTIONS = {
         "openai": [
-            ("GPT-4.1-nano - Ultra-lightweight model for basic operations", "gpt-4.1-nano"),
-            ("GPT-4.1-mini - Compact model with good performance", "gpt-4.1-mini"),
-            ("GPT-4o - Standard model with solid capabilities", "gpt-4o"),
-            ("o4-mini - Specialized reasoning model (compact)", "o4-mini"),
-            ("o3-mini - Advanced reasoning model (lightweight)", "o3-mini"),
-            ("o3 - Full advanced reasoning model", "o3"),
-            ("o1 - Premier reasoning and problem-solving model", "o1"),
+            ("GPT-5.2 - Latest flagship", "gpt-5.2"),
+            ("GPT-5.1 - Flexible reasoning", "gpt-5.1"),
+            ("GPT-5 - Advanced reasoning", "gpt-5"),
+            ("GPT-4.1 - Smartest non-reasoning, 1M context", "gpt-4.1"),
+            ("GPT-5 Mini - Cost-optimized reasoning", "gpt-5-mini"),
+            ("GPT-5 Nano - Ultra-fast, high-throughput", "gpt-5-nano"),
         ],
         "anthropic": [
-            ("Claude Haiku 3.5 - Fast inference and standard capabilities", "claude-3-5-haiku-latest"),
-            ("Claude Sonnet 3.5 - Highly capable standard model", "claude-3-5-sonnet-latest"),
-            ("Claude Sonnet 3.7 - Exceptional hybrid reasoning and agentic capabilities", "claude-3-7-sonnet-latest"),
-            ("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"),
-            ("Claude Opus 4 - Most powerful Anthropic model", "	claude-opus-4-0"),
+            ("Claude Sonnet 4.5 - Best for agents/coding", "claude-sonnet-4-5"),
+            ("Claude Opus 4.5 - Premium, max intelligence", "claude-opus-4-5"),
+            ("Claude Opus 4.1 - Most capable model", "claude-opus-4-1-20250805"),
+            ("Claude Haiku 4.5 - Fast + extended thinking", "claude-haiku-4-5"),
+            ("Claude Sonnet 4 - High-performance", "claude-sonnet-4-20250514"),
         ],
         "google": [
-            ("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"),
-            ("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"),
-            ("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"),
-            ("Gemini 2.5 Pro", "gemini-2.5-pro-preview-06-05"),
+            ("Gemini 3 Pro - Reasoning-first", "gemini-3-pro-preview"),
+            ("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
+            ("Gemini 2.5 Flash - Balanced, recommended", "gemini-2.5-flash"),
+        ],
+        "xai": [
+            ("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
+            ("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"),
+            ("Grok 4 - Flagship model", "grok-4-0709"),
+            ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
+            ("Grok 4 Fast (Non-Reasoning) - Speed optimized", "grok-4-fast-non-reasoning"),
         ],
         "openrouter": [
             ("DeepSeek V3 - a 685B-parameter, mixture-of-experts model", "deepseek/deepseek-chat-v3-0324:free"),
@@ -246,6 +258,7 @@ def select_llm_provider() -> tuple[str, str]:
         ("OpenAI", "https://api.openai.com/v1"),
         ("Anthropic", "https://api.anthropic.com/"),
         ("Google", "https://generativelanguage.googleapis.com/v1"),
+        ("xAI", "https://api.x.ai/v1"),
         ("Openrouter", "https://openrouter.ai/api/v1"),
         ("Ollama", "http://localhost:11434/v1"),
     ]
@@ -274,3 +287,41 @@ def select_llm_provider() -> tuple[str, str]:
     print(f"You selected: {display_name}\tURL: {url}")
 
     return display_name, url
+
+
+def ask_openai_reasoning_effort() -> str:
+    """Ask for OpenAI reasoning effort level."""
+    choices = [
+        questionary.Choice("Medium (Default)", "medium"),
+        questionary.Choice("High (More thorough)", "high"),
+        questionary.Choice("Low (Faster)", "low"),
+    ]
+    return questionary.select(
+        "Select Reasoning Effort:",
+        choices=choices,
+        style=questionary.Style([
+            ("selected", "fg:cyan noinherit"),
+            ("highlighted", "fg:cyan noinherit"),
+            ("pointer", "fg:cyan noinherit"),
+        ]),
+    ).ask()
+
+
+def ask_gemini_thinking_config() -> str | None:
+    """Ask for Gemini thinking configuration.
+
+    Returns thinking_level: "high" or "minimal".
+    Client maps to appropriate API param based on model series.
+    """
+    return questionary.select(
+        "Select Thinking Mode:",
+        choices=[
+            questionary.Choice("Enable Thinking (recommended)", "high"),
+            questionary.Choice("Minimal/Disable Thinking", "minimal"),
+        ],
+        style=questionary.Style([
+            ("selected", "fg:green noinherit"),
+            ("highlighted", "fg:green noinherit"),
+            ("pointer", "fg:green noinherit"),
+        ]),
+    ).ask()

