.dockerignore

@@ -1,15 +0,0 @@
-.git
-.venv
-.env
-.claude
-.idea
-.vscode
-.DS_Store
-__pycache__
-*.egg-info
-build
-dist
-results
-eval_results
-Dockerfile
-docker-compose.yml

.env.enterprise.example

@@ -1,5 +0,0 @@
-# Azure OpenAI
-AZURE_OPENAI_API_KEY=
-AZURE_OPENAI_ENDPOINT=https://your-resource-name.openai.azure.com/
-AZURE_OPENAI_DEPLOYMENT_NAME=
-# OPENAI_API_VERSION=2024-10-21  # optional, required for non-v1 API

.env.example

@@ -3,7 +3,4 @@ OPENAI_API_KEY=
 GOOGLE_API_KEY=
 ANTHROPIC_API_KEY=
 XAI_API_KEY=
-DEEPSEEK_API_KEY=
-DASHSCOPE_API_KEY=
-ZHIPU_API_KEY=
 OPENROUTER_API_KEY=

.python-version

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

Dockerfile

@@ -1,27 +0,0 @@
-FROM python:3.12-slim AS builder
-
-ENV PYTHONDONTWRITEBYTECODE=1 \
-    PIP_DISABLE_PIP_VERSION_CHECK=1
-
-RUN python -m venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
-
-WORKDIR /build
-COPY . .
-RUN pip install --no-cache-dir .
-
-FROM python:3.12-slim
-
-ENV PYTHONDONTWRITEBYTECODE=1 \
-    PYTHONUNBUFFERED=1
-
-COPY --from=builder /opt/venv /opt/venv
-ENV PATH="/opt/venv/bin:$PATH"
-
-RUN useradd --create-home appuser
-USER appuser
-WORKDIR /home/appuser/app
-
-COPY --from=builder --chown=appuser:appuser /build .
-
-ENTRYPOINT ["tradingagents"]

README.md

@@ -28,8 +28,6 @@
 # TradingAgents: Multi-Agents LLM Financial Trading Framework
 
 ## News
-- [2026-03] **TradingAgents v0.2.3** released with multi-language support, GPT-5.4 family models, unified model catalog, backtesting date fidelity, and proxy support.
-- [2026-03] **TradingAgents v0.2.2** released with GPT-5.4/Gemini 3.1/Claude 4.6 model coverage, five-tier rating scale, OpenAI Responses API, Anthropic effort control, and cross-platform stability.
 - [2026-02] **TradingAgents v0.2.0** released with multi-provider LLM support (GPT-5.x, Gemini 3.x, Claude 4.x, Grok 4.x) and improved system architecture.
 - [2026-01] **Trading-R1** [Technical Report](https://arxiv.org/abs/2509.11420) released, with [Terminal](https://github.com/TauricResearch/Trading-R1) expected to land soon.
 
@@ -113,22 +111,9 @@ conda create -n tradingagents python=3.13
 conda activate tradingagents
 ```
 
-Install the package and its dependencies:
+Install dependencies:
 ```bash
-pip install .
-```
-
-### Docker
-
-Alternatively, run with Docker:
-```bash
-cp .env.example .env  # add your API keys
-docker compose run --rm tradingagents
-```
-
-For local models with Ollama:
-```bash
-docker compose --profile ollama run --rm tradingagents-ollama
+pip install -r requirements.txt
 ```
 
 ### Required APIs
@@ -140,15 +125,10 @@ export OPENAI_API_KEY=...          # OpenAI (GPT)
 export GOOGLE_API_KEY=...          # Google (Gemini)
 export ANTHROPIC_API_KEY=...       # Anthropic (Claude)
 export XAI_API_KEY=...             # xAI (Grok)
-export DEEPSEEK_API_KEY=...        # DeepSeek
-export DASHSCOPE_API_KEY=...       # Qwen (Alibaba DashScope)
-export ZHIPU_API_KEY=...           # GLM (Zhipu)
 export OPENROUTER_API_KEY=...      # OpenRouter
 export ALPHA_VANTAGE_API_KEY=...   # Alpha Vantage
 ```
 
-For enterprise providers (e.g. Azure OpenAI, AWS Bedrock), copy `.env.enterprise.example` to `.env.enterprise` and fill in your credentials.
-
 For local models, configure Ollama with `llm_provider: "ollama"` in your config.
 
 Alternatively, copy `.env.example` to `.env` and fill in your keys:
@@ -158,12 +138,11 @@ cp .env.example .env
 
 ### CLI Usage
 
-Launch the interactive CLI:
+You can also try out the CLI directly by running:
 ```bash
-tradingagents          # installed command
-python -m cli.main     # alternative: run directly from source
+python -m cli.main
 ```
-You will see a screen where you can select your desired tickers, analysis date, LLM provider, research depth, and more.
+You will see a screen where you can select your desired tickers, date, LLMs, research depth, etc.
 
 <p align="center">
   <img src="assets/cli/cli_init.png" width="100%" style="display: inline-block; margin: 0 2%;">
@@ -208,8 +187,8 @@ from tradingagents.default_config import DEFAULT_CONFIG
 
 config = DEFAULT_CONFIG.copy()
 config["llm_provider"] = "openai"        # openai, google, anthropic, xai, openrouter, ollama
-config["deep_think_llm"] = "gpt-5.4"     # Model for complex reasoning
-config["quick_think_llm"] = "gpt-5.4-mini" # Model for quick tasks
+config["deep_think_llm"] = "gpt-5.2"     # Model for complex reasoning
+config["quick_think_llm"] = "gpt-5-mini" # Model for quick tasks
 config["max_debate_rounds"] = 2
 
 ta = TradingAgentsGraph(debug=True, config=config)

cli/main.py

@@ -6,9 +6,8 @@ from functools import wraps
 from rich.console import Console
 from dotenv import load_dotenv
 
-# Load environment variables
+# Load environment variables from .env file
 load_dotenv()
-load_dotenv(".env.enterprise", override=False)
 from rich.panel import Panel
 from rich.spinner import Spinner
 from rich.live import Live
@@ -80,7 +79,7 @@ class MessageBuffer:
         self.current_agent = None
         self.report_sections = {}
         self.selected_analysts = []
-        self._processed_message_ids = set()
+        self._last_message_id = None
 
     def init_for_analysis(self, selected_analysts):
         """Initialize agent status and report sections based on selected analysts.
@@ -115,7 +114,7 @@ class MessageBuffer:
         self.current_agent = None
         self.messages.clear()
         self.tool_calls.clear()
-        self._processed_message_ids.clear()
+        self._last_message_id = None
 
     def get_completed_reports_count(self):
         """Count reports that are finalized (their finalizing agent is completed).
@@ -463,7 +462,7 @@ def update_display(layout, spinner_text=None, stats_handler=None, start_time=Non
 def get_user_selections():
     """Get all user selections before starting the analysis display."""
     # Display ASCII art welcome message
-    with open(Path(__file__).parent / "static" / "welcome.txt", "r") as f:
+    with open("./cli/static/welcome.txt", "r") as f:
         welcome_ascii = f.read()
 
     # Create welcome box content
@@ -502,9 +501,7 @@ def get_user_selections():
     # Step 1: Ticker symbol
     console.print(
         create_question_box(
-            "Step 1: Ticker Symbol",
-            "Enter the exact ticker symbol to analyze, including exchange suffix when needed (examples: SPY, CNC.TO, 7203.T, 0700.HK)",
-            "SPY",
+            "Step 1: Ticker Symbol", "Enter the ticker symbol to analyze", "SPY"
         )
     )
     selected_ticker = get_ticker()
@@ -520,19 +517,10 @@ def get_user_selections():
     )
     analysis_date = get_analysis_date()
 
-    # Step 3: Output language
+    # Step 3: Select analysts
     console.print(
         create_question_box(
-            "Step 3: Output Language",
-            "Select the language for analyst reports and final decision"
-        )
-    )
-    output_language = ask_output_language()
-
-    # Step 4: Select analysts
-    console.print(
-        create_question_box(
-            "Step 4: Analysts Team", "Select your LLM analyst agents for the analysis"
+            "Step 3: Analysts Team", "Select your LLM analyst agents for the analysis"
         )
     )
     selected_analysts = select_analysts()
@@ -540,41 +528,40 @@ def get_user_selections():
         f"[green]Selected analysts:[/green] {', '.join(analyst.value for analyst in selected_analysts)}"
     )
 
-    # Step 5: Research depth
+    # Step 4: Research depth
     console.print(
         create_question_box(
-            "Step 5: Research Depth", "Select your research depth level"
+            "Step 4: Research Depth", "Select your research depth level"
         )
     )
     selected_research_depth = select_research_depth()
 
-    # Step 6: LLM Provider
+    # Step 5: OpenAI backend
     console.print(
         create_question_box(
-            "Step 6: LLM Provider", "Select your LLM provider"
+            "Step 5: OpenAI backend", "Select which service to talk to"
         )
     )
     selected_llm_provider, backend_url = select_llm_provider()
-
-    # Step 7: Thinking agents
+    
+    # Step 6: Thinking agents
     console.print(
         create_question_box(
-            "Step 7: Thinking Agents", "Select your thinking agents for analysis"
+            "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)
 
-    # Step 8: Provider-specific thinking configuration
+    # Step 7: Provider-specific thinking configuration
     thinking_level = None
     reasoning_effort = None
-    anthropic_effort = None
 
     provider_lower = selected_llm_provider.lower()
     if provider_lower == "google":
         console.print(
             create_question_box(
-                "Step 8: Thinking Mode",
+                "Step 7: Thinking Mode",
                 "Configure Gemini thinking mode"
             )
         )
@@ -582,19 +569,11 @@ def get_user_selections():
     elif provider_lower == "openai":
         console.print(
             create_question_box(
-                "Step 8: Reasoning Effort",
+                "Step 7: Reasoning Effort",
                 "Configure OpenAI reasoning effort level"
             )
         )
         reasoning_effort = ask_openai_reasoning_effort()
-    elif provider_lower == "anthropic":
-        console.print(
-            create_question_box(
-                "Step 8: Effort Level",
-                "Configure Claude effort level"
-            )
-        )
-        anthropic_effort = ask_anthropic_effort()
 
     return {
         "ticker": selected_ticker,
@@ -607,8 +586,6 @@ def get_user_selections():
         "deep_thinker": selected_deep_thinker,
         "google_thinking_level": thinking_level,
         "openai_reasoning_effort": reasoning_effort,
-        "anthropic_effort": anthropic_effort,
-        "output_language": output_language,
     }
 
 
@@ -811,11 +788,9 @@ ANALYST_REPORT_MAP = {
 
 
 def update_analyst_statuses(message_buffer, chunk):
-    """Update analyst statuses based on accumulated report state.
+    """Update all analyst statuses based on current report state.
 
     Logic:
-    - Store new report content from the current chunk if present
-    - Check accumulated report_sections (not just current chunk) for status
     - Analysts with reports = completed
     - First analyst without report = in_progress
     - Remaining analysts without reports = pending
@@ -830,16 +805,11 @@ def update_analyst_statuses(message_buffer, chunk):
 
         agent_name = ANALYST_AGENT_NAMES[analyst_key]
         report_key = ANALYST_REPORT_MAP[analyst_key]
-
-        # Capture new report content from current chunk
-        if chunk.get(report_key):
-            message_buffer.update_report_section(report_key, chunk[report_key])
-
-        # Determine status from accumulated sections, not just current chunk
-        has_report = bool(message_buffer.report_sections.get(report_key))
+        has_report = bool(chunk.get(report_key))
 
         if has_report:
             message_buffer.update_agent_status(agent_name, "completed")
+            message_buffer.update_report_section(report_key, chunk[report_key])
         elif not found_active:
             message_buffer.update_agent_status(agent_name, "in_progress")
             found_active = True
@@ -941,8 +911,6 @@ def run_analysis():
     # Provider-specific thinking configuration
     config["google_thinking_level"] = selections.get("google_thinking_level")
     config["openai_reasoning_effort"] = selections.get("openai_reasoning_effort")
-    config["anthropic_effort"] = selections.get("anthropic_effort")
-    config["output_language"] = selections.get("output_language", "English")
 
     # Create stats callback handler for tracking LLM/tool calls
     stats_handler = StatsCallbackHandler()
@@ -1004,9 +972,8 @@ def run_analysis():
                 content = obj.report_sections[section_name]
                 if content:
                     file_name = f"{section_name}.md"
-                    text = "\n".join(str(item) for item in content) if isinstance(content, list) else content
                     with open(report_dir / file_name, "w") as f:
-                        f.write(text)
+                        f.write(content)
         return wrapper
 
     message_buffer.add_message = save_message_decorator(message_buffer, "add_message")
@@ -1053,24 +1020,28 @@ def run_analysis():
         # Stream the analysis
         trace = []
         for chunk in graph.graph.stream(init_agent_state, **args):
-            # Process all messages in chunk, deduplicating by message ID
-            for message in chunk.get("messages", []):
-                msg_id = getattr(message, "id", None)
-                if msg_id is not None:
-                    if msg_id in message_buffer._processed_message_ids:
-                        continue
-                    message_buffer._processed_message_ids.add(msg_id)
+            # Process messages if present (skip duplicates via message ID)
+            if len(chunk["messages"]) > 0:
+                last_message = chunk["messages"][-1]
+                msg_id = getattr(last_message, "id", None)
 
-                msg_type, content = classify_message_type(message)
-                if content and content.strip():
-                    message_buffer.add_message(msg_type, content)
+                if msg_id != message_buffer._last_message_id:
+                    message_buffer._last_message_id = msg_id
 
-                if hasattr(message, "tool_calls") and message.tool_calls:
-                    for tool_call in message.tool_calls:
-                        if isinstance(tool_call, dict):
-                            message_buffer.add_tool_call(tool_call["name"], tool_call["args"])
-                        else:
-                            message_buffer.add_tool_call(tool_call.name, tool_call.args)
+                    # Add message to buffer
+                    msg_type, content = classify_message_type(last_message)
+                    if content and content.strip():
+                        message_buffer.add_message(msg_type, content)
+
+                    # Handle tool calls
+                    if hasattr(last_message, "tool_calls") and last_message.tool_calls:
+                        for tool_call in last_message.tool_calls:
+                            if isinstance(tool_call, dict):
+                                message_buffer.add_tool_call(
+                                    tool_call["name"], tool_call["args"]
+                                )
+                            else:
+                                message_buffer.add_tool_call(tool_call.name, tool_call.args)
 
