[Update] Default LLM (#3587)

docs/components/llms/models/langchain.mdx

@@ -20,7 +20,7 @@ os.environ["OPENAI_API_KEY"] = "your-api-key"
 
 # Initialize a LangChain model directly
 openai_model = ChatOpenAI(
-    model="gpt-4o",
+    model="gpt-4.1-nano-2025-04-14",
     temperature=0.2,
     max_tokens=2000
 )

docs/components/llms/models/litellm.mdx

@@ -12,7 +12,7 @@ config = {
     "llm": {
         "provider": "litellm",
         "config": {
-            "model": "gpt-4o-mini",
+            "model": "gpt-4.1-nano-2025-04-14",
             "temperature": 0.2,
             "max_tokens": 2000,
         }

docs/components/llms/models/openai.mdx

@@ -19,7 +19,7 @@ config = {
     "llm": {
         "provider": "openai",
         "config": {
-            "model": "gpt-4o",
+            "model": "gpt-4.1-nano-2025-04-14",
             "temperature": 0.2,
             "max_tokens": 2000,
         }
@@ -85,7 +85,7 @@ config = {
     "llm": {
         "provider": "openai_structured",
         "config": {
-            "model": "gpt-4o-2024-08-06",
+            "model": "gpt-4.1-nano-2025-04-14",
             "temperature": 0.0,
         }
     }

docs/examples/ai_companion_js.mdx

@@ -45,7 +45,7 @@ ${memoriesStr}`;
   ];
   
   const response = await openaiClient.chat.completions.create({
-    model: "gpt-4o-mini",
+    model: "gpt-4.1-nano-2025-04-14",
     messages: messages
   });
   

docs/examples/collaborative-task-agent.mdx

@@ -51,7 +51,7 @@ class CollaborativeAgent:
             {"role": "user", "content": f"Prompt: {prompt}\nContext:\n{context}"}
         ]
         reply = client.chat.completions.create(
-            model="gpt-4o-mini",
+            model="gpt-4.1-nano-2025-04-14",
             messages=messages
         ).choices[0].message.content.strip()
         self.add_message("assistant", "assistant", reply)

docs/examples/eliza_os.mdx

@@ -44,7 +44,7 @@ MEM0_API_KEY= # Mem0 API Key (get from https://app.mem0.ai/dashboard/api-keys)
 MEM0_USER_ID= # Default: eliza-os-user
 MEM0_PROVIDER= # Default: openai
 MEM0_PROVIDER_API_KEY= # API Key for the provider (OpenAI, Anthropic, etc.)
-SMALL_MEM0_MODEL= # Default: gpt-4o-mini
+SMALL_MEM0_MODEL= # Default: gpt-4.1-nano
 MEDIUM_MEM0_MODEL= # Default: gpt-4o
 LARGE_MEM0_MODEL= # Default: gpt-4o
 ```

docs/examples/llama-index-mem0.mdx

@@ -20,7 +20,7 @@ import os
 from llama_index.llms.openai import OpenAI
 
 os.environ["OPENAI_API_KEY"] = "<your-openai-api-key>"
-llm = OpenAI(model="gpt-4o")
+llm = OpenAI(model="gpt-4.1-nano-2025-04-14")
 ```
 
 Initialize the Mem0 client. You can find your API key [here](https://app.mem0.ai/dashboard/api-keys). Read about Mem0 [Open Source](https://docs.mem0.ai/open-source/overview).

