fix

CHANGELOG.md

@@ -0,0 +1,22 @@
+# Changelog
+
+All notable changes to the **TradingAgents** project will be documented in this file.
+
+## [Unreleased] - 2026-01-09
+
+### Added
+- **API Key Verification**: Added `verify_google_key.py` script to isolate and verify Google API Key functionality for embeddings.
+- **Environment Management**: Added `load_dotenv` to `cli/main.py` and `verify_google_key.py` to ensure `.env` variables are correctly loaded.
+- **Start Script Enhancements**: Updated `start.sh` to check for `GOOGLE_API_KEY` existence and warn the user.
+- **Debug Logging**: Added temporary debug logging (commented out) in `memory.py` for API key verification.
+
+### Fixed
+- **Embedding Model Error**: Fixed `BadRequestError` / `404 Not Found` when using Google (Gemini) provider by explicitly setting `text-embedding-004` and using the Google-compatible OpenAI endpoint (`generativelanguage.googleapis.com`).
+- **Data Fetching Failure**: Resolved `RuntimeError: All vendor implementations failed for method 'get_fundamentals'` by implementing a fallback to `yfinance` in `tradingagents/dataflows/y_finance.py` and registering it in `interface.py`.
+- **Report Saving Crash**: Fixed `TypeError: write() argument must be str, not list` in `cli/main.py` by converting structured list content to string before writing to files.
+- **API Rate Limiting**: Added `max_retries` handling (exponential backoff) to both `ChatGoogleGenerativeAI` (10 retries) and `OpenAI` embedding client (5 retries) to robustly handle `429 RESOURCE_EXHAUSTED` errors.
+- **Payload Size Error**: Implemented input truncation (max 9000 chars) in `memory.py`'s `get_embedding` method to prevent massive payloads from crashing the API.
+
+### Changed
+- **LLM Configuration**: Updated `tradingagents/default_config.py` and `cli/utils.py` to use valid Gemini model names (e.g., `gemini-1.5-flash`, `gemini-1.5-pro`) and `gemini-pro`.
+- **Vendor Configuration**: Updated default `fundamental_data` vendor to "alpha_vantage, yfinance" to ensure fallback availability.

cli/main.py

@@ -789,11 +789,12 @@ def run_analysis():
         def wrapper(section_name, content):
             func(section_name, content)
             if section_name in obj.report_sections and obj.report_sections[section_name] is not None:
-                content = obj.report_sections[section_name]
+                raw_content = obj.report_sections[section_name]
-                if content:
+                if raw_content:
+                    content_str = extract_content_string(raw_content)
                     file_name = f"{section_name}.md"
                     with open(report_dir / file_name, "w") as f:
-                        f.write(content)
+                        f.write(content_str)
         return wrapper
 
     message_buffer.add_message = save_message_decorator(message_buffer, "add_message")

cli/utils.py

@@ -140,10 +140,11 @@ def select_shallow_thinking_agent(provider) -> str:
             ("Claude Sonnet 4 - High performance and excellent reasoning", "claude-sonnet-4-0"),
         ],
         "google": [
-            ("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"),
+            ("Gemini 2.5 Flash-Lite - Cost efficiency and low latency", "gemini-2.5-flash-lite"),
-            ("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"),
+            ("Gemini 2.5 Flash - Next generation features, speed, and thinking", "gemini-2.5-flash"),
-            ("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"),
+            ("Gemini 3.0 Flash - Next generation features, speed, and thinking", "gemini-3-flash-preview"),
-        ],
+            ("Gemini 3.0 Pro - Adaptive thinking, cost efficiency", "gemini-3-pro-preview"),
+            ("Gemini 2.5 Pro", "gemini-2.5-pro")],
         "openrouter": [
             ("Meta: Llama 4 Scout", "meta-llama/llama-4-scout:free"),
             ("Meta: Llama 3.3 8B Instruct - A lightweight and ultra-fast variant of Llama 3.3 70B", "meta-llama/llama-3.3-8b-instruct:free"),
@@ -202,10 +203,10 @@ def select_deep_thinking_agent(provider) -> str:
             ("Claude Opus 4 - Most powerful Anthropic model", "	claude-opus-4-0"),
         ],
         "google": [
-            ("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"),
+            ("Gemini 2.5 Flash - Next generation features, speed, and thinking", "gemini-2.5-flash"),
-            ("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"),
+            ("Gemini 3.0 Flash - Next generation features, speed, and thinking", "gemini-3-flash-preview"),
-            ("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"),
+            ("Gemini 3.0 Pro - Adaptive thinking, cost efficiency", "gemini-3-pro-preview"),
-            ("Gemini 2.5 Pro", "gemini-2.5-pro-preview-06-05"),
+            ("Gemini 2.5 Pro", "gemini-2.5-pro"),
         ],
         "openrouter": [
             ("DeepSeek V3 - a 685B-parameter, mixture-of-experts model", "deepseek/deepseek-chat-v3-0324:free"),

