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

@@ -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(
@@ -754,7 +801,7 @@ 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 +122,43 @@ 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 = [
+    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(
@@ -158,11 +179,12 @@ 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 +192,34 @@ 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": [
+            ("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

index.html

@@ -1,447 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head>
-  <meta charset="utf-8">
-  <meta name="description" content="TradingAgents: Multi-Agents LLM Financial Trading Framework">
-  <meta name="keywords" content="TradingAgents, LLM, Financial Trading, Multi-Agent Systems, Financial Markets, AI Trading">
-  <meta name="viewport" content="width=device-width, initial-scale=1">
-  <title>TradingAgents: Multi-Agents LLM Financial Trading Framework</title>
-
-  <script async src="https://www.googletagmanager.com/gtag/js?id=G-PYVRSFMDRL"></script>
-  <script>
-    window.dataLayer = window.dataLayer || [];
-    function gtag() { dataLayer.push(arguments); }
-    gtag('js', new Date());
-    gtag('config', 'G-PYVRSFMDRL');
-  </script>
-
-  <link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro" rel="stylesheet">
-  <link rel="stylesheet" href="./static/css/bulma.min.css">
-  <link rel="stylesheet" href="./static/css/bulma-carousel.min.css">
-  <link rel="stylesheet" href="./static/css/bulma-slider.min.css">
-  <link rel="stylesheet" href="./static/css/fontawesome.all.min.css">
-  <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/jpswalsh/academicons@1/css/academicons.min.css">
-  <link rel="stylesheet" href="./static/css/index.css">
-  <link rel="icon" href="./static/images/TradingAgents.png">
-  <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
-  <script defer src="./static/js/fontawesome.all.min.js"></script>
-  <script src="./static/js/bulma-carousel.min.js"></script>
-  <script src="./static/js/bulma-slider.min.js"></script>
-  <script src="./static/js/index.js"></script>
-</head>
-<body>
-
-<nav class="navbar" role="navigation" aria-label="main navigation">
-  <div class="navbar-brand">
-    <a role="button" class="navbar-burger" aria-label="menu" aria-expanded="false">
-      <span aria-hidden="true"></span>
-      <span aria-hidden="true"></span>
-      <span aria-hidden="true"></span>
-    </a>
-  </div>
-  <div class="navbar-menu">
-    <div class="navbar-start" style="flex-grow: 1; justify-content: center;">
-      <a class="navbar-item" href="https://yijia-xiao.github.io/">
-        <span class="icon">
-          <i class="fas fa-home"></i>
-        </span>
-      </a>
-      <div class="navbar-item has-dropdown is-hoverable">
-        <a class="navbar-link">More Research</a>
-        <div class="navbar-dropdown">
-          <a class="navbar-item" href="https://arxiv.org/abs/2408.11363">ProteinGPT</a>
-          <a class="navbar-item" href="https://arxiv.org/abs/2310.02469">PrivacyMind</a>
-          <a class="navbar-item" href="https://arxiv.org/abs/2412.20138">TradingAgents</a>
-        </div>
-      </div>
-    </div>
-  </div>
-</nav>
-
-<section class="hero">
-  <div class="hero-body">
-    <div class="container is-max-desktop">
-      <div class="columns is-centered">
-        <div class="column has-text-centered">
-          <h1 class="title is-1 publication-title">TradingAgents: Multi-Agents LLM Financial Trading Framework</h1>
-          <div class="is-size-5 publication-authors">
-            <a href="https://yijia-xiao.github.io/" target="_blank" rel="noopener noreferrer"><span class="author-block">Yijia Xiao<sup>1</sup>,</span></a>
-            <span class="author-block">Edward Sun<sup>1</sup>,</span>
-            <span class="author-block">Di Luo<sup>1,2</sup>,</span>
-            <span class="author-block">Wei Wang<sup>1</sup></span>
-          </div>
-          <div class="is-size-5 publication-authors">
-            <span class="author-block"><sup>1</sup>University of California, Los Angeles,</span>
-            <span class="author-block"><sup>2</sup>Massachusetts Institute of Technology</span>
-          </div>
-          <div class="column has-text-centered">
-            <div class="publication-links">
-              <span class="link-block"><a href="https://arxiv.org/abs/2412.20138" class="external-link button is-normal is-rounded is-dark"><span class="icon"><i class="fas fa-file-pdf"></i></span><span>Paper</span></a></span>