pyproject.toml

@@ -8,7 +8,6 @@ dependencies = [
     "akshare>=1.16.98",
     "backtrader>=1.9.78.123",
     "chainlit>=2.5.5",
-    "chromadb>=1.0.12",
     "eodhd>=1.0.32",
     "feedparser>=6.0.11",
     "finnhub-python>=2.4.23",
@@ -23,6 +22,7 @@ dependencies = [
     "praw>=7.8.1",
     "pytz>=2025.2",
     "questionary>=2.1.0",
+    "rank-bm25>=0.2.2",
     "redis>=6.2.0",
     "requests>=2.32.4",
     "rich>=14.0.0",

requirements.txt

@@ -8,7 +8,7 @@ feedparser
 stockstats
 eodhd
 langgraph
-chromadb
+rank-bm25
 setuptools
 backtrader
 akshare

tradingagents/agents/utils/memory.py

@@ -1,75 +1,106 @@
-import chromadb
-from chromadb.config import Settings
-from openai import OpenAI
+"""Financial situation memory using BM25 for lexical similarity matching.
+
+Uses BM25 (Best Matching 25) algorithm for retrieval - no API calls,
+no token limits, works offline with any LLM provider.
+"""
+
+from rank_bm25 import BM25Okapi
+from typing import List, Tuple
+import re
 
 
 class FinancialSituationMemory:
-    def __init__(self, name, config):
-        if config["backend_url"] == "http://localhost:11434/v1":
-            self.embedding = "nomic-embed-text"
+    """Memory system for storing and retrieving financial situations using BM25."""
+
+    def __init__(self, name: str, config: dict = None):
+        """Initialize the memory system.
+
+        Args:
+            name: Name identifier for this memory instance
+            config: Configuration dict (kept for API compatibility, not used for BM25)
+        """
+        self.name = name
+        self.documents: List[str] = []
+        self.recommendations: List[str] = []
+        self.bm25 = None
+
+    def _tokenize(self, text: str) -> List[str]:
+        """Tokenize text for BM25 indexing.
+
+        Simple whitespace + punctuation tokenization with lowercasing.
+        """
+        # Lowercase and split on non-alphanumeric characters
+        tokens = re.findall(r'\b\w+\b', text.lower())
+        return tokens
+
+    def _rebuild_index(self):
+        """Rebuild the BM25 index after adding documents."""
+        if self.documents:
+            tokenized_docs = [self._tokenize(doc) for doc in self.documents]
+            self.bm25 = BM25Okapi(tokenized_docs)
         else:
-            self.embedding = "text-embedding-3-small"
-        self.client = OpenAI(base_url=config["backend_url"])
-        self.chroma_client = chromadb.Client(Settings(allow_reset=True))
-        self.situation_collection = self.chroma_client.create_collection(name=name)
+            self.bm25 = None
 
-    def get_embedding(self, text):
-        """Get OpenAI embedding for a text"""
+    def add_situations(self, situations_and_advice: List[Tuple[str, str]]):
+        """Add financial situations and their corresponding advice.
 
-        response = self.client.embeddings.create(
-            model=self.embedding, input=text
-        )
-        return response.data[0].embedding
+        Args:
+            situations_and_advice: List of tuples (situation, recommendation)
+        """
+        for situation, recommendation in situations_and_advice:
+            self.documents.append(situation)
+            self.recommendations.append(recommendation)
 
-    def add_situations(self, situations_and_advice):
-        """Add financial situations and their corresponding advice. Parameter is a list of tuples (situation, rec)"""
+        # Rebuild BM25 index with new documents
+        self._rebuild_index()
 
-        situations = []
-        advice = []
-        ids = []
-        embeddings = []
+    def get_memories(self, current_situation: str, n_matches: int = 1) -> List[dict]:
+        """Find matching recommendations using BM25 similarity.
 
