Merge pull request #49 from Zhongyi-Lu/a

Exclude `.env` from Git.

.gitignore

@@ -6,3 +6,4 @@ src/
 eval_results/
 eval_data/
 *.egg-info/
+.env

.python-version

@@ -0,0 +1 @@
+3.10

README.md

@@ -11,6 +11,18 @@
   <a href="https://github.com/TauricResearch/" target="_blank"><img alt="Community" src="https://img.shields.io/badge/Join_GitHub_Community-TauricResearch-14C290?logo=discourse"/></a>
 </div>
 
+<div align="center">
+  <!-- Keep these links. Translations will automatically update with the README. -->
+  <a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=de">Deutsch</a> | 
+  <a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=es">Español</a> | 
+  <a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=fr">français</a> | 
+  <a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=ja">日本語</a> | 
+  <a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=ko">한국어</a> | 
+  <a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=pt">Português</a> | 
+  <a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=ru">Русский</a> | 
+  <a href="https://www.readme-i18n.com/TauricResearch/TradingAgents?lang=zh">中文</a>
+</div>
+
 ---
 
 # TradingAgents: Multi-Agents LLM Financial Trading Framework 
@@ -19,6 +31,16 @@
 >
 > So we decided to fully open-source the framework. Looking forward to building impactful projects with you!
 
+<div align="center">
+<a href="https://www.star-history.com/#TauricResearch/TradingAgents&Date">
+ <picture>
+   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date&theme=dark" />
+   <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date" />
+   <img alt="TradingAgents Star History" src="https://api.star-history.com/svg?repos=TauricResearch/TradingAgents&type=Date" style="width: 80%; height: auto;" />
+ </picture>
+</a>
+</div>
+
 <div align="center">
 
 🚀 [TradingAgents](#tradingagents-framework) | ⚡ [Installation & CLI](#installation-and-cli) | 🎬 [Demo](https://www.youtube.com/watch?v=90gr5lwjIho) | 📦 [Package Usage](#tradingagents-package) | 🤝 [Contributing](#contributing) | 📄 [Citation](#citation)
@@ -58,7 +80,7 @@ Our framework decomposes complex trading tasks into specialized roles. This ensu
 - Composes reports from the analysts and researchers to make informed trading decisions. It determines the timing and magnitude of trades based on comprehensive market insights.
 
 <p align="center">
-  <img src="assets/risk.png" width="70%" style="display: inline-block; margin: 0 2%;">
+  <img src="assets/trader.png" width="70%" style="display: inline-block; margin: 0 2%;">
 </p>
 
 ### Risk Management and Portfolio Manager
@@ -66,7 +88,7 @@ Our framework decomposes complex trading tasks into specialized roles. This ensu
 - The Portfolio Manager approves/rejects the transaction proposal. If approved, the order will be sent to the simulated exchange and executed.
 
 <p align="center">
-  <img src="assets/trader.png" width="70%" style="display: inline-block; margin: 0 2%;">
+  <img src="assets/risk.png" width="70%" style="display: inline-block; margin: 0 2%;">
 </p>
 