             # Update analyst statuses based on report state (runs on every chunk)
             update_analyst_statuses(message_buffer, chunk)

cli/utils.py

@@ -1,14 +1,7 @@
 import questionary
 from typing import List, Optional, Tuple, Dict
 
-from rich.console import Console
-
 from cli.models import AnalystType
-from tradingagents.llm_clients.model_catalog import get_model_options
-
-console = Console()
-
-TICKER_INPUT_EXAMPLES = "Examples: SPY, CNC.TO, 7203.T, 0700.HK"
 
 ANALYST_ORDER = [
     ("Market Analyst", AnalystType.MARKET),
@@ -21,7 +14,7 @@ ANALYST_ORDER = [
 def get_ticker() -> str:
     """Prompt the user to enter a ticker symbol."""
     ticker = questionary.text(
-        f"Enter the exact ticker symbol to analyze ({TICKER_INPUT_EXAMPLES}):",
+        "Enter the ticker symbol to analyze:",
         validate=lambda x: len(x.strip()) > 0 or "Please enter a valid ticker symbol.",
         style=questionary.Style(
             [
@@ -35,11 +28,6 @@ def get_ticker() -> str:
         console.print("\n[red]No ticker symbol provided. Exiting...[/red]")
         exit(1)
 
-    return normalize_ticker_symbol(ticker)
-
-
-def normalize_ticker_symbol(ticker: str) -> str:
-    """Normalize ticker input while preserving exchange suffixes."""
     return ticker.strip().upper()
 
 
@@ -134,70 +122,51 @@ def select_research_depth() -> int:
     return choice
 
 
-def _fetch_openrouter_models() -> List[Tuple[str, str]]:
-    """Fetch available models from the OpenRouter API."""
-    import requests
-    try:
-        resp = requests.get("https://openrouter.ai/api/v1/models", timeout=10)
-        resp.raise_for_status()
-        models = resp.json().get("data", [])
-        return [(m.get("name") or m["id"], m["id"]) for m in models]
-    except Exception as e:
-        console.print(f"\n[yellow]Could not fetch OpenRouter models: {e}[/yellow]")
-        return []
+def select_shallow_thinking_agent(provider) -> str:
+    """Select shallow thinking llm engine using an interactive selection."""
 
-
-def select_openrouter_model() -> str:
-    """Select an OpenRouter model from the newest available, or enter a custom ID."""
-    models = _fetch_openrouter_models()
-
-    choices = [questionary.Choice(name, value=mid) for name, mid in models[:5]]
-    choices.append(questionary.Choice("Custom model ID", value="custom"))
+    # Define shallow thinking llm engine options with their corresponding model names
+    SHALLOW_AGENT_OPTIONS = {
+        "openai": [
+            ("GPT-5 Mini - Cost-optimized reasoning", "gpt-5-mini"),
+            ("GPT-5 Nano - Ultra-fast, high-throughput", "gpt-5-nano"),
+            ("GPT-5.2 - Latest flagship", "gpt-5.2"),
+            ("GPT-5.1 - Flexible reasoning", "gpt-5.1"),
+            ("GPT-4.1 - Smartest non-reasoning, 1M context", "gpt-4.1"),
+        ],
+        "anthropic": [
+            ("Claude Haiku 4.5 - Fast + extended thinking", "claude-haiku-4-5"),
+            ("Claude Sonnet 4.5 - Best for agents/coding", "claude-sonnet-4-5"),
+            ("Claude Sonnet 4 - High-performance", "claude-sonnet-4-20250514"),
+        ],
+        "google": [
+            ("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
+            ("Gemini 2.5 Flash - Balanced, recommended", "gemini-2.5-flash"),
+            ("Gemini 3 Pro - Reasoning-first", "gemini-3-pro-preview"),
+            ("Gemini 2.5 Flash Lite - Fast, low-cost", "gemini-2.5-flash-lite"),
+        ],
+        "xai": [
+            ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
+            ("Grok 4 Fast (Non-Reasoning) - Speed optimized", "grok-4-fast-non-reasoning"),
+            ("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
+            ("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"),
+        ],
+        "openrouter": [
+            ("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"),
+            ("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"),
+        ],
+        "ollama": [
+            ("Qwen3:latest (8B, local)", "qwen3:latest"),
+            ("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
+            ("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
+        ],
+    }
 
     choice = questionary.select(
-        "Select OpenRouter Model (latest available):",
-        choices=choices,
-        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 or choice == "custom":
-        return questionary.text(
-            "Enter OpenRouter model ID (e.g. google/gemma-4-26b-a4b-it):",
-            validate=lambda x: len(x.strip()) > 0 or "Please enter a model ID.",
-        ).ask().strip()
-
-    return choice
-
-
-def _prompt_custom_model_id() -> str:
-    """Prompt user to type a custom model ID."""
-    return questionary.text(
-        "Enter model ID:",
-        validate=lambda x: len(x.strip()) > 0 or "Please enter a model ID.",
-    ).ask().strip()
-
-
-def _select_model(provider: str, mode: str) -> str:
-    """Select a model for the given provider and mode (quick/deep)."""
-    if provider.lower() == "openrouter":
-        return select_openrouter_model()
-
-    if provider.lower() == "azure":
-        return questionary.text(
-            f"Enter Azure deployment name ({mode}-thinking):",
-            validate=lambda x: len(x.strip()) > 0 or "Please enter a deployment name.",
-        ).ask().strip()
-
-    choice = questionary.select(
-        f"Select Your [{mode.title()}-Thinking LLM Engine]:",
+        "Select Your [Quick-Thinking LLM Engine]:",
         choices=[
             questionary.Choice(display, value=value)
-            for display, value in get_model_options(provider, mode)
+            for display, value in SHALLOW_AGENT_OPTIONS[provider.lower()]
         ],
         instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
         style=questionary.Style(
@@ -210,45 +179,96 @@ def _select_model(provider: str, mode: str) -> str:
     ).ask()
 
     if choice is None:
-        console.print(f"\n[red]No {mode} thinking llm engine selected. Exiting...[/red]")
+        console.print(
+            "\n[red]No shallow thinking llm engine selected. Exiting...[/red]"
+        )
         exit(1)
 
-    if choice == "custom":
-        return _prompt_custom_model_id()
-
     return choice
 
 
-def select_shallow_thinking_agent(provider) -> str:
-    """Select shallow thinking llm engine using an interactive selection."""
-    return _select_model(provider, "quick")
-
-
 def select_deep_thinking_agent(provider) -> str:
     """Select deep thinking llm engine using an interactive selection."""
-    return _select_model(provider, "deep")
 
-def select_llm_provider() -> tuple[str, str | None]:
-    """Select the LLM provider and its API endpoint."""
-    # (display_name, provider_key, base_url)
-    PROVIDERS = [
-        ("OpenAI", "openai", "https://api.openai.com/v1"),
-        ("Google", "google", None),
-        ("Anthropic", "anthropic", "https://api.anthropic.com/"),
-        ("xAI", "xai", "https://api.x.ai/v1"),
-        ("DeepSeek", "deepseek", "https://api.deepseek.com"),
-        ("Qwen", "qwen", "https://dashscope.aliyuncs.com/compatible-mode/v1"),
-        ("GLM", "glm", "https://open.bigmodel.cn/api/paas/v4/"),
-        ("OpenRouter", "openrouter", "https://openrouter.ai/api/v1"),
-        ("Azure OpenAI", "azure", None),
-        ("Ollama", "ollama", "http://localhost:11434/v1"),
+    # Define deep thinking llm engine options with their corresponding model names
+    DEEP_AGENT_OPTIONS = {
+        "openai": [
+            ("GPT-5.2 - Latest flagship", "gpt-5.2"),
+            ("GPT-5.1 - Flexible reasoning", "gpt-5.1"),
+            ("GPT-5 - Advanced reasoning", "gpt-5"),
+            ("GPT-4.1 - Smartest non-reasoning, 1M context", "gpt-4.1"),
+            ("GPT-5 Mini - Cost-optimized reasoning", "gpt-5-mini"),
+            ("GPT-5 Nano - Ultra-fast, high-throughput", "gpt-5-nano"),
+        ],
+        "anthropic": [
+            ("Claude Sonnet 4.5 - Best for agents/coding", "claude-sonnet-4-5"),
+            ("Claude Opus 4.5 - Premium, max intelligence", "claude-opus-4-5"),
+            ("Claude Opus 4.1 - Most capable model", "claude-opus-4-1-20250805"),
+            ("Claude Haiku 4.5 - Fast + extended thinking", "claude-haiku-4-5"),
+            ("Claude Sonnet 4 - High-performance", "claude-sonnet-4-20250514"),
+        ],
+        "google": [
+            ("Gemini 3 Pro - Reasoning-first", "gemini-3-pro-preview"),
+            ("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
+            ("Gemini 2.5 Flash - Balanced, recommended", "gemini-2.5-flash"),
+        ],
+        "xai": [
+            ("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
+            ("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"),
+            ("Grok 4 - Flagship model", "grok-4-0709"),
+            ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
+            ("Grok 4 Fast (Non-Reasoning) - Speed optimized", "grok-4-fast-non-reasoning"),
+        ],
+        "openrouter": [
+            ("Z.AI GLM 4.5 Air (free)", "z-ai/glm-4.5-air:free"),
+            ("NVIDIA Nemotron 3 Nano 30B (free)", "nvidia/nemotron-3-nano-30b-a3b:free"),
+        ],
+        "ollama": [
+            ("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
+            ("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
+            ("Qwen3:latest (8B, local)", "qwen3:latest"),
+        ],
+    }
+
+    choice = questionary.select(
+        "Select Your [Deep-Thinking LLM Engine]:",
+        choices=[
+            questionary.Choice(display, value=value)
+            for display, value in DEEP_AGENT_OPTIONS[provider.lower()]
+        ],
+        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 deep thinking llm engine selected. Exiting...[/red]")
+        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"),
+        ("Google", "https://generativelanguage.googleapis.com/v1"),
+        ("Anthropic", "https://api.anthropic.com/"),
+        ("xAI", "https://api.x.ai/v1"),
+        ("Openrouter", "https://openrouter.ai/api/v1"),
+        ("Ollama", "http://localhost:11434/v1"),
     ]
-
+    
     choice = questionary.select(
         "Select your LLM Provider:",
         choices=[
-            questionary.Choice(display, value=(provider_key, url))
-            for display, provider_key, url in PROVIDERS
+            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(
@@ -261,11 +281,13 @@ def select_llm_provider() -> tuple[str, str | None]:
     ).ask()
     
     if choice is None:
-        console.print("\n[red]No LLM provider selected. Exiting...[/red]")
+        console.print("\n[red]no OpenAI backend selected. Exiting...[/red]")
         exit(1)
+    
+    display_name, url = choice
+    print(f"You selected: {display_name}\tURL: {url}")
 
-    provider, url = choice
-    return provider, url
+    return display_name, url
 
 
 def ask_openai_reasoning_effort() -> str:
@@ -286,26 +308,6 @@ def ask_openai_reasoning_effort() -> str:
     ).ask()
 
 
-def ask_anthropic_effort() -> str | None:
-    """Ask for Anthropic effort level.
-
-    Controls token usage and response thoroughness on Claude 4.5+ and 4.6 models.
-    """
-    return questionary.select(
-        "Select Effort Level:",
-        choices=[
-            questionary.Choice("High (recommended)", "high"),
-            questionary.Choice("Medium (balanced)", "medium"),
-            questionary.Choice("Low (faster, cheaper)", "low"),
-        ],
-        style=questionary.Style([
-            ("selected", "fg:cyan noinherit"),
-            ("highlighted", "fg:cyan noinherit"),
-            ("pointer", "fg:cyan noinherit"),
-        ]),
-    ).ask()
-
-
 def ask_gemini_thinking_config() -> str | None:
     """Ask for Gemini thinking configuration.
 
@@ -324,37 +326,3 @@ def ask_gemini_thinking_config() -> str | None:
             ("pointer", "fg:green noinherit"),
         ]),
     ).ask()
-
-
-def ask_output_language() -> str:
-    """Ask for report output language."""
-    choice = questionary.select(
-        "Select Output Language:",
-        choices=[
-            questionary.Choice("English (default)", "English"),
-            questionary.Choice("Chinese (中文)", "Chinese"),
-            questionary.Choice("Japanese (日本語)", "Japanese"),
-            questionary.Choice("Korean (한국어)", "Korean"),
-            questionary.Choice("Hindi (हिन्दी)", "Hindi"),
-            questionary.Choice("Spanish (Español)", "Spanish"),
-            questionary.Choice("Portuguese (Português)", "Portuguese"),
-            questionary.Choice("French (Français)", "French"),
-            questionary.Choice("German (Deutsch)", "German"),
-            questionary.Choice("Arabic (العربية)", "Arabic"),
-            questionary.Choice("Russian (Русский)", "Russian"),
-            questionary.Choice("Custom language", "custom"),
-        ],
-        style=questionary.Style([
-            ("selected", "fg:yellow noinherit"),
-            ("highlighted", "fg:yellow noinherit"),
-            ("pointer", "fg:yellow noinherit"),
-        ]),
-    ).ask()
-
-    if choice == "custom":
-        return questionary.text(
-            "Enter language name (e.g. Turkish, Vietnamese, Thai, Indonesian):",
-            validate=lambda x: len(x.strip()) > 0 or "Please enter a language name.",
-        ).ask().strip()
-
-    return choice

docker-compose.yml

@@ -1,35 +0,0 @@
-services:
-  tradingagents:
-    build: .
-    env_file:
-      - .env
-    volumes:
-      - tradingagents_data:/home/appuser/.tradingagents
-    tty: true
-    stdin_open: true
-
-  ollama:
-    image: ollama/ollama:latest
-    volumes:
-      - ollama_data:/root/.ollama
-    profiles:
-      - ollama
-
-  tradingagents-ollama:
-    build: .
-    env_file:
-      - .env
-    environment:
-      - LLM_PROVIDER=ollama
-    volumes:
-      - tradingagents_data:/home/appuser/.tradingagents
-    depends_on:
-      - ollama
-    tty: true
-    stdin_open: true
-    profiles:
-      - ollama
-
-volumes:
-  tradingagents_data:
-  ollama_data:

main.py

@@ -8,8 +8,8 @@ load_dotenv()
 
