Add support for accessing LLM models on Azure Foundry

.env.example

@@ -4,3 +4,4 @@ GOOGLE_API_KEY=
 ANTHROPIC_API_KEY=
 XAI_API_KEY=
 OPENROUTER_API_KEY=
+AZURE_FOUNDRY_API_KEY=

cli/main.py

@@ -536,28 +536,31 @@ def get_user_selections():
     )
     selected_research_depth = select_research_depth()
 
-    # Step 5: OpenAI backend
+    # Step 5: LLM Provider
     console.print(
         create_question_box(
-            "Step 5: OpenAI backend", "Select which service to talk to"
+            "Step 5: LLM Provider", "Select which service to talk to"
         )
     )
     selected_llm_provider, backend_url = select_llm_provider()
 
+    # Normalize provider key for dict lookups: "Azure Foundry" -> "azure_foundry"
+    provider_key = selected_llm_provider.lower().replace(" ", "_")
+    
     # Step 6: Thinking agents
     console.print(
         create_question_box(
             "Step 6: Thinking Agents", "Select your thinking agents for analysis"
         )
     )
-    selected_shallow_thinker = select_shallow_thinking_agent(selected_llm_provider)
+    selected_shallow_thinker = select_shallow_thinking_agent(provider_key)
-    selected_deep_thinker = select_deep_thinking_agent(selected_llm_provider)
+    selected_deep_thinker = select_deep_thinking_agent(provider_key)
 
     # Step 7: Provider-specific thinking configuration
     thinking_level = None
     reasoning_effort = None
 
-    provider_lower = selected_llm_provider.lower()
+    provider_lower = provider_key
     if provider_lower == "google":
         console.print(
             create_question_box(
@@ -580,7 +583,7 @@ def get_user_selections():
         "analysis_date": analysis_date,
         "analysts": selected_analysts,
         "research_depth": selected_research_depth,
-        "llm_provider": selected_llm_provider.lower(),
+        "llm_provider": provider_key,
         "backend_url": backend_url,
         "shallow_thinker": selected_shallow_thinker,
         "deep_thinker": selected_deep_thinker,
@@ -623,19 +626,19 @@ def save_report_to_disk(final_state, ticker: str, save_path: Path):
     analyst_parts = []
     if final_state.get("market_report"):
         analysts_dir.mkdir(exist_ok=True)
-        (analysts_dir / "market.md").write_text(final_state["market_report"])
+        (analysts_dir / "market.md").write_text(final_state["market_report"], encoding="utf-8")
         analyst_parts.append(("Market Analyst", final_state["market_report"]))
     if final_state.get("sentiment_report"):
         analysts_dir.mkdir(exist_ok=True)
-        (analysts_dir / "sentiment.md").write_text(final_state["sentiment_report"])
+        (analysts_dir / "sentiment.md").write_text(final_state["sentiment_report"], encoding="utf-8")
         analyst_parts.append(("Social Analyst", final_state["sentiment_report"]))
     if final_state.get("news_report"):
         analysts_dir.mkdir(exist_ok=True)
-        (analysts_dir / "news.md").write_text(final_state["news_report"])
+        (analysts_dir / "news.md").write_text(final_state["news_report"], encoding="utf-8")
         analyst_parts.append(("News Analyst", final_state["news_report"]))
     if final_state.get("fundamentals_report"):
         analysts_dir.mkdir(exist_ok=True)
-        (analysts_dir / "fundamentals.md").write_text(final_state["fundamentals_report"])
+        (analysts_dir / "fundamentals.md").write_text(final_state["fundamentals_report"], encoding="utf-8")
         analyst_parts.append(("Fundamentals Analyst", final_state["fundamentals_report"]))
     if analyst_parts:
         content = "\n\n".join(f"### {name}\n{text}" for name, text in analyst_parts)
@@ -648,15 +651,15 @@ def save_report_to_disk(final_state, ticker: str, save_path: Path):
         research_parts = []
         if debate.get("bull_history"):
             research_dir.mkdir(exist_ok=True)
-            (research_dir / "bull.md").write_text(debate["bull_history"])
+            (research_dir / "bull.md").write_text(debate["bull_history"], encoding="utf-8")
             research_parts.append(("Bull Researcher", debate["bull_history"]))
         if debate.get("bear_history"):
             research_dir.mkdir(exist_ok=True)
-            (research_dir / "bear.md").write_text(debate["bear_history"])
+            (research_dir / "bear.md").write_text(debate["bear_history"], encoding="utf-8")
             research_parts.append(("Bear Researcher", debate["bear_history"]))
         if debate.get("judge_decision"):
             research_dir.mkdir(exist_ok=True)
-            (research_dir / "manager.md").write_text(debate["judge_decision"])
+            (research_dir / "manager.md").write_text(debate["judge_decision"], encoding="utf-8")
             research_parts.append(("Research Manager", debate["judge_decision"]))
         if research_parts:
             content = "\n\n".join(f"### {name}\n{text}" for name, text in research_parts)
@@ -666,7 +669,7 @@ def save_report_to_disk(final_state, ticker: str, save_path: Path):
     if final_state.get("trader_investment_plan"):
         trading_dir = save_path / "3_trading"
         trading_dir.mkdir(exist_ok=True)
-        (trading_dir / "trader.md").write_text(final_state["trader_investment_plan"])
+        (trading_dir / "trader.md").write_text(final_state["trader_investment_plan"], encoding="utf-8")
         sections.append(f"## III. Trading Team Plan\n\n### Trader\n{final_state['trader_investment_plan']}")
 