-        offset = self.situation_collection.count()
+        Args:
+            current_situation: The current financial situation to match against
+            n_matches: Number of top matches to return
 
-        for i, (situation, recommendation) in enumerate(situations_and_advice):
-            situations.append(situation)
-            advice.append(recommendation)
-            ids.append(str(offset + i))
-            embeddings.append(self.get_embedding(situation))
+        Returns:
+            List of dicts with matched_situation, recommendation, and similarity_score
+        """
+        if not self.documents or self.bm25 is None:
+            return []
 
-        self.situation_collection.add(
-            documents=situations,
-            metadatas=[{"recommendation": rec} for rec in advice],
-            embeddings=embeddings,
-            ids=ids,
-        )
+        # Tokenize query
+        query_tokens = self._tokenize(current_situation)
 
-    def get_memories(self, current_situation, n_matches=1):
-        """Find matching recommendations using OpenAI embeddings"""
-        query_embedding = self.get_embedding(current_situation)
+        # Get BM25 scores for all documents
+        scores = self.bm25.get_scores(query_tokens)
 
-        results = self.situation_collection.query(
-            query_embeddings=[query_embedding],
-            n_results=n_matches,
-            include=["metadatas", "documents", "distances"],
-        )
+        # Get top-n indices sorted by score (descending)
+        top_indices = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:n_matches]
 
-        matched_results = []
-        for i in range(len(results["documents"][0])):
-            matched_results.append(
-                {
-                    "matched_situation": results["documents"][0][i],
-                    "recommendation": results["metadatas"][0][i]["recommendation"],
-                    "similarity_score": 1 - results["distances"][0][i],
-                }
-            )
+        # Build results
+        results = []
+        max_score = max(scores) if max(scores) > 0 else 1  # Normalize scores
 
-        return matched_results
+        for idx in top_indices:
+            # Normalize score to 0-1 range for consistency
+            normalized_score = scores[idx] / max_score if max_score > 0 else 0
+            results.append({
+                "matched_situation": self.documents[idx],
+                "recommendation": self.recommendations[idx],
+                "similarity_score": normalized_score,
+            })
+
+        return results
+
+    def clear(self):
+        """Clear all stored memories."""
+        self.documents = []
+        self.recommendations = []
+        self.bm25 = None
 
 
 if __name__ == "__main__":
     # Example usage
-    matcher = FinancialSituationMemory()
+    matcher = FinancialSituationMemory("test_memory")
 
     # Example data
     example_data = [

tradingagents/dataflows/config.py

@@ -3,24 +3,21 @@ from typing import Dict, Optional
 
 # Use default config but allow it to be overridden
 _config: Optional[Dict] = None
-DATA_DIR: Optional[str] = None
 
 
 def initialize_config():
     """Initialize the configuration with default values."""
-    global _config, DATA_DIR
+    global _config
     if _config is None:
         _config = default_config.DEFAULT_CONFIG.copy()
-        DATA_DIR = _config["data_dir"]
 
 
 def set_config(config: Dict):
     """Update the configuration with custom values."""
-    global _config, DATA_DIR
+    global _config
     if _config is None:
         _config = default_config.DEFAULT_CONFIG.copy()
     _config.update(config)
-    DATA_DIR = _config["data_dir"]
 
 
 def get_config() -> Dict:

tradingagents/dataflows/interface.py

@@ -1,10 +1,15 @@
 from typing import Annotated
 
 # Import from vendor-specific modules
-from .local import get_YFin_data, get_finnhub_news, get_finnhub_company_insider_sentiment, get_finnhub_company_insider_transactions, get_simfin_balance_sheet, get_simfin_cashflow, get_simfin_income_statements, get_reddit_global_news, get_reddit_company_news
-from .y_finance import get_YFin_data_online, get_stock_stats_indicators_window, get_balance_sheet as get_yfinance_balance_sheet, get_cashflow as get_yfinance_cashflow, get_income_statement as get_yfinance_income_statement, get_insider_transactions as get_yfinance_insider_transactions
-from .google import get_google_news
-from .openai import get_stock_news_openai, get_global_news_openai, get_fundamentals_openai
+from .y_finance import (
+    get_YFin_data_online,
+    get_stock_stats_indicators_window,
+    get_balance_sheet as get_yfinance_balance_sheet,
+    get_cashflow as get_yfinance_cashflow,
+    get_income_statement as get_yfinance_income_statement,
+    get_insider_transactions as get_yfinance_insider_transactions,
+)
+from .yfinance_news import get_news_yfinance, get_global_news_yfinance
 from .alpha_vantage import (
     get_stock as get_alpha_vantage_stock,
     get_indicator as get_alpha_vantage_indicator,
@@ -13,7 +18,7 @@ from .alpha_vantage import (
     get_cashflow as get_alpha_vantage_cashflow,
     get_income_statement as get_alpha_vantage_income_statement,
     get_insider_transactions as get_alpha_vantage_insider_transactions,
-    get_news as get_alpha_vantage_news
+    get_news as get_alpha_vantage_news,
 )
 from .alpha_vantage_common import AlphaVantageRateLimitError
 
