diff --git a/.env.example b/.env.example
index 1328b838..fd1468e2 100644
--- a/.env.example
+++ b/.env.example
@@ -4,3 +4,7 @@ GOOGLE_API_KEY=
 ANTHROPIC_API_KEY=
 XAI_API_KEY=
 OPENROUTER_API_KEY=
+
+# vllm Configuration (optional)
+VLLM_API_BASE=http://localhost:8000/v1
+VLLM_API_KEY=
diff --git a/tradingagents/default_config.py b/tradingagents/default_config.py
index ecf0dc29..305ed4b3 100644
--- a/tradingagents/default_config.py
+++ b/tradingagents/default_config.py
@@ -15,6 +15,7 @@ DEFAULT_CONFIG = {
     # Provider-specific thinking configuration
     "google_thinking_level": None,      # "high", "minimal", etc.
     "openai_reasoning_effort": None,    # "medium", "high", "low"
+    "vllm_api_base": None,              # vllm API base URL (defaults to http://localhost:8000/v1)
     # Debate and discussion settings
     "max_debate_rounds": 1,
     "max_risk_discuss_rounds": 1,
diff --git a/tradingagents/graph/trading_graph.py b/tradingagents/graph/trading_graph.py
index 44ecca0c..f5eb1902 100644
--- a/tradingagents/graph/trading_graph.py
+++ b/tradingagents/graph/trading_graph.py
@@ -145,6 +145,13 @@ class TradingAgentsGraph:
             if reasoning_effort:
                 kwargs["reasoning_effort"] = reasoning_effort
 
+        elif provider == "vllm":
+            # vllm specific settings
+            api_base = self.config.get("vllm_api_base")
+            if api_base:
+                kwargs["base_url"] = api_base
+            # Add any vllm-specific parameters here if needed
+
         return kwargs
 
     def _create_tool_nodes(self) -> Dict[str, ToolNode]:
diff --git a/tradingagents/llm_clients/factory.py b/tradingagents/llm_clients/factory.py
index 028c88a2..e10e83da 100644
--- a/tradingagents/llm_clients/factory.py
+++ b/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 .vllm_client import VLLMClient
 
 
 def create_llm_client(
@@ -15,7 +16,7 @@ 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 (openai, anthropic, google, xai, ollama, openrouter, vllm)
         model: Model name/identifier
         base_url: Optional base URL for API endpoint
         **kwargs: Additional provider-specific arguments
@@ -40,4 +41,7 @@ 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}")
diff --git a/tradingagents/llm_clients/vllm_client.py b/tradingagents/llm_clients/vllm_client.py
new file mode 100644
index 00000000..2779313b
--- /dev/null
+++ b/tradingagents/llm_clients/vllm_client.py
@@ -0,0 +1,32 @@
+import os
+from typing import Any, Optional
+
+from langchain_openai import ChatOpenAI
+
+from .base_client import BaseLLMClient
+
+
+class VLLMClient(BaseLLMClient):
+    """Client for vllm models. Uses OpenAI-compatible API."""
+
+    def __init__(self, model: str, base_url: Optional[str] = None, **kwargs):
+        super().__init__(model, base_url, **kwargs)
+
+    def get_llm(self) -> Any:
+        """Return configured ChatOpenAI instance for vllm."""
+        llm_kwargs = {
+            "model": self.model,
+            "base_url": self.base_url or os.environ.get("VLLM_API_BASE", "http://localhost:8000/v1"),
+            "api_key": self.kwargs.get("api_key") or os.environ.get("VLLM_API_KEY") or "vllm",
+        }
+
+        # Add supported parameters
+        for key in ("temperature", "top_p", "max_tokens", "timeout", "max_retries", "callbacks"):
+            if key in self.kwargs:
+                llm_kwargs[key] = self.kwargs[key]
+
+        return ChatOpenAI(**llm_kwargs)
+
+    def validate_model(self) -> bool:
+        """Validate model name is provided."""
+        return bool(self.model and self.model.strip())