start.sh

@@ -0,0 +1,37 @@
+#!/bin/bash
+
+# 1. Activate Virtual Environment
+if [ -d ".venv" ]; then
+    source .venv/bin/activate
+    echo "✅ Virtual Environment (.venv) Activated"
+else
+    echo "❌ Virtual Environment not found! Run 'uv venv --python 3.13' first."
+    exit 1
+fi
+
+# 2. Export API Keys (PLACEHOLDERS - PLEASE UPDATE)
+# You can also load these from a .env file if preferred
+if [ -f ".env" ]; then
+    export $(grep -v '^#' .env | xargs)
+    echo "✅ Loaded keys from .env"
+else
+    echo "⚠️  No .env file found. Using default/exported keys."
+    # START: REPLACE WITH YOUR ACTUAL KEYS IF NOT USING .ENV
+    # export OPENAI_API_KEY="sk-your-key-here"
+    # export ALPHA_VANTAGE_API_KEY="your-key-here"
+    # export GOOGLE_API_KEY="your-key-here"
+    # END
+fi
+
+# Check if keys are set
+if [ -z "$OPENAI_API_KEY" ]; then
+    echo "⚠️  OPENAI_API_KEY is missing! Set it if using OpenAI."
+fi
+
+if [ -z "$GOOGLE_API_KEY" ]; then
+    echo "⚠️  GOOGLE_API_KEY is missing! Set it if using Gemini."
+fi
+
+# 3. Start the Shadow Run (Daily Execution)
+echo "🚀 Starting Shadow Run Daily Execution..."
+python3 -m cli.main

tradingagents/agents/utils/memory.py

@@ -1,3 +1,4 @@
+import os
 import chromadb
 from chromadb.config import Settings
 from openai import OpenAI
@@ -5,17 +6,44 @@ from openai import OpenAI
 
 class FinancialSituationMemory:
     def __init__(self, name, config):
-        if config["backend_url"] == "http://localhost:11434/v1":
+        if config.get("llm_provider") == "google":
+            self.embedding = "text-embedding-004"
+            
+            google_api_key = os.getenv("GOOGLE_API_KEY")
+            if not google_api_key:
+                raise ValueError("❌ GOOGLE_API_KEY not found in environment. Please add it to your .env file or export it.")
+                
+            # Use Google's OpenAI-compatible endpoint with retries
+            self.client = OpenAI(
+                api_key=google_api_key,
+                base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
+                max_retries=5
+            )
+        elif config["backend_url"] == "http://localhost:11434/v1" or config.get("llm_provider") == "ollama":
             self.embedding = "nomic-embed-text"
+            self.client = OpenAI(base_url=config["backend_url"])
         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)
 
     def get_embedding(self, text):
         """Get OpenAI embedding for a text"""
         
+        # DEBUG: Check API Key
+        if hasattr(self, 'client') and self.client.api_key:
+             masked_key = self.client.api_key[:4] + "..."
+             # print(f"DEBUG: Using API Key: {masked_key}")
+        
+        # Truncate text if too long (Google's limit is ~2048 tokens / 8k chars, allow buffer)
+        # OpenAI text-embedding-3 is 8191 tokens (~32k chars)
+        # Use safe limit of 9000 chars
+        if len(text) > 9000:
+             # print(f"WARNING: Truncating text for embedding. Length {len(text)} > 9000")
+             text = text[:9000]
+        
         response = self.client.embeddings.create(
             model=self.embedding, input=text
         )

tradingagents/dataflows/interface.py

@@ -2,7 +2,7 @@ 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 .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, get_fundamentals as get_fundamentals_yfinance
 from .google import get_google_news
 from .openai import get_stock_news_openai, get_global_news_openai, get_fundamentals_openai
 from .alpha_vantage import (
@@ -78,6 +78,7 @@ VENDOR_METHODS = {
     # fundamental_data
     "get_fundamentals": {
         "alpha_vantage": get_alpha_vantage_fundamentals,
+        "yfinance": get_fundamentals_yfinance,
         "openai": get_fundamentals_openai,
     },
     "get_balance_sheet": {

