refactor: clean up codebase and streamline documentation

- Remove debug prints from vendor routing (interface.py)
- Simplify vendor fallback to only handle rate limits
- Reorder CLI provider menu: OpenAI, Google, Anthropic, xAI, OpenRouter, Ollama
- Remove dead files: local.py, reddit_utils.py, openai.py, google.py, googlenews_utils.py, yfin_utils.py

README.md

@@ -114,21 +114,23 @@ pip install -r requirements.txt
 
 ### Required APIs
 
-You will need the OpenAI API for all the agents, and [Alpha Vantage API](https://www.alphavantage.co/support/#api-key) for fundamental and news data (default configuration).
+TradingAgents supports multiple LLM providers. Set the API key for your chosen provider:
 
 ```bash
-export OPENAI_API_KEY=$YOUR_OPENAI_API_KEY
-export ALPHA_VANTAGE_API_KEY=$YOUR_ALPHA_VANTAGE_API_KEY
+export OPENAI_API_KEY=...          # OpenAI
+export GOOGLE_API_KEY=...          # Google (Gemini)
+export ANTHROPIC_API_KEY=...       # Anthropic (Claude)
+export OPENROUTER_API_KEY=...      # OpenRouter
+export ALPHA_VANTAGE_API_KEY=...   # Alpha Vantage
 ```
 
-Alternatively, you can create a `.env` file in the project root with your API keys (see `.env.example` for reference):
+For local models, configure Ollama with `llm_provider: "ollama"` in your config.
+
+Alternatively, copy `.env.example` to `.env` and fill in your keys:
 ```bash
 cp .env.example .env
-# Edit .env with your actual API keys
 ```
 
-**Note:** We are happy to partner with Alpha Vantage to provide robust API support for TradingAgents. You can get a free AlphaVantage API [here](https://www.alphavantage.co/support/#api-key), TradingAgents-sourced requests also have increased rate limits to 60 requests per minute with no daily limits. Typically the quota is sufficient for performing complex tasks with TradingAgents thanks to Alpha Vantage’s open-source support program. If you prefer to use OpenAI for these data sources instead, you can modify the data vendor settings in `tradingagents/default_config.py`.
-
 ### CLI Usage
 
 You can also try out the CLI directly by running:
@@ -155,7 +157,7 @@ An interface will appear showing results as they load, letting you track the age
 
 ### Implementation Details
 
-We built TradingAgents with LangGraph to ensure flexibility and modularity. We utilize `o1-preview` and `gpt-4o` as our deep thinking and fast thinking LLMs for our experiments. However, for testing purposes, we recommend you use `o4-mini` and `gpt-4.1-mini` to save on costs as our framework makes **lots of** API calls.
+We built TradingAgents with LangGraph to ensure flexibility and modularity. The framework supports multiple LLM providers: OpenAI, Google, Anthropic, OpenRouter, and Ollama.
 
 ### Python Usage
 
@@ -168,7 +170,7 @@ from tradingagents.default_config import DEFAULT_CONFIG
 ta = TradingAgentsGraph(debug=True, config=DEFAULT_CONFIG.copy())
 
 # forward propagate
-_, decision = ta.propagate("NVDA", "2024-05-10")
+_, decision = ta.propagate("NVDA", "2026-01-15")
 print(decision)
 ```
 
@@ -178,31 +180,18 @@ You can also adjust the default configuration to set your own choice of LLMs, de
 from tradingagents.graph.trading_graph import TradingAgentsGraph
 from tradingagents.default_config import DEFAULT_CONFIG
 
-# Create a custom config
 config = DEFAULT_CONFIG.copy()
-config["deep_think_llm"] = "gpt-4.1-nano"  # Use a different model
-config["quick_think_llm"] = "gpt-4.1-nano"  # Use a different model
-config["max_debate_rounds"] = 1  # Increase debate rounds
+config["llm_provider"] = "openai"        # openai, google, anthropic, openrouter, ollama
+config["deep_think_llm"] = "gpt-5.2"     # Model for complex reasoning
+config["quick_think_llm"] = "gpt-5-mini" # Model for quick tasks
+config["max_debate_rounds"] = 2
 
-# Configure data vendors (default uses yfinance and Alpha Vantage)
-config["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
-}
-
-# Initialize with custom config
 ta = TradingAgentsGraph(debug=True, config=config)
-
-# forward propagate
-_, decision = ta.propagate("NVDA", "2024-05-10")
+_, decision = ta.propagate("NVDA", "2026-01-15")
 print(decision)
 ```
 
-> The default configuration uses yfinance for stock price and technical data, and Alpha Vantage for fundamental and news data. For production use or if you encounter rate limits, consider upgrading to [Alpha Vantage Premium](https://www.alphavantage.co/premium/) for more stable and reliable data access. For offline experimentation, there's a local data vendor option that uses our **Tauric TradingDB**, a curated dataset for backtesting, though this is still in development. We're currently refining this dataset and plan to release it soon alongside our upcoming projects. Stay tuned!
-
-You can view the full list of configurations in `tradingagents/default_config.py`.
+See `tradingagents/default_config.py` for all configuration options.
 
 ## Contributing