Update initialization of LLM client for agent base

Signed-off-by: Roberto Rodriguez <9653181+Cyb3rWard0g@users.noreply.github.com>

dapr_agents/agents/base.py

@@ -27,7 +27,6 @@ from typing import (
 )
 from pydantic import BaseModel, Field, PrivateAttr, model_validator, ConfigDict
 from dapr_agents.llm.chat import ChatClientBase
-from dapr_agents.llm.openai import OpenAIChatClient
 
 logger = logging.getLogger(__name__)
 
@@ -66,8 +65,8 @@ class AgentBase(BaseModel, ABC):
         default=None,
         description="A custom system prompt, overriding name, role, goal, and instructions.",
     )
-    llm: ChatClientBase = Field(
-        default_factory=OpenAIChatClient,
+    llm: Optional[ChatClientBase] = Field(
+        default=None,
         description="Language model client for generating responses.",
     )
     prompt_template: Optional[PromptTemplateBase] = Field(
@@ -136,12 +135,16 @@ Your role is {role}.
     @model_validator(mode="after")
     def validate_llm(cls, values):
         """Validate that LLM is properly configured."""
-        if hasattr(values, "llm") and values.llm:
-            try:
-                # Validate LLM is properly configured by accessing it as this is required to be set.
-                _ = values.llm
-            except Exception as e:
-                raise ValueError(f"Failed to initialize LLM: {e}") from e
+        if hasattr(values, "llm"):
+            if values.llm is None:
+                logger.warning("LLM client is None, some functionality may be limited.")
+            else:
+                try:
+                    # Validate LLM is properly configured by accessing it as this is required to be set.
+                    _ = values.llm
+                except Exception as e:
+                    logger.error(f"Failed to initialize LLM: {e}")
+                    values.llm = None
 
         return values
 
@@ -160,10 +163,15 @@ Your role is {role}.
         if self.tool_choice is None:
             self.tool_choice = "auto" if self.tools else None
 
+        # Initialize LLM if not provided
+        if self.llm is None:
+            self.llm = self._create_default_llm()
+
         # Centralize prompt template selection logic
         self.prompt_template = self._initialize_prompt_template()
         # Ensure LLM client and agent both reference the same template
-        self.llm.prompt_template = self.prompt_template
+        if self.llm is not None:
+            self.llm.prompt_template = self.prompt_template
 
         self._validate_prompt_template()
         self.prefill_agent_attributes()
@@ -174,6 +182,18 @@ Your role is {role}.
 
         super().model_post_init(__context)
 
+    def _create_default_llm(self) -> Optional[ChatClientBase]:
+        """
+        Creates a default LLM client when none is provided.
+        Returns None if the default LLM cannot be created due to missing configuration.
+        """
+        try:
+            from dapr_agents.llm.openai import OpenAIChatClient
+            return OpenAIChatClient()
+        except Exception as e:
+            logger.warning(f"Failed to create default OpenAI client: {e}. LLM will be None.")
+            return None
+
     def _initialize_prompt_template(self) -> PromptTemplateBase:
         """
         Determines which prompt template to use for the agent:
@@ -190,7 +210,7 @@ Your role is {role}.
             return self.prompt_template
 
         # 2) LLM client has one?
-        if self.llm.prompt_template:
+        if self.llm and hasattr(self.llm, 'prompt_template') and self.llm.prompt_template:
             logger.debug("🔄 Syncing from llm.prompt_template")
             return self.llm.prompt_template