 # Create a custom config
 config = DEFAULT_CONFIG.copy()
-config["deep_think_llm"] = "gpt-5.4-mini"  # Use a different model
-config["quick_think_llm"] = "gpt-5.4-mini"  # Use a different model
+config["deep_think_llm"] = "gpt-5-mini"  # Use a different model
+config["quick_think_llm"] = "gpt-5-mini"  # Use a different model
 config["max_debate_rounds"] = 1  # Increase debate rounds
 
 # Configure data vendors (default uses yfinance, no extra API keys needed)

pyproject.toml

@@ -1,19 +1,15 @@
-[build-system]
-requires = ["setuptools>=61.0"]
-build-backend = "setuptools.build_meta"
-
 [project]
 name = "tradingagents"
-version = "0.2.3"
-description = "TradingAgents: Multi-Agents LLM Financial Trading Framework"
+version = "0.1.0"
+description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
-    "langchain-core>=0.3.81",
     "backtrader>=1.9.78.123",
+    "chainlit>=2.5.5",
     "langchain-anthropic>=0.3.15",
     "langchain-experimental>=0.3.4",
-    "langchain-google-genai>=4.0.0",
+    "langchain-google-genai>=2.1.5",
     "langchain-openai>=0.3.23",
     "langgraph>=0.4.8",
     "pandas>=2.3.0",
@@ -31,12 +27,3 @@ dependencies = [
     "typing-extensions>=4.14.0",
     "yfinance>=0.2.63",
 ]
-
-[project.scripts]
-tradingagents = "cli.main:app"
-
-[tool.setuptools.packages.find]
-include = ["tradingagents*", "cli*"]
-
-[tool.setuptools.package-data]
-cli = ["static/*"]

requirements.txt

@@ -1 +1,21 @@
-.
+typing-extensions
+langchain-openai
+langchain-experimental
+pandas
+yfinance
+stockstats
+langgraph
+rank-bm25
+setuptools
+backtrader
+parsel
+requests
+tqdm
+pytz
+redis
+chainlit
+rich
+typer
+questionary
+langchain_anthropic
+langchain-google-genai

setup.py

@@ -0,0 +1,43 @@
+"""
+Setup script for the TradingAgents package.
+"""
+
+from setuptools import setup, find_packages
+
+setup(
+    name="tradingagents",
+    version="0.1.0",
+    description="Multi-Agents LLM Financial Trading Framework",
+    author="TradingAgents Team",
+    author_email="yijia.xiao@cs.ucla.edu",
+    url="https://github.com/TauricResearch",
+    packages=find_packages(),
+    install_requires=[
+        "langchain>=0.1.0",
+        "langchain-openai>=0.0.2",
+        "langchain-experimental>=0.0.40",
+        "langgraph>=0.0.20",
+        "numpy>=1.24.0",
+        "pandas>=2.0.0",
+        "praw>=7.7.0",
+        "stockstats>=0.5.4",
+        "yfinance>=0.2.31",
+        "typer>=0.9.0",
+        "rich>=13.0.0",
+        "questionary>=2.0.1",
+    ],
+    python_requires=">=3.10",
+    entry_points={
+        "console_scripts": [
+            "tradingagents=cli.main:app",
+        ],
+    },
+    classifiers=[
+        "Development Status :: 3 - Alpha",
+        "Intended Audience :: Financial and Trading Industry",
+        "License :: OSI Approved :: Apache Software License",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.10",
+        "Topic :: Office/Business :: Financial :: Investment",
+    ],
+)

tests/test_google_api_key.py

@@ -1,28 +0,0 @@
-import unittest
-from unittest.mock import patch
-
-from tradingagents.llm_clients.google_client import GoogleClient
-
-
-class TestGoogleApiKeyStandardization(unittest.TestCase):
-    """Verify GoogleClient accepts unified api_key parameter."""
-
-    @patch("tradingagents.llm_clients.google_client.NormalizedChatGoogleGenerativeAI")
-    def test_api_key_handling(self, mock_chat):
-        test_cases = [
-            ("unified api_key is mapped", {"api_key": "test-key-123"}, "test-key-123"),
-            ("legacy google_api_key still works", {"google_api_key": "legacy-key-456"}, "legacy-key-456"),
-            ("unified api_key takes precedence", {"api_key": "unified", "google_api_key": "legacy"}, "unified"),
-        ]
-
-        for msg, kwargs, expected_key in test_cases:
-            with self.subTest(msg=msg):
-                mock_chat.reset_mock()
-                client = GoogleClient("gemini-2.5-flash", **kwargs)
-                client.get_llm()
-                call_kwargs = mock_chat.call_args[1]
-                self.assertEqual(call_kwargs.get("google_api_key"), expected_key)
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_model_validation.py

@@ -1,52 +0,0 @@
-import unittest
-import warnings
-
-from tradingagents.llm_clients.base_client import BaseLLMClient
-from tradingagents.llm_clients.model_catalog import get_known_models
-from tradingagents.llm_clients.validators import validate_model
-
-
-class DummyLLMClient(BaseLLMClient):
-    def __init__(self, provider: str, model: str):
-        self.provider = provider
-        super().__init__(model)
-
-    def get_llm(self):
-        self.warn_if_unknown_model()
-        return object()
-
-    def validate_model(self) -> bool:
-        return validate_model(self.provider, self.model)
-
-
-class ModelValidationTests(unittest.TestCase):
-    def test_cli_catalog_models_are_all_validator_approved(self):
-        for provider, models in get_known_models().items():
-            if provider in ("ollama", "openrouter"):
-                continue
-
-            for model in models:
-                with self.subTest(provider=provider, model=model):
-                    self.assertTrue(validate_model(provider, model))
-
-    def test_unknown_model_emits_warning_for_strict_provider(self):
-        client = DummyLLMClient("openai", "not-a-real-openai-model")
-
-        with warnings.catch_warnings(record=True) as caught:
-            warnings.simplefilter("always")
-            client.get_llm()
-
-        self.assertEqual(len(caught), 1)
-        self.assertIn("not-a-real-openai-model", str(caught[0].message))
-        self.assertIn("openai", str(caught[0].message))
-
-    def test_openrouter_and_ollama_accept_custom_models_without_warning(self):
-        for provider in ("openrouter", "ollama"):
-            client = DummyLLMClient(provider, "custom-model-name")
-
-            with self.subTest(provider=provider):
-                with warnings.catch_warnings(record=True) as caught:
-                    warnings.simplefilter("always")
-                    client.get_llm()
-
-                self.assertEqual(caught, [])

tests/test_ticker_symbol_handling.py

@@ -1,18 +0,0 @@
-import unittest
-
-from cli.utils import normalize_ticker_symbol
-from tradingagents.agents.utils.agent_utils import build_instrument_context
-
-
-class TickerSymbolHandlingTests(unittest.TestCase):
-    def test_normalize_ticker_symbol_preserves_exchange_suffix(self):
-        self.assertEqual(normalize_ticker_symbol(" cnc.to "), "CNC.TO")
-
-    def test_build_instrument_context_mentions_exact_symbol(self):
-        context = build_instrument_context("7203.T")
-        self.assertIn("7203.T", context)
-        self.assertIn("exchange suffix", context)
-
-
-if __name__ == "__main__":
-    unittest.main()

tradingagents/__init__.py

@@ -1,2 +0,0 @@
-import os
-os.environ.setdefault("PYTHONUTF8", "1")

tradingagents/agents/__init__.py

@@ -15,7 +15,7 @@ from .risk_mgmt.conservative_debator import create_conservative_debator
 from .risk_mgmt.neutral_debator import create_neutral_debator
 
 from .managers.research_manager import create_research_manager
-from .managers.portfolio_manager import create_portfolio_manager
+from .managers.risk_manager import create_risk_manager
 
 from .trader.trader import create_trader
 
@@ -33,7 +33,7 @@ __all__ = [
     "create_neutral_debator",
     "create_news_analyst",
     "create_aggressive_debator",
-    "create_portfolio_manager",
+    "create_risk_manager",
     "create_conservative_debator",
     "create_social_media_analyst",
     "create_trader",

tradingagents/agents/analysts/fundamentals_analyst.py

@@ -1,20 +1,15 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from tradingagents.agents.utils.agent_utils import (
-    build_instrument_context,
-    get_balance_sheet,
-    get_cashflow,
-    get_fundamentals,
-    get_income_statement,
-    get_insider_transactions,
-    get_language_instruction,
-)
+import time
+import json
+from tradingagents.agents.utils.agent_utils import get_fundamentals, get_balance_sheet, get_cashflow, get_income_statement, get_insider_transactions
 from tradingagents.dataflows.config import get_config
 
 
 def create_fundamentals_analyst(llm):
     def fundamentals_analyst_node(state):
         current_date = state["trade_date"]
-        instrument_context = build_instrument_context(state["company_of_interest"])
+        ticker = state["company_of_interest"]
+        company_name = state["company_of_interest"]
 
         tools = [
             get_fundamentals,
@@ -24,10 +19,9 @@ def create_fundamentals_analyst(llm):
         ]
 
         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, and company financial history to gain a full view of the company's fundamental information to inform traders. Make sure to include as much detail as possible. Provide specific, actionable insights with supporting evidence to help traders make informed decisions."
+            "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, and company financial history 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 Markdown table at the end of the report to organize key points in the report, organized and easy to read."
-            + " Use the available tools: `get_fundamentals` for comprehensive company analysis, `get_balance_sheet`, `get_cashflow`, and `get_income_statement` for specific financial statements."
-            + get_language_instruction(),
+            + " Use the available tools: `get_fundamentals` for comprehensive company analysis, `get_balance_sheet`, `get_cashflow`, and `get_income_statement` for specific financial statements.",
         )
 
         prompt = ChatPromptTemplate.from_messages(
@@ -41,7 +35,7 @@ def create_fundamentals_analyst(llm):
                     " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
                     " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
                     " You have access to the following tools: {tool_names}.\n{system_message}"
-                    "For your reference, the current date is {current_date}. {instrument_context}",
+                    "For your reference, the current date is {current_date}. The company we want to look at is {ticker}",
                 ),
                 MessagesPlaceholder(variable_name="messages"),
             ]
@@ -50,7 +44,7 @@ def create_fundamentals_analyst(llm):
         prompt = prompt.partial(system_message=system_message)
         prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
         prompt = prompt.partial(current_date=current_date)
-        prompt = prompt.partial(instrument_context=instrument_context)
+        prompt = prompt.partial(ticker=ticker)
 
         chain = prompt | llm.bind_tools(tools)
 

tradingagents/agents/analysts/market_analyst.py

@@ -1,10 +1,7 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from tradingagents.agents.utils.agent_utils import (
-    build_instrument_context,
-    get_indicators,
-    get_language_instruction,
-    get_stock_data,
-)
+import time
+import json
+from tradingagents.agents.utils.agent_utils import get_stock_data, get_indicators
 from tradingagents.dataflows.config import get_config
 
 
@@ -12,7 +9,8 @@ def create_market_analyst(llm):
 
     def market_analyst_node(state):
         current_date = state["trade_date"]
-        instrument_context = build_instrument_context(state["company_of_interest"])
+        ticker = state["company_of_interest"]
+        company_name = state["company_of_interest"]
 
         tools = [
             get_stock_data,
@@ -44,9 +42,8 @@ Volatility Indicators:
 Volume-Based Indicators:
 - vwma: VWMA: A moving average weighted by volume. Usage: Confirm trends by integrating price action with volume data. Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses.
 
-- Select indicators that provide diverse and complementary information. Avoid redundancy (e.g., do not select both rsi and stochrsi). Also briefly explain why they are suitable for the given market context. When you tool call, please use the exact name of the indicators provided above as they are defined parameters, otherwise your call will fail. Please make sure to call get_stock_data first to retrieve the CSV that is needed to generate indicators. Then use get_indicators with the specific indicator names. Write a very detailed and nuanced report of the trends you observe. Provide specific, actionable insights with supporting evidence to help traders make informed decisions."""
+- Select indicators that provide diverse and complementary information. Avoid redundancy (e.g., do not select both rsi and stochrsi). Also briefly explain why they are suitable for the given market context. When you tool call, please use the exact name of the indicators provided above as they are defined parameters, otherwise your call will fail. Please make sure to call get_stock_data first to retrieve the CSV that is needed to generate indicators. Then use get_indicators with the specific indicator names. Write a very detailed and nuanced report of the trends you observe. 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 Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
-            + get_language_instruction()
         )
 
         prompt = ChatPromptTemplate.from_messages(
@@ -60,7 +57,7 @@ Volume-Based Indicators:
                     " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
                     " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
                     " You have access to the following tools: {tool_names}.\n{system_message}"
-                    "For your reference, the current date is {current_date}. {instrument_context}",
+                    "For your reference, the current date is {current_date}. The company we want to look at is {ticker}",
                 ),
                 MessagesPlaceholder(variable_name="messages"),
             ]
@@ -69,7 +66,7 @@ Volume-Based Indicators:
         prompt = prompt.partial(system_message=system_message)
         prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
         prompt = prompt.partial(current_date=current_date)
-        prompt = prompt.partial(instrument_context=instrument_context)
+        prompt = prompt.partial(ticker=ticker)
 
         chain = prompt | llm.bind_tools(tools)
 

tradingagents/agents/analysts/news_analyst.py

@@ -1,17 +1,14 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from tradingagents.agents.utils.agent_utils import (
-    build_instrument_context,
-    get_global_news,
-    get_language_instruction,
-    get_news,
-)
+import time
+import json
+from tradingagents.agents.utils.agent_utils import get_news, get_global_news
 from tradingagents.dataflows.config import get_config
 
 
 def create_news_analyst(llm):
     def news_analyst_node(state):
         current_date = state["trade_date"]
-        instrument_context = build_instrument_context(state["company_of_interest"])
+        ticker = state["company_of_interest"]
 
         tools = [
             get_news,
@@ -19,9 +16,8 @@ def create_news_analyst(llm):
         ]
 
         system_message = (
-            "You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Use the available tools: get_news(query, start_date, end_date) for company-specific or targeted news searches, and get_global_news(curr_date, look_back_days, limit) for broader macroeconomic news. Provide specific, actionable insights with supporting evidence to help traders make informed decisions."
+            "You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Use the available tools: get_news(query, start_date, end_date) for company-specific or targeted news searches, and get_global_news(curr_date, look_back_days, limit) for broader macroeconomic news. 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 Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
-            + get_language_instruction()
         )
 