@@ -44,21 +49,18 @@ TOOLS_CATEGORIES = {
         ]
     },
     "news_data": {
-        "description": "News (public/insiders, original/processed)",
+        "description": "News and insider data",
         "tools": [
             "get_news",
             "get_global_news",
-            "get_insider_sentiment",
             "get_insider_transactions",
         ]
     }
 }
 
 VENDOR_LIST = [
-    "local",
     "yfinance",
-    "openai",
-    "google"
+    "alpha_vantage",
 ]
 
 # Mapping of methods to their vendor-specific implementations
@@ -67,52 +69,39 @@ VENDOR_METHODS = {
     "get_stock_data": {
         "alpha_vantage": get_alpha_vantage_stock,
         "yfinance": get_YFin_data_online,
-        "local": get_YFin_data,
     },
     # technical_indicators
     "get_indicators": {
         "alpha_vantage": get_alpha_vantage_indicator,
         "yfinance": get_stock_stats_indicators_window,
-        "local": get_stock_stats_indicators_window
     },
     # fundamental_data
     "get_fundamentals": {
         "alpha_vantage": get_alpha_vantage_fundamentals,
-        "openai": get_fundamentals_openai,
     },
     "get_balance_sheet": {
         "alpha_vantage": get_alpha_vantage_balance_sheet,
         "yfinance": get_yfinance_balance_sheet,
-        "local": get_simfin_balance_sheet,
     },
     "get_cashflow": {
         "alpha_vantage": get_alpha_vantage_cashflow,
         "yfinance": get_yfinance_cashflow,
-        "local": get_simfin_cashflow,
     },
     "get_income_statement": {
         "alpha_vantage": get_alpha_vantage_income_statement,
         "yfinance": get_yfinance_income_statement,
-        "local": get_simfin_income_statements,
     },
     # news_data
     "get_news": {
         "alpha_vantage": get_alpha_vantage_news,
-        "openai": get_stock_news_openai,
-        "google": get_google_news,
-        "local": [get_finnhub_news, get_reddit_company_news, get_google_news],
+        "yfinance": get_news_yfinance,
     },
     "get_global_news": {
-        "openai": get_global_news_openai,
-        "local": get_reddit_global_news
-    },
-    "get_insider_sentiment": {
-        "local": get_finnhub_company_insider_sentiment
+        "yfinance": get_global_news_yfinance,
     },
     "get_insider_transactions": {
         "alpha_vantage": get_alpha_vantage_insider_transactions,
         "yfinance": get_yfinance_insider_transactions,
-        "local": get_finnhub_company_insider_transactions,
     },
 }
 

tradingagents/dataflows/stockstats_utils.py

@@ -3,7 +3,7 @@ import yfinance as yf
 from stockstats import wrap
 from typing import Annotated
 import os
-from .config import get_config, DATA_DIR
+from .config import get_config
 
 
 class StockstatsUtils:
@@ -17,40 +17,22 @@ class StockstatsUtils:
             str, "curr date for retrieving stock price data, YYYY-mm-dd"
         ],
     ):
-        # Get config and set up data directory path
         config = get_config()
-        online = config["data_vendors"]["technical_indicators"] != "local"
 
-        df = None
-        data = None
-
-        if not online:
-            try:
-                data = pd.read_csv(
-                    os.path.join(
-                        DATA_DIR,
-                        f"{symbol}-YFin-data-2015-01-01-2025-03-25.csv",
-                    )
-                )
-                df = wrap(data)
-            except FileNotFoundError:
-                raise Exception("Stockstats fail: Yahoo Finance data not fetched yet!")
-        else:
-            # Get today's date as YYYY-mm-dd to add to cache
         today_date = pd.Timestamp.today()
-            curr_date = pd.to_datetime(curr_date)
+        curr_date_dt = pd.to_datetime(curr_date)
 
         end_date = today_date
         start_date = today_date - pd.DateOffset(years=15)
-            start_date = start_date.strftime("%Y-%m-%d")
-            end_date = end_date.strftime("%Y-%m-%d")
+        start_date_str = start_date.strftime("%Y-%m-%d")
+        end_date_str = end_date.strftime("%Y-%m-%d")
 