docs/examples/llamaindex-multiagent-learning-system.mdx

@@ -81,7 +81,7 @@ 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"}

docs/examples/mem0-mastra.mdx

@@ -97,7 +97,7 @@ export const mem0Agent = new Agent({
   instructions: `
     You are a helpful assistant that has the ability to memorize and remember facts using Mem0.
   `,
-  model: openai('gpt-4o'),
+  model: openai('gpt-4.1-nano'),
   tools: { mem0RememberTool, mem0MemorizeTool },
 });
 ```

docs/examples/mem0-openai-voice-demo.mdx

@@ -162,7 +162,7 @@ def create_memory_voice_agent():
             Use the search_memories tool when you need context from past conversations or user asks you to recall something.
             """,
         ),
-        model="gpt-4o",
+        model="gpt-4.1-nano-2025-04-14",
         tools=[save_memories, search_memories],
     )
     
@@ -171,7 +171,7 @@ def create_memory_voice_agent():
 
 This function:
 - Creates an OpenAI Agent with specific instructions
-- Configures it to use gpt-4o (you can use other models)
+- Configures it to use gpt-4.1-nano (you can use other models)
 - Registers the memory-related tools with the agent
 - Uses `prompt_with_handoff_instructions` to include standard voice agent behaviors
 
@@ -369,7 +369,7 @@ def create_memory_voice_agent():
             Use the search_memories tool when you need context from past conversations or user asks you to recall something.
             """,
         ),
-        model="gpt-4o",
+        model="gpt-4.1-nano-2025-04-14",
         tools=[save_memories, search_memories],
     )
     

docs/examples/memory-guided-content-writing.mdx

@@ -103,7 +103,7 @@ Preferences:
     ]
 
     response = openai.chat.completions.create(
-        model="gpt-4o-mini",
+        model="gpt-4.1-nano-2025-04-14",
         messages=messages
     )
     clean_response = response.choices[0].message.content.strip()

docs/examples/openai-inbuilt-tools.mdx

@@ -119,7 +119,7 @@ const carRecommendationTool = zodResponsesFunction({
 
 // Use the tool in your OpenAI request
 const response = await openAIClient.responses.create({
-    model: "gpt-4o",
+    model: "gpt-4.1-nano-2025-04-14",
     tools: [{ type: "web_search_preview" }, carRecommendationTool],
     input: `${getMemoryString(relevantMemories)}\n${userInput}`,
 });
@@ -131,7 +131,7 @@ Combine memory with web search for up-to-date recommendations:
 
 ```javascript
 const response = await openAIClient.responses.create({
-    model: "gpt-4o",
+    model: "gpt-4.1-nano-2025-04-14",
     tools: [{ type: "web_search_preview" }, carRecommendationTool],
     input: `${getMemoryString(relevantMemories)}\n${userInput}`,
 });
@@ -202,7 +202,7 @@ async function main(memory = false) {
   }
 
   const response = await openAIClient.responses.create({
-    model: "gpt-4o",
+    model: "gpt-4.1-nano-2025-04-14",
     tools: [{ type: "web_search_preview" }, tool],
     input: `${getMemoryString(relevantMemories)}\n${input}`,
   });

docs/examples/personal-ai-tutor.mdx

@@ -58,7 +58,7 @@ class PersonalAITutor:
         """
         # Start a streaming response request to the AI
         response = self.client.responses.create(
-            model="gpt-4o",
+            model="gpt-4.1-nano-2025-04-14",
             instructions="You are a personal AI Tutor.",
             input=question,
             stream=True

docs/examples/personal-travel-assistant.mdx

@@ -35,7 +35,7 @@ config = {
     "llm": {
         "provider": "openai",
         "config": {
-            "model": "gpt-4o",
+            "model": "gpt-4.1-nano-2025-04-14",
             "temperature": 0.1,
             "max_tokens": 2000,
         }
@@ -76,7 +76,7 @@ class PersonalTravelAssistant:
 
         # Generate response using Responses API
         response = self.client.responses.create(
-            model="gpt-4o",
+            model="gpt-4.1-nano-2025-04-14",
             input=prompt
         )
 
@@ -140,9 +140,9 @@ class PersonalTravelAssistant:
             prompt = f"User input: {question}\n Previous memories: {previous_memories}"
         self.messages.append({"role": "user", "content": prompt})
 