-              <span class="link-block"><a href="https://github.com/TradingAgents-AI" class="external-link button is-normal is-rounded is-dark"><span class="icon"><i class="fab fa-github"></i></span><span>Code</span></a></span>
-              <span class="link-block"><a href="#citation-ref" class="external-link button is-normal is-rounded is-dark"><span class="icon"><i class="fas fa-quote-right"></i></span><span>Citation</span></a></span>
-            </div>
-          </div>
-        </div>
-      </div>
-    </div>
-  </div>
-</section>
-
-<section class="section">
-  <div class="container is-max-desktop">
-    <div class="columns is-centered has-text-centered">
-      <div class="column is-four-fifths">
-        <h2 class="title is-3">Abstract</h2>
-        <div class="content has-text-justified">
-          <p>We introduce <strong>TradingAgents</strong>, a novel stock trading framework inspired by trading firms, utilizing multiple LLM-powered agents with specialized roles such as fundamental, sentiment, and technical analysts, as well as traders with diverse risk profiles. The system features Bull and Bear researchers evaluating market conditions, a risk management team overseeing exposure, and traders integrating insights from debates and historical data to make informed decisions. This collaborative, dynamic environment enhances trading performance, as demonstrated by our comprehensive experiments showing significant improvements in cumulative returns, Sharpe ratio, and maximum drawdown compared to baseline models. Our results highlight the effectiveness of multi-agent LLM frameworks in financial trading.</p>
-        </div>
-      </div>
-    </div>
-  </div>
-</section>
-
-<section class="section">
-  <div class="container is-max-desktop">
-    <div class="columns is-centered">
-      <div class="column is-full-width">
-        <h2 class="title is-3">TradingAgents: Overview</h2>
-        <div class="content has-text-justified">
-          <p><strong>TradingAgents</strong> leverages a multi-agent framework to simulate a professional trading firm with distinct roles: fundamental, sentiment, and technical analysts; researchers; traders; and risk managers. These agents collaborate through structured communication and debates, enhancing decision-making and optimizing trading strategies.</p>
-          <figure class="image">
-            <img src="./static/images/schema.png" alt="TradingAgents Overall Framework Organization">
-            <figcaption class="has-text-centered"><strong>Figure 1:</strong> TradingAgents Overall Framework Organization.<br><strong>I. Analysts Team</strong>: Four analysts concurrently gather relevant market information.<br><strong>II. Research Team</strong>: The team discusses and evaluates the collected data.<br><strong>III. Trader</strong>: Based on the researchers' analysis, the trader makes the trading decision.<br><strong>IV. Risk Management Team</strong>: Risk guardians assess the decision against current market conditions to mitigate risks.<br><strong>V. Fund Manager</strong>: The fund manager approves and executes the trade.</em></figcaption>
-          </figure>
-        </div>
-      </div>
-    </div>
-  </div>
-</section>
-
-<section class="section">
-  <div class="container is-max-desktop">
-    <div class="columns is-centered">
-      <div class="column is-full-width">
-        <h2 class="title is-3">TradingAgents: Role Specialization</h2>
-        <div class="content has-text-justified">
-          <p>Assigning specific roles to LLM agents allows complex trading objectives to be broken down into manageable tasks. Inspired by trading firms, <strong>TradingAgents</strong> features seven distinct roles: Fundamentals Analyst, Sentiment Analyst, News Analyst, Technical Analyst, Researcher, Trader, and Risk Manager. Each agent is equipped with specialized tools and constraints tailored to their function, ensuring comprehensive market analysis and informed decision-making.</p>
-          
-          <h3 class="title is-4">Analyst Team</h3>
-          <div class="content has-text-justified">
-            <p>The Analyst Team gathers and analyzes market data across various domains:</p>
-            <ul>
-              <li><strong>Fundamental Analysts:</strong> Assess company fundamentals to identify undervalued or overvalued stocks.</li>
-              <li><strong>Sentiment Analysts:</strong> Analyze social media and public sentiment to gauge market mood.</li>
-              <li><strong>News Analysts:</strong> Evaluate news and macroeconomic indicators to predict market movements.</li>