-            # Get config and ensure cache directory exists
+        # Ensure cache directory exists
         os.makedirs(config["data_cache_dir"], exist_ok=True)
 
         data_file = os.path.join(
             config["data_cache_dir"],
-                f"{symbol}-YFin-data-{start_date}-{end_date}.csv",
+            f"{symbol}-YFin-data-{start_date_str}-{end_date_str}.csv",
         )
 
         if os.path.exists(data_file):
@@ -59,8 +41,8 @@ class StockstatsUtils:
         else:
             data = yf.download(
                 symbol,
-                    start=start_date,
-                    end=end_date,
+                start=start_date_str,
+                end=end_date_str,
                 multi_level_index=False,
                 progress=False,
                 auto_adjust=True,
@@ -70,10 +52,10 @@ class StockstatsUtils:
 
         df = wrap(data)
         df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
-            curr_date = curr_date.strftime("%Y-%m-%d")
+        curr_date_str = curr_date_dt.strftime("%Y-%m-%d")
 
         df[indicator]  # trigger stockstats to calculate the indicator
-        matching_rows = df[df["Date"].str.startswith(curr_date)]
+        matching_rows = df[df["Date"].str.startswith(curr_date_str)]
 
         if not matching_rows.empty:
             indicator_value = matching_rows[indicator].values[0]

tradingagents/dataflows/yfinance_news.py

@@ -0,0 +1,190 @@
+"""yfinance-based news data fetching functions."""
+
+import yfinance as yf
+from datetime import datetime
+from dateutil.relativedelta import relativedelta
+
+
+def _extract_article_data(article: dict) -> dict:
+    """Extract article data from yfinance news format (handles nested 'content' structure)."""
+    # Handle nested content structure
+    if "content" in article:
+        content = article["content"]
+        title = content.get("title", "No title")
+        summary = content.get("summary", "")
+        provider = content.get("provider", {})
+        publisher = provider.get("displayName", "Unknown")
+
+        # Get URL from canonicalUrl or clickThroughUrl
+        url_obj = content.get("canonicalUrl") or content.get("clickThroughUrl") or {}
+        link = url_obj.get("url", "")
+
+        # Get publish date
+        pub_date_str = content.get("pubDate", "")
+        pub_date = None
+        if pub_date_str:
+            try:
+                pub_date = datetime.fromisoformat(pub_date_str.replace("Z", "+00:00"))
+            except (ValueError, AttributeError):
+                pass
+
+        return {
+            "title": title,
+            "summary": summary,
+            "publisher": publisher,
+            "link": link,
+            "pub_date": pub_date,
+        }
+    else:
+        # Fallback for flat structure
+        return {
+            "title": article.get("title", "No title"),
+            "summary": article.get("summary", ""),
+            "publisher": article.get("publisher", "Unknown"),
+            "link": article.get("link", ""),
+            "pub_date": None,
+        }
+
+
+def get_news_yfinance(
+    ticker: str,
+    start_date: str,
+    end_date: str,
+) -> str:
+    """
+    Retrieve news for a specific stock ticker using yfinance.
+
+    Args:
+        ticker: Stock ticker symbol (e.g., "AAPL")
+        start_date: Start date in yyyy-mm-dd format
+        end_date: End date in yyyy-mm-dd format
+
+    Returns:
+        Formatted string containing news articles
+    """
+    try:
+        stock = yf.Ticker(ticker)
+        news = stock.get_news(count=20, tab="news")
+
+        if not news:
+            return f"No news found for {ticker}"
+
+        # Parse date range for filtering
+        start_dt = datetime.strptime(start_date, "%Y-%m-%d")
+        end_dt = datetime.strptime(end_date, "%Y-%m-%d")
+
+        news_str = ""
+        filtered_count = 0
+
+        for article in news:
+            data = _extract_article_data(article)
+
+            # Filter by date if publish time is available
+            if data["pub_date"]:
+                pub_date_naive = data["pub_date"].replace(tzinfo=None)
+                if not (start_dt <= pub_date_naive <= end_dt + relativedelta(days=1)):
+                    continue
+
+            news_str += f"### {data['title']} (source: {data['publisher']})\n"
+            if data["summary"]:
+                news_str += f"{data['summary']}\n"
+            if data["link"]:
+                news_str += f"Link: {data['link']}\n"
+            news_str += "\n"
+            filtered_count += 1
+
+        if filtered_count == 0:
+            return f"No news found for {ticker} between {start_date} and {end_date}"
+
+        return f"## {ticker} News, from {start_date} to {end_date}:\n\n{news_str}"
+
+    except Exception as e:
+        return f"Error fetching news for {ticker}: {str(e)}"
+
+
+def get_global_news_yfinance(
+    curr_date: str,
+    look_back_days: int = 7,
+    limit: int = 10,
+) -> str:
+    """
+    Retrieve global/macro economic news using yfinance Search.
+
+    Args:
+        curr_date: Current date in yyyy-mm-dd format
+        look_back_days: Number of days to look back
+        limit: Maximum number of articles to return
+
+    Returns:
+        Formatted string containing global news articles
+    """
+    # Search queries for macro/global news
+    search_queries = [
+        "stock market economy",
+        "Federal Reserve interest rates",
+        "inflation economic outlook",
+        "global markets trading",
+    ]
+
+    all_news = []
+    seen_titles = set()
+
+    try:
+        for query in search_queries:
+            search = yf.Search(
+                query=query,
+                news_count=limit,
+                enable_fuzzy_query=True,
+            )
+
+            if search.news:
+                for article in search.news:
+                    # Handle both flat and nested structures
+                    if "content" in article:
+                        data = _extract_article_data(article)
+                        title = data["title"]
+                    else:
+                        title = article.get("title", "")
+
+                    # Deduplicate by title
+                    if title and title not in seen_titles:
+                        seen_titles.add(title)
+                        all_news.append(article)
+
+            if len(all_news) >= limit:
+                break
+
+        if not all_news:
+            return f"No global news found for {curr_date}"
+
+        # Calculate date range
+        curr_dt = datetime.strptime(curr_date, "%Y-%m-%d")
+        start_dt = curr_dt - relativedelta(days=look_back_days)
+        start_date = start_dt.strftime("%Y-%m-%d")
+
+        news_str = ""
+        for article in all_news[:limit]:
+            # Handle both flat and nested structures
+            if "content" in article:
+                data = _extract_article_data(article)
+                title = data["title"]
+                publisher = data["publisher"]
+                link = data["link"]
+                summary = data["summary"]
+            else:
+                title = article.get("title", "No title")
+                publisher = article.get("publisher", "Unknown")
+                link = article.get("link", "")
+                summary = ""
+
+            news_str += f"### {title} (source: {publisher})\n"
+            if summary:
+                news_str += f"{summary}\n"
+            if link:
+                news_str += f"Link: {link}\n"
+            news_str += "\n"
+
+        return f"## Global Market News, from {start_date} to {curr_date}:\n\n{news_str}"
+
+    except Exception as e:
+        return f"Error fetching global news: {str(e)}"