-        # Generate response using GPT-4o
+        # Generate response using gpt-4.1-nano
         response = self.client.chat.completions.create(
-            model="gpt-4o",
+            model="gpt-4.1-nano-2025-04-14"2025-04-14",
             messages=self.messages
         )
         answer = response.choices[0].message.content

docs/integrations/agentops.mdx

@@ -49,7 +49,7 @@ local_config = {
     "llm": {
         "provider": "openai",
         "config": {
-            "model": "gpt-4o-mini",
+            "model": "gpt-4.1-nano-2025-04-14",
             "temperature": 0.1,
             "max_tokens": 2000,
         },

docs/integrations/agno.mdx

@@ -36,7 +36,7 @@ from agno.tools.mem0 import Mem0Tools
 
 agent = Agent(
     name="Memory Agent",
-    model=OpenAIChat(id="gpt-4o-mini"),
+    model=OpenAIChat(id="gpt-4.1-nano-2025-04-14"),
     tools=[Mem0Tools()],
     description="An assistant that remembers and personalizes using Mem0 memory."
 )

docs/integrations/keywords.mdx

@@ -55,7 +55,7 @@ config = {
     "llm": {
         "provider": "openai",
         "config": {
-            "model": "gpt-4o-mini",
+            "model": "gpt-4.1-nano-2025-04-14",
             "temperature": 0.0,
             "api_key": keywordsai_api_key,
             "openai_base_url": base_url,
@@ -101,7 +101,7 @@ messages = [
 
 # Add memory and generate a response
 response = client.chat.completions.create(
-    model="openai/gpt-4o",
+    model="openai/gpt-4.1-nano",
     messages=messages,
     extra_body={
         "mem0_params": {

docs/integrations/langchain.mdx

@@ -39,7 +39,7 @@ load_dotenv()
 # os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
 
 # Initialize LangChain and Mem0
-llm = ChatOpenAI(model="gpt-4o-mini")
+llm = ChatOpenAI(model="gpt-4.1-nano-2025-04-14")
 mem0 = MemoryClient()
 ```
 

docs/integrations/livekit.mdx

@@ -147,7 +147,7 @@ async def entrypoint(ctx: JobContext):
 
     session = AgentSession(
         stt=deepgram.STT(),
-        llm=openai.LLM(model="gpt-4o-mini"),
+        llm=openai.LLM(model="gpt-4.1-nano-2025-04-14"),
         tts=openai.TTS(voice="ash",),
         turn_detection=EnglishModel(),
         vad=silero.VAD.load(),

docs/integrations/llama-index.mdx

@@ -83,7 +83,7 @@ config = {
     "llm": {
         "provider": "openai",
         "config": {
-            "model": "gpt-4o",
+            "model": "gpt-4.1-nano-2025-04-14",
             "temperature": 0.2,
             "max_tokens": 2000,
         },
@@ -116,7 +116,7 @@ from dotenv import load_dotenv
 load_dotenv()
 
 # os.environ["OPENAI_API_KEY"] = "<your-openai-api-key>"
-llm = OpenAI(model="gpt-4o-mini")
+llm = OpenAI(model="gpt-4.1-nano-2025-04-14")
 ```
 