         prompt = ChatPromptTemplate.from_messages(
@@ -35,7 +31,7 @@ def create_news_analyst(llm):
                     " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
                     " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
                     " You have access to the following tools: {tool_names}.\n{system_message}"
-                    "For your reference, the current date is {current_date}. {instrument_context}",
+                    "For your reference, the current date is {current_date}. We are looking at the company {ticker}",
                 ),
                 MessagesPlaceholder(variable_name="messages"),
             ]
@@ -44,7 +40,7 @@ def create_news_analyst(llm):
         prompt = prompt.partial(system_message=system_message)
         prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
         prompt = prompt.partial(current_date=current_date)
-        prompt = prompt.partial(instrument_context=instrument_context)
+        prompt = prompt.partial(ticker=ticker)
 
         chain = prompt | llm.bind_tools(tools)
         result = chain.invoke(state["messages"])

tradingagents/agents/analysts/social_media_analyst.py

@@ -1,21 +1,23 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-from tradingagents.agents.utils.agent_utils import build_instrument_context, get_language_instruction, get_news
+import time
+import json
+from tradingagents.agents.utils.agent_utils import get_news
 from tradingagents.dataflows.config import get_config
 
 
 def create_social_media_analyst(llm):
     def social_media_analyst_node(state):
         current_date = state["trade_date"]
-        instrument_context = build_instrument_context(state["company_of_interest"])
+        ticker = state["company_of_interest"]
+        company_name = state["company_of_interest"]
 
         tools = [
             get_news,
         ]
 
         system_message = (
-            "You are a social media and company specific news researcher/analyst tasked with analyzing social media posts, recent company news, and public sentiment for a specific company over the past week. You will be given a company's name your objective is to write a comprehensive long report detailing your analysis, insights, and implications for traders and investors on this company's current state after looking at social media and what people are saying about that company, analyzing sentiment data of what people feel each day about the company, and looking at recent company news. Use the get_news(query, start_date, end_date) tool to search for company-specific news and social media discussions. Try to look at all sources possible from social media to sentiment to news. Provide specific, actionable insights with supporting evidence to help traders make informed decisions."
-            + """ 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."""
-            + get_language_instruction()
+            "You are a social media and company specific news researcher/analyst tasked with analyzing social media posts, recent company news, and public sentiment for a specific company over the past week. You will be given a company's name your objective is to write a comprehensive long report detailing your analysis, insights, and implications for traders and investors on this company's current state after looking at social media and what people are saying about that company, analyzing sentiment data of what people feel each day about the company, and looking at recent company news. Use the get_news(query, start_date, end_date) tool to search for company-specific news and social media discussions. Try to look at all sources possible from social media to sentiment to news. 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 Markdown table at the end of the report to organize key points in the report, organized and easy to read.""",
         )
 
         prompt = ChatPromptTemplate.from_messages(
@@ -29,7 +31,7 @@ def create_social_media_analyst(llm):
                     " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
                     " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
                     " You have access to the following tools: {tool_names}.\n{system_message}"
-                    "For your reference, the current date is {current_date}. {instrument_context}",
+                    "For your reference, the current date is {current_date}. The current company we want to analyze is {ticker}",
                 ),
                 MessagesPlaceholder(variable_name="messages"),
             ]
@@ -38,7 +40,7 @@ def create_social_media_analyst(llm):
         prompt = prompt.partial(system_message=system_message)
         prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
         prompt = prompt.partial(current_date=current_date)
-        prompt = prompt.partial(instrument_context=instrument_context)
+        prompt = prompt.partial(ticker=ticker)
 
         chain = prompt | llm.bind_tools(tools)
 

tradingagents/agents/managers/portfolio_manager.py

@@ -1,77 +0,0 @@
-from tradingagents.agents.utils.agent_utils import build_instrument_context, get_language_instruction
-
-
-def create_portfolio_manager(llm, memory):
-    def portfolio_manager_node(state) -> dict:
-
-        instrument_context = build_instrument_context(state["company_of_interest"])
-
-        history = state["risk_debate_state"]["history"]
-        risk_debate_state = state["risk_debate_state"]
-        market_research_report = state["market_report"]
-        news_report = state["news_report"]
-        fundamentals_report = state["fundamentals_report"]
-        sentiment_report = state["sentiment_report"]
-        research_plan = state["investment_plan"]
-        trader_plan = state["trader_investment_plan"]
-
-        curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
-        past_memories = memory.get_memories(curr_situation, n_matches=2)
-
-        past_memory_str = ""
-        for i, rec in enumerate(past_memories, 1):
-            past_memory_str += rec["recommendation"] + "\n\n"
-
-        prompt = f"""As the Portfolio Manager, synthesize the risk analysts' debate and deliver the final trading decision.
-
-{instrument_context}
-
----
-
-**Rating Scale** (use exactly one):
-- **Buy**: Strong conviction to enter or add to position
-- **Overweight**: Favorable outlook, gradually increase exposure
-- **Hold**: Maintain current position, no action needed
-- **Underweight**: Reduce exposure, take partial profits
-- **Sell**: Exit position or avoid entry
-
-**Context:**
-- Research Manager's investment plan: **{research_plan}**
-- Trader's transaction proposal: **{trader_plan}**
-- Lessons from past decisions: **{past_memory_str}**
-
-**Required Output Structure:**
-1. **Rating**: State one of Buy / Overweight / Hold / Underweight / Sell.
-2. **Executive Summary**: A concise action plan covering entry strategy, position sizing, key risk levels, and time horizon.
-3. **Investment Thesis**: Detailed reasoning anchored in the analysts' debate and past reflections.
-
----
-
-**Risk Analysts Debate History:**
-{history}
-
----
-
-Be decisive and ground every conclusion in specific evidence from the analysts.{get_language_instruction()}"""
-
-        response = llm.invoke(prompt)
-
-        new_risk_debate_state = {
-            "judge_decision": response.content,
-            "history": risk_debate_state["history"],
-            "aggressive_history": risk_debate_state["aggressive_history"],
-            "conservative_history": risk_debate_state["conservative_history"],
-            "neutral_history": risk_debate_state["neutral_history"],
-            "latest_speaker": "Judge",
-            "current_aggressive_response": risk_debate_state["current_aggressive_response"],
-            "current_conservative_response": risk_debate_state["current_conservative_response"],
-            "current_neutral_response": risk_debate_state["current_neutral_response"],
-            "count": risk_debate_state["count"],
-        }
-
-        return {
-            "risk_debate_state": new_risk_debate_state,
-            "final_trade_decision": response.content,
-        }
-
-    return portfolio_manager_node

tradingagents/agents/managers/research_manager.py

@@ -1,10 +1,9 @@
-
-from tradingagents.agents.utils.agent_utils import build_instrument_context
+import time
+import json
 
 
 def create_research_manager(llm, memory):
     def research_manager_node(state) -> dict:
-        instrument_context = build_instrument_context(state["company_of_interest"])
         history = state["investment_debate_state"].get("history", "")
         market_research_report = state["market_report"]
         sentiment_report = state["sentiment_report"]
@@ -34,8 +33,6 @@ Take into account your past mistakes on similar situations. Use these insights t
 Here are your past reflections on mistakes:
 \"{past_memory_str}\"
 
-{instrument_context}
-
 Here is the debate:
 Debate History:
 {history}"""

tradingagents/agents/managers/risk_manager.py

@@ -0,0 +1,66 @@
+import time
+import json
+
+
+def create_risk_manager(llm, memory):
+    def risk_manager_node(state) -> dict:
+
+        company_name = state["company_of_interest"]
+
+        history = state["risk_debate_state"]["history"]
+        risk_debate_state = state["risk_debate_state"]
+        market_research_report = state["market_report"]
+        news_report = state["news_report"]
+        fundamentals_report = state["news_report"]
+        sentiment_report = state["sentiment_report"]
+        trader_plan = state["investment_plan"]
+
+        curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
+        past_memories = memory.get_memories(curr_situation, n_matches=2)
+
+        past_memory_str = ""
+        for i, rec in enumerate(past_memories, 1):
+            past_memory_str += rec["recommendation"] + "\n\n"
+
+        prompt = f"""As the Risk Management Judge and Debate Facilitator, your goal is to evaluate the debate between three risk analysts—Aggressive, Neutral, and Conservative—and determine the best course of action for the trader. Your decision must result in a clear recommendation: Buy, Sell, or Hold. Choose Hold only if strongly justified by specific arguments, not as a fallback when all sides seem valid. Strive for clarity and decisiveness.
+
+Guidelines for Decision-Making:
+1. **Summarize Key Arguments**: Extract the strongest points from each analyst, focusing on relevance to the context.
+2. **Provide Rationale**: Support your recommendation with direct quotes and counterarguments from the debate.
+3. **Refine the Trader's Plan**: Start with the trader's original plan, **{trader_plan}**, and adjust it based on the analysts' insights.
+4. **Learn from Past Mistakes**: Use lessons from **{past_memory_str}** to address prior misjudgments and improve the decision you are making now to make sure you don't make a wrong BUY/SELL/HOLD call that loses money.
+
+Deliverables:
+- A clear and actionable recommendation: Buy, Sell, or Hold.
+- Detailed reasoning anchored in the debate and past reflections.
+
+---
+
+**Analysts Debate History:**  
+{history}
+
+---
+
+Focus on actionable insights and continuous improvement. Build on past lessons, critically evaluate all perspectives, and ensure each decision advances better outcomes."""
+
+        response = llm.invoke(prompt)
+
+        new_risk_debate_state = {
+            "judge_decision": response.content,
+            "history": risk_debate_state["history"],
+            "aggressive_history": risk_debate_state["aggressive_history"],
+            "conservative_history": risk_debate_state["conservative_history"],
+            "neutral_history": risk_debate_state["neutral_history"],
+            "latest_speaker": "Judge",
+            "current_aggressive_response": risk_debate_state["current_aggressive_response"],
+            "current_conservative_response": risk_debate_state["current_conservative_response"],
+            "current_neutral_response": risk_debate_state["current_neutral_response"],
+            "count": risk_debate_state["count"],
+        }
+
+        return {
+            "risk_debate_state": new_risk_debate_state,
+            "final_trade_decision": response.content,
+        }
+
+    return risk_manager_node

tradingagents/agents/researchers/bear_researcher.py

@@ -1,3 +1,6 @@
+from langchain_core.messages import AIMessage
+import time
+import json
 
 
 def create_bear_researcher(llm, memory):

tradingagents/agents/researchers/bull_researcher.py

@@ -1,3 +1,6 @@
+from langchain_core.messages import AIMessage
+import time
+import json
 
 
 def create_bull_researcher(llm, memory):

tradingagents/agents/risk_mgmt/aggressive_debator.py

@@ -1,3 +1,5 @@
+import time
+import json
 
 
 def create_aggressive_debator(llm):
@@ -26,7 +28,7 @@ Market Research Report: {market_research_report}
 Social Media Sentiment Report: {sentiment_report}
 Latest World Affairs Report: {news_report}
 Company Fundamentals Report: {fundamentals_report}
-Here is the current conversation history: {history} Here are the last arguments from the conservative analyst: {current_conservative_response} Here are the last arguments from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints yet, present your own argument based on the available data.
+Here is the current conversation history: {history} Here are the last arguments from the conservative analyst: {current_conservative_response} Here are the last arguments from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints, do not hallucinate and just present your point.
 
 Engage actively by addressing any specific concerns raised, refuting the weaknesses in their logic, and asserting the benefits of risk-taking to outpace market norms. Maintain a focus on debating and persuading, not just presenting data. Challenge each counterpoint to underscore why a high-risk approach is optimal. Output conversationally as if you are speaking without any special formatting."""
 

tradingagents/agents/risk_mgmt/conservative_debator.py

@@ -1,3 +1,6 @@
+from langchain_core.messages import AIMessage
+import time
+import json
 
 
 def create_conservative_debator(llm):
@@ -26,7 +29,7 @@ Market Research Report: {market_research_report}
 Social Media Sentiment Report: {sentiment_report}
 Latest World Affairs Report: {news_report}
 Company Fundamentals Report: {fundamentals_report}
-Here is the current conversation history: {history} Here is the last response from the aggressive analyst: {current_aggressive_response} Here is the last response from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints yet, present your own argument based on the available data.
+Here is the current conversation history: {history} Here is the last response from the aggressive analyst: {current_aggressive_response} Here is the last response from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints, do not hallucinate and just present your point.
 
 Engage by questioning their optimism and emphasizing the potential downsides they may have overlooked. Address each of their counterpoints to showcase why a conservative stance is ultimately the safest path for the firm's assets. Focus on debating and critiquing their arguments to demonstrate the strength of a low-risk strategy over their approaches. Output conversationally as if you are speaking without any special formatting."""
 

tradingagents/agents/risk_mgmt/neutral_debator.py

@@ -1,3 +1,5 @@
+import time
+import json
 
 
 def create_neutral_debator(llm):
@@ -26,7 +28,7 @@ Market Research Report: {market_research_report}
 Social Media Sentiment Report: {sentiment_report}
 Latest World Affairs Report: {news_report}
 Company Fundamentals Report: {fundamentals_report}
-Here is the current conversation history: {history} Here is the last response from the aggressive analyst: {current_aggressive_response} Here is the last response from the conservative analyst: {current_conservative_response}. If there are no responses from the other viewpoints yet, present your own argument based on the available data.
+Here is the current conversation history: {history} Here is the last response from the aggressive analyst: {current_aggressive_response} Here is the last response from the conservative analyst: {current_conservative_response}. If there are no responses from the other viewpoints, do not hallucinate and just present your point.
 