-              <li><strong>Technical Analysts:</strong> Use technical indicators to forecast price trends and trading opportunities.</li>
-            </ul>
-            <p>Combined, their insights provide a holistic market view, feeding into the Researcher Team for further evaluation.</p>
-            
-            <figure class="image">
-              <img src="./static/images/Analyst.png" alt="TradingAgents Analyst Team" style="width: 65%;">
-              <figcaption class="has-text-centered"><strong>Figure 2:</strong> TradingAgents Analyst Team</figcaption>
-            </figure>
-          </div>
-          
-          <h3 class="title is-4">Researcher Team</h3>
-          <div class="content has-text-justified">
-            <p>The Researcher Team critically evaluates analyst data through a dialectical process involving bullish and bearish perspectives. This debate ensures balanced analysis, identifying both opportunities and risks to inform trading strategies.</p>
-            
-            <div class="columns">
-              <div class="column">
-                <figure class="image">
-                  <img src="./static/images/Researcher.png" alt="TradingAgents Researcher Team">
-                  <figcaption class="has-text-centered"><strong>Figure 3:</strong> TradingAgents Researcher Team</figcaption>
-                </figure>
-              </div>
-              <div class="column">
-                <figure class="image">
-                  <img src="./static/images/Trader.png" alt="TradingAgents Trader Decision-Making Process">
-                  <figcaption class="has-text-centered"><strong>Figure 4:</strong> TradingAgents Trader Decision-Making Process</figcaption>
-                </figure>
-              </div>
-              <div class="column">
-                <figure class="image">
-                  <img src="./static/images/RiskMGMT.png" alt="TradingAgents Risk Management Team Workflow">
-                  <figcaption class="has-text-centered"><strong>Figure 5:</strong> TradingAgents Risk Management Workflow</figcaption>
-                </figure>
-              </div>
-            </div>
-            
-            <ul>
-              <li><strong>Bullish Researchers:</strong> Highlight positive market indicators and growth potential.</li>
-              <li><strong>Bearish Researchers:</strong> Focus on risks and negative market signals.</li>
-            </ul>
-            
-            <p>This process ensures a balanced understanding of market conditions, aiding Trader Agents in making informed decisions.</p>
-          </div>
-          
-          <h3 class="title is-4">Trader Agents</h3>
-          <div class="content has-text-justified">
-            <p>Trader Agents execute decisions based on comprehensive analyses. They evaluate insights from analysts and researchers to determine optimal trading actions, balancing returns and risks in a dynamic market environment.</p>
-            
-            <ul>
-              <li>Assessing analyst and researcher recommendations.</li>
-              <li>Determining trade timing and size.</li>
-              <li>Executing buy/sell orders.</li>
-              <li>Adjusting portfolios in response to market changes.</li>
-            </ul>
-            
-            <p>Precision and strategic thinking are essential for their role in maximizing performance.</p>
-          </div>
-          
-          <h3 class="title is-4">Risk Management Team</h3>
-          <div class="content has-text-justified">
-            <p>The Risk Management Team oversees the firm's exposure to market risks, ensuring trading activities stay within predefined limits.</p>
-            
-            <ul>
-              <li>Assessing market volatility and liquidity.</li>
-              <li>Implementing risk mitigation strategies.</li>
-              <li>Advising Trader Agents on risk exposures.</li>
-              <li>Aligning portfolio with risk tolerance.</li>
-            </ul>
-            
-            <p>They ensure financial stability and safeguard assets through effective risk control.</p>
-            
-            <p>All agents utilize the ReAct prompting framework, facilitating a collaborative and dynamic decision-making process reflective of real-world trading systems.</p>
-          </div>
-        </div>
-      </div>
-    </div>
-  </div>
-</section>
-
-<section class="section">
-  <div class="container is-max-desktop">
-    <div class="columns is-centered">
-      <div class="column is-full-width">
-        <h2 class="title is-3">TradingAgents: Agent Workflow</h2>
-        <div class="content has-text-justified">
-          <h3 class="title is-4">Communication Protocol</h3>