 ### SimpleChatEngine

docs/integrations/mastra.mdx

@@ -106,7 +106,7 @@ const mem0Agent = new Agent({
     Use the Mem0-memorize tool to save important information that might be useful later.
     Use the Mem0-remember tool to recall previously saved information when answering questions.
   `,
-  model: openai('gpt-4o'),
+  model: openai('gpt-4.1-nano'),
   tools: { mem0RememberTool, mem0MemorizeTool },
 });
 ```

docs/integrations/openai-agents-sdk.mdx

@@ -63,7 +63,7 @@ agent = Agent(
     Use the save_memory tool to store important information about the user.
     Always personalize your responses based on available memory.""",
     tools=[search_memory, save_memory],
-    model="gpt-4o"
+    model="gpt-4.1-nano-2025-04-14"
 )
 
 def chat_with_agent(user_input: str, user_id: str) -> str:
@@ -114,7 +114,7 @@ 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(
@@ -123,7 +123,7 @@ 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
@@ -134,7 +134,7 @@ triage_agent = Agent(
     For health-related questions (fitness, diet, wellness, exercise), hand off to the Health Advisor.
     For general questions, handle them directly using available tools.""",
     handoffs=[travel_agent, health_agent],
-    model="gpt-4o"
+    model="gpt-4.1-nano-2025-04-14"
 )
 
 def chat_with_handoffs(user_input: str, user_id: str) -> str:

docs/open-source/features/async-memory.mdx

@@ -202,7 +202,7 @@ async def chat_with_memories(message: str, user_id: str = "default_user") -> str
         # Generate assistant response
         system_prompt = f"You are a helpful AI. Answer the question based on query and memories.\nUser Memories:\n{memories_str}"
         messages = [{"role": "system", "content": system_prompt}, {"role": "user", "content": message}]
-        response = await async_openai_client.chat.completions.create(model="gpt-4o-mini", messages=messages)
+        response = await async_openai_client.chat.completions.create(model="gpt-4.1-nano-2025-04-14", messages=messages)
         assistant_response = response.choices[0].message.content
 
         # Create new memories from the conversation
@@ -249,7 +249,7 @@ async def handle_initialization_errors():
         # Initialize with custom config
         config = MemoryConfig(
             vector_store={"provider": "chroma", "config": {"path": "./chroma_db"}},
-            llm={"provider": "openai", "config": {"model": "gpt-4o-mini"}}
+            llm={"provider": "openai", "config": {"model": "gpt-4.1-nano-2025-04-14"}}
         )
         memory = AsyncMemory(config=config)
         print("AsyncMemory initialized successfully")

docs/open-source/features/custom-fact-extraction-prompt.mdx

@@ -76,7 +76,7 @@ config = {
     "llm": {
         "provider": "openai",
         "config": {
-            "model": "gpt-4o",
+            "model": "gpt-4.1-nano-2025-04-14",
             "temperature": 0.2,
             "max_tokens": 2000,
         }

docs/open-source/features/openai_compatibility.mdx

@@ -27,7 +27,7 @@ messages = [
 user_id = "alice"
 chat_completion = client.chat.completions.create(
     messages=messages,
-    model="gpt-4o-mini",
+    model="gpt-4.1-nano-2025-04-14",
     user_id=user_id
 )
 # Memory saved after this will look like: "Loves Indian food. Allergic to cheese and cannot eat pizza."
@@ -42,7 +42,7 @@ messages = [
 
 chat_completion = client.chat.completions.create(
     messages=messages,
-    model="gpt-4o-mini",
+    model="gpt-4.1-nano-2025-04-14",
     user_id=user_id
 )
 print(chat_completion.choices[0].message.content)
@@ -73,7 +73,7 @@ chat_completion = client.chat.completions.create(
             "content": "What's the capital of France?",
         }
     ],
-    model="gpt-4o",
+    model="gpt-4.1-nano-2025-04-14",
 )
 ```
 

docs/open-source/graph_memory/overview.mdx

@@ -54,7 +54,7 @@ You can also customize the LLM for Graph Memory from the [Supported LLM list](ht
 
 1. **Main Configuration**: If `llm` is set in the main config, it will be used for all graph operations.
 2. **Graph Store Configuration**: If `llm` is set in the graph_store config, it will override the main config `llm` and be used specifically for graph operations.
-3. **Default Configuration**: If no custom LLM is set, the default LLM (`gpt-4o-2024-08-06`) will be used for all graph operations.
+3. **Default Configuration**: If no custom LLM is set, the default LLM (`gpt-4.1-nano-2025-04-14) will be used for all graph operations.
 
