fix: process all chunk messages for tool call logging, harden memory score normalization (#534, #531)

.dockerignore

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

.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=

Dockerfile

@@ -0,0 +1,27 @@
+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

@@ -118,6 +118,19 @@ Install the package and its dependencies:
 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
+```
+
 ### Required APIs
 
 TradingAgents supports multiple LLM providers. Set the API key for your chosen provider:
@@ -127,10 +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

@@ -134,14 +134,70 @@ def select_research_depth() -> int:
     return choice
 
 
-def select_shallow_thinking_agent(provider) -> str:
-    """Select shallow thinking llm engine using an interactive selection."""
+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_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"))
 
     choice = questionary.select(
-        "Select Your [Quick-Thinking LLM Engine]:",
+        "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]:",
         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(
@@ -154,56 +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."""
+    return _select_model(provider, "deep")
 
-    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
-
-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"),
+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"),
     ]
-    
+
     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(
@@ -216,13 +261,11 @@ def select_llm_provider() -> tuple[str, str]:
     ).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

@@ -0,0 +1,35 @@
+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:

pyproject.toml

@@ -13,7 +13,7 @@ dependencies = [
     "backtrader>=1.9.78.123",
     "langchain-anthropic>=0.3.15",
     "langchain-experimental>=0.3.4",
-    "langchain-google-genai>=2.1.5",
+    "langchain-google-genai>=4.0.0",
     "langchain-openai>=0.3.23",
     "langgraph>=0.4.8",
     "pandas>=2.3.0",

tradingagents/agents/analysts/fundamentals_analyst.py

@@ -1,6 +1,4 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import time
-import json
 from tradingagents.agents.utils.agent_utils import (
     build_instrument_context,
     get_balance_sheet,

tradingagents/agents/analysts/market_analyst.py

@@ -1,6 +1,4 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import time
-import json
 from tradingagents.agents.utils.agent_utils import (
     build_instrument_context,
     get_indicators,

tradingagents/agents/analysts/news_analyst.py

@@ -1,6 +1,4 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import time
-import json
 from tradingagents.agents.utils.agent_utils import (
     build_instrument_context,
     get_global_news,

tradingagents/agents/analysts/social_media_analyst.py

@@ -1,6 +1,4 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import time
-import json
 from tradingagents.agents.utils.agent_utils import build_instrument_context, get_language_instruction, get_news
 from tradingagents.dataflows.config import get_config
 

tradingagents/agents/managers/portfolio_manager.py

@@ -12,7 +12,8 @@ def create_portfolio_manager(llm, memory):
         news_report = state["news_report"]
         fundamentals_report = state["fundamentals_report"]
         sentiment_report = state["sentiment_report"]
-        trader_plan = state["investment_plan"]
+        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)
@@ -35,7 +36,8 @@ def create_portfolio_manager(llm, memory):
 - **Sell**: Exit position or avoid entry
 
 **Context:**
-- Trader's proposed plan: **{trader_plan}**
+- Research Manager's investment plan: **{research_plan}**
+- Trader's transaction proposal: **{trader_plan}**
 - Lessons from past decisions: **{past_memory_str}**
 
 **Required Output Structure:**

tradingagents/agents/managers/research_manager.py

@@ -1,5 +1,3 @@
-import time
-import json
 
 from tradingagents.agents.utils.agent_utils import build_instrument_context
 

tradingagents/agents/researchers/bear_researcher.py

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

tradingagents/agents/researchers/bull_researcher.py

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

tradingagents/agents/risk_mgmt/aggressive_debator.py

@@ -1,5 +1,3 @@
-import time
-import json
 
 
 def create_aggressive_debator(llm):

tradingagents/agents/risk_mgmt/conservative_debator.py

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

tradingagents/agents/risk_mgmt/neutral_debator.py

@@ -1,5 +1,3 @@
-import time
-import json
 
 
 def create_neutral_debator(llm):

tradingagents/agents/trader/trader.py

@@ -1,6 +1,4 @@
 import functools
-import time
-import json
 
 from tradingagents.agents.utils.agent_utils import build_instrument_context
 

tradingagents/agents/utils/agent_states.py

@@ -1,10 +1,6 @@
-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
+from typing import Annotated
+from typing_extensions import TypedDict
+from langgraph.graph import MessagesState
 
