feat: add footer statistics tracking with LangChain callbacks

- Add StatsCallbackHandler for tracking LLM calls, tool calls, and tokens
- Integrate callbacks into TradingAgentsGraph and all LLM clients
- Dynamic agent/report counts based on selected analysts
- Fix report completion counting (tied to agent completion)

tradingagents/graph/propagation.py

@@ -1,6 +1,6 @@
 # TradingAgents/graph/propagation.py
 
-from typing import Dict, Any
+from typing import Dict, Any, List, Optional
 from tradingagents.agents.utils.agent_states import (
     AgentState,
     InvestDebateState,
@@ -41,9 +41,17 @@ class Propagator:
             "news_report": "",
         }
 
-    def get_graph_args(self) -> Dict[str, Any]:
-        """Get arguments for the graph invocation."""
+    def get_graph_args(self, callbacks: Optional[List] = None) -> Dict[str, Any]:
+        """Get arguments for the graph invocation.
+
+        Args:
+            callbacks: Optional list of callback handlers for tool execution tracking.
+                       Note: LLM callbacks are handled separately via LLM constructor.
+        """
+        config = {"recursion_limit": self.max_recur_limit}
+        if callbacks:
+            config["callbacks"] = callbacks
         return {
             "stream_mode": "values",
-            "config": {"recursion_limit": self.max_recur_limit},
+            "config": config,
         }

tradingagents/graph/trading_graph.py

@@ -48,6 +48,7 @@ class TradingAgentsGraph:
         selected_analysts=["market", "social", "news", "fundamentals"],
         debug=False,
         config: Dict[str, Any] = None,
+        callbacks: Optional[List] = None,
     ):
         """Initialize the trading agents graph and components.
 
@@ -55,9 +56,11 @@ class TradingAgentsGraph:
             selected_analysts: List of analyst types to include
             debug: Whether to run in debug mode
             config: Configuration dictionary. If None, uses default config
+            callbacks: Optional list of callback handlers (e.g., for tracking LLM/tool stats)
         """
         self.debug = debug
         self.config = config or DEFAULT_CONFIG
+        self.callbacks = callbacks or []
 
         # Update the interface's config
         set_config(self.config)
@@ -71,6 +74,10 @@ class TradingAgentsGraph:
         # Initialize LLMs with provider-specific thinking configuration
         llm_kwargs = self._get_provider_kwargs()
 
+        # Add callbacks to kwargs if provided (passed to LLM constructor)
+        if self.callbacks:
+            llm_kwargs["callbacks"] = self.callbacks
+
         deep_client = create_llm_client(
             provider=self.config["llm_provider"],
             model=self.config["deep_think_llm"],
@@ -83,6 +90,7 @@ class TradingAgentsGraph:
             base_url=self.config.get("backend_url"),
             **llm_kwargs,
         )
+
         self.deep_thinking_llm = deep_client.get_llm()
         self.quick_thinking_llm = quick_client.get_llm()
         

tradingagents/llm_clients/anthropic_client.py

@@ -16,7 +16,7 @@ class AnthropicClient(BaseLLMClient):
         """Return configured ChatAnthropic instance."""
         llm_kwargs = {"model": self.model}
 
-        for key in ("timeout", "max_retries", "api_key", "max_tokens"):
+        for key in ("timeout", "max_retries", "api_key", "max_tokens", "callbacks"):
             if key in self.kwargs:
                 llm_kwargs[key] = self.kwargs[key]
 

tradingagents/llm_clients/factory.py

@@ -4,7 +4,6 @@ from .base_client import BaseLLMClient
 from .openai_client import OpenAIClient
 from .anthropic_client import AnthropicClient
 from .google_client import GoogleClient
-from .vllm_client import VLLMClient
 
 
 def create_llm_client(
@@ -16,7 +15,7 @@ def create_llm_client(
     """Create an LLM client for the specified provider.
 
     Args:
-        provider: LLM provider (openai, anthropic, google, xai, ollama, openrouter, vllm)
+        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
@@ -41,7 +40,4 @@ def create_llm_client(
     if provider_lower == "google":
         return GoogleClient(model, base_url, **kwargs)
 
-    if provider_lower == "vllm":
-        return VLLMClient(model, base_url, **kwargs)
-
     raise ValueError(f"Unsupported LLM provider: {provider}")

tradingagents/llm_clients/google_client.py

@@ -38,7 +38,7 @@ class GoogleClient(BaseLLMClient):
         """Return configured ChatGoogleGenerativeAI instance."""
         llm_kwargs = {"model": self.model}
 
-        for key in ("timeout", "max_retries", "google_api_key"):
+        for key in ("timeout", "max_retries", "google_api_key", "callbacks"):
             if key in self.kwargs:
                 llm_kwargs[key] = self.kwargs[key]
 

tradingagents/llm_clients/openai_client.py

@@ -60,7 +60,7 @@ class OpenAIClient(BaseLLMClient):
         elif self.base_url:
             llm_kwargs["base_url"] = self.base_url
 
-        for key in ("timeout", "max_retries", "reasoning_effort", "api_key"):
+        for key in ("timeout", "max_retries", "reasoning_effort", "api_key", "callbacks"):
             if key in self.kwargs:
                 llm_kwargs[key] = self.kwargs[key]
 

tradingagents/llm_clients/validators.py

@@ -69,11 +69,11 @@ VALID_MODELS = {
 def validate_model(provider: str, model: str) -> bool:
     """Check if model name is valid for the given provider.
 
-    For ollama, openrouter, vllm - any model is accepted.
+    For ollama, openrouter - any model is accepted.
     """
     provider_lower = provider.lower()
 
-    if provider_lower in ("ollama", "openrouter", "vllm"):
+    if provider_lower in ("ollama", "openrouter"):
         return True
 
     if provider_lower not in VALID_MODELS:

tradingagents/llm_clients/vllm_client.py

@@ -1,18 +0,0 @@
-from typing import Any, Optional
-
-from .base_client import BaseLLMClient
-
-
-class VLLMClient(BaseLLMClient):
-    """Client for vLLM (placeholder for future implementation)."""
-
-    def __init__(self, model: str, base_url: Optional[str] = None, **kwargs):
-        super().__init__(model, base_url, **kwargs)
-
-    def get_llm(self) -> Any:
-        """Return configured vLLM instance."""
-        raise NotImplementedError("vLLM client not yet implemented")
-
-    def validate_model(self) -> bool:
-        """Validate model for vLLM."""
-        return True