 ## Installation and CLI
@@ -92,7 +114,7 @@ pip install -r requirements.txt
 
 ### Required APIs
 
-You will also need the FinnHub API and EODHD API for financial data. All of our code is implemented with the free tier.
+You will also need the FinnHub API for financial data. All of our code is implemented with the free tier.
 ```bash
 export FINNHUB_API_KEY=$YOUR_FINNHUB_API_KEY
 ```
@@ -136,8 +158,9 @@ To use TradingAgents inside your code, you can import the `tradingagents` module
 
 ```python
 from tradingagents.graph.trading_graph import TradingAgentsGraph
+from tradingagents.default_config import DEFAULT_CONFIG
 
-ta = TradingAgentsGraph(debug=True, config=config)
+ta = TradingAgentsGraph(debug=True, config=DEFAULT_CONFIG.copy())
 
 # forward propagate
 _, decision = ta.propagate("NVDA", "2024-05-10")

cli/main.py

@@ -1,6 +1,8 @@
 from typing import Optional
 import datetime
 import typer
+from pathlib import Path
+from functools import wraps
 from rich.console import Console
 from rich.panel import Panel
 from rich.spinner import Spinner
@@ -295,10 +297,27 @@ def update_display(layout, spinner_text=None):
 
     # Add regular messages
     for timestamp, msg_type, content in message_buffer.messages:
+        # Convert content to string if it's not already
+        content_str = content
+        if isinstance(content, list):
+            # Handle list of content blocks (Anthropic 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))
+            content_str = ' '.join(text_parts)
+        elif not isinstance(content_str, str):
+            content_str = str(content)
+            
         # Truncate message content if too long
-        if isinstance(content, str) and len(content) > 200:
+        if len(content_str) > 200:
-            content = content[:197] + "..."
+            content_str = content_str[:197] + "..."
-        all_messages.append((timestamp, msg_type, content))
+        all_messages.append((timestamp, msg_type, content_str))
 
     # Sort by timestamp
     all_messages.sort(key=lambda x: x[0])
@@ -444,20 +463,30 @@ def get_user_selections():
     )
     selected_research_depth = select_research_depth()
 
-    # Step 5: Thinking agents
+    # Step 5: OpenAI backend
     console.print(
         create_question_box(
-            "Step 5: Thinking Agents", "Select your thinking agents for analysis"
+            "Step 5: OpenAI backend", "Select which service to talk to"
         )
     )
-    selected_shallow_thinker = select_shallow_thinking_agent()
+    selected_llm_provider, backend_url = select_llm_provider()
-    selected_deep_thinker = select_deep_thinking_agent()
+    
+    # Step 6: Thinking agents
+    console.print(
+        create_question_box(
+            "Step 6: Thinking Agents", "Select your thinking agents for analysis"
+        )
+    )
+    selected_shallow_thinker = select_shallow_thinking_agent(selected_llm_provider)
+    selected_deep_thinker = select_deep_thinking_agent(selected_llm_provider)
 
     return {
         "ticker": selected_ticker,
         "analysis_date": analysis_date,
         "analysts": selected_analysts,
         "research_depth": selected_research_depth,
+        "llm_provider": selected_llm_provider.lower(),
+        "backend_url": backend_url,
         "shallow_thinker": selected_shallow_thinker,
         "deep_thinker": selected_deep_thinker,
     }
@@ -683,6 +712,24 @@ def update_research_team_status(status):
     for agent in research_team:
         message_buffer.update_agent_status(agent, status)
 
+def extract_content_string(content):
+    """Extract string content from various message formats."""
+    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)
 
 def run_analysis():
     # First get all user selections
@@ -694,12 +741,61 @@ def run_analysis():
     config["max_risk_discuss_rounds"] = selections["research_depth"]
     config["quick_think_llm"] = selections["shallow_thinker"]
     config["deep_think_llm"] = selections["deep_thinker"]
+    config["backend_url"] = selections["backend_url"]
+    config["llm_provider"] = selections["llm_provider"].lower()
 
     # Initialize the graph
     graph = TradingAgentsGraph(
         [analyst.value for analyst in selections["analysts"]], config=config, debug=True
     )
 
+    # Create result directory
+    results_dir = Path(config["results_dir"]) / selections["ticker"] / selections["analysis_date"]
+    results_dir.mkdir(parents=True, exist_ok=True)
+    report_dir = results_dir / "reports"
+    report_dir.mkdir(parents=True, exist_ok=True)
+    log_file = results_dir / "message_tool.log"
+    log_file.touch(exist_ok=True)