-          <p>To enhance communication efficiency, <strong>TradingAgents</strong> employs a structured protocol that combines clear, structured outputs with natural language dialogue. This approach minimizes information loss and maintains context over long interactions, ensuring focused and effective communication among agents.</p>
-          
-          <h3 class="title is-4">Types of Agent Interactions</h3>
-          <p>Unlike previous frameworks that rely heavily on unstructured dialogue, our agents communicate through structured reports and diagrams, preserving essential information and enabling direct queries from the global state.</p>
-          
-          <ul>
-            <li><strong>Analyst Team:</strong> Compiles research into concise analysis reports.</li>
-            <li><strong>Traders:</strong> Review analyst reports and produce decision signals with detailed rationales.</li>
-          </ul>
-          
-          <p>Natural language dialogue is reserved for specific interactions, such as debates within the Researcher and Risk Management teams, fostering deeper reasoning and balanced decision-making.</p>
-          
-          <ul>
-            <li><strong>Researcher Team:</strong> Engages in debates to form balanced perspectives.</li>
-            <li><strong>Risk Management Team:</strong> Deliberates on trading plans from multiple risk perspectives.</li>
-            <li><strong>Fund Manager:</strong> Reviews and approves risk-adjusted trading decisions.</li>
-          </ul>
-          
-          <h3 class="title is-4">Backbone LLMs</h3>
-          <p>We select LLMs based on task requirements, using quick-thinking models for data retrieval and deep-thinking models for in-depth analysis and decision-making. This strategic alignment ensures efficiency and robust reasoning, allowing <strong>TradingAgents</strong> to operate without the need for GPUs and enabling easy integration of alternative models in the future.</p>
-        </div>
-      </div>
-    </div>
-  </div>
-</section>
-
-<section class="section">
-  <div class="container is-max-desktop">
-    <div class="columns is-centered">
-      <div class="column is-full-width">
-        <h2 class="title is-3">Experiments</h2>
-        <div class="content has-text-justified">
-          <p>We evaluated <strong>TradingAgents</strong> using a comprehensive experimental setup to assess its performance against various baselines.</p>
-          
-          <h3 class="title is-4">Back Trading</h3>
-          <p>Our simulation utilized a multi-asset, multi-modal financial dataset including historical stock prices, news articles, social media sentiments, insider transactions, financial reports, and technical indicators from January to March 2024.</p>
-          
-          <h3 class="title is-4">Simulation Setup</h3>
-          <p>The trading environment spanned from June to November 2024. Agents operated on a daily basis, making decisions based on available data without future information, ensuring unbiased results.</p>
-          
-          <h3 class="title is-4">Baseline Models</h3>
-          <p>We compared <strong>TradingAgents</strong> against the following strategies:</p>
-          
-          <ul>
-            <li><strong>Buy and Hold:</strong> Investing equally across selected stocks throughout the period.</li>
-            <li><strong>MACD:</strong> Momentum strategy based on MACD crossovers.</li>
-            <li><strong>KDJ & RSI:</strong> Combined momentum indicators for trading signals.</li>
-            <li><strong>ZMR:</strong> Mean reversion strategy based on price deviations.</li>
-            <li><strong>SMA:</strong> Trend-following strategy using moving average crossovers.</li>
-          </ul>
-          
-          <h3 class="title is-4">Evaluation Metrics</h3>
-          
-          <div class="columns">
-            <div class="column">
-              <figure class="image">
-                <img src="./static/images/CumulativeReturns_AAPL.png" alt="Cumulative Returns on AAPL">
-                <figcaption class="has-text-centered"><strong>(a)</strong> Cumulative Returns on AAPL</figcaption>
-              </figure>
-            </div>
-            
-            <div class="column">
-              <figure class="image">
-                <img src="./static/images/TradingAgents_Transactions_AAPL.png" alt="TradingAgents Transactions for AAPL">
-                <figcaption class="has-text-centered">
-                  <strong>(b)</strong> TradingAgents Transactions for AAPL.<br>
-                  Green / Red Arrows for Long / Short Positions.