 Engage actively by analyzing both sides critically, addressing weaknesses in the aggressive and conservative arguments to advocate for a more balanced approach. Challenge each of their points to illustrate why a moderate risk strategy might offer the best of both worlds, providing growth potential while safeguarding against extreme volatility. Focus on debating rather than simply presenting data, aiming to show that a balanced view can lead to the most reliable outcomes. Output conversationally as if you are speaking without any special formatting."""
 

tradingagents/agents/trader/trader.py

@@ -1,12 +1,11 @@
 import functools
-
-from tradingagents.agents.utils.agent_utils import build_instrument_context
+import time
+import json
 
 
 def create_trader(llm, memory):
     def trader_node(state, name):
         company_name = state["company_of_interest"]
-        instrument_context = build_instrument_context(company_name)
         investment_plan = state["investment_plan"]
         market_research_report = state["market_report"]
         sentiment_report = state["sentiment_report"]
@@ -25,13 +24,13 @@ def create_trader(llm, memory):
 
         context = {
             "role": "user",
-            "content": f"Based on a comprehensive analysis by a team of analysts, here is an investment plan tailored for {company_name}. {instrument_context} This plan incorporates insights from current technical market trends, macroeconomic indicators, and social media sentiment. Use this plan as a foundation for evaluating your next trading decision.\n\nProposed Investment Plan: {investment_plan}\n\nLeverage these insights to make an informed and strategic decision.",
+            "content": f"Based on a comprehensive analysis by a team of analysts, here is an investment plan tailored for {company_name}. This plan incorporates insights from current technical market trends, macroeconomic indicators, and social media sentiment. Use this plan as a foundation for evaluating your next trading decision.\n\nProposed Investment Plan: {investment_plan}\n\nLeverage these insights to make an informed and strategic decision.",
         }
 
         messages = [
             {
                 "role": "system",
-                "content": f"""You are a trading agent analyzing market data to make investment decisions. Based on your analysis, provide a specific recommendation to buy, sell, or hold. End with a firm decision and always conclude your response with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**' to confirm your recommendation. Apply lessons from past decisions to strengthen your analysis. Here are reflections from similar situations you traded in and the lessons learned: {past_memory_str}""",
+                "content": f"""You are a trading agent analyzing market data to make investment decisions. Based on your analysis, provide a specific recommendation to buy, sell, or hold. End with a firm decision and always conclude your response with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**' to confirm your recommendation. Do not forget to utilize lessons from past decisions to learn from your mistakes. Here is some reflections from similar situatiosn you traded in and the lessons learned: {past_memory_str}""",
             },
             context,
         ]

tradingagents/agents/utils/agent_states.py

@@ -1,6 +1,10 @@
-from typing import Annotated
-from typing_extensions import TypedDict
-from langgraph.graph import MessagesState
+from typing import Annotated, Sequence
+from datetime import date, timedelta, datetime
+from typing_extensions import TypedDict, Optional
+from langchain_openai import ChatOpenAI
+from tradingagents.agents import *
+from langgraph.prebuilt import ToolNode
+from langgraph.graph import END, StateGraph, START, MessagesState
 
 
 # Researcher team state

tradingagents/agents/utils/agent_utils.py

@@ -19,29 +19,6 @@ from tradingagents.agents.utils.news_data_tools import (
     get_global_news
 )
 
-
-def get_language_instruction() -> str:
-    """Return a prompt instruction for the configured output language.
-
-    Returns empty string when English (default), so no extra tokens are used.
-    Only applied to user-facing agents (analysts, portfolio manager).
-    Internal debate agents stay in English for reasoning quality.
-    """
-    from tradingagents.dataflows.config import get_config
-    lang = get_config().get("output_language", "English")
-    if lang.strip().lower() == "english":
-        return ""
-    return f" Write your entire response in {lang}."
-
-
-def build_instrument_context(ticker: str) -> str:
-    """Describe the exact instrument so agents preserve exchange-qualified tickers."""
-    return (
-        f"The instrument to analyze is `{ticker}`. "
-        "Use this exact ticker in every tool call, report, and recommendation, "
-        "preserving any exchange suffix (e.g. `.TO`, `.L`, `.HK`, `.T`)."
-    )
-
 def create_msg_delete():
     def delete_messages(state):
         """Clear messages and add placeholder for Anthropic compatibility"""
@@ -58,4 +35,4 @@ def create_msg_delete():
     return delete_messages
 
 
-        
+        

tradingagents/agents/utils/memory.py

@@ -78,7 +78,7 @@ class FinancialSituationMemory:
 
         # Build results
         results = []
-        max_score = float(scores.max()) if len(scores) > 0 and scores.max() > 0 else 1.0
+        max_score = max(scores) if max(scores) > 0 else 1  # Normalize scores
 
         for idx in top_indices:
             # Normalize score to 0-1 range for consistency

tradingagents/agents/utils/technical_indicators_tools.py

@@ -10,23 +10,14 @@ def get_indicators(
     look_back_days: Annotated[int, "how many days to look back"] = 30,
 ) -> str:
     """
-    Retrieve a single technical indicator for a given ticker symbol.
+    Retrieve technical indicators for a given ticker symbol.
     Uses the configured technical_indicators vendor.
     Args:
         symbol (str): Ticker symbol of the company, e.g. AAPL, TSM
-        indicator (str): A single technical indicator name, e.g. 'rsi', 'macd'. Call this tool once per indicator.
+        indicator (str): Technical indicator to get the analysis and report of
         curr_date (str): The current trading date you are trading on, YYYY-mm-dd
         look_back_days (int): How many days to look back, default is 30
     Returns:
         str: A formatted dataframe containing the technical indicators for the specified ticker symbol and indicator.
     """
-    # LLMs sometimes pass multiple indicators as a comma-separated string;
-    # split and process each individually.
-    indicators = [i.strip().lower() for i in indicator.split(",") if i.strip()]
-    results = []
-    for ind in indicators:
-        try:
-            results.append(route_to_vendor("get_indicators", symbol, ind, curr_date, look_back_days))
-        except ValueError as e:
-            results.append(str(e))
-    return "\n\n".join(results)
+    return route_to_vendor("get_indicators", symbol, indicator, curr_date, look_back_days)

tradingagents/dataflows/alpha_vantage_fundamentals.py

@@ -1,23 +1,6 @@
 from .alpha_vantage_common import _make_api_request
 
 
-def _filter_reports_by_date(result, curr_date: str):
-    """Filter annualReports/quarterlyReports to exclude entries after curr_date.
-
-    Prevents look-ahead bias by removing fiscal periods that end after
-    the simulation's current date.
-    """
-    if not curr_date or not isinstance(result, dict):
-        return result
-    for key in ("annualReports", "quarterlyReports"):
-        if key in result:
-            result[key] = [
-                r for r in result[key]
-                if r.get("fiscalDateEnding", "") <= curr_date
-            ]
-    return result
-
-
 def get_fundamentals(ticker: str, curr_date: str = None) -> str:
     """
     Retrieve comprehensive fundamental data for a given ticker symbol using Alpha Vantage.
@@ -36,20 +19,59 @@ def get_fundamentals(ticker: str, curr_date: str = None) -> str:
     return _make_api_request("OVERVIEW", params)
 
 
-def get_balance_sheet(ticker: str, freq: str = "quarterly", curr_date: str = None):
-    """Retrieve balance sheet data for a given ticker symbol using Alpha Vantage."""
-    result = _make_api_request("BALANCE_SHEET", {"symbol": ticker})
-    return _filter_reports_by_date(result, curr_date)
+def get_balance_sheet(ticker: str, freq: str = "quarterly", curr_date: str = None) -> str:
+    """
+    Retrieve balance sheet data for a given ticker symbol using Alpha Vantage.
+
+    Args:
+        ticker (str): Ticker symbol of the company
+        freq (str): Reporting frequency: annual/quarterly (default quarterly) - not used for Alpha Vantage
+        curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage)
+
+    Returns:
+        str: Balance sheet data with normalized fields
+    """
+    params = {
+        "symbol": ticker,
+    }
+
+    return _make_api_request("BALANCE_SHEET", params)
 
 
-def get_cashflow(ticker: str, freq: str = "quarterly", curr_date: str = None):
-    """Retrieve cash flow statement data for a given ticker symbol using Alpha Vantage."""
-    result = _make_api_request("CASH_FLOW", {"symbol": ticker})
-    return _filter_reports_by_date(result, curr_date)
+def get_cashflow(ticker: str, freq: str = "quarterly", curr_date: str = None) -> str:
+    """
+    Retrieve cash flow statement data for a given ticker symbol using Alpha Vantage.
+
+    Args:
+        ticker (str): Ticker symbol of the company
+        freq (str): Reporting frequency: annual/quarterly (default quarterly) - not used for Alpha Vantage
+        curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage)
+
+    Returns:
+        str: Cash flow statement data with normalized fields
+    """
+    params = {
+        "symbol": ticker,
+    }
+
+    return _make_api_request("CASH_FLOW", params)
 
 
-def get_income_statement(ticker: str, freq: str = "quarterly", curr_date: str = None):
-    """Retrieve income statement data for a given ticker symbol using Alpha Vantage."""
-    result = _make_api_request("INCOME_STATEMENT", {"symbol": ticker})
-    return _filter_reports_by_date(result, curr_date)
+def get_income_statement(ticker: str, freq: str = "quarterly", curr_date: str = None) -> str:
+    """
+    Retrieve income statement data for a given ticker symbol using Alpha Vantage.
+
+    Args:
+        ticker (str): Ticker symbol of the company
+        freq (str): Reporting frequency: annual/quarterly (default quarterly) - not used for Alpha Vantage
+        curr_date (str): Current date you are trading at, yyyy-mm-dd (not used for Alpha Vantage)
+
+    Returns:
+        str: Income statement data with normalized fields
+    """
+    params = {
+        "symbol": ticker,
+    }
+
+    return _make_api_request("INCOME_STATEMENT", params)
 

tradingagents/dataflows/stockstats_utils.py

@@ -1,106 +1,10 @@
-import time
-import logging
-
 import pandas as pd
 import yfinance as yf
-from yfinance.exceptions import YFRateLimitError
 from stockstats import wrap
 from typing import Annotated
 import os
 from .config import get_config
 
-logger = logging.getLogger(__name__)
-
-
-def yf_retry(func, max_retries=3, base_delay=2.0):
-    """Execute a yfinance call with exponential backoff on rate limits.
-
-    yfinance raises YFRateLimitError on HTTP 429 responses but does not
-    retry them internally. This wrapper adds retry logic specifically
-    for rate limits. Other exceptions propagate immediately.
-    """
-    for attempt in range(max_retries + 1):
-        try:
-            return func()
-        except YFRateLimitError:
-            if attempt < max_retries:
-                delay = base_delay * (2 ** attempt)
-                logger.warning(f"Yahoo Finance rate limited, retrying in {delay:.0f}s (attempt {attempt + 1}/{max_retries})")
-                time.sleep(delay)
-            else:
-                raise
-
-
-def _clean_dataframe(data: pd.DataFrame) -> pd.DataFrame:
-    """Normalize a stock DataFrame for stockstats: parse dates, drop invalid rows, fill price gaps."""
-    data["Date"] = pd.to_datetime(data["Date"], errors="coerce")
-    data = data.dropna(subset=["Date"])
-
-    price_cols = [c for c in ["Open", "High", "Low", "Close", "Volume"] if c in data.columns]
-    data[price_cols] = data[price_cols].apply(pd.to_numeric, errors="coerce")
-    data = data.dropna(subset=["Close"])
-    data[price_cols] = data[price_cols].ffill().bfill()
-
-    return data
-
-
-def load_ohlcv(symbol: str, curr_date: str) -> pd.DataFrame:
-    """Fetch OHLCV data with caching, filtered to prevent look-ahead bias.
-
-    Downloads 15 years of data up to today and caches per symbol. On
-    subsequent calls the cache is reused. Rows after curr_date are
-    filtered out so backtests never see future prices.
-    """
-    config = get_config()
-    curr_date_dt = pd.to_datetime(curr_date)
-
-    # Cache uses a fixed window (15y to today) so one file per symbol
-    today_date = pd.Timestamp.today()
-    start_date = today_date - pd.DateOffset(years=5)
-    start_str = start_date.strftime("%Y-%m-%d")
-    end_str = today_date.strftime("%Y-%m-%d")
-
-    os.makedirs(config["data_cache_dir"], exist_ok=True)
-    data_file = os.path.join(
-        config["data_cache_dir"],
-        f"{symbol}-YFin-data-{start_str}-{end_str}.csv",
-    )
-
-    if os.path.exists(data_file):
-        data = pd.read_csv(data_file, on_bad_lines="skip")
-    else:
-        data = yf_retry(lambda: yf.download(
-            symbol,
-            start=start_str,
-            end=end_str,
-            multi_level_index=False,
-            progress=False,
-            auto_adjust=True,
-        ))
-        data = data.reset_index()
-        data.to_csv(data_file, index=False)
-
-    data = _clean_dataframe(data)
-
-    # Filter to curr_date to prevent look-ahead bias in backtesting
-    data = data[data["Date"] <= curr_date_dt]
-
-    return data
-
-
-def filter_financials_by_date(data: pd.DataFrame, curr_date: str) -> pd.DataFrame:
-    """Drop financial statement columns (fiscal period timestamps) after curr_date.
-
-    yfinance financial statements use fiscal period end dates as columns.
-    Columns after curr_date represent future data and are removed to
-    prevent look-ahead bias.
-    """
-    if not curr_date or data.empty:
-        return data
-    cutoff = pd.Timestamp(curr_date)
-    mask = pd.to_datetime(data.columns, errors="coerce") <= cutoff
-    return data.loc[:, mask]
-
 
 class StockstatsUtils:
     @staticmethod
@@ -113,10 +17,42 @@ class StockstatsUtils:
             str, "curr date for retrieving stock price data, YYYY-mm-dd"
         ],
     ):
-        data = load_ohlcv(symbol, curr_date)
+        config = get_config()
+
+        today_date = pd.Timestamp.today()
+        curr_date_dt = pd.to_datetime(curr_date)
+
+        end_date = today_date
+        start_date = today_date - pd.DateOffset(years=15)
+        start_date_str = start_date.strftime("%Y-%m-%d")
+        end_date_str = end_date.strftime("%Y-%m-%d")
+
+        # Ensure cache directory exists
+        os.makedirs(config["data_cache_dir"], exist_ok=True)
+
+        data_file = os.path.join(
+            config["data_cache_dir"],
+            f"{symbol}-YFin-data-{start_date_str}-{end_date_str}.csv",
+        )
+
+        if os.path.exists(data_file):
+            data = pd.read_csv(data_file)
+            data["Date"] = pd.to_datetime(data["Date"])
+        else:
+            data = yf.download(
+                symbol,
+                start=start_date_str,
+                end=end_date_str,
+                multi_level_index=False,
+                progress=False,
+                auto_adjust=True,
+            )
+            data = data.reset_index()
+            data.to_csv(data_file, index=False)
+
         df = wrap(data)
         df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
-        curr_date_str = pd.to_datetime(curr_date).strftime("%Y-%m-%d")
+        curr_date_str = curr_date_dt.strftime("%Y-%m-%d")
 
         df[indicator]  # trigger stockstats to calculate the indicator
         matching_rows = df[df["Date"].str.startswith(curr_date_str)]

tradingagents/dataflows/y_finance.py

@@ -1,10 +1,9 @@
 from typing import Annotated
 from datetime import datetime
 from dateutil.relativedelta import relativedelta
-import pandas as pd
 import yfinance as yf
 import os
-from .stockstats_utils import StockstatsUtils, _clean_dataframe, yf_retry, load_ohlcv, filter_financials_by_date
+from .stockstats_utils import StockstatsUtils
 
 def get_YFin_data_online(
     symbol: Annotated[str, "ticker symbol of the company"],
@@ -19,7 +18,7 @@ def get_YFin_data_online(
     ticker = yf.Ticker(symbol.upper())
 