tradingagents/default_config.py

@@ -3,16 +3,18 @@ import os
 DEFAULT_CONFIG = {
     "project_dir": os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
     "results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", "./results"),
-    "data_dir": "/Users/yluo/Documents/Code/ScAI/FR1-data",
     "data_cache_dir": os.path.join(
         os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
         "dataflows/data_cache",
     ),
     # LLM settings
     "llm_provider": "openai",
-    "deep_think_llm": "o4-mini",
-    "quick_think_llm": "gpt-4o-mini",
+    "deep_think_llm": "gpt-5.2",
+    "quick_think_llm": "gpt-5-mini",
     "backend_url": "https://api.openai.com/v1",
+    # Provider-specific thinking configuration
+    "google_thinking_level": None,      # "high", "minimal", etc.
+    "openai_reasoning_effort": None,    # "medium", "high", "low"
     # Debate and discussion settings
     "max_debate_rounds": 1,
     "max_risk_discuss_rounds": 1,
@@ -20,14 +22,13 @@ DEFAULT_CONFIG = {
     # Data vendor configuration
     # Category-level configuration (default for all tools in category)
     "data_vendors": {
-        "core_stock_apis": "yfinance",       # Options: yfinance, alpha_vantage, local
-        "technical_indicators": "yfinance",  # Options: yfinance, alpha_vantage, local
-        "fundamental_data": "alpha_vantage", # Options: openai, alpha_vantage, local
-        "news_data": "alpha_vantage",        # Options: openai, alpha_vantage, google, local
+        "core_stock_apis": "yfinance",       # Options: yfinance, alpha_vantage
+        "technical_indicators": "yfinance",  # Options: yfinance, alpha_vantage
+        "fundamental_data": "alpha_vantage", # Options: alpha_vantage, yfinance
+        "news_data": "yfinance",             # Options: yfinance, alpha_vantage
     },
     # Tool-level configuration (takes precedence over category-level)
     "tool_vendors": {
         # Example: "get_stock_data": "alpha_vantage",  # Override category default
-        # Example: "get_news": "openai",               # Override category default
     },
 }

tradingagents/graph/trading_graph.py

@@ -69,16 +69,20 @@ class TradingAgentsGraph:
             exist_ok=True,
         )
 