-                </figcaption>
-              </figure>
-            </div>
-          </div>          
-
-          <table class="table is-striped is-fullwidth is-centered">
-            <thead>
-              <tr>
-                <th>Categories</th>
-                <th>Models</th>
-                <th colspan="4">AAPL</th>
-                <th></th>
-                <th colspan="4">GOOGL</th>
-                <th></th>
-                <th colspan="4">AMZN</th>
-              </tr>
-              <tr>
-                <th></th>
-                <th></th>
-                <th>CR%↑</th>
-                <th>ARR%↑</th>
-                <th>SR↑</th>
-                <th>MDD%↓</th>
-                <th></th>
-                <th>CR%↑</th>
-                <th>ARR%↑</th>
-                <th>SR↑</th>
-                <th>MDD%↓</th>
-                <th></th>
-                <th>CR%↑</th>
-                <th>ARR%↑</th>
-                <th>SR↑</th>
-                <th>MDD%↓</th>
-              </tr>
-            </thead>
-            <tbody>
-              <tr>
-                <td>Market</td>
-                <td>B&H</td>
-                <td>-5.23</td><td>-5.09</td><td>-1.29</td><td>11.90</td>
-                <td></td>
-                <td>7.78</td><td>8.09</td><td>1.35</td><td>13.04</td>
-                <td></td>
-                <td>17.1</td><td>17.6</td><td>3.53</td><td>3.80</td>
-              </tr>
-              <tr>
-                <td rowspan="4">Rule-based</td>
-                <td>MACD</td>
-                <td>-1.49</td><td>-1.48</td><td>-0.81</td><td>4.53</td>
-                <td></td>
-                <td>6.20</td><td>6.26</td><td>2.31</td><td>1.22</td>
-                <td></td>
-                <td>-</td><td>-</td><td>-</td><td>-</td>
-              </tr>
-              <tr>
-                <td>KDJ&RSI</td>
-                <td>2.05</td><td>2.07</td><td>1.64</td><td>1.09</td>
-                <td></td>
-                <td>0.4</td><td>0.4</td><td>0.02</td><td>1.58</td>
-                <td></td>
-                <td>-0.77</td><td>-0.76</td><td>-2.25</td><td>1.08</td>
-              </tr>
-              <tr>
-                <td>ZMR</td>
-                <td>0.57</td><td>0.57</td><td>0.17</td><td>0.86</td>
-                <td></td>
-                <td>-0.58</td><td>0.58</td><td>2.12</td><td>2.34</td>
-                <td></td>
-                <td>-0.77</td><td>-0.77</td><td>-2.45</td><td>0.82</td>
-              </tr>
-              <tr>
-                <td>SMA</td>
-                <td>-3.2</td><td>-2.97</td><td>-1.72</td><td>3.67</td>
-                <td></td>
-                <td>6.23</td><td>6.43</td><td>2.12</td><td>2.34</td>
-                <td></td>
-                <td>11.01</td><td>11.6</td><td>2.22</td><td>3.97</td>
-              </tr>
-              <tr>
-                <td rowspan="1">Ours</td>
-                <td><strong>TradingAgents</strong></td>
-                <td><strong style="color:green;">26.62</strong></td><td><strong style="color:green;">30.5</strong></td><td><strong style="color:green;">8.21</strong></td><td>0.91</td>
-                <td></td>
-                <td><strong style="color:green;">24.36</strong></td><td><strong style="color:green;">27.58</strong></td><td><strong style="color:green;">6.39</strong></td><td>1.69</td>
-                <td></td>
-                <td><strong style="color:green;">23.21</strong></td><td><strong style="color:green;">24.90</strong></td><td><strong style="color:green;">5.60</strong></td><td>2.11</td>
-              </tr>
-              <tr>
-                <td colspan="2">Improvement(%)</td>
-                <td>24.57</td><td>28.43</td><td>6.57</td><td>-</td>
-                <td></td>
-                <td>16.58</td><td>19.49</td><td>4.26</td><td>-</td>
-                <td></td>
-                <td>6.10</td><td>7.30</td><td>2.07</td><td>-</td>