+
+    def save_message_decorator(obj, func_name):
+        func = getattr(obj, func_name)
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            func(*args, **kwargs)
+            timestamp, message_type, content = obj.messages[-1]
+            content = content.replace("\n", " ")  # Replace newlines with spaces
+            with open(log_file, "a") as f:
+                f.write(f"{timestamp} [{message_type}] {content}\n")
+        return wrapper
+    
+    def save_tool_call_decorator(obj, func_name):
+        func = getattr(obj, func_name)
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            func(*args, **kwargs)
+            timestamp, tool_name, args = obj.tool_calls[-1]
+            args_str = ", ".join(f"{k}={v}" for k, v in args.items())
+            with open(log_file, "a") as f:
+                f.write(f"{timestamp} [Tool Call] {tool_name}({args_str})\n")
+        return wrapper
+
+    def save_report_section_decorator(obj, func_name):
+        func = getattr(obj, func_name)
+        @wraps(func)
+        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]
+                if content:
+                    file_name = f"{section_name}.md"
+                    with open(report_dir / file_name, "w") as f:
+                        f.write(content)
+        return wrapper
+
+    message_buffer.add_message = save_message_decorator(message_buffer, "add_message")
+    message_buffer.add_tool_call = save_tool_call_decorator(message_buffer, "add_tool_call")
+    message_buffer.update_report_section = save_report_section_decorator(message_buffer, "update_report_section")
+
     # Now start the display layout
     layout = create_layout()
 
@@ -754,7 +850,7 @@ def run_analysis():
 
                 # Extract message content and type
                 if hasattr(last_message, "content"):
-                    content = last_message.content
+                    content = extract_content_string(last_message.content)  # Use the helper function
                     msg_type = "Reasoning"
                 else:
                     content = str(last_message)

cli/utils.py

@@ -122,22 +122,44 @@ def select_research_depth() -> int:
     return choice
 
 
-def select_shallow_thinking_agent() -> str:
+def select_shallow_thinking_agent(provider) -> str:
     """Select shallow thinking llm engine using an interactive selection."""
 
     # Define shallow thinking llm engine options with their corresponding model names
-    SHALLOW_AGENT_OPTIONS = [
+    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"),
+        ],
+        "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"),
+        ],
+        "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"),
+        ],
+        "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"),
+            ("google/gemini-2.0-flash-exp:free - Gemini Flash 2.0 offers a significantly faster time to first token", "google/gemini-2.0-flash-exp:free"),
+        ],
+        "ollama": [
+            ("llama3.1 local", "llama3.1"),
+            ("llama3.2 local", "llama3.2"),
         ]
+    }
 
     choice = questionary.select(
         "Select Your [Quick-Thinking LLM Engine]:",
         choices=[
             questionary.Choice(display, value=value)
-            for display, value in SHALLOW_AGENT_OPTIONS
+            for display, value in SHALLOW_AGENT_OPTIONS[provider.lower()]
         ],
         instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
         style=questionary.Style(
@@ -158,11 +180,12 @@ def select_shallow_thinking_agent() -> str:
     return choice
 
 
-def select_deep_thinking_agent() -> str:
+def select_deep_thinking_agent(provider) -> str:
     """Select deep thinking llm engine using an interactive selection."""
 
     # Define deep thinking llm engine options with their corresponding model names
-    DEEP_AGENT_OPTIONS = [
+    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"),
@@ -170,13 +193,35 @@ def select_deep_thinking_agent() -> str:
             ("o3-mini - Advanced reasoning model (lightweight)", "o3-mini"),
             ("o3 - Full advanced reasoning model", "o3"),
             ("o1 - Premier reasoning and problem-solving model", "o1"),
+        ],
+        "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"),
+        ],
+        "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"),
+        ],
+        "openrouter": [
+            ("DeepSeek V3 - a 685B-parameter, mixture-of-experts model", "deepseek/deepseek-chat-v3-0324:free"),
+            ("Deepseek - latest iteration of the flagship chat model family from the DeepSeek team.", "deepseek/deepseek-chat-v3-0324:free"),
