[Update] Default LLM (#3587)

examples/graph-db-demo/kuzu-example.ipynb

@@ -225,7 +225,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 24,
+      "execution_count": null,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -239,7 +239,7 @@
         "    # Generate Assistant response\n",
         "    system_prompt = f\"You are a helpful AI. Answer the question based on query and memories.\\nUser Memories:\\n{memories_str}\"\n",
         "    messages = [{\"role\": \"system\", \"content\": system_prompt}, {\"role\": \"user\", \"content\": message}]\n",
-        "    response = openai_client.chat.completions.create(model=\"gpt-4o-mini\", messages=messages)\n",
+        "    response = openai_client.chat.completions.create(model=\"gpt-4.1-nano-2025-04-14\", messages=messages)\n",
         "    assistant_response = response.choices[0].message.content\n",
         "\n",
         "    # Create new memories from the conversation\n",

examples/misc/diet_assistant_voice_cartesia.py

@@ -37,7 +37,7 @@ food_agent = Agent(
     name="Personal Food Assistant",
     description="Provides personalized food recommendations with memory and generates voice responses using Cartesia TTS tools.",
     instructions=agent_instructions,
-    model=OpenAIChat(id="gpt-4o"),
+    model=OpenAIChat(id="gpt-4.1-nano-2025-04-14"),
     tools=[CartesiaTools(voice_localize_enabled=True)],
     show_tool_calls=True,
 )

examples/misc/fitness_checker.py

@@ -1,6 +1,6 @@
 """
 Simple Fitness Memory Tracker that tracks your fitness progress and knows your health priorities.
-Uses Mem0 for memory and GPT-4o for image understanding.
+Uses Mem0 for memory and gpt-4.1-nano for image understanding.
 
 In order to run this file, you need to set up your Mem0 API at Mem0 platform and also need an OpenAI API key.
 export OPENAI_API_KEY="your_openai_api_key"
@@ -18,7 +18,7 @@ USER_ID = "Anish"
 
 agent = Agent(
     name="Fitness Agent",
-    model=OpenAIChat(id="gpt-4o"),
+    model=OpenAIChat(id="gpt-4.1-nano-2025-04-14"),
     description="You are a helpful fitness assistant who remembers past logs and gives personalized suggestions for Anish's training and diet.",
     markdown=True,
 )

examples/misc/multillm_memory.py

@@ -34,7 +34,7 @@ memory = MemoryClient()
 # Research team models with specialized roles
 RESEARCH_TEAM = {
     "tech_analyst": {
-        "model": "gpt-4o",
+        "model": "gpt-4.1-nano-2025-04-14",
         "role": "Technical Analyst - Code review, architecture, and technical decisions",
     },
     "writer": {
@@ -42,7 +42,7 @@ RESEARCH_TEAM = {
         "role": "Documentation Writer - Clear explanations and user guides",
     },
     "data_analyst": {
-        "model": "gpt-4o-mini",
+        "model": "gpt-4.1-nano-2025-04-14",
         "role": "Data Analyst - Insights, trends, and data-driven recommendations",
     },
 }

examples/misc/personal_assistant_agno.py

@@ -21,7 +21,7 @@ client = MemoryClient()
 # Define the agent
 agent = Agent(
     name="Personal Agent",
-    model=OpenAIChat(id="gpt-4o"),
+    model=OpenAIChat(id="gpt-4.1-nano-2025-04-14"),
     description="You are a helpful personal agent that helps me with day to day activities."
     "You can process both text and images.",
     markdown=True,

examples/misc/personalized_search.py

@@ -35,7 +35,7 @@ BE IT TIME, LOCATION, USER'S PERSONAL LIFE, CHOICES, USER'S PREFERENCES, we need
 '''
 )
 
-llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.2)
+llm = ChatOpenAI(model="gpt-4.1-nano-2025-04-14", temperature=0.2)
 
 
 def setup_user_history(user_id):

examples/misc/test.py

@@ -35,7 +35,7 @@ travel_agent = Agent(
     understand the user's travel preferences and history before making recommendations.
     After providing your response, use store_conversation to save important details.""",
     tools=[search_memory, save_memory],
-    model="gpt-4o",
+    model="gpt-4.1-nano-2025-04-14",
 )
 
 health_agent = Agent(
@@ -44,7 +44,7 @@ health_agent = Agent(
     understand the user's health goals and dietary preferences.
     After providing advice, use store_conversation to save relevant information.""",
     tools=[search_memory, save_memory],
-    model="gpt-4o",
+    model="gpt-4.1-nano-2025-04-14",
 )
 
 # Triage agent with handoffs
@@ -55,7 +55,7 @@ triage_agent = Agent(
     For health-related questions (fitness, diet, wellness, exercise), hand off to Health Advisor.
     For general questions, you can handle them directly using available tools.""",
     handoffs=[travel_agent, health_agent],
-    model="gpt-4o",
+    model="gpt-4.1-nano-2025-04-14",
 )
 
 

examples/multiagents/llamaindex_learning_system.py

@@ -42,7 +42,7 @@ class MultiAgentLearningSystem:
 
     def __init__(self, student_id: str):
         self.student_id = student_id
-        self.llm = OpenAI(model="gpt-4o", temperature=0.2)
+        self.llm = OpenAI(model="gpt-4.1-nano-2025-04-14", temperature=0.2)
 
         # Memory context for this student
         self.memory_context = {"user_id": student_id, "app": "learning_assistant"}

examples/multimodal-demo/src/hooks/useChat.ts

@@ -172,7 +172,7 @@ export const useChat = ({ user, mem0ApiKey, openaiApiKey }: UseChatProps): UseCh
       }
 
       const completion = await openai.chat.completions.create({
-        model: "gpt-4o-mini",
+        model: "gpt-4.1-nano-2025-04-14",
         // eslint-disable-next-line @typescript-eslint/ban-ts-comment
         // @ts-expect-error
         messages: messagesForLLM.map(msg => ({

examples/multimodal-demo/useChat.ts

@@ -172,7 +172,7 @@ export const useChat = ({ user, mem0ApiKey, openaiApiKey }: UseChatProps): UseCh
       }
 
       const completion = await openai.chat.completions.create({
-        model: "gpt-4o-mini",
+        model: "gpt-4.1-nano-2025-04-14",
         // eslint-disable-next-line @typescript-eslint/ban-ts-comment
         // @ts-expect-error
         messages: messagesForLLM.map(msg => ({