     # Fetch historical data for the specified date range
-    data = yf_retry(lambda: ticker.history(start=start_date, end=end_date))
+    data = ticker.history(start=start_date, end=end_date)
 
     # Check if data is empty
     if data.empty:
@@ -195,11 +194,60 @@ def _get_stock_stats_bulk(
     Fetches data once and calculates indicator for all available dates.
     Returns dict mapping date strings to indicator values.
     """
+    from .config import get_config
+    import pandas as pd
     from stockstats import wrap
-
-    data = load_ohlcv(symbol, curr_date)
-    df = wrap(data)
-    df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
+    import os
+    
+    config = get_config()
+    online = config["data_vendors"]["technical_indicators"] != "local"
+    
+    if not online:
+        # Local data path
+        try:
+            data = pd.read_csv(
+                os.path.join(
+                    config.get("data_cache_dir", "data"),
+                    f"{symbol}-YFin-data-2015-01-01-2025-03-25.csv",
+                )
+            )
+            df = wrap(data)
+        except FileNotFoundError:
+            raise Exception("Stockstats fail: Yahoo Finance data not fetched yet!")
+    else:
+        # Online data fetching with caching
+        today_date = pd.Timestamp.today()
+        curr_date_dt = pd.to_datetime(curr_date)
+        
+        end_date = today_date
+        start_date = today_date - pd.DateOffset(years=15)
+        start_date_str = start_date.strftime("%Y-%m-%d")
+        end_date_str = end_date.strftime("%Y-%m-%d")
+        
+        os.makedirs(config["data_cache_dir"], exist_ok=True)
+        
+        data_file = os.path.join(
+            config["data_cache_dir"],
+            f"{symbol}-YFin-data-{start_date_str}-{end_date_str}.csv",
+        )
+        
+        if os.path.exists(data_file):
+            data = pd.read_csv(data_file)
+            data["Date"] = pd.to_datetime(data["Date"])
+        else:
+            data = yf.download(
+                symbol,
+                start=start_date_str,
+                end=end_date_str,
+                multi_level_index=False,
+                progress=False,
+                auto_adjust=True,
+            )
+            data = data.reset_index()
+            data.to_csv(data_file, index=False)
+        
+        df = wrap(data)
+        df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
     
     # Calculate the indicator for all rows at once
     df[indicator]  # This triggers stockstats to calculate the indicator
@@ -252,7 +300,7 @@ def get_fundamentals(
     """Get company fundamentals overview from yfinance."""
     try:
         ticker_obj = yf.Ticker(ticker.upper())
-        info = yf_retry(lambda: ticker_obj.info)
+        info = ticker_obj.info
 
         if not info:
             return f"No fundamentals data found for symbol '{ticker}'"
@@ -305,19 +353,17 @@ def get_fundamentals(
 def get_balance_sheet(
     ticker: Annotated[str, "ticker symbol of the company"],
     freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly",
-    curr_date: Annotated[str, "current date in YYYY-MM-DD format"] = None
+    curr_date: Annotated[str, "current date (not used for yfinance)"] = None
 ):
     """Get balance sheet data from yfinance."""
     try:
         ticker_obj = yf.Ticker(ticker.upper())
-
+        
         if freq.lower() == "quarterly":
-            data = yf_retry(lambda: ticker_obj.quarterly_balance_sheet)
+            data = ticker_obj.quarterly_balance_sheet
         else:
-            data = yf_retry(lambda: ticker_obj.balance_sheet)
-
-        data = filter_financials_by_date(data, curr_date)
-
+            data = ticker_obj.balance_sheet
+            
         if data.empty:
             return f"No balance sheet data found for symbol '{ticker}'"
             
@@ -337,19 +383,17 @@ def get_balance_sheet(
 def get_cashflow(
     ticker: Annotated[str, "ticker symbol of the company"],
     freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly",
-    curr_date: Annotated[str, "current date in YYYY-MM-DD format"] = None
+    curr_date: Annotated[str, "current date (not used for yfinance)"] = None
 ):
     """Get cash flow data from yfinance."""
     try:
         ticker_obj = yf.Ticker(ticker.upper())
-
+        
         if freq.lower() == "quarterly":
-            data = yf_retry(lambda: ticker_obj.quarterly_cashflow)
+            data = ticker_obj.quarterly_cashflow
         else:
-            data = yf_retry(lambda: ticker_obj.cashflow)
-
-        data = filter_financials_by_date(data, curr_date)
-
+            data = ticker_obj.cashflow
+            
         if data.empty:
             return f"No cash flow data found for symbol '{ticker}'"
             
@@ -369,19 +413,17 @@ def get_cashflow(
 def get_income_statement(
     ticker: Annotated[str, "ticker symbol of the company"],
     freq: Annotated[str, "frequency of data: 'annual' or 'quarterly'"] = "quarterly",
-    curr_date: Annotated[str, "current date in YYYY-MM-DD format"] = None
+    curr_date: Annotated[str, "current date (not used for yfinance)"] = None
 ):
     """Get income statement data from yfinance."""
     try:
         ticker_obj = yf.Ticker(ticker.upper())
-
+        
         if freq.lower() == "quarterly":
-            data = yf_retry(lambda: ticker_obj.quarterly_income_stmt)
+            data = ticker_obj.quarterly_income_stmt
         else:
-            data = yf_retry(lambda: ticker_obj.income_stmt)
-
-        data = filter_financials_by_date(data, curr_date)
-
+            data = ticker_obj.income_stmt
+            
         if data.empty:
             return f"No income statement data found for symbol '{ticker}'"
             
@@ -404,7 +446,7 @@ def get_insider_transactions(
     """Get insider transactions data from yfinance."""
     try:
         ticker_obj = yf.Ticker(ticker.upper())
-        data = yf_retry(lambda: ticker_obj.insider_transactions)
+        data = ticker_obj.insider_transactions
         
         if data is None or data.empty:
             return f"No insider transactions data found for symbol '{ticker}'"

tradingagents/dataflows/yfinance_news.py

@@ -4,8 +4,6 @@ import yfinance as yf
 from datetime import datetime
 from dateutil.relativedelta import relativedelta
 
-from .stockstats_utils import yf_retry
-
 
 def _extract_article_data(article: dict) -> dict:
     """Extract article data from yfinance news format (handles nested 'content' structure)."""
@@ -66,7 +64,7 @@ def get_news_yfinance(
     """
     try:
         stock = yf.Ticker(ticker)
-        news = yf_retry(lambda: stock.get_news(count=20))
+        news = stock.get_news(count=20)
 
         if not news:
             return f"No news found for {ticker}"
@@ -133,11 +131,11 @@ def get_global_news_yfinance(
 
     try:
         for query in search_queries:
-            search = yf_retry(lambda q=query: yf.Search(
-                query=q,
+            search = yf.Search(
+                query=query,
                 news_count=limit,
                 enable_fuzzy_query=True,
-            ))
+            )
 
             if search.news:
                 for article in search.news:
@@ -169,11 +167,6 @@ def get_global_news_yfinance(
             # Handle both flat and nested structures
             if "content" in article:
                 data = _extract_article_data(article)
-                # Skip articles published after curr_date (look-ahead guard)
-                if data.get("pub_date"):
-                    pub_naive = data["pub_date"].replace(tzinfo=None) if hasattr(data["pub_date"], "replace") else data["pub_date"]
-                    if pub_naive > curr_dt + relativedelta(days=1):
-                        continue
                 title = data["title"]
                 publisher = data["publisher"]
                 link = data["link"]

tradingagents/default_config.py

@@ -1,23 +1,20 @@
 import os
 
-_TRADINGAGENTS_HOME = os.path.join(os.path.expanduser("~"), ".tradingagents")
-
 DEFAULT_CONFIG = {
     "project_dir": os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
-    "results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", os.path.join(_TRADINGAGENTS_HOME, "logs")),
-    "data_cache_dir": os.getenv("TRADINGAGENTS_CACHE_DIR", os.path.join(_TRADINGAGENTS_HOME, "cache")),
+    "results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", "./results"),
+    "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": "gpt-5.4",
-    "quick_think_llm": "gpt-5.4-mini",
+    "deep_think_llm": "gpt-5.2",
+    "quick_think_llm": "gpt-5-mini",
     "backend_url": "https://api.openai.com/v1",
     # Provider-specific thinking configuration
     "google_thinking_level": None,      # "high", "minimal", etc.
     "openai_reasoning_effort": None,    # "medium", "high", "low"
-    "anthropic_effort": None,           # "high", "medium", "low"
-    # Output language for analyst reports and final decision
-    # Internal agent debate stays in English for reasoning quality
-    "output_language": "English",
     # Debate and discussion settings
     "max_debate_rounds": 1,
     "max_risk_discuss_rounds": 1,

tradingagents/graph/conditional_logic.py

@@ -59,7 +59,7 @@ class ConditionalLogic:
         if (
             state["risk_debate_state"]["count"] >= 3 * self.max_risk_discuss_rounds
         ):  # 3 rounds of back-and-forth between 3 agents
-            return "Portfolio Manager"
+            return "Risk Judge"
         if state["risk_debate_state"]["latest_speaker"].startswith("Aggressive"):
             return "Conservative Analyst"
         if state["risk_debate_state"]["latest_speaker"].startswith("Conservative"):

tradingagents/graph/propagation.py

@@ -24,26 +24,14 @@ class Propagator:
             "company_of_interest": company_name,
             "trade_date": str(trade_date),
             "investment_debate_state": InvestDebateState(
-                {
-                    "bull_history": "",
-                    "bear_history": "",
-                    "history": "",
-                    "current_response": "",
-                    "judge_decision": "",
-                    "count": 0,
-                }
+                {"history": "", "current_response": "", "count": 0}
             ),
             "risk_debate_state": RiskDebateState(
                 {
-                    "aggressive_history": "",
-                    "conservative_history": "",
-                    "neutral_history": "",
                     "history": "",
-                    "latest_speaker": "",
                     "current_aggressive_response": "",
                     "current_conservative_response": "",
                     "current_neutral_response": "",
-                    "judge_decision": "",
                     "count": 0,
                 }
             ),

tradingagents/graph/reflection.py

@@ -1,12 +1,13 @@
 # TradingAgents/graph/reflection.py
 
-from typing import Any, Dict
+from typing import Dict, Any
+from langchain_openai import ChatOpenAI
 
 
 class Reflector:
     """Handles reflection on decisions and updating memory."""
 
-    def __init__(self, quick_thinking_llm: Any):
+    def __init__(self, quick_thinking_llm: ChatOpenAI):
         """Initialize the reflector with an LLM."""
         self.quick_thinking_llm = quick_thinking_llm
         self.reflection_system_prompt = self._get_reflection_prompt()
@@ -109,12 +110,12 @@ Adhere strictly to these instructions, and ensure your output is detailed, accur
         )
         invest_judge_memory.add_situations([(situation, result)])
 
-    def reflect_portfolio_manager(self, current_state, returns_losses, portfolio_manager_memory):
-        """Reflect on portfolio manager's decision and update memory."""
+    def reflect_risk_manager(self, current_state, returns_losses, risk_manager_memory):
+        """Reflect on risk manager's decision and update memory."""
         situation = self._extract_current_situation(current_state)
         judge_decision = current_state["risk_debate_state"]["judge_decision"]
 
         result = self._reflect_on_component(
-            "PORTFOLIO MANAGER", judge_decision, situation, returns_losses
+            "RISK JUDGE", judge_decision, situation, returns_losses
         )
-        portfolio_manager_memory.add_situations([(situation, result)])
+        risk_manager_memory.add_situations([(situation, result)])

tradingagents/graph/setup.py

@@ -1,7 +1,8 @@
 # TradingAgents/graph/setup.py
 
-from typing import Any, Dict
-from langgraph.graph import END, START, StateGraph
+from typing import Dict, Any
+from langchain_openai import ChatOpenAI
+from langgraph.graph import END, StateGraph, START
 from langgraph.prebuilt import ToolNode
 
 from tradingagents.agents import *
@@ -15,14 +16,14 @@ class GraphSetup:
 
     def __init__(
         self,
-        quick_thinking_llm: Any,
-        deep_thinking_llm: Any,
+        quick_thinking_llm: ChatOpenAI,
+        deep_thinking_llm: ChatOpenAI,
         tool_nodes: Dict[str, ToolNode],
         bull_memory,
         bear_memory,
         trader_memory,
         invest_judge_memory,
-        portfolio_manager_memory,
+        risk_manager_memory,
         conditional_logic: ConditionalLogic,
     ):
         """Initialize with required components."""
@@ -33,7 +34,7 @@ class GraphSetup:
         self.bear_memory = bear_memory
         self.trader_memory = trader_memory
         self.invest_judge_memory = invest_judge_memory
-        self.portfolio_manager_memory = portfolio_manager_memory
+        self.risk_manager_memory = risk_manager_memory
         self.conditional_logic = conditional_logic
 
     def setup_graph(
@@ -100,8 +101,8 @@ class GraphSetup:
         aggressive_analyst = create_aggressive_debator(self.quick_thinking_llm)
         neutral_analyst = create_neutral_debator(self.quick_thinking_llm)
         conservative_analyst = create_conservative_debator(self.quick_thinking_llm)
-        portfolio_manager_node = create_portfolio_manager(
-            self.deep_thinking_llm, self.portfolio_manager_memory
+        risk_manager_node = create_risk_manager(
+            self.deep_thinking_llm, self.risk_manager_memory
         )
 
         # Create workflow
@@ -123,7 +124,7 @@ class GraphSetup:
         workflow.add_node("Aggressive Analyst", aggressive_analyst)
         workflow.add_node("Neutral Analyst", neutral_analyst)
         workflow.add_node("Conservative Analyst", conservative_analyst)
-        workflow.add_node("Portfolio Manager", portfolio_manager_node)
+        workflow.add_node("Risk Judge", risk_manager_node)
 
         # Define edges
         # Start with the first analyst
@@ -175,7 +176,7 @@ class GraphSetup:
             self.conditional_logic.should_continue_risk_analysis,
             {
                 "Conservative Analyst": "Conservative Analyst",
-                "Portfolio Manager": "Portfolio Manager",
+                "Risk Judge": "Risk Judge",
             },
         )
         workflow.add_conditional_edges(
@@ -183,7 +184,7 @@ class GraphSetup:
             self.conditional_logic.should_continue_risk_analysis,
             {
                 "Neutral Analyst": "Neutral Analyst",
-                "Portfolio Manager": "Portfolio Manager",
+                "Risk Judge": "Risk Judge",
             },
         )
         workflow.add_conditional_edges(
@@ -191,11 +192,11 @@ class GraphSetup:
             self.conditional_logic.should_continue_risk_analysis,
             {
                 "Aggressive Analyst": "Aggressive Analyst",
-                "Portfolio Manager": "Portfolio Manager",
+                "Risk Judge": "Risk Judge",
             },
         )
 
-        workflow.add_edge("Portfolio Manager", END)
+        workflow.add_edge("Risk Judge", END)
 
         # Compile and return
         return workflow.compile()

tradingagents/graph/signal_processing.py

@@ -1,12 +1,12 @@
 # TradingAgents/graph/signal_processing.py
 
-from typing import Any
+from langchain_openai import ChatOpenAI
 
 
 class SignalProcessor:
     """Processes trading signals to extract actionable decisions."""
 