     # 4. Risk Management
@@ -676,15 +679,15 @@ def save_report_to_disk(final_state, ticker: str, save_path: Path):
         risk_parts = []
         if risk.get("aggressive_history"):
             risk_dir.mkdir(exist_ok=True)
-            (risk_dir / "aggressive.md").write_text(risk["aggressive_history"])
+            (risk_dir / "aggressive.md").write_text(risk["aggressive_history"], encoding="utf-8")
             risk_parts.append(("Aggressive Analyst", risk["aggressive_history"]))
         if risk.get("conservative_history"):
             risk_dir.mkdir(exist_ok=True)
-            (risk_dir / "conservative.md").write_text(risk["conservative_history"])
+            (risk_dir / "conservative.md").write_text(risk["conservative_history"], encoding="utf-8")
             risk_parts.append(("Conservative Analyst", risk["conservative_history"]))
         if risk.get("neutral_history"):
             risk_dir.mkdir(exist_ok=True)
-            (risk_dir / "neutral.md").write_text(risk["neutral_history"])
+            (risk_dir / "neutral.md").write_text(risk["neutral_history"], encoding="utf-8")
             risk_parts.append(("Neutral Analyst", risk["neutral_history"]))
         if risk_parts:
             content = "\n\n".join(f"### {name}\n{text}" for name, text in risk_parts)
@@ -694,12 +697,12 @@ def save_report_to_disk(final_state, ticker: str, save_path: Path):
         if risk.get("judge_decision"):
             portfolio_dir = save_path / "5_portfolio"
             portfolio_dir.mkdir(exist_ok=True)
-            (portfolio_dir / "decision.md").write_text(risk["judge_decision"])
+            (portfolio_dir / "decision.md").write_text(risk["judge_decision"], encoding="utf-8")
             sections.append(f"## V. Portfolio Manager Decision\n\n### Portfolio Manager\n{risk['judge_decision']}")
 
     # Write consolidated report
     header = f"# Trading Analysis Report: {ticker}\n\nGenerated: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
-    (save_path / "complete_report.md").write_text(header + "\n\n".join(sections))
+    (save_path / "complete_report.md").write_text(header + "\n\n".join(sections), encoding="utf-8")
     return save_path / "complete_report.md"
 
 
@@ -907,7 +910,12 @@ def run_analysis():
     config["quick_think_llm"] = selections["shallow_thinker"]
     config["deep_think_llm"] = selections["deep_thinker"]
     config["backend_url"] = selections["backend_url"]
-    config["llm_provider"] = selections["llm_provider"].lower()
+    config["llm_provider"] = selections["llm_provider"]
+    # Azure Foundry: use endpoint from config/env, not backend_url
+    if selections["llm_provider"] == "azure_foundry":
+        # Keep the default azure_foundry_endpoint from config (or env var)
+        # Don't overwrite it with the empty backend_url from the provider list
+        pass
     # Provider-specific thinking configuration
     config["google_thinking_level"] = selections.get("google_thinking_level")
     config["openai_reasoning_effort"] = selections.get("openai_reasoning_effort")
@@ -948,7 +956,7 @@ def run_analysis():
             func(*args, **kwargs)
             timestamp, message_type, content = obj.messages[-1]
             content = content.replace("\n", " ")  # Replace newlines with spaces
-            with open(log_file, "a") as f:
+            with open(log_file, "a", encoding="utf-8") as f:
                 f.write(f"{timestamp} [{message_type}] {content}\n")
         return wrapper
     