 Here's how you can do it:
 
@@ -100,7 +100,7 @@ config = {
     "llm": {
         "provider": "openai",
         "config": {
-            "model": "gpt-4o",
+            "model": "gpt-4.1-nano-2025-04-14",
             "temperature": 0.2,
             "max_tokens": 2000,
         }
@@ -115,7 +115,7 @@ config = {
         "llm" : {
             "provider": "openai",
             "config": {
-                "model": "gpt-4o-mini",
+                "model": "gpt-4.1-nano-2025-04-14",
                 "temperature": 0.0,
             }
         }
@@ -130,7 +130,7 @@ const config = {
     llm: {
         provider: "openai",
         config: {
-            model: "gpt-4o",
+            model: "gpt-4.1-nano-2025-04-14",
             temperature: 0.2,
             max_tokens: 2000,
         }
@@ -146,7 +146,7 @@ const config = {
         llm: {
             provider: "openai",
             config: {
-                model: "gpt-4o-mini",
+                model: "gpt-4.1-nano-2025-04-14",
                 temperature: 0.0,
             }
         }
@@ -177,7 +177,7 @@ You can also customize the LLM for Graph Memory from the [Supported LLM list](ht
 
 1. **Main Configuration**: If `llm` is set in the main config, it will be used for all graph operations.
 2. **Graph Store Configuration**: If `llm` is set in the graph_store config, it will override the main config `llm` and be used specifically for graph operations.
-3. **Default Configuration**: If no custom LLM is set, the default LLM (`gpt-4o-2024-08-06`) will be used for all graph operations.
+3. **Default Configuration**: If no custom LLM is set, the default LLM (`gpt-4.1-nano-2025-04-142025-04-14) will be used for all graph operations.
 
 Here's how you can do it:
 
@@ -241,7 +241,7 @@ User can also customize the LLM for Graph Memory from the [Supported LLM list](h
 
 1. **Main Configuration**: If `llm` is set in the main config, it will be used for all graph operations.
 2. **Graph Store Configuration**: If `llm` is set in the graph_store config, it will override the main config `llm` and be used specifically for graph operations.
-3. **Default Configuration**: If no custom LLM is set, the default LLM (`gpt-4o-2024-08-06`) will be used for all graph operations.
+3. **Default Configuration**: If no custom LLM is set, the default LLM (`gpt-4.1-nano-2025-04-14) will be used for all graph operations.
 
 Here's how you can do it:
 
@@ -290,7 +290,7 @@ User can also customize the LLM for Graph Memory from the [Supported LLM list](h
 
 1. **Main Configuration**: If `llm` is set in the main config, it will be used for all graph operations.
 2. **Graph Store Configuration**: If `llm` is set in the graph_store config, it will override the main config `llm` and be used specifically for graph operations.
-3. **Default Configuration**: If no custom LLM is set, the default LLM (`gpt-4o-2024-08-06`) will be used for all graph operations.
+3. **Default Configuration**: If no custom LLM is set, the default LLM (`gpt-4.1-nano-2025-04-14) will be used for all graph operations.
 
 Here's how you can do it:
 

docs/open-source/python-quickstart.mdx

@@ -504,7 +504,7 @@ chat_completion = client.chat.completions.create(
             "content": "What's the capital of France?",
         }
     ],
-    model="gpt-4o",
+    model="gpt-4.1-nano-2025-04-14",
 )
 ```
 

docs/v0x/components/llms/models/langchain.mdx

@@ -20,7 +20,7 @@ os.environ["OPENAI_API_KEY"] = "your-api-key"
 
 # Initialize a LangChain model directly
 openai_model = ChatOpenAI(
-    model="gpt-4o",
+    model="gpt-4.1-nano-2025-04-14",
     temperature=0.2,
     max_tokens=2000
 )

docs/v0x/components/llms/models/litellm.mdx

@@ -12,7 +12,7 @@ config = {
     "llm": {
         "provider": "litellm",
         "config": {
-            "model": "gpt-4o-mini",
+            "model": "gpt-4.1-nano-2025-04-14",
             "temperature": 0.2,
             "max_tokens": 2000,
         }

docs/v0x/components/llms/models/openai.mdx

@@ -19,7 +19,7 @@ config = {
     "llm": {
         "provider": "openai",
         "config": {
-            "model": "gpt-4o",
+            "model": "gpt-4.1-nano-2025-04-14",
             "temperature": 0.2,
             "max_tokens": 2000,
         }
@@ -85,7 +85,7 @@ config = {
     "llm": {
         "provider": "openai_structured",
         "config": {
-            "model": "gpt-4o-2024-08-06",
+            "model": "gpt-4.1-nano-2025-04-14",
             "temperature": 0.0,
         }
     }

docs/v0x/examples/ai_companion_js.mdx

@@ -45,7 +45,7 @@ ${memoriesStr}`;
   ];
   
   const response = await openaiClient.chat.completions.create({
-    model: "gpt-4o-mini",
+    model: "gpt-4.1-nano-2025-04-14",
     messages: messages
   });
   

docs/v0x/examples/collaborative-task-agent.mdx

@@ -51,7 +51,7 @@ class CollaborativeAgent:
             {"role": "user", "content": f"Prompt: {prompt}\nContext:\n{context}"}
         ]
         reply = client.chat.completions.create(
-            model="gpt-4o-mini",
+            model="gpt-4.1-nano-2025-04-14",
             messages=messages
         ).choices[0].message.content.strip()
         self.add_message("assistant", "assistant", reply)

docs/v0x/examples/eliza_os.mdx

@@ -43,7 +43,7 @@ MEM0_API_KEY= # Mem0 API Key ( Get from https://app.mem0.ai/dashboard/api-keys )
 MEM0_USER_ID= # Default: eliza-os-user
 MEM0_PROVIDER= # Default: openai
 MEM0_PROVIDER_API_KEY= # API Key for the provider (openai, anthropic, etc.)
-SMALL_MEM0_MODEL= # Default: gpt-4o-mini
+SMALL_MEM0_MODEL= # Default: gpt-4.1-nano
 MEDIUM_MEM0_MODEL= # Default: gpt-4o
 LARGE_MEM0_MODEL= # Default: gpt-4o
 ```

docs/v0x/examples/llama-index-mem0.mdx

@@ -18,7 +18,7 @@ import os
 from llama_index.llms.openai import OpenAI
 
 os.environ["OPENAI_API_KEY"] = "<your-openai-api-key>"
-llm = OpenAI(model="gpt-4o")
+llm = OpenAI(model="gpt-4.1-nano-2025-04-14")
 ```
 
 Initialize the Mem0 client. You can find your API key [here](https://app.mem0.ai/dashboard/api-keys). Read about Mem0 [Open Source](https://docs.mem0.ai/open-source/overview).

docs/v0x/examples/llamaindex-multiagent-learning-system.mdx

@@ -81,7 +81,7 @@ 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"}

docs/v0x/examples/mem0-mastra.mdx

@@ -98,7 +98,7 @@ export const mem0Agent = new Agent({
   instructions: `
     You are a helpful assistant that has the ability to memorize and remember facts using Mem0.
   `,
-  model: openai('gpt-4o'),
+  model: openai('gpt-4.1-nano'),
   tools: { mem0RememberTool, mem0MemorizeTool },
 });
 ```

docs/v0x/examples/mem0-openai-voice-demo.mdx

@@ -162,7 +162,7 @@ def create_memory_voice_agent():
             Use the search_memories tool when you need context from past conversations or user asks you to recall something.
             """,
         ),
-        model="gpt-4o",
+        model="gpt-4.1-nano-2025-04-14",
         tools=[save_memories, search_memories],
     )
     
@@ -171,7 +171,7 @@ def create_memory_voice_agent():
 
 This function:
 - Creates an OpenAI Agent with specific instructions
-- Configures it to use gpt-4o (you can use other models)
+- Configures it to use gpt-4.1-nano (you can use other models)
 - Registers the memory-related tools with the agent
 - Uses `prompt_with_handoff_instructions` to include standard voice agent behaviors
 
@@ -369,7 +369,7 @@ def create_memory_voice_agent():
             Use the search_memories tool when you need context from past conversations or user asks you to recall something.
             """,
         ),
-        model="gpt-4o",
+        model="gpt-4.1-nano-2025-04-14",
         tools=[save_memories, search_memories],
     )
     

docs/v0x/examples/memory-guided-content-writing.mdx

@@ -103,7 +103,7 @@ Preferences:
     ]
 
     response = openai.chat.completions.create(
-        model="gpt-4o-mini",
+        model="gpt-4.1-nano-2025-04-14",
         messages=messages
     )
     clean_response = response.choices[0].message.content.strip()

docs/v0x/examples/openai-inbuilt-tools.mdx

@@ -119,7 +119,7 @@ const carRecommendationTool = zodResponsesFunction({
 
 // Use the tool in your OpenAI request
 const response = await openAIClient.responses.create({
-    model: "gpt-4o",
+    model: "gpt-4.1-nano-2025-04-14",
     tools: [{ type: "web_search_preview" }, carRecommendationTool],
     input: `${getMemoryString(relevantMemories)}\n${userInput}`,
 });
@@ -131,7 +131,7 @@ Combine memory with web search for up-to-date recommendations:
 
 ```javascript
 const response = await openAIClient.responses.create({
-    model: "gpt-4o",
+    model: "gpt-4.1-nano-2025-04-14",
     tools: [{ type: "web_search_preview" }, carRecommendationTool],
     input: `${getMemoryString(relevantMemories)}\n${userInput}`,
 });
@@ -202,7 +202,7 @@ async function main(memory = false) {
   }
 
   const response = await openAIClient.responses.create({
-    model: "gpt-4o",
+    model: "gpt-4.1-nano-2025-04-14",
     tools: [{ type: "web_search_preview" }, tool],
     input: `${getMemoryString(relevantMemories)}\n${input}`,
   });

docs/v0x/examples/personal-ai-tutor.mdx

@@ -57,7 +57,7 @@ class PersonalAITutor:
         """
         # Start a streaming response request to the AI
         response = self.client.responses.create(
-            model="gpt-4o",
+            model="gpt-4.1-nano-2025-04-14",
             instructions="You are a personal AI Tutor.",
             input=question,
             stream=True

docs/v0x/examples/personal-travel-assistant.mdx

@@ -35,7 +35,7 @@ config = {
     "llm": {
         "provider": "openai",
         "config": {
-            "model": "gpt-4o",
+            "model": "gpt-4.1-nano-2025-04-14",
             "temperature": 0.1,
             "max_tokens": 2000,
         }
@@ -76,7 +76,7 @@ class PersonalTravelAssistant:
 
         # Generate response using Responses API
         response = self.client.responses.create(
-            model="gpt-4o",
+            model="gpt-4.1-nano-2025-04-14",
             input=prompt
         )
 
@@ -140,9 +140,9 @@ class PersonalTravelAssistant:
             prompt = f"User input: {question}\n Previous memories: {previous_memories}"
         self.messages.append({"role": "user", "content": prompt})
 
-        # Generate response using GPT-4o
+        # Generate response using gpt-4.1-nano
         response = self.client.chat.completions.create(
-            model="gpt-4o",
+            model="gpt-4.1-nano-2025-04-14"2025-04-14",
             messages=self.messages
         )
         answer = response.choices[0].message.content

docs/v0x/integrations/agentops.mdx

@@ -49,7 +49,7 @@ local_config = {
     "llm": {
         "provider": "openai",
         "config": {
-            "model": "gpt-4o-mini",
+            "model": "gpt-4.1-nano-2025-04-14",
             "temperature": 0.1,
             "max_tokens": 2000,
         },

docs/v0x/integrations/agno.mdx

@@ -36,7 +36,7 @@ from agno.tools.mem0 import Mem0Tools
 
 agent = Agent(
     name="Memory Agent",
-    model=OpenAIChat(id="gpt-4o-mini"),
+    model=OpenAIChat(id="gpt-4.1-nano-2025-04-14"2025-04-14"),
     tools=[Mem0Tools()],
     description="An assistant that remembers and personalizes using Mem0 memory."
 )

docs/v0x/integrations/keywords.mdx

@@ -55,7 +55,7 @@ config = {
     "llm": {
         "provider": "openai",
         "config": {
-            "model": "gpt-4o-mini",
+            "model": "gpt-4.1-nano-2025-04-14",
             "temperature": 0.0,
             "api_key": keywordsai_api_key,
             "openai_base_url": base_url,
@@ -101,7 +101,7 @@ messages = [
 
 # Add memory and generate a response
 response = client.chat.completions.create(
-    model="openai/gpt-4o",
+    model="openai/gpt-4.1-nano",
     messages=messages,
     extra_body={
         "mem0_params": {

docs/v0x/integrations/langchain.mdx

@@ -39,7 +39,7 @@ load_dotenv()
 # os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
 
 # Initialize LangChain and Mem0
-llm = ChatOpenAI(model="gpt-4o-mini")
+llm = ChatOpenAI(model="gpt-4.1-nano-2025-04-14")
 mem0 = MemoryClient()
 ```
 