-    def __init__(self, quick_thinking_llm: Any):
+    def __init__(self, quick_thinking_llm: ChatOpenAI):
         """Initialize with an LLM for processing."""
         self.quick_thinking_llm = quick_thinking_llm
 
@@ -18,14 +18,12 @@ class SignalProcessor:
             full_signal: Complete trading signal text
 
         Returns:
-            Extracted rating (BUY, OVERWEIGHT, HOLD, UNDERWEIGHT, or SELL)
+            Extracted decision (BUY, SELL, or HOLD)
         """
         messages = [
             (
                 "system",
-                "You are an efficient assistant that extracts the trading decision from analyst reports. "
-                "Extract the rating as exactly one of: BUY, OVERWEIGHT, HOLD, UNDERWEIGHT, SELL. "
-                "Output only the single rating word, nothing else.",
+                "You are an efficient assistant designed to analyze paragraphs or financial reports provided by a group of analysts. Your task is to extract the investment decision: SELL, BUY, or HOLD. Provide only the extracted decision (SELL, BUY, or HOLD) as your output, without adding any additional text or information.",
             ),
             ("human", full_signal),
         ]

tradingagents/graph/trading_graph.py

@@ -66,8 +66,10 @@ class TradingAgentsGraph:
         set_config(self.config)
 
         # Create necessary directories
-        os.makedirs(self.config["data_cache_dir"], exist_ok=True)
-        os.makedirs(self.config["results_dir"], exist_ok=True)
+        os.makedirs(
+            os.path.join(self.config["project_dir"], "dataflows/data_cache"),
+            exist_ok=True,
+        )
 
         # Initialize LLMs with provider-specific thinking configuration
         llm_kwargs = self._get_provider_kwargs()
@@ -97,16 +99,13 @@ class TradingAgentsGraph:
         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.portfolio_manager_memory = FinancialSituationMemory("portfolio_manager_memory", self.config)
+        self.risk_manager_memory = FinancialSituationMemory("risk_manager_memory", self.config)
 
         # Create tool nodes
         self.tool_nodes = self._create_tool_nodes()
 
         # Initialize components
-        self.conditional_logic = ConditionalLogic(
-            max_debate_rounds=self.config["max_debate_rounds"],
-            max_risk_discuss_rounds=self.config["max_risk_discuss_rounds"],
-        )
+        self.conditional_logic = ConditionalLogic()
         self.graph_setup = GraphSetup(
             self.quick_thinking_llm,
             self.deep_thinking_llm,
@@ -115,7 +114,7 @@ class TradingAgentsGraph:
             self.bear_memory,
             self.trader_memory,
             self.invest_judge_memory,
-            self.portfolio_manager_memory,
+            self.risk_manager_memory,
             self.conditional_logic,
         )
 
@@ -146,11 +145,6 @@ class TradingAgentsGraph:
             if reasoning_effort:
                 kwargs["reasoning_effort"] = reasoning_effort
 
-        elif provider == "anthropic":
-            effort = self.config.get("anthropic_effort")
-            if effort:
-                kwargs["effort"] = effort
-
         return kwargs
 
     def _create_tool_nodes(self) -> Dict[str, ToolNode]:
@@ -257,12 +251,14 @@ class TradingAgentsGraph:
         }
 
         # Save to file
-        directory = Path(self.config["results_dir"]) / self.ticker / "TradingAgentsStrategy_logs"
+        directory = Path(f"eval_results/{self.ticker}/TradingAgentsStrategy_logs/")
         directory.mkdir(parents=True, exist_ok=True)
 
-        log_path = directory / f"full_states_log_{trade_date}.json"
-        with open(log_path, "w", encoding="utf-8") as f:
-            json.dump(self.log_states_dict[str(trade_date)], f, indent=4)
+        with open(
+            f"eval_results/{self.ticker}/TradingAgentsStrategy_logs/full_states_log_{trade_date}.json",
+            "w",
+        ) as f:
+            json.dump(self.log_states_dict, f, indent=4)
 
     def reflect_and_remember(self, returns_losses):
         """Reflect on decisions and update memory based on returns."""
@@ -278,8 +274,8 @@ class TradingAgentsGraph:
         self.reflector.reflect_invest_judge(
             self.curr_state, returns_losses, self.invest_judge_memory
         )
-        self.reflector.reflect_portfolio_manager(
-            self.curr_state, returns_losses, self.portfolio_manager_memory
+        self.reflector.reflect_risk_manager(
+            self.curr_state, returns_losses, self.risk_manager_memory
         )
 
     def process_signal(self, full_signal):

tradingagents/llm_clients/TODO.md

@@ -5,11 +5,20 @@
 ### 1. `validate_model()` is never called
 - Add validation call in `get_llm()` with warning (not error) for unknown models
 
-### 2. ~~Inconsistent parameter handling~~ (Fixed)
-- GoogleClient now accepts unified `api_key` and maps it to `google_api_key`
+### 2. Inconsistent parameter handling
+| Client | API Key Param | Special Params |
+|--------|---------------|----------------|
+| OpenAI | `api_key` | `reasoning_effort` |
+| Anthropic | `api_key` | `thinking_config` → `thinking` |
+| Google | `google_api_key` | `thinking_budget` |
 
-### 3. ~~`base_url` accepted but ignored~~ (Fixed)
-- All clients now pass `base_url` to their respective LLM constructors
+**Fix:** Standardize with unified `api_key` that maps to provider-specific keys
 
-### 4. ~~Update validators.py with models from CLI~~ (Fixed)
-- Synced in v0.2.2
+### 3. `base_url` accepted but ignored
+- `AnthropicClient`: accepts `base_url` but never uses it
+- `GoogleClient`: accepts `base_url` but never uses it (correct - Google doesn't support it)
+
+**Fix:** Remove unused `base_url` from clients that don't support it
+
+### 4. Update validators.py with models from CLI
+- Sync `VALID_MODELS` dict with CLI model options after Feature 2 is complete

tradingagents/llm_clients/anthropic_client.py

@@ -2,26 +2,9 @@ from typing import Any, Optional
 
 from langchain_anthropic import ChatAnthropic
 
-from .base_client import BaseLLMClient, normalize_content
+from .base_client import BaseLLMClient
 from .validators import validate_model
 
-_PASSTHROUGH_KWARGS = (
-    "timeout", "max_retries", "api_key", "max_tokens",
-    "callbacks", "http_client", "http_async_client", "effort",
-)
-
-
-class NormalizedChatAnthropic(ChatAnthropic):
-    """ChatAnthropic with normalized content output.
-
-    Claude models with extended thinking or tool use return content as a
-    list of typed blocks. This normalizes to string for consistent
-    downstream handling.
-    """
-
-    def invoke(self, input, config=None, **kwargs):
-        return normalize_content(super().invoke(input, config, **kwargs))
-
 
 class AnthropicClient(BaseLLMClient):
     """Client for Anthropic Claude models."""
@@ -31,17 +14,13 @@ class AnthropicClient(BaseLLMClient):
 
     def get_llm(self) -> Any:
         """Return configured ChatAnthropic instance."""
-        self.warn_if_unknown_model()
         llm_kwargs = {"model": self.model}
 
-        if self.base_url:
-            llm_kwargs["base_url"] = self.base_url
-
-        for key in _PASSTHROUGH_KWARGS:
+        for key in ("timeout", "max_retries", "api_key", "max_tokens", "callbacks"):
             if key in self.kwargs:
                 llm_kwargs[key] = self.kwargs[key]
 
-        return NormalizedChatAnthropic(**llm_kwargs)
+        return ChatAnthropic(**llm_kwargs)
 
     def validate_model(self) -> bool:
         """Validate model for Anthropic."""

tradingagents/llm_clients/azure_client.py

@@ -1,52 +0,0 @@
-import os
-from typing import Any, Optional
-
-from langchain_openai import AzureChatOpenAI
-
-from .base_client import BaseLLMClient, normalize_content
-from .validators import validate_model
-
-_PASSTHROUGH_KWARGS = (
-    "timeout", "max_retries", "api_key", "reasoning_effort",
-    "callbacks", "http_client", "http_async_client",
-)
-
-
-class NormalizedAzureChatOpenAI(AzureChatOpenAI):
-    """AzureChatOpenAI with normalized content output."""
-
-    def invoke(self, input, config=None, **kwargs):
-        return normalize_content(super().invoke(input, config, **kwargs))
-
-
-class AzureOpenAIClient(BaseLLMClient):
-    """Client for Azure OpenAI deployments.
-
-    Requires environment variables:
-        AZURE_OPENAI_API_KEY: API key
-        AZURE_OPENAI_ENDPOINT: Endpoint URL (e.g. https://<resource>.openai.azure.com/)
-        AZURE_OPENAI_DEPLOYMENT_NAME: Deployment name
-        OPENAI_API_VERSION: API version (e.g. 2025-03-01-preview)
-    """
-
-    def __init__(self, model: str, base_url: Optional[str] = None, **kwargs):
-        super().__init__(model, base_url, **kwargs)
-
-    def get_llm(self) -> Any:
-        """Return configured AzureChatOpenAI instance."""
-        self.warn_if_unknown_model()
-
-        llm_kwargs = {
-            "model": self.model,
-            "azure_deployment": os.environ.get("AZURE_OPENAI_DEPLOYMENT_NAME", self.model),
-        }
-
-        for key in _PASSTHROUGH_KWARGS:
-            if key in self.kwargs:
-                llm_kwargs[key] = self.kwargs[key]
-
-        return NormalizedAzureChatOpenAI(**llm_kwargs)
-
-    def validate_model(self) -> bool:
-        """Azure accepts any deployed model name."""
-        return True

tradingagents/llm_clients/base_client.py

@@ -1,25 +1,5 @@
 from abc import ABC, abstractmethod
 from typing import Any, Optional
-import warnings
-
-
-def normalize_content(response):
-    """Normalize LLM response content to a plain string.
-
-    Multiple providers (OpenAI Responses API, Google Gemini 3) return content
-    as a list of typed blocks, e.g. [{'type': 'reasoning', ...}, {'type': 'text', 'text': '...'}].
-    Downstream agents expect response.content to be a string. This extracts
-    and joins the text blocks, discarding reasoning/metadata blocks.
-    """
-    content = response.content
-    if isinstance(content, list):
-        texts = [
-            item.get("text", "") if isinstance(item, dict) and item.get("type") == "text"
-            else item if isinstance(item, str) else ""
-            for item in content
-        ]
-        response.content = "\n".join(t for t in texts if t)
-    return response
 
 
 class BaseLLMClient(ABC):
@@ -30,27 +10,6 @@ class BaseLLMClient(ABC):
         self.base_url = base_url
         self.kwargs = kwargs
 
-    def get_provider_name(self) -> str:
-        """Return the provider name used in warning messages."""
-        provider = getattr(self, "provider", None)
-        if provider:
-            return str(provider)
-        return self.__class__.__name__.removesuffix("Client").lower()
-
-    def warn_if_unknown_model(self) -> None:
-        """Warn when the model is outside the known list for the provider."""
-        if self.validate_model():
-            return
-
-        warnings.warn(
-            (
-                f"Model '{self.model}' is not in the known model list for "
-                f"provider '{self.get_provider_name()}'. Continuing anyway."
-            ),
-            RuntimeWarning,
-            stacklevel=2,
-        )
-
     @abstractmethod
     def get_llm(self) -> Any:
         """Return the configured LLM instance."""

tradingagents/llm_clients/factory.py

@@ -4,12 +4,6 @@ from .base_client import BaseLLMClient
 from .openai_client import OpenAIClient
 from .anthropic_client import AnthropicClient
 from .google_client import GoogleClient
-from .azure_client import AzureOpenAIClient
-
-# Providers that use the OpenAI-compatible chat completions API
-_OPENAI_COMPATIBLE = (
-    "openai", "xai", "deepseek", "qwen", "glm", "ollama", "openrouter",
-)
 
 
 def create_llm_client(
@@ -21,7 +15,7 @@ def create_llm_client(
     """Create an LLM client for the specified provider.
 
     Args:
-        provider: LLM provider name
+        provider: LLM provider (openai, anthropic, google, xai, ollama, openrouter)
         model: Model name/identifier
         base_url: Optional base URL for API endpoint
         **kwargs: Additional provider-specific arguments
@@ -34,16 +28,16 @@ def create_llm_client(
     """
     provider_lower = provider.lower()
 
-    if provider_lower in _OPENAI_COMPATIBLE:
+    if provider_lower in ("openai", "ollama", "openrouter"):
         return OpenAIClient(model, base_url, provider=provider_lower, **kwargs)
 
+    if provider_lower == "xai":
+        return OpenAIClient(model, base_url, provider="xai", **kwargs)
+
     if provider_lower == "anthropic":
         return AnthropicClient(model, base_url, **kwargs)
 
     if provider_lower == "google":
         return GoogleClient(model, base_url, **kwargs)
 
-    if provider_lower == "azure":
-        return AzureOpenAIClient(model, base_url, **kwargs)
-
     raise ValueError(f"Unsupported LLM provider: {provider}")

tradingagents/llm_clients/google_client.py

@@ -2,19 +2,30 @@ from typing import Any, Optional
 
 from langchain_google_genai import ChatGoogleGenerativeAI
 
-from .base_client import BaseLLMClient, normalize_content
+from .base_client import BaseLLMClient
 from .validators import validate_model
 
 
 class NormalizedChatGoogleGenerativeAI(ChatGoogleGenerativeAI):
     """ChatGoogleGenerativeAI with normalized content output.
 