-        # Initialize LLMs
+        # Initialize LLMs with provider-specific thinking configuration
+        llm_kwargs = self._get_provider_kwargs()
+
         deep_client = create_llm_client(
             provider=self.config["llm_provider"],
             model=self.config["deep_think_llm"],
             base_url=self.config.get("backend_url"),
+            **llm_kwargs,
         )
         quick_client = create_llm_client(
             provider=self.config["llm_provider"],
             model=self.config["quick_think_llm"],
             base_url=self.config.get("backend_url"),
+            **llm_kwargs,
         )
         self.deep_thinking_llm = deep_client.get_llm()
         self.quick_thinking_llm = quick_client.get_llm()
@@ -119,6 +123,23 @@ class TradingAgentsGraph:
         # Set up the graph
         self.graph = self.graph_setup.setup_graph(selected_analysts)
 
+    def _get_provider_kwargs(self) -> Dict[str, Any]:
+        """Get provider-specific kwargs for LLM client creation."""
+        kwargs = {}
+        provider = self.config.get("llm_provider", "").lower()
+
+        if provider == "google":
+            thinking_level = self.config.get("google_thinking_level")
+            if thinking_level:
+                kwargs["thinking_level"] = thinking_level
+
+        elif provider == "openai":
+            reasoning_effort = self.config.get("openai_reasoning_effort")
+            if reasoning_effort:
+                kwargs["reasoning_effort"] = reasoning_effort
+
+        return kwargs
+
     def _create_tool_nodes(self) -> Dict[str, ToolNode]:
         """Create tool nodes for different data sources using abstract methods."""
         return {

tradingagents/llm_clients/anthropic_client.py

@@ -14,18 +14,12 @@ class AnthropicClient(BaseLLMClient):
 
     def get_llm(self) -> Any:
         """Return configured ChatAnthropic instance."""
-        llm_kwargs = {
-            "model": self.model,
-            "max_tokens": self.kwargs.get("max_tokens", 4096),
-        }
+        llm_kwargs = {"model": self.model}
 
-        for key in ("timeout", "max_retries", "api_key"):
+        for key in ("timeout", "max_retries", "api_key", "max_tokens"):
             if key in self.kwargs:
                 llm_kwargs[key] = self.kwargs[key]
 
-        if "thinking_config" in self.kwargs:
-            llm_kwargs["thinking"] = self.kwargs["thinking_config"]
-
         return ChatAnthropic(**llm_kwargs)
 
     def validate_model(self) -> bool:

tradingagents/llm_clients/google_client.py

@@ -6,6 +6,28 @@ from .base_client import BaseLLMClient
 from .validators import validate_model
 
 
+class NormalizedChatGoogleGenerativeAI(ChatGoogleGenerativeAI):
+    """ChatGoogleGenerativeAI with normalized content output.
+
+    Gemini 3 models return content as list: [{'type': 'text', 'text': '...'}]
+    This normalizes to string for consistent downstream handling.
+    """
+
+    def _normalize_content(self, response):
+        content = response.content
+        if isinstance(content, list):
+            texts = [
+                item.get("text", "") if isinstance(item, dict) and item.get("type") == "text"
+                else item if isinstance(item, str) else ""
+                for item in content
+            ]
+            response.content = "\n".join(t for t in texts if t)
+        return response
+
+    def invoke(self, input, config=None, **kwargs):
+        return self._normalize_content(super().invoke(input, config, **kwargs))
+
+
 class GoogleClient(BaseLLMClient):
     """Client for Google Gemini models."""
 
@@ -20,14 +42,23 @@ class GoogleClient(BaseLLMClient):
             if key in self.kwargs:
                 llm_kwargs[key] = self.kwargs[key]
 
-        if "thinking_budget" in self.kwargs and self._is_preview_model():
-            llm_kwargs["thinking_budget"] = self.kwargs["thinking_budget"]
+        # Map thinking_level to appropriate API param based on model
+        # Gemini 3 Pro: low, high
+        # Gemini 3 Flash: minimal, low, medium, high
+        # Gemini 2.5: thinking_budget (0=disable, -1=dynamic)
+        thinking_level = self.kwargs.get("thinking_level")
+        if thinking_level:
+            model_lower = self.model.lower()
+            if "gemini-3" in model_lower:
+                # Gemini 3 Pro doesn't support "minimal", use "low" instead
+                if "pro" in model_lower and thinking_level == "minimal":
+                    thinking_level = "low"
+                llm_kwargs["thinking_level"] = thinking_level
+            else:
+                # Gemini 2.5: map to thinking_budget
+                llm_kwargs["thinking_budget"] = -1 if thinking_level == "high" else 0
 
-        return ChatGoogleGenerativeAI(**llm_kwargs)
-
-    def _is_preview_model(self) -> bool:
-        """Check if this is a preview model that supports thinking budget."""
-        return "preview" in self.model.lower()
+        return NormalizedChatGoogleGenerativeAI(**llm_kwargs)
 
     def validate_model(self) -> bool:
         """Validate model for Google."""

tradingagents/llm_clients/validators.py

@@ -1,58 +1,75 @@
-from typing import Dict, List
+"""Model name validators for each provider.
 