+        ],
+        "ollama": [
+            ("llama3.1 local", "llama3.1"),
+            ("qwen3", "qwen3"),
         ]
+    }
     
     choice = questionary.select(
         "Select Your [Deep-Thinking LLM Engine]:",
         choices=[
             questionary.Choice(display, value=value)
-            for display, value in DEEP_AGENT_OPTIONS
+            for display, value in DEEP_AGENT_OPTIONS[provider.lower()]
         ],
         instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
         style=questionary.Style(
@@ -193,3 +238,39 @@ def select_deep_thinking_agent() -> str:
         exit(1)
 
     return choice
+
+def select_llm_provider() -> tuple[str, str]:
+    """Select the OpenAI api url using interactive selection."""
+    # Define OpenAI api options with their corresponding endpoints
+    BASE_URLS = [
+        ("OpenAI", "https://api.openai.com/v1"),
+        ("Anthropic", "https://api.anthropic.com/"),
+        ("Google", "https://generativelanguage.googleapis.com/v1"),
+        ("Openrouter", "https://openrouter.ai/api/v1"),
+        ("Ollama", "http://localhost:11434/v1"),        
+    ]
+    
+    choice = questionary.select(
+        "Select your LLM Provider:",
+        choices=[
+            questionary.Choice(display, value=(display, value))
+            for display, value in BASE_URLS
+        ],
+        instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
+        style=questionary.Style(
+            [
+                ("selected", "fg:magenta noinherit"),
+                ("highlighted", "fg:magenta noinherit"),
+                ("pointer", "fg:magenta noinherit"),
+            ]
+        ),
+    ).ask()
+    
+    if choice is None:
+        console.print("\n[red]no OpenAI backend selected. Exiting...[/red]")
+        exit(1)
+    
+    display_name, url = choice
+    print(f"You selected: {display_name}\tURL: {url}")
+    
+    return display_name, url

main.py

@@ -3,8 +3,10 @@ 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["llm_provider"] = "google"  # Use a different model
-config["quick_think_llm"] = "gpt-4.1-nano"  # Use a different model
+config["backend_url"] = "https://generativelanguage.googleapis.com/v1"  # Use a different backend
+config["deep_think_llm"] = "gemini-2.0-flash"  # Use a different model
+config["quick_think_llm"] = "gemini-2.0-flash"  # Use a different model
 config["max_debate_rounds"] = 1  # Increase debate rounds
 config["online_tools"] = True  # Increase debate rounds
 

pyproject.toml

@@ -0,0 +1,34 @@
+[project]
+name = "tradingagents"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.10"
+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",
+    "langchain-anthropic>=0.3.15",
+    "langchain-experimental>=0.3.4",
+    "langchain-google-genai>=2.1.5",
+    "langchain-openai>=0.3.23",
+    "langgraph>=0.4.8",
+    "pandas>=2.3.0",
+    "parsel>=1.10.0",
+    "praw>=7.8.1",
+    "pytz>=2025.2",
+    "questionary>=2.1.0",
+    "redis>=6.2.0",
+    "requests>=2.32.4",
+    "rich>=14.0.0",
+    "setuptools>=80.9.0",
+    "stockstats>=0.6.5",
+    "tqdm>=4.67.1",
+    "tushare>=1.4.21",
+    "typing-extensions>=4.14.0",
+    "yfinance>=0.2.63",
+]

requirements.txt

@@ -22,3 +22,5 @@ redis
 chainlit
 rich
 questionary
+langchain_anthropic
+langchain-google-genai

tradingagents/agents/analysts/fundamentals_analyst.py

@@ -22,7 +22,7 @@ def create_fundamentals_analyst(llm, toolkit):
 
         system_message = (
             "You are a researcher tasked with analyzing fundamental information over the past week about a company. Please write a comprehensive report of the company's fundamental information such as financial documents, company profile, basic company financials, company financial history, insider sentiment and insider transactions to gain a full view of the company's fundamental information to inform traders. Make sure to include as much detail as possible. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
-            + " Make sure to append a Makrdown table at the end of the report to organize key points in the report, organized and easy to read.",
+            + " Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read.",
         )
 
         prompt = ChatPromptTemplate.from_messages(
@@ -51,9 +51,14 @@ def create_fundamentals_analyst(llm, toolkit):
 
         result = chain.invoke(state["messages"])
 
+        report = ""
+
+        if len(result.tool_calls) == 0:
+            report = result.content
+
         return {
             "messages": [result],
-            "fundamentals_report": result.content,