-              </tr>
-            </tbody>
-          </table>
-          <p class="has-text-centered"><strong>Table 1:</strong> TradingAgents: Performance Metrics Comparison across AAPL, GOOGL, and AMZN.</p>
-          
-          <h3 class="title is-4">Sharpe Ratio</h3>
-          <p><strong>TradingAgents</strong> achieves superior risk-adjusted returns, consistently outperforming all baselines across AAPL, GOOGL, and AMZN. The enhanced Sharpe Ratios demonstrate the framework's effectiveness in balancing returns with risk, highlighting its robustness in diverse market conditions.</p>
-          
-          <h3 class="title is-4">Maximum Drawdown</h3>
-          <p>While rule-based strategies excel in controlling risk, <strong>TradingAgents</strong> maintains a low maximum drawdown without sacrificing high returns. This balance underscores the framework's ability to maximize profits while effectively managing risk.</p>
-          
-          <h3 class="title is-4">Explainability</h3>
-          <p>Unlike traditional deep learning models, <strong>TradingAgents</strong> offers transparent decision-making through natural language explanations. Each agent's actions are accompanied by detailed reasoning and tool usage, making the system's operations easily interpretable and debuggable, which is crucial for real-world financial applications.</p>
-        </div>
-      </div>
-    </div>
-  </div>
-</section>
-
-<section class="section">
-  <div class="container is-max-desktop">
-    <div class="columns is-centered">
-      <div class="column is-full-width">
-        <h2 class="title is-3">Conclusion</h2>
-        <div class="content has-text-justified">
-          <p>We presented <strong>TradingAgents</strong>, a multi-agent LLM-driven stock trading framework that emulates a realistic trading firm with specialized agents collaborating through debates and structured communication. Our framework leverages diverse data sources and multi-agent interactions to enhance trading decisions, achieving superior performance in cumulative returns, Sharpe ratio, and risk management compared to traditional strategies. Future work includes live deployment, expanding agent roles, and integrating real-time data processing to further improve performance.</p>
-        </div>
-      </div>
-    </div>
-  </div>
-</section>
-
-<section id="citation-ref" class="section">
-  <div class="container content">
-    <h2 class="title">BibTeX</h2>
-    <pre><code>@inproceedings{xiao2025tradingagentsmultiagentsllmfinancial,
-      title        = {TradingAgents: Multi-Agents LLM Financial Trading Framework},
-      author       = {Yijia Xiao and Edward Sun and Di Luo and Wei Wang},
-      booktitle    = {Multi-Agent AI in the Real World @ AAAI 2025},
-      year         = {2025},
-      eprint       = {2412.20138},
-      archivePrefix= {arXiv},
-      primaryClass = {q-fin.TR},
-      url          = {https://arxiv.org/abs/2412.20138},
-      note         = {Workshop paper},
-    }</code></pre>
-  </div>
-</section>
-
-<footer class="footer">
-  <div class="container">
-    <div class="content has-text-centered">
-      <a class="icon-link" href="https://arxiv.org/abs/2412.20138"><i class="fas fa-file-pdf"></i></a>
-      <a class="icon-link" href="https://github.com/TradingAgents-AI"><i class="fab fa-github"></i></a>
-    </div>
-    <div class="columns is-centered">
-      <div class="column is-8">
-        <div class="content">
-          <p>This website is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.</p>
-        </div>
-      </div>
-    </div>
-  </div>
-</footer>
-
-</body>
-</html>

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

@@ -12,13 +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
 

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):
+        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()