-VALID_MODELS: Dict[str, List[str]] = {
+Only validates model names - does NOT enforce limits.
+Let LLM providers use their own defaults for unspecified params.
+"""
+
+VALID_MODELS = {
     "openai": [
+        # GPT-5 series (2025)
+        "gpt-5.2",
+        "gpt-5.1",
+        "gpt-5",
+        "gpt-5-mini",
+        "gpt-5-nano",
+        # GPT-4.1 series (2025)
+        "gpt-4.1",
+        "gpt-4.1-mini",
+        "gpt-4.1-nano",
+        # o-series reasoning models
+        "o4-mini",
+        "o3",
+        "o3-mini",
+        "o1",
+        "o1-preview",
+        # GPT-4o series (legacy but still supported)
         "gpt-4o",
         "gpt-4o-mini",
-        "gpt-4-turbo",
-        "gpt-4",
-        "gpt-3.5-turbo",
-        "o1",
-        "o1-mini",
-        "o1-preview",
-        "o3-mini",
-        "gpt-5-nano",
-        "gpt-5-mini",
-        "gpt-5",
     ],
     "anthropic": [
-        "claude-3-5-sonnet-20241022",
-        "claude-3-5-haiku-20241022",
-        "claude-3-opus-20240229",
-        "claude-3-sonnet-20240229",
-        "claude-3-haiku-20240307",
+        # Claude 4.5 series (2025)
+        "claude-opus-4-5",
+        "claude-sonnet-4-5",
+        "claude-haiku-4-5",
+        # Claude 4.x series
+        "claude-opus-4-1-20250805",
         "claude-sonnet-4-20250514",
-        "claude-haiku-4-5-20251001",
-        "claude-opus-4-5-20251101",
+        # Claude 3.7 series
+        "claude-3-7-sonnet-20250219",
+        # Claude 3.5 series (legacy)
+        "claude-3-5-haiku-20241022",
+        "claude-3-5-sonnet-20241022",
     ],
     "google": [
-        "gemini-1.5-pro",
-        "gemini-1.5-flash",
-        "gemini-2.0-flash",
-        "gemini-2.0-flash-lite",
-        "gemini-2.5-pro-preview-05-06",
-        "gemini-2.5-flash-preview-05-20",
+        # Gemini 3 series (preview)
         "gemini-3-pro-preview",
         "gemini-3-flash-preview",
+        # Gemini 2.5 series
+        "gemini-2.5-pro",
+        "gemini-2.5-flash",
+        "gemini-2.5-flash-lite",
+        # Gemini 2.0 series
+        "gemini-2.0-flash",
+        "gemini-2.0-flash-lite",
     ],
     "xai": [
-        "grok-beta",
-        "grok-2",
-        "grok-2-mini",
-        "grok-3",
-        "grok-3-mini",
+        # Grok 4.1 series
+        "grok-4-1-fast",
+        "grok-4-1-fast-reasoning",
+        "grok-4-1-fast-non-reasoning",
+        # Grok 4 series
+        "grok-4",
+        "grok-4-0709",
+        "grok-4-fast-reasoning",
+        "grok-4-fast-non-reasoning",
     ],
-    "ollama": [],
-    "openrouter": [],
-    "vllm": [],
 }
 
 
 def validate_model(provider: str, model: str) -> bool:
-    """Validate that a model is supported by the provider.
+    """Check if model name is valid for the given provider.
 
-    For ollama, openrouter, and vllm, any model is accepted.
-    For other providers, checks against VALID_MODELS.
+    For ollama, openrouter, vllm - any model is accepted.
     """
     provider_lower = provider.lower()
 
@@ -60,10 +77,6 @@ def validate_model(provider: str, model: str) -> bool:
         return True
 
     if provider_lower not in VALID_MODELS:
-        return False
-
-    valid = VALID_MODELS[provider_lower]
-    if not valid:
         return True
 
-    return model in valid
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