+            "fundamentals_report": report,
         }
 
     return fundamentals_analyst_node

tradingagents/agents/analysts/market_analyst.py

@@ -76,9 +76,14 @@ Volume-Based Indicators:
 
         result = chain.invoke(state["messages"])
 
+        report = ""
+
+        if len(result.tool_calls) == 0:
+            report = result.content
+       
         return {
             "messages": [result],
-            "market_report": result.content,
+            "market_report": report,
         }
 
     return market_analyst_node

tradingagents/agents/analysts/news_analyst.py

@@ -47,9 +47,14 @@ def create_news_analyst(llm, toolkit):
         chain = prompt | llm.bind_tools(tools)
         result = chain.invoke(state["messages"])
 
+        report = ""
+
+        if len(result.tool_calls) == 0:
+            report = result.content
+
         return {
             "messages": [result],
-            "news_report": result.content,
+            "news_report": report,
         }
 
     return news_analyst_node

tradingagents/agents/analysts/social_media_analyst.py

@@ -47,9 +47,14 @@ def create_social_media_analyst(llm, toolkit):
 
         result = chain.invoke(state["messages"])
 
+        report = ""
+
+        if len(result.tool_calls) == 0:
+            report = result.content
+
         return {
             "messages": [result],
-            "sentiment_report": result.content,
+            "sentiment_report": report,
         }
 
     return social_media_analyst_node

tradingagents/agents/utils/agent_utils.py

@@ -12,13 +12,21 @@ from dateutil.relativedelta import relativedelta
 from langchain_openai import ChatOpenAI
 import tradingagents.dataflows.interface as interface
 from tradingagents.default_config import DEFAULT_CONFIG
+from langchain_core.messages import HumanMessage
 
 
 def create_msg_delete():
     def delete_messages(state):
-        """To prevent message history from overflowing, regularly clear message history after a stage of the pipeline is done"""
+        """Clear messages and add placeholder for Anthropic compatibility"""
         messages = state["messages"]
-        return {"messages": [RemoveMessage(id=m.id) for m in messages]}
+        
+        # Remove all messages
+        removal_operations = [RemoveMessage(id=m.id) for m in messages]
+        
+        # Add a minimal placeholder message
+        placeholder = HumanMessage(content="Continue")
+        
+        return {"messages": removal_operations + [placeholder]}
     
     return delete_messages
 
@@ -116,7 +124,7 @@ class Toolkit:
     def get_YFin_data(
         symbol: Annotated[str, "ticker symbol of the company"],
         start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
-        end_date: Annotated[str, "Start date in yyyy-mm-dd format"],
+        end_date: Annotated[str, "End date in yyyy-mm-dd format"],
     ) -> str:
         """
         Retrieve the stock price data for a given ticker symbol from Yahoo Finance.
@@ -137,7 +145,7 @@ class Toolkit:
     def get_YFin_data_online(
         symbol: Annotated[str, "ticker symbol of the company"],
         start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
-        end_date: Annotated[str, "Start date in yyyy-mm-dd format"],
+        end_date: Annotated[str, "End date in yyyy-mm-dd format"],
     ) -> str:
         """
         Retrieve the stock price data for a given ticker symbol from Yahoo Finance.

tradingagents/agents/utils/memory.py

@@ -1,19 +1,23 @@
 import chromadb
 from chromadb.config import Settings
 from openai import OpenAI
-import numpy as np
 
 
 class FinancialSituationMemory:
-    def __init__(self, name):
+    def __init__(self, name, config):
-        self.client = OpenAI()
+        if config["backend_url"] == "http://localhost:11434/v1":
+            self.embedding = "nomic-embed-text"
+        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"""
+        
         response = self.client.embeddings.create(
-            model="text-embedding-ada-002", input=text
+            model=self.embedding, input=text
         )
         return response.data[0].embedding
 

tradingagents/dataflows/interface.py

@@ -628,7 +628,7 @@ def get_YFin_data_window(
 def get_YFin_data_online(
     symbol: Annotated[str, "ticker symbol of the company"],
     start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
-    end_date: Annotated[str, "Start date in yyyy-mm-dd format"],
+    end_date: Annotated[str, "End date in yyyy-mm-dd format"],
 ):
 
     datetime.strptime(start_date, "%Y-%m-%d")
@@ -670,7 +670,7 @@ def get_YFin_data_online(
 def get_YFin_data(
     symbol: Annotated[str, "ticker symbol of the company"],
     start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
-    end_date: Annotated[str, "Start date in yyyy-mm-dd format"],
+    end_date: Annotated[str, "End date in yyyy-mm-dd format"],
 ) -> str:
     # read in data
     data = pd.read_csv(
@@ -703,17 +703,18 @@ def get_YFin_data(
 
 
 def get_stock_news_openai(ticker, curr_date):
-    client = OpenAI()
+    config = get_config()
+    client = OpenAI(base_url=config["backend_url"])
 
     response = client.responses.create(
-        model="gpt-4.1-mini",
+        model=config["quick_think_llm"],
         input=[
             {
                 "role": "system",
                 "content": [
                     {
                         "type": "input_text",
-                        "text": f"Can you search Social Media for {ticker} on TSLA from 7 days before {curr_date} to {curr_date}? Make sure you only get the data posted during that period.",
+                        "text": f"Can you search Social Media for {ticker} from 7 days before {curr_date} to {curr_date}? Make sure you only get the data posted during that period.",
                     }
                 ],
             }
@@ -737,10 +738,11 @@ def get_stock_news_openai(ticker, curr_date):
 
 
 def get_global_news_openai(curr_date):
-    client = OpenAI()
+    config = get_config()
+    client = OpenAI(base_url=config["backend_url"])
 
     response = client.responses.create(
-        model="gpt-4.1-mini",
+        model=config["quick_think_llm"],
         input=[
             {
                 "role": "system",
@@ -771,10 +773,11 @@ def get_global_news_openai(curr_date):
 
 
 def get_fundamentals_openai(ticker, curr_date):
-    client = OpenAI()
+    config = get_config()
+    client = OpenAI(base_url=config["backend_url"])
 
     response = client.responses.create(
-        model="gpt-4.1-mini",
+        model=config["quick_think_llm"],
         input=[
             {
                 "role": "system",

tradingagents/default_config.py

@@ -2,14 +2,17 @@ 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",
+    "backend_url": "https://api.openai.com/v1",
     # Debate and discussion settings
     "max_debate_rounds": 1,
     "max_risk_discuss_rounds": 1,

tradingagents/graph/trading_graph.py

@@ -7,6 +7,9 @@ from datetime import date
 from typing import Dict, Any, Tuple, List, Optional
 
 from langchain_openai import ChatOpenAI
+from langchain_anthropic import ChatAnthropic
+from langchain_google_genai import ChatGoogleGenerativeAI
+
 from langgraph.prebuilt import ToolNode
 
 from tradingagents.agents import *
@@ -55,18 +58,26 @@ class TradingAgentsGraph:
         )
 
         # Initialize LLMs
-        self.deep_thinking_llm = ChatOpenAI(model=self.config["deep_think_llm"])
+        if self.config["llm_provider"].lower() == "openai" or self.config["llm_provider"] == "ollama" or self.config["llm_provider"] == "openrouter":
-        self.quick_thinking_llm = ChatOpenAI(
+            self.deep_thinking_llm = ChatOpenAI(model=self.config["deep_think_llm"], base_url=self.config["backend_url"])
-            model=self.config["quick_think_llm"], temperature=0.1
+            self.quick_thinking_llm = ChatOpenAI(model=self.config["quick_think_llm"], base_url=self.config["backend_url"])
-        )
+        elif self.config["llm_provider"].lower() == "anthropic":
+            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.quick_thinking_llm = ChatGoogleGenerativeAI(model=self.config["quick_think_llm"])
+        else:
+            raise ValueError(f"Unsupported LLM provider: {self.config['llm_provider']}")
+        
         self.toolkit = Toolkit(config=self.config)
 
         # Initialize memories
-        self.bull_memory = FinancialSituationMemory("bull_memory")
+        self.bull_memory = FinancialSituationMemory("bull_memory", self.config)
-        self.bear_memory = FinancialSituationMemory("bear_memory")
+        self.bear_memory = FinancialSituationMemory("bear_memory", self.config)
-        self.trader_memory = FinancialSituationMemory("trader_memory")
+        self.trader_memory = FinancialSituationMemory("trader_memory", self.config)
-        self.invest_judge_memory = FinancialSituationMemory("invest_judge_memory")
+        self.invest_judge_memory = FinancialSituationMemory("invest_judge_memory", self.config)
-        self.risk_manager_memory = FinancialSituationMemory("risk_manager_memory")
+        self.risk_manager_memory = FinancialSituationMemory("risk_manager_memory", self.config)
 
         # Create tool nodes
         self.tool_nodes = self._create_tool_nodes()