-    Gemini 3 models return content as list of typed blocks.
+    Gemini 3 models return content as list: [{'type': 'text', 'text': '...'}]
     This normalizes to string for consistent downstream handling.
     """
 
+    def _normalize_content(self, response):
+        content = response.content
+        if isinstance(content, list):
+            texts = [
+                item.get("text", "") if isinstance(item, dict) and item.get("type") == "text"
+                else item if isinstance(item, str) else ""
+                for item in content
+            ]
+            response.content = "\n".join(t for t in texts if t)
+        return response
+
     def invoke(self, input, config=None, **kwargs):
-        return normalize_content(super().invoke(input, config, **kwargs))
+        return self._normalize_content(super().invoke(input, config, **kwargs))
 
 
 class GoogleClient(BaseLLMClient):
@@ -25,21 +36,12 @@ class GoogleClient(BaseLLMClient):
 
     def get_llm(self) -> Any:
         """Return configured ChatGoogleGenerativeAI instance."""
-        self.warn_if_unknown_model()
         llm_kwargs = {"model": self.model}
 
-        if self.base_url:
-            llm_kwargs["base_url"] = self.base_url
-
-        for key in ("timeout", "max_retries", "callbacks", "http_client", "http_async_client"):
+        for key in ("timeout", "max_retries", "google_api_key", "callbacks"):
             if key in self.kwargs:
                 llm_kwargs[key] = self.kwargs[key]
 
-        # Unified api_key maps to provider-specific google_api_key
-        google_api_key = self.kwargs.get("api_key") or self.kwargs.get("google_api_key")
-        if google_api_key:
-            llm_kwargs["google_api_key"] = google_api_key
-
         # Map thinking_level to appropriate API param based on model
         # Gemini 3 Pro: low, high
         # Gemini 3 Flash: minimal, low, medium, high

tradingagents/llm_clients/model_catalog.py

@@ -1,134 +0,0 @@
-"""Shared model catalog for CLI selections and validation."""
-
-from __future__ import annotations
-
-from typing import Dict, List, Tuple
-
-ModelOption = Tuple[str, str]
-ProviderModeOptions = Dict[str, Dict[str, List[ModelOption]]]
-
-
-MODEL_OPTIONS: ProviderModeOptions = {
-    "openai": {
-        "quick": [
-            ("GPT-5.4 Mini - Fast, strong coding and tool use", "gpt-5.4-mini"),
-            ("GPT-5.4 Nano - Cheapest, high-volume tasks", "gpt-5.4-nano"),
-            ("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
-            ("GPT-4.1 - Smartest non-reasoning model", "gpt-4.1"),
-        ],
-        "deep": [
-            ("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
-            ("GPT-5.2 - Strong reasoning, cost-effective", "gpt-5.2"),
-            ("GPT-5.4 Mini - Fast, strong coding and tool use", "gpt-5.4-mini"),
-            ("GPT-5.4 Pro - Most capable, expensive ($30/$180 per 1M tokens)", "gpt-5.4-pro"),
-        ],
-    },
-    "anthropic": {
-        "quick": [
-            ("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
-            ("Claude Haiku 4.5 - Fast, near-instant responses", "claude-haiku-4-5"),
-            ("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
-        ],
-        "deep": [
-            ("Claude Opus 4.6 - Most intelligent, agents and coding", "claude-opus-4-6"),
-            ("Claude Opus 4.5 - Premium, max intelligence", "claude-opus-4-5"),
-            ("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
-            ("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
-        ],
-    },
-    "google": {
-        "quick": [
-            ("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
-            ("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
-            ("Gemini 3.1 Flash Lite - Most cost-efficient", "gemini-3.1-flash-lite-preview"),
-            ("Gemini 2.5 Flash Lite - Fast, low-cost", "gemini-2.5-flash-lite"),
-        ],
-        "deep": [
-            ("Gemini 3.1 Pro - Reasoning-first, complex workflows", "gemini-3.1-pro-preview"),
-            ("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
-            ("Gemini 2.5 Pro - Stable pro model", "gemini-2.5-pro"),
-            ("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
-        ],
-    },
-    "xai": {
-        "quick": [
-            ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
-            ("Grok 4 Fast (Non-Reasoning) - Speed optimized", "grok-4-fast-non-reasoning"),
-            ("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
-        ],
-        "deep": [
-            ("Grok 4 - Flagship model", "grok-4-0709"),
-            ("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
-            ("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"),
-            ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
-        ],
-    },
-    "deepseek": {
-        "quick": [
-            ("DeepSeek V3.2", "deepseek-chat"),
-            ("Custom model ID", "custom"),
-        ],
-        "deep": [
-            ("DeepSeek V3.2 (thinking)", "deepseek-reasoner"),
-            ("DeepSeek V3.2", "deepseek-chat"),
-            ("Custom model ID", "custom"),
-        ],
-    },
-    "qwen": {
-        "quick": [
-            ("Qwen 3.5 Flash", "qwen3.5-flash"),
-            ("Qwen Plus", "qwen-plus"),
-            ("Custom model ID", "custom"),
-        ],
-        "deep": [
-            ("Qwen 3.6 Plus", "qwen3.6-plus"),
-            ("Qwen 3.5 Plus", "qwen3.5-plus"),
-            ("Qwen 3 Max", "qwen3-max"),
-            ("Custom model ID", "custom"),
-        ],
-    },
-    "glm": {
-        "quick": [
-            ("GLM-4.7", "glm-4.7"),
-            ("GLM-5", "glm-5"),
-            ("Custom model ID", "custom"),
-        ],
-        "deep": [
-            ("GLM-5.1", "glm-5.1"),
-            ("GLM-5", "glm-5"),
-            ("Custom model ID", "custom"),
-        ],
-    },
-    # OpenRouter: fetched dynamically. Azure: any deployed model name.
-    "ollama": {
-        "quick": [
-            ("Qwen3:latest (8B, local)", "qwen3:latest"),
-            ("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
-            ("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
-        ],
-        "deep": [
-            ("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
-            ("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
-            ("Qwen3:latest (8B, local)", "qwen3:latest"),
-        ],
-    },
-}
-
-
-def get_model_options(provider: str, mode: str) -> List[ModelOption]:
-    """Return shared model options for a provider and selection mode."""
-    return MODEL_OPTIONS[provider.lower()][mode]
-
-
-def get_known_models() -> Dict[str, List[str]]:
-    """Build known model names from the shared CLI catalog."""
-    return {
-        provider: sorted(
-            {
-                value
-                for options in mode_options.values()
-                for _, value in options
-            }
-        )
-        for provider, mode_options in MODEL_OPTIONS.items()
-    }

tradingagents/llm_clients/openai_client.py

@@ -3,46 +3,33 @@ from typing import Any, Optional
 
 from langchain_openai import ChatOpenAI
 
-from .base_client import BaseLLMClient, normalize_content
+from .base_client import BaseLLMClient
 from .validators import validate_model
 
 
-class NormalizedChatOpenAI(ChatOpenAI):
-    """ChatOpenAI with normalized content output.
+class UnifiedChatOpenAI(ChatOpenAI):
+    """ChatOpenAI subclass that strips incompatible params for certain models."""
 
-    The Responses API returns content as a list of typed blocks
-    (reasoning, text, etc.). This normalizes to string for consistent
-    downstream handling.
-    """
+    def __init__(self, **kwargs):
+        model = kwargs.get("model", "")
+        if self._is_reasoning_model(model):
+            kwargs.pop("temperature", None)
+            kwargs.pop("top_p", None)
+        super().__init__(**kwargs)
 
-    def invoke(self, input, config=None, **kwargs):
-        return normalize_content(super().invoke(input, config, **kwargs))
-
-# Kwargs forwarded from user config to ChatOpenAI
-_PASSTHROUGH_KWARGS = (
-    "timeout", "max_retries", "reasoning_effort",
-    "api_key", "callbacks", "http_client", "http_async_client",
-)
-
-# Provider base URLs and API key env vars
-_PROVIDER_CONFIG = {
-    "xai": ("https://api.x.ai/v1", "XAI_API_KEY"),
-    "deepseek": ("https://api.deepseek.com", "DEEPSEEK_API_KEY"),
-    "qwen": ("https://dashscope-intl.aliyuncs.com/compatible-mode/v1", "DASHSCOPE_API_KEY"),
-    "glm": ("https://api.z.ai/api/paas/v4/", "ZHIPU_API_KEY"),
-    "openrouter": ("https://openrouter.ai/api/v1", "OPENROUTER_API_KEY"),
-    "ollama": ("http://localhost:11434/v1", None),
-}
+    @staticmethod
+    def _is_reasoning_model(model: str) -> bool:
+        """Check if model is a reasoning model that doesn't support temperature."""
+        model_lower = model.lower()
+        return (
+            model_lower.startswith("o1")
+            or model_lower.startswith("o3")
+            or "gpt-5" in model_lower
+        )
 
 
 class OpenAIClient(BaseLLMClient):
-    """Client for OpenAI, Ollama, OpenRouter, and xAI providers.
-
-    For native OpenAI models, uses the Responses API (/v1/responses) which
-    supports reasoning_effort with function tools across all model families
-    (GPT-4.1, GPT-5). Third-party compatible providers (xAI, OpenRouter,
-    Ollama) use standard Chat Completions.
-    """
+    """Client for OpenAI, Ollama, OpenRouter, and xAI providers."""
 
     def __init__(
         self,
@@ -56,33 +43,29 @@ class OpenAIClient(BaseLLMClient):
 
     def get_llm(self) -> Any:
         """Return configured ChatOpenAI instance."""
-        self.warn_if_unknown_model()
         llm_kwargs = {"model": self.model}
 
-        # Provider-specific base URL and auth
-        if self.provider in _PROVIDER_CONFIG:
-            base_url, api_key_env = _PROVIDER_CONFIG[self.provider]
-            llm_kwargs["base_url"] = base_url
-            if api_key_env:
-                api_key = os.environ.get(api_key_env)
-                if api_key:
-                    llm_kwargs["api_key"] = api_key
-            else:
-                llm_kwargs["api_key"] = "ollama"
+        if self.provider == "xai":
+            llm_kwargs["base_url"] = "https://api.x.ai/v1"
+            api_key = os.environ.get("XAI_API_KEY")
+            if api_key:
+                llm_kwargs["api_key"] = api_key
+        elif self.provider == "openrouter":
+            llm_kwargs["base_url"] = "https://openrouter.ai/api/v1"
+            api_key = os.environ.get("OPENROUTER_API_KEY")
+            if api_key:
+                llm_kwargs["api_key"] = api_key
+        elif self.provider == "ollama":
+            llm_kwargs["base_url"] = "http://localhost:11434/v1"
+            llm_kwargs["api_key"] = "ollama"  # Ollama doesn't require auth
         elif self.base_url:
             llm_kwargs["base_url"] = self.base_url
 
-        # Forward user-provided kwargs
-        for key in _PASSTHROUGH_KWARGS:
+        for key in ("timeout", "max_retries", "reasoning_effort", "api_key", "callbacks"):
             if key in self.kwargs:
                 llm_kwargs[key] = self.kwargs[key]
 
-        # Native OpenAI: use Responses API for consistent behavior across
-        # all model families. Third-party providers use Chat Completions.
-        if self.provider == "openai":
-            llm_kwargs["use_responses_api"] = True
-
-        return NormalizedChatOpenAI(**llm_kwargs)
+        return UnifiedChatOpenAI(**llm_kwargs)
 
     def validate_model(self) -> bool:
         """Validate model for the provider."""

tradingagents/llm_clients/validators.py

@@ -1,12 +1,68 @@
-"""Model name validators for each provider."""
-
-from .model_catalog import get_known_models
+"""Model name validators for each provider.
 
+Only validates model names - does NOT enforce limits.
+Let LLM providers use their own defaults for unspecified params.
+"""
 
 VALID_MODELS = {
-    provider: models
-    for provider, models in get_known_models().items()
-    if provider not in ("ollama", "openrouter")
+    "openai": [
+        # GPT-5 series (2025)
+        "gpt-5.2",
+        "gpt-5.1",
+        "gpt-5",
+        "gpt-5-mini",
+        "gpt-5-nano",
+        # GPT-4.1 series (2025)
+        "gpt-4.1",
+        "gpt-4.1-mini",
+        "gpt-4.1-nano",
+        # o-series reasoning models
+        "o4-mini",
+        "o3",
+        "o3-mini",
+        "o1",
+        "o1-preview",
+        # GPT-4o series (legacy but still supported)
+        "gpt-4o",
+        "gpt-4o-mini",
+    ],
+    "anthropic": [
+        # Claude 4.5 series (2025)
+        "claude-opus-4-5",
+        "claude-sonnet-4-5",
+        "claude-haiku-4-5",
+        # Claude 4.x series
+        "claude-opus-4-1-20250805",
+        "claude-sonnet-4-20250514",
+        # Claude 3.7 series
+        "claude-3-7-sonnet-20250219",
+        # Claude 3.5 series (legacy)
+        "claude-3-5-haiku-20241022",
+        "claude-3-5-sonnet-20241022",
+    ],
+    "google": [
+        # Gemini 3 series (preview)
+        "gemini-3-pro-preview",
+        "gemini-3-flash-preview",
+        # Gemini 2.5 series
+        "gemini-2.5-pro",
+        "gemini-2.5-flash",
+        "gemini-2.5-flash-lite",
+        # Gemini 2.0 series
+        "gemini-2.0-flash",
+        "gemini-2.0-flash-lite",
+    ],
+    "xai": [
+        # Grok 4.1 series
+        "grok-4-1-fast",
+        "grok-4-1-fast-reasoning",
+        "grok-4-1-fast-non-reasoning",
+        # Grok 4 series
+        "grok-4",
+        "grok-4-0709",
+        "grok-4-fast-reasoning",
+        "grok-4-fast-non-reasoning",
+    ],
 }
 
 

uv.lock

@@ -1134,7 +1134,7 @@ wheels = [
 
 [[package]]
 name = "langchain-core"
-version = "0.3.83"
+version = "0.3.65"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jsonpatch" },
@@ -1144,11 +1144,10 @@ dependencies = [
     { name = "pyyaml" },
     { name = "tenacity" },
     { name = "typing-extensions" },
-    { name = "uuid-utils" },
 ]
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