@@ -959,7 +967,7 @@ def run_analysis():
             func(*args, **kwargs)
             timestamp, tool_name, args = obj.tool_calls[-1]
             args_str = ", ".join(f"{k}={v}" for k, v in args.items())
-            with open(log_file, "a") as f:
+            with open(log_file, "a", encoding="utf-8") as f:
                 f.write(f"{timestamp} [Tool Call] {tool_name}({args_str})\n")
         return wrapper
 
@@ -972,7 +980,7 @@ def run_analysis():
                 content = obj.report_sections[section_name]
                 if content:
                     file_name = f"{section_name}.md"
-                    with open(report_dir / file_name, "w") as f:
+                    with open(report_dir / file_name, "w", encoding="utf-8") as f:
                         f.write(content)
         return wrapper
 

cli/utils.py

@@ -160,6 +160,20 @@ def select_shallow_thinking_agent(provider) -> str:
             ("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
             ("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
         ],
+        "azure_foundry": [
+            ("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"),
+            ("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"),
+            ("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"),
+        ],
     }
 
     choice = questionary.select(
@@ -228,6 +242,22 @@ def select_deep_thinking_agent(provider) -> str:
             ("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
             ("Qwen3:latest (8B, local)", "qwen3:latest"),
         ],
+        "azure_foundry": [
+            ("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"),
+            ("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"),
+            ("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"),
+        ],
     }
 
     choice = questionary.select(
@@ -260,6 +290,7 @@ def select_llm_provider() -> tuple[str, str]:
         ("Google", "https://generativelanguage.googleapis.com/v1"),
         ("Anthropic", "https://api.anthropic.com/"),
         ("xAI", "https://api.x.ai/v1"),
+        ("Azure Foundry", ""),  # Endpoint read from config/env var
         ("Openrouter", "https://openrouter.ai/api/v1"),
         ("Ollama", "http://localhost:11434/v1"),
     ]

tradingagents/agents/managers/risk_manager.py

@@ -11,7 +11,7 @@ def create_risk_manager(llm, memory):
         risk_debate_state = state["risk_debate_state"]
         market_research_report = state["market_report"]
         news_report = state["news_report"]
-        fundamentals_report = state["news_report"]
+        fundamentals_report = state["fundamentals_report"]
         sentiment_report = state["sentiment_report"]
         trader_plan = state["investment_plan"]
 

tradingagents/default_config.py

@@ -8,6 +8,7 @@ DEFAULT_CONFIG = {
         "dataflows/data_cache",
     ),
     # LLM settings
+    # Supported providers: openai, anthropic, google, xai, ollama, openrouter, azure_foundry
     "llm_provider": "openai",
     "deep_think_llm": "gpt-5.2",
     "quick_think_llm": "gpt-5-mini",
@@ -15,7 +16,9 @@ DEFAULT_CONFIG = {
     # Provider-specific thinking configuration
     "google_thinking_level": None,      # "high", "minimal", etc.
     "openai_reasoning_effort": None,    # "medium", "high", "low"
-    # Debate and discussion settings
+    # Azure Foundry settings (set env var AZURE_FOUNDRY_API_KEY in .env,
+    # and AZURE_FOUNDRY_ENDPOINT here or as env var)
+    "azure_foundry_endpoint": "https://<resource>.openai.azure.com/openai/v1/",
     "max_debate_rounds": 1,
     "max_risk_discuss_rounds": 1,
     "max_recur_limit": 100,

tradingagents/graph/trading_graph.py

@@ -145,6 +145,14 @@ class TradingAgentsGraph:
             if reasoning_effort:
                 kwargs["reasoning_effort"] = reasoning_effort
 
+        elif provider == "azure_foundry":
+            endpoint = self.config.get("azure_foundry_endpoint")
+            if endpoint:
+                kwargs["azure_foundry_endpoint"] = endpoint
+            api_key = self.config.get("azure_foundry_api_key")
+            if api_key:
+                kwargs["api_key"] = api_key
+
         return kwargs
 
     def _create_tool_nodes(self) -> Dict[str, ToolNode]:

