Merge ccf375eafd into fa4d01c23a

Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

.env.enterprise.example

@@ -0,0 +1,5 @@
+# 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

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

README.md

@@ -140,10 +140,15 @@ 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:

cli/main.py

@@ -6,8 +6,9 @@ from functools import wraps
 from rich.console import Console
 from dotenv import load_dotenv
 
-# Load environment variables from .env file
+# Load environment variables
 load_dotenv()
+load_dotenv(".env.enterprise", override=False)
 from rich.panel import Panel
 from rich.spinner import Spinner
 from rich.live import Live
@@ -79,7 +80,7 @@ class MessageBuffer:
         self.current_agent = None
         self.report_sections = {}
         self.selected_analysts = []
-        self._last_message_id = None
+        self._processed_message_ids = set()
 
     def init_for_analysis(self, selected_analysts):
         """Initialize agent status and report sections based on selected analysts.
@@ -114,7 +115,7 @@ class MessageBuffer:
         self.current_agent = None
         self.messages.clear()
         self.tool_calls.clear()
-        self._last_message_id = None
+        self._processed_message_ids.clear()
 
     def get_completed_reports_count(self):
         """Count reports that are finalized (their finalizing agent is completed).
@@ -1052,28 +1053,24 @@ def run_analysis():
         # Stream the analysis
         trace = []
         for chunk in graph.graph.stream(init_agent_state, **args):
-            # 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)
+            # 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)
 
-                if msg_id != message_buffer._last_message_id:
-                    message_buffer._last_message_id = msg_id
+                msg_type, content = classify_message_type(message)
+                if content and content.strip():
+                    message_buffer.add_message(msg_type, content)
 
-                    # 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)
+                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)
 
             # Update analyst statuses based on report state (runs on every chunk)
             update_analyst_statuses(message_buffer, chunk)

cli/utils.py

@@ -174,17 +174,30 @@ def select_openrouter_model() -> str:
     return choice
 
 
-def select_shallow_thinking_agent(provider) -> str:
-    """Select shallow thinking llm engine using an interactive selection."""
+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(
-        "Select Your [Quick-Thinking LLM Engine]:",
+        f"Select Your [{mode.title()}-Thinking LLM Engine]:",
         choices=[
             questionary.Choice(display, value=value)
-            for display, value in get_model_options(provider, "quick")
+            for display, value in get_model_options(provider, mode)
         ],
         instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
         style=questionary.Style(
@@ -197,58 +210,45 @@ def select_shallow_thinking_agent(provider) -> str:
     ).ask()
 
     if choice is None:
-        console.print(
-            "\n[red]No shallow thinking llm engine selected. Exiting...[/red]"
-        )
+        console.print(f"\n[red]No {mode} 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."""
-
-    if provider.lower() == "openrouter":
-        return select_openrouter_model()
-
-    choice = questionary.select(
-        "Select Your [Deep-Thinking LLM Engine]:",
-        choices=[
-            questionary.Choice(display, value=value)
-            for display, value in get_model_options(provider, "deep")
-        ],
-        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
+    return _select_model(provider, "deep")
 
 def select_llm_provider() -> tuple[str, str | None]:
     """Select the LLM provider and its API endpoint."""
-    BASE_URLS = [
-        ("OpenAI", "https://api.openai.com/v1"),
-        ("Google", None),  # google-genai SDK manages its own endpoint
-        ("Anthropic", "https://api.anthropic.com/"),
-        ("xAI", "https://api.x.ai/v1"),
-        ("Openrouter", "https://openrouter.ai/api/v1"),
-        ("Ollama", "http://localhost:11434/v1"),
+    # (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"),
     ]
-    
+
     choice = questionary.select(
         "Select your LLM Provider:",
         choices=[
-            questionary.Choice(display, value=(display, value))
-            for display, value in BASE_URLS
+            questionary.Choice(display, value=(provider_key, url))
+            for display, provider_key, url in PROVIDERS
         ],
         instruction="\n- Use arrow keys to navigate\n- Press Enter to select",
         style=questionary.Style(
@@ -261,13 +261,11 @@ def select_llm_provider() -> tuple[str, str | None]:
     ).ask()
     
     if choice is None:
-        console.print("\n[red]no OpenAI backend selected. Exiting...[/red]")
+        console.print("\n[red]No LLM provider selected. Exiting...[/red]")
         exit(1)
-    
-    display_name, url = choice
-    print(f"You selected: {display_name}\tURL: {url}")
 
-    return display_name, url
+    provider, url = choice
+    return provider, url
 
 
 def ask_openai_reasoning_effort() -> str:

docker-compose.yml

@@ -4,7 +4,7 @@ services:
     env_file:
       - .env
     volumes:
-      - ./results:/home/appuser/app/results
+      - tradingagents_data:/home/appuser/.tradingagents
     tty: true
     stdin_open: true
 
@@ -22,7 +22,7 @@ services:
     environment:
       - LLM_PROVIDER=ollama
     volumes:
-      - ./results:/home/appuser/app/results
+      - tradingagents_data:/home/appuser/.tradingagents
     depends_on:
       - ollama
     tty: true
@@ -31,4 +31,5 @@ services:
       - ollama
 
 volumes:
+  tradingagents_data:
   ollama_data:

examples/portfolio_analysis.py

@@ -0,0 +1,37 @@
+"""Portfolio analysis example.
+
+Analyzes multiple stocks in a portfolio and produces a comparative
+recommendation for each position (KEEP / REDUCE / EXIT).
+
+Related GitHub issues: #60, #406
+"""
+
+from tradingagents.graph.trading_graph import TradingAgentsGraph
+from tradingagents.default_config import DEFAULT_CONFIG
+from dotenv import load_dotenv
+
+load_dotenv()
+
+config = DEFAULT_CONFIG.copy()
+# Customize LLM provider and models as needed:
+# config["llm_provider"] = "anthropic"  # or "openai", "google"
+# config["deep_think_llm"] = "claude-sonnet-4-20250514"
+# config["quick_think_llm"] = "claude-haiku-4-5-20251001"
+config["max_debate_rounds"] = 1
+
+ta = TradingAgentsGraph(debug=False, config=config)
+
+# Define your portfolio
+portfolio = ["NVDA", "AAPL", "MSFT", "GOOGL", "AMZN"]
+
+# Run the portfolio analysis
+results = ta.propagate_portfolio(portfolio, "2025-03-23")
+
+# Print individual signals
+print("\n=== INDIVIDUAL SIGNALS ===")
+for ticker, result in results["individual_results"].items():
+    print(f"  {ticker}: {result['signal']}")
+
+# Print the comparative portfolio summary
+print("\n=== PORTFOLIO SUMMARY ===")
+print(results["portfolio_summary"])

tradingagents/agents/utils/memory.py

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

tradingagents/default_config.py

@@ -1,12 +1,11 @@
 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", "./results"),
-    "data_cache_dir": os.path.join(
-        os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
-        "dataflows/data_cache",
-    ),
+    "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")),
     # LLM settings
     "llm_provider": "openai",
     "deep_think_llm": "gpt-5.4",

tradingagents/graph/__init__.py

@@ -6,6 +6,7 @@ from .setup import GraphSetup
 from .propagation import Propagator
 from .reflection import Reflector
 from .signal_processing import SignalProcessor
+from .portfolio_analysis import PortfolioAnalyzer
 
 __all__ = [
     "TradingAgentsGraph",
@@ -14,4 +15,5 @@ __all__ = [
     "Propagator",
     "Reflector",
     "SignalProcessor",
+    "PortfolioAnalyzer",
 ]

tradingagents/graph/portfolio_analysis.py

@@ -0,0 +1,192 @@
+# TradingAgents/graph/portfolio_analysis.py
+
+import json
+import re
+import traceback
+from pathlib import Path
+from typing import Any, Callable, Dict, List, Tuple
+
+from langchain_core.language_models.chat_models import BaseChatModel
+
+
+class PortfolioAnalyzer:
+    """Analyzes multiple stocks and produces a comparative portfolio recommendation.
+
+    Follows the same delegation pattern as SignalProcessor and Reflector —
+    the orchestrator (TradingAgentsGraph) owns the graph and LLMs, this class
+    owns the portfolio-level prompt, comparison logic, and logging.
+    """
+
+    def __init__(self, deep_thinking_llm: BaseChatModel, config: Dict[str, Any]):
+        """Initialize with the deep thinking LLM for comparative analysis.
+
+        Args:
+            deep_thinking_llm: The LLM instance used for the portfolio summary.
+            config: The configuration dictionary for the application.
+        """
+        self.deep_thinking_llm = deep_thinking_llm
+        self.config = config
+
+    def analyze(
+        self,
+        tickers: List[str],
+        trade_date: str,
+        propagate_fn: Callable[[str, str], Tuple[Dict[str, Any], str]],
+        debug: bool = False,
+    ) -> Dict[str, Any]:
+        """Run analysis on multiple stocks and produce a comparative summary.
+
+        Args:
+            tickers: List of ticker symbols to analyze.
+            trade_date: The trade date string (e.g., "2026-03-23").
+            propagate_fn: The single-stock propagation function (typically
+                TradingAgentsGraph.propagate).
+            debug: Whether to print progress output.
+
+        Returns:
+            Dictionary with:
+                - "individual_results": dict mapping ticker to its decision and signal
+                - "portfolio_summary": the comparative LLM analysis
+
+        Raises:
+            ValueError: If tickers is empty.
+        """
+        if not tickers:
+            raise ValueError("tickers must be a non-empty list")
+
+        individual_results = self._analyze_individual(
+            tickers, trade_date, propagate_fn, debug
+        )
+
+        portfolio_summary = self._generate_summary(
+            individual_results, trade_date, debug
+        )
+
+        try:
+            self._log_portfolio(trade_date, tickers, individual_results, portfolio_summary)
+        except Exception as e:
+            if debug:
+                print(f"Warning: failed to save portfolio log: {e}")
+
+        return {
+            "individual_results": individual_results,
+            "portfolio_summary": portfolio_summary,
+        }
+
+    def _analyze_individual(
+        self,
+        tickers: List[str],
+        trade_date: str,
+        propagate_fn: Callable[[str, str], Tuple[Dict[str, Any], str]],
+        debug: bool,
+    ) -> Dict[str, Dict[str, str]]:
+        """Run the agent pipeline on each ticker, collecting results."""
+        individual_results = {}
+
+        for ticker in tickers:
+            if debug:
+                print(f"\n{'='*60}")
+                print(f"Analyzing {ticker}...")
+                print(f"{'='*60}\n")
+
+            try:
+                final_state, signal = propagate_fn(ticker, trade_date)
+                individual_results[ticker] = {
+                    "signal": signal,
+                    "final_trade_decision": final_state["final_trade_decision"],
+                }
+            except Exception as e:
+                if debug:
+                    print(f"Error analyzing {ticker}: {e}")
+                error_msg = f"Analysis failed: {e}"
+                if debug:
+                    error_msg += f"\n{traceback.format_exc()}"
+                individual_results[ticker] = {
+                    "signal": "ERROR",
+                    "final_trade_decision": error_msg,
+                }
+
+        return individual_results
+
+    def _generate_summary(
+        self,
+        individual_results: Dict[str, Dict[str, str]],
+        trade_date: str,
+        debug: bool = False,
+    ) -> str:
+        """Use the deep thinking LLM to compare all positions."""
+        # Skip summary if all tickers failed
+        successful = {
+            t: r for t, r in individual_results.items() if r["signal"] != "ERROR"
+        }
+        if not successful:
+            return "Portfolio summary unavailable — all individual analyses failed."
+
+        analyses_text = self._build_analyses_text(successful)
+        messages = [
+            ("system", self._get_system_prompt()),
+            (
+                "human",
+                f"Here are the individual analyses for my portfolio positions "
+                f"as of {trade_date}:\n{analyses_text}\n\n"
+                f"Please provide a comparative portfolio recommendation.",
+            ),
+        ]
+
+        try:
+            return self.deep_thinking_llm.invoke(messages).content
+        except Exception as e:
+            error_msg = f"Portfolio summary generation failed: {e}"
+            if debug:
+                error_msg += f"\n{traceback.format_exc()}"
+            signals = ", ".join(f"{t}: {r['signal']}" for t, r in individual_results.items())
+            return f"{error_msg}\nIndividual signals were: {signals}"
+
+    def _build_analyses_text(self, results: Dict[str, Dict[str, str]]) -> str:
+        """Format individual results into a text block for the LLM prompt."""
+        parts = []
+        for ticker, result in results.items():
+            parts.append(
+                f"--- {ticker} ---\n"
+                f"Rating: {result['signal']}\n"
+                f"Full Analysis:\n{result['final_trade_decision']}"
+            )
+        return "\n".join(parts)
+
+    def _get_system_prompt(self) -> str:
+        """Return the system prompt for the portfolio comparison LLM call."""
+        return (
+            "You are a senior portfolio strategist. You have received individual "
+            "stock analyses for all positions in a portfolio. Your job is to compare "
+            "them relative to each other and provide a clear, actionable portfolio "
+            "recommendation.\n\n"
+            "For each stock, assign one of: KEEP, REDUCE, or EXIT.\n\n"
+            "Structure your response as:\n"
+            "1. A ranked summary table (best to worst) with ticker, action, and "
+            "one-line rationale.\n"
+            "2. A brief portfolio-level commentary covering overall risk exposure, "
+            "sector concentration, and any suggested rebalancing.\n\n"
+            "Be direct and concise. This is for an experienced investor."
+        )
+
+    def _log_portfolio(
+        self,
+        trade_date: str,
+        tickers: List[str],
+        individual_results: Dict[str, Dict[str, str]],
+        portfolio_summary: str,
+    ) -> None:
+        """Log the portfolio analysis results to a JSON file."""
+        directory = Path(self.config.get("portfolio_log_dir", "eval_results/portfolio/"))
+        directory.mkdir(parents=True, exist_ok=True)
+
+        log_data = {
+            "trade_date": trade_date,
+            "tickers": tickers,
+            "individual_results": individual_results,
+            "portfolio_summary": portfolio_summary,
+        }
+
+        log_file = directory / f"portfolio_analysis_{re.sub(r'[^\w.-]', '_', trade_date)}.json"
+        with log_file.open("w", encoding="utf-8") as f:
+            json.dump(log_data, f, indent=4)