docs/v0x/integrations/livekit.mdx

@@ -147,7 +147,7 @@ async def entrypoint(ctx: JobContext):
 
     session = AgentSession(
         stt=deepgram.STT(),
-        llm=openai.LLM(model="gpt-4o-mini"),
+        llm=openai.LLM(model="gpt-4.1-nano-2025-04-14"2025-04-14"),
         tts=openai.TTS(voice="ash",),
         turn_detection=EnglishModel(),
         vad=silero.VAD.load(),

docs/v0x/integrations/llama-index.mdx

@@ -83,7 +83,7 @@ config = {
     "llm": {
         "provider": "openai",
         "config": {
-            "model": "gpt-4o",
+            "model": "gpt-4.1-nano-2025-04-14",
             "temperature": 0.2,
             "max_tokens": 2000,
         },
@@ -116,7 +116,7 @@ from dotenv import load_dotenv
 load_dotenv()
 
 # os.environ["OPENAI_API_KEY"] = "<your-openai-api-key>"
-llm = OpenAI(model="gpt-4o-mini")
+llm = OpenAI(model="gpt-4.1-nano-2025-04-14"2025-04-14")
 ```
 
 ### SimpleChatEngine

docs/v0x/integrations/mastra.mdx

@@ -106,7 +106,7 @@ const mem0Agent = new Agent({
     Use the Mem0-memorize tool to save important information that might be useful later.
     Use the Mem0-remember tool to recall previously saved information when answering questions.
   `,
-  model: openai('gpt-4o'),
+  model: openai('gpt-4.1-nano'),
   tools: { mem0RememberTool, mem0MemorizeTool },
 });
 ```

docs/v0x/integrations/openai-agents-sdk.mdx

@@ -63,7 +63,7 @@ agent = Agent(
     Use the save_memory tool to store important information about the user.
     Always personalize your responses based on available memory.""",
     tools=[search_memory, save_memory],
-    model="gpt-4o"
+    model="gpt-4.1-nano-2025-04-14"
 )
 
 def chat_with_agent(user_input: str, user_id: str) -> str:
@@ -114,7 +114,7 @@ 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(
@@ -123,7 +123,7 @@ 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
@@ -134,7 +134,7 @@ 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"
 )
 
 def chat_with_handoffs(user_input: str, user_id: str) -> str:

docs/v0x/open-source/python-quickstart.mdx

@@ -504,7 +504,7 @@ chat_completion = client.chat.completions.create(
             "content": "What's the capital of France?",
         }
     ],
-    model="gpt-4o",
+    model="gpt-4.1-nano-2025-04-14",
 )
 ```