tradingagents/llm_clients/azure_foundry_client.py

@@ -0,0 +1,73 @@
+import os
+from typing import Any, Optional
+
+from langchain_openai import ChatOpenAI
+
+from .base_client import BaseLLMClient
+from .validators import validate_model
+
+
+class AzureFoundryClient(BaseLLMClient):
+    """Client for models hosted on Azure AI Foundry.
+
+    Azure AI Foundry exposes an OpenAI-compatible chat completions endpoint,
+    so we use ChatOpenAI with a custom base_url pointing to your Foundry
+    deployment.
+
+    Required environment variables (unless passed explicitly):
+        AZURE_FOUNDRY_ENDPOINT: Your Azure Foundry inference endpoint URI
+            e.g. https://<resource>.services.ai.azure.com/models
+                 https://<endpoint>.<region>.models.ai.azure.com/v1
+        AZURE_FOUNDRY_API_KEY: Your Azure Foundry API key
+    """
+
+    def __init__(
+        self,
+        model: str,
+        base_url: Optional[str] = None,
+        **kwargs,
+    ):
+        super().__init__(model, base_url, **kwargs)
+
+    def get_llm(self) -> Any:
+        """Return a ChatOpenAI instance configured for Azure Foundry."""
+        # Resolve endpoint: explicit kwarg > base_url > env var
+        endpoint = (
+            self.kwargs.get("azure_foundry_endpoint")
+            or self.base_url
+            or os.environ.get("AZURE_FOUNDRY_ENDPOINT")
+        )
+        if not endpoint:
+            raise ValueError(
+                "Azure Foundry endpoint is required. Set the AZURE_FOUNDRY_ENDPOINT "
+                "environment variable, pass 'backend_url' in the config, or provide "
+                "'azure_foundry_endpoint' in kwargs."
+            )
+
+        # Resolve API key: explicit kwarg > env var
+        api_key = (
+            self.kwargs.get("api_key")
+            or os.environ.get("AZURE_FOUNDRY_API_KEY")
+        )
+        if not api_key:
+            raise ValueError(
+                "Azure Foundry API key is required. Set the AZURE_FOUNDRY_API_KEY "
+                "environment variable or pass 'api_key' in kwargs."
+            )
+
+        llm_kwargs = {
+            "model": self.model,
+            "base_url": endpoint,
+            "api_key": api_key,
+        }
+
+        # Forward optional params
+        for key in ("timeout", "max_retries", "temperature", "max_tokens", "callbacks"):
+            if key in self.kwargs:
+                llm_kwargs[key] = self.kwargs[key]
+
+        return ChatOpenAI(**llm_kwargs)
+
+    def validate_model(self) -> bool:
+        """Validate model for Azure Foundry."""
+        return validate_model("azure_foundry", self.model)

tradingagents/llm_clients/factory.py

@@ -4,6 +4,7 @@ from .base_client import BaseLLMClient
 from .openai_client import OpenAIClient
 from .anthropic_client import AnthropicClient
 from .google_client import GoogleClient
+from .azure_foundry_client import AzureFoundryClient
 
 
 def create_llm_client(
@@ -40,4 +41,7 @@ def create_llm_client(
     if provider_lower == "google":
         return GoogleClient(model, base_url, **kwargs)
 
+    if provider_lower == "azure_foundry":
+        return AzureFoundryClient(model, base_url, **kwargs)
+
     raise ValueError(f"Unsupported LLM provider: {provider}")

tradingagents/llm_clients/validators.py

@@ -63,6 +63,31 @@ VALID_MODELS = {
         "grok-4-fast-reasoning",
         "grok-4-fast-non-reasoning",
     ],
+    # Azure Foundry can host any model from the catalog;
+    # list common ones here but any model name is accepted.
+    "azure_foundry": [
+        # OpenAI models on Azure
+        "gpt-4o",
+        "gpt-4o-mini",
+        "gpt-4.1",
+        "gpt-4.1-mini",
+        "gpt-4.1-nano",
+        "o4-mini",
+        "o3",
+        "o3-mini",
+        # Meta Llama models
+        "Meta-Llama-3.3-70B-Instruct",
+        "Meta-Llama-3.1-405B-Instruct",
+        # Mistral models
+        "Mistral-Large-2",
+        "Mistral-Small",
+        # Cohere models
+        "Cohere-command-r-plus",
+        "Cohere-command-r",
+        # DeepSeek models
+        "DeepSeek-R1",
+        "DeepSeek-V3",
+    ],
 }
 
 
@@ -73,7 +98,7 @@ def validate_model(provider: str, model: str) -> bool:
     """
     provider_lower = provider.lower()
 
-    if provider_lower in ("ollama", "openrouter"):
+    if provider_lower in ("ollama", "openrouter", "azure_foundry"):
         return True
 
     if provider_lower not in VALID_MODELS: