fixed anthropic support. Anthropic has different format of response when it has tool calls. Explicit handling added

Add support for other backends, such as OpenRouter and Ollama

.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)
@@ -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

@@ -97,7 +97,7 @@ class MessageBuffer:
             if content is not None:
                 latest_section = section
                 latest_content = content
-
+               
         if latest_section and latest_content:
             # Format the current section for display
             section_titles = {
@@ -295,10 +295,27 @@ 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:
-            content = content[:197] + "..."
-        all_messages.append((timestamp, msg_type, content))
+        if len(content_str) > 200:
+            content_str = content_str[:197] + "..."
+        all_messages.append((timestamp, msg_type, content_str))
 
     # Sort by timestamp
     all_messages.sort(key=lambda x: x[0])
@@ -444,20 +461,30 @@ 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_deep_thinker = select_deep_thinking_agent()
+    selected_llm_provider, backend_url = select_llm_provider()
+    
+    # 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 +710,24 @@ 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
@@ -694,6 +739,8 @@ 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(
@@ -754,14 +801,14 @@ 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)
                     msg_type = "System"
 
                 # Add message to buffer
-                message_buffer.add_message(msg_type, 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"):

cli/utils.py

@@ -122,22 +122,43 @@ 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 = [
-        ("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"),
-    ]
+    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": [
+            ("llama3.2 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(
@@ -158,25 +179,47 @@ 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 = [
-        ("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"),
-    ]
-
+    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"),
+        ],
+        "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": [
+            ("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 +236,39 @@ 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["quick_think_llm"] = "gpt-4.1-nano"  # Use a different model
+config["llm_provider"] = "google"  # 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",
+]

tradingagents/agents/analysts/fundamentals_analyst.py

@@ -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

@@ -12,14 +12,22 @@ 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
 
 

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):
-        self.client = OpenAI()
+    def __init__(self, name, config):
+        if config["backend_url"] == "http://localhost:11434/v1":
+            self.embedding = "nomic-embed-text"
+        else:
+            self.embedding = "text-embedding-3-small"
+            self.client = OpenAI()
         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

@@ -703,6 +703,7 @@ def get_YFin_data(
 
 
 def get_stock_news_openai(ticker, curr_date):
+    config = get_config()
     client = OpenAI()
 
     response = client.responses.create(
@@ -713,7 +714,7 @@ def get_stock_news_openai(ticker, curr_date):
                 "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.",
                     }
                 ],
             }
@@ -737,6 +738,7 @@ def get_stock_news_openai(ticker, curr_date):
 
 
 def get_global_news_openai(curr_date):
+    config = get_config()
     client = OpenAI()
 
     response = client.responses.create(
@@ -771,6 +773,7 @@ def get_global_news_openai(curr_date):
 
 
 def get_fundamentals_openai(ticker, curr_date):
+    config = get_config()
     client = OpenAI()
 
     response = client.responses.create(

tradingagents/default_config.py

@@ -8,8 +8,10 @@ DEFAULT_CONFIG = {
         "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"])
-        self.quick_thinking_llm = ChatOpenAI(
-            model=self.config["quick_think_llm"], temperature=0.1
-        )
+        if self.config["llm_provider"].lower() == "openai" or self.config["llm_provider"] == "ollama" or self.config["llm_provider"] == "openrouter":
+            self.deep_thinking_llm = ChatOpenAI(model=self.config["deep_think_llm"], base_url=self.config["backend_url"])
+            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.bear_memory = FinancialSituationMemory("bear_memory")
-        self.trader_memory = FinancialSituationMemory("trader_memory")
-        self.invest_judge_memory = FinancialSituationMemory("invest_judge_memory")
-        self.risk_manager_memory = FinancialSituationMemory("risk_manager_memory")
+        self.bull_memory = FinancialSituationMemory("bull_memory", self.config)
+        self.bear_memory = FinancialSituationMemory("bear_memory", self.config)
+        self.trader_memory = FinancialSituationMemory("trader_memory", self.config)
+        self.invest_judge_memory = FinancialSituationMemory("invest_judge_memory", self.config)
+        self.risk_manager_memory = FinancialSituationMemory("risk_manager_memory", self.config)
 
         # Create tool nodes
         self.tool_nodes = self._create_tool_nodes()