tradingagents/dataflows/y_finance.py

@@ -405,3 +405,52 @@ def get_insider_transactions(
         
     except Exception as e:
         return f"Error retrieving insider transactions for {ticker}: {str(e)}"
+def get_fundamentals(
+    ticker: Annotated[str, "ticker symbol of the company"],
+    curr_date: Annotated[str, "current date (not used for yfinance info)"] = None
+):
+    """Get fundamental data from yfinance ticker.info."""
+    try:
+        ticker_obj = yf.Ticker(ticker.upper())
+        info = ticker_obj.info
+        
+        if not info:
+            return f"No fundamental data found for symbol '{ticker}'"
+            
+        keys_of_interest = [
+            "shortName", "longName", "sector", "industry", "fullTimeEmployees",
+            "marketCap", "enterpriseValue", "totalRevenue", "profitMargins",
+            "floatShares", "sharesOutstanding", "impliedSharesOutstanding",
+            "bookValue", "priceToBook", "trailingEps", "forwardEps",
+            "pegRatio", "priceToSalesTrailing12Months", "forwardPE", "trailingPE",
+            "dividendRate", "dividendYield", "payoutRatio",
+            "beta", "52WeekChange", "SnP52WeekChange", "lastDividendValue", "lastDividendDate",
+            "currentPrice", "targetHighPrice", "targetLowPrice", "targetMeanPrice",
+            "recommendationMean", "recommendationKey", "numberOfAnalystOpinions",
+            "totalCash", "totalCashPerShare", "ebitda", "totalDebt",
+            "quickRatio", "currentRatio", "revenueGrowth", "debtToEquity",
+            "returnOnAssets", "returnOnEquity", "grossProfits", "freeCashflow",
+            "operatingCashflow", "earningsGrowth", "revenueGrowth", "grossMargins",
+            "ebitdaMargins", "operatingMargins", "auditRisk", "boardRisk",
+            "compensationRisk", "shareHolderRightsRisk", "overallRisk"
+        ]
+        
+        report_lines = []
+        report_lines.append(f"# Fundamental Data for {ticker.upper()}")
+        report_lines.append(f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} (Real-time/Latest)")
+        report_lines.append(f"# Note: YFinance 'info' provides current data, not historical.")
+        report_lines.append("")
+        
+        for key in keys_of_interest:
+            if key in info and info[key] is not None:
+                report_lines.append(f"{key}: {info[key]}")
+                
+        if "longBusinessSummary" in info:
+             report_lines.append("")
+             report_lines.append("## Business Summary")
+             report_lines.append(info["longBusinessSummary"])
+        
+        return "\n".join(report_lines)
+        
+    except Exception as e:
+        return f"Error retrieving fundamentals for {ticker}: {str(e)}"

tradingagents/default_config.py

@@ -10,8 +10,8 @@ DEFAULT_CONFIG = {
     ),
     # LLM settings
     "llm_provider": "openai",
-    "deep_think_llm": "o4-mini",
+    "deep_think_llm": "gemini-pro",
-    "quick_think_llm": "gpt-4o-mini",
+    "quick_think_llm": "gemini-pro",
     "backend_url": "https://api.openai.com/v1",
     # Debate and discussion settings
     "max_debate_rounds": 1,

tradingagents/graph/trading_graph.py

@@ -79,8 +79,16 @@ class TradingAgentsGraph:
             self.deep_thinking_llm = ChatAnthropic(model=self.config["deep_think_llm"], base_url=self.config["backend_url"])
             self.quick_thinking_llm = ChatAnthropic(model=self.config["quick_think_llm"], base_url=self.config["backend_url"])
         elif self.config["llm_provider"].lower() == "google":
-            self.deep_thinking_llm = ChatGoogleGenerativeAI(model=self.config["deep_think_llm"])
+            self.deep_thinking_llm = ChatGoogleGenerativeAI(
-            self.quick_thinking_llm = ChatGoogleGenerativeAI(model=self.config["quick_think_llm"])
+                model=self.config["deep_think_llm"],
+                max_retries=10,
+                request_timeout=60
+            )
+            self.quick_thinking_llm = ChatGoogleGenerativeAI(
+                model=self.config["quick_think_llm"],
+                max_retries=10,
+                request_timeout=60
+            )
         else:
             raise ValueError(f"Unsupported LLM provider: {self.config['llm_provider']}")