tradingagents/graph/trading_graph.py

@@ -38,6 +38,7 @@ from .setup import GraphSetup
 from .propagation import Propagator
 from .reflection import Reflector
 from .signal_processing import SignalProcessor
+from .portfolio_analysis import PortfolioAnalyzer
 
 
 class TradingAgentsGraph:
@@ -66,10 +67,8 @@ class TradingAgentsGraph:
         set_config(self.config)
 
         # Create necessary directories
-        os.makedirs(
-            os.path.join(self.config["project_dir"], "dataflows/data_cache"),
-            exist_ok=True,
-        )
+        os.makedirs(self.config["data_cache_dir"], exist_ok=True)
+        os.makedirs(self.config["results_dir"], exist_ok=True)
 
         # Initialize LLMs with provider-specific thinking configuration
         llm_kwargs = self._get_provider_kwargs()
@@ -124,6 +123,7 @@ class TradingAgentsGraph:
         self.propagator = Propagator()
         self.reflector = Reflector(self.quick_thinking_llm)
         self.signal_processor = SignalProcessor(self.quick_thinking_llm)
+        self.portfolio_analyzer = PortfolioAnalyzer(self.deep_thinking_llm, self.config)
 
         # State tracking
         self.curr_state = None
@@ -266,6 +266,26 @@ class TradingAgentsGraph:
         with open(log_path, "w", encoding="utf-8") as f:
             json.dump(self.log_states_dict[str(trade_date)], f, indent=4)
 
+    def propagate_portfolio(
+        self, tickers: List[str], trade_date: str
+    ) -> Dict[str, Any]:
+        """Run analysis on multiple stocks and produce a comparative portfolio summary.
+
+        Delegates to PortfolioAnalyzer.analyze — see that class for full details.
+
+        This method preserves the instance's ticker and curr_state attributes,
+        restoring them after the portfolio analysis is complete.
+        """
+        original_ticker = self.ticker
+        original_curr_state = self.curr_state
+        try:
+            return self.portfolio_analyzer.analyze(
+                tickers, trade_date, self.propagate, debug=self.debug
+            )
+        finally:
+            self.ticker = original_ticker
+            self.curr_state = original_curr_state
+
     def reflect_and_remember(self, returns_losses):
         """Reflect on decisions and update memory based on returns."""
         self.reflector.reflect_bull_researcher(

tradingagents/llm_clients/azure_client.py

@@ -0,0 +1,52 @@
+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/factory.py

@@ -4,6 +4,12 @@ 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(
@@ -15,16 +21,10 @@ def create_llm_client(
     """Create an LLM client for the specified provider.
 
     Args:
-        provider: LLM provider (openai, anthropic, google, xai, ollama, openrouter)
+        provider: LLM provider name
         model: Model name/identifier
         base_url: Optional base URL for API endpoint
         **kwargs: Additional provider-specific arguments
-            - http_client: Custom httpx.Client for SSL proxy or certificate customization
-            - http_async_client: Custom httpx.AsyncClient for async operations
-            - timeout: Request timeout in seconds
-            - max_retries: Maximum retry attempts
-            - api_key: API key for the provider
-            - callbacks: LangChain callbacks
 
     Returns:
         Configured BaseLLMClient instance
@@ -34,16 +34,16 @@ def create_llm_client(
     """
     provider_lower = provider.lower()
 
-    if provider_lower in ("openai", "ollama", "openrouter"):
+    if provider_lower in _OPENAI_COMPATIBLE:
         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/model_catalog.py

@@ -63,8 +63,43 @@ MODEL_OPTIONS: ProviderModeOptions = {
             ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
         ],
     },
-    # OpenRouter models are fetched dynamically at CLI runtime.
-    # No static entries needed; any model ID is accepted by the validator.
+    "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"),

tradingagents/llm_clients/openai_client.py

@@ -27,6 +27,9 @@ _PASSTHROUGH_KWARGS = (
 # 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),
 }