 
 # Researcher team state

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/agents/utils/technical_indicators_tools.py

@@ -22,7 +22,7 @@ def get_indicators(
     """
     # LLMs sometimes pass multiple indicators as a comma-separated string;
     # split and process each individually.
-    indicators = [i.strip() for i in indicator.split(",") if i.strip()]
+    indicators = [i.strip().lower() for i in indicator.split(",") if i.strip()]
     results = []
     for ind in indicators:
         try:

tradingagents/dataflows/y_finance.py

@@ -1,6 +1,7 @@
 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

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/reflection.py

@@ -1,13 +1,12 @@
 # TradingAgents/graph/reflection.py
 
-from typing import Dict, Any
-from langchain_openai import ChatOpenAI
+from typing import Any, Dict
 
 
 class Reflector:
     """Handles reflection on decisions and updating memory."""
 
-    def __init__(self, quick_thinking_llm: ChatOpenAI):
+    def __init__(self, quick_thinking_llm: Any):
         """Initialize the reflector with an LLM."""
         self.quick_thinking_llm = quick_thinking_llm
         self.reflection_system_prompt = self._get_reflection_prompt()

tradingagents/graph/setup.py

@@ -1,8 +1,7 @@
 # TradingAgents/graph/setup.py
 
-from typing import Dict, Any
-from langchain_openai import ChatOpenAI
-from langgraph.graph import END, StateGraph, START
+from typing import Any, Dict
+from langgraph.graph import END, START, StateGraph
 from langgraph.prebuilt import ToolNode
 
 from tradingagents.agents import *
@@ -16,8 +15,8 @@ class GraphSetup:
 
     def __init__(
         self,
-        quick_thinking_llm: ChatOpenAI,
-        deep_thinking_llm: ChatOpenAI,
+        quick_thinking_llm: Any,
+        deep_thinking_llm: Any,
         tool_nodes: Dict[str, ToolNode],
         bull_memory,
         bear_memory,

tradingagents/graph/signal_processing.py

@@ -1,12 +1,12 @@
 # TradingAgents/graph/signal_processing.py
 
-from langchain_openai import ChatOpenAI
+from typing import Any
 
 
 class SignalProcessor:
     """Processes trading signals to extract actionable decisions."""
 
-    def __init__(self, quick_thinking_llm: ChatOpenAI):
+    def __init__(self, quick_thinking_llm: Any):
         """Initialize with an LLM for processing."""
         self.quick_thinking_llm = quick_thinking_llm
 

tradingagents/graph/trading_graph.py

@@ -66,10 +66,8 @@ 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()
@@ -259,15 +257,12 @@ class TradingAgentsGraph:
         }
 
         # Save to file
-        directory = Path(f"eval_results/{self.ticker}/TradingAgentsStrategy_logs/")
+        directory = Path(self.config["results_dir"]) / self.ticker / "TradingAgentsStrategy_logs"
         directory.mkdir(parents=True, exist_ok=True)
 
-        with open(
-            f"eval_results/{self.ticker}/TradingAgentsStrategy_logs/full_states_log_{trade_date}.json",
-            "w",
-            encoding="utf-8",
-        ) as f:
-            json.dump(self.log_states_dict, f, indent=4)
+        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)
 
     def reflect_and_remember(self, returns_losses):
         """Reflect on decisions and update memory based on returns."""

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

@@ -63,16 +63,43 @@ MODEL_OPTIONS: ProviderModeOptions = {
             ("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
         ],
     },
-    "openrouter": {
+    "deepseek": {
         "quick": [
-            ("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"),
+            ("DeepSeek V3.2", "deepseek-chat"),
+            ("Custom model ID", "custom"),
         ],
         "deep": [
-            ("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"),
+            ("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),
 }