refactor: v2 search and update examples

docs/docs.json

@@ -330,9 +330,7 @@
                         "pages": [
                           "api-reference/memory/add-memories",
                           "api-reference/memory/v2-search-memories",
-                          "api-reference/memory/v1-search-memories",
                           "api-reference/memory/v2-get-memories",
-                          "api-reference/memory/v1-get-memories",
                           "api-reference/memory/history-memory",
                           "api-reference/memory/get-memory",
                           "api-reference/memory/update-memory",

docs/examples/email_processing.mdx

@@ -112,7 +112,6 @@ class EmailProcessor:
             query=query,
             user_id=user_id,
             categories=["email"],
-            output_format="v1.1",
             version="v2"
         )
 
@@ -136,8 +135,7 @@ class EmailProcessor:
 
         thread = self.client.get_all(
             version="v2",
-            filters=filters,
-            output_format="v1.1"
+            filters=filters
         )
 
         return thread

docs/examples/mem0-agentic-tool.mdx

@@ -112,7 +112,7 @@ async def search_memory(
         query: The search query.
     """
     user_id = context.context.user_id or "default_user"
-    memories = await client.search(query, user_id=user_id, output_format="v1.1")
+    memories = await client.search(query, user_id=user_id)
     results = '\n'.join([result["memory"] for result in memories["results"]])
     return str(results)
 ```
@@ -126,7 +126,7 @@ async def get_all_memory(
 ) -> str:
     """Retrieve all memories from Mem0"""
     user_id = context.context.user_id or "default_user"
-    memories = await client.get_all(user_id=user_id, output_format="v1.1")
+    memories = await client.get_all(user_id=user_id)
     results = '\n'.join([result["memory"] for result in memories["results"]])
     return str(results)
 ```

docs/examples/mem0-google-adk-healthcare-assistant.mdx

@@ -80,8 +80,7 @@ def retrieve_patient_info(query: str) -> dict:
         query,
         user_id=USER_ID,
         limit=5,
-        threshold=0.7,  # Higher threshold for more relevant results
-        output_format="v1.1"
+        threshold=0.7  # Higher threshold for more relevant results
     )
 
     # Format and return the results

docs/examples/personalized-search-tavily-mem0.mdx

@@ -76,7 +76,7 @@ def setup_user_history(user_id):
     ]
 
     for conversation in conversations:
-        mem0_client.add(conversation, user_id=user_id, output_format="v1.1")
+        mem0_client.add(conversation, user_id=user_id)
 ```
 This gives the agent a baseline understanding of the user’s lifestyle and needs.
 

docs/integrations/agno.mdx

@@ -125,7 +125,7 @@ def chat_user(
 
     if user_input:
         # Search for relevant memories
-        memories = client.search(user_input, user_id=user_id, output_format='v1.1')
+        memories = client.search(user_input, user_id=user_id)
         memory_context = "\n".join(f"- {m['memory']}" for m in memories['results'])
 
         # Construct the prompt

docs/integrations/autogen.mdx

@@ -62,7 +62,7 @@ conversation = [
     {"role": "assistant", "content": "Thank you for the information. Let's troubleshoot this issue..."}
 ]
 
-memory_client.add(messages=conversation, user_id=USER_ID, output_format="v1.1")
+memory_client.add(messages=conversation, user_id=USER_ID)
 print("Conversation added to memory.")
 ```
 
@@ -72,7 +72,7 @@ Create a function to get context-aware responses based on user's question and pr
 
 ```python
 def get_context_aware_response(question):
-    relevant_memories = memory_client.search(question, user_id=USER_ID, output_format='v1.1')
+    relevant_memories = memory_client.search(question, user_id=USER_ID)
     context = "\n".join([m["memory"] for m in relevant_memories.get('results', [])])
 
     prompt = f"""Answer the user question considering the previous interactions:
@@ -104,7 +104,7 @@ manager = ConversableAgent(
 )
 
 def escalate_to_manager(question):
-    relevant_memories = memory_client.search(question, user_id=USER_ID, output_format='v1.1')
+    relevant_memories = memory_client.search(question, user_id=USER_ID)
     context = "\n".join([m["memory"] for m in relevant_memories.get('results', [])])
 
     prompt = f"""

docs/integrations/google-ai-adk.mdx

@@ -50,7 +50,7 @@ mem0 = MemoryClient()
 # Define memory function tools
 def search_memory(query: str, user_id: str) -> dict:
     """Search through past conversations and memories"""
-    memories = mem0.search(query, user_id=user_id, output_format='v1.1')
+    memories = mem0.search(query, user_id=user_id)
     if memories.get('results', []):
         memory_list = memories['results']
         memory_context = "\n".join([f"- {mem['memory']}" for mem in memory_list])
@@ -60,7 +60,7 @@ def search_memory(query: str, user_id: str) -> dict:
 def save_memory(content: str, user_id: str) -> dict:
     """Save important information to memory"""
     try:
-        result = mem0.add([{"role": "user", "content": content}], user_id=user_id, output_format='v1.1')
+        result = mem0.add([{"role": "user", "content": content}], user_id=user_id)
         return {"status": "success", "message": "Information saved to memory", "result": result}
     except Exception as e:
         return {"status": "error", "message": f"Failed to save memory: {str(e)}"}

docs/integrations/langchain.mdx

@@ -65,7 +65,7 @@ Create functions to handle context retrieval, response generation, and addition
 def retrieve_context(query: str, user_id: str) -> List[Dict]:
     """Retrieve relevant context from Mem0"""
     try:
-        memories = mem0.search(query, user_id=user_id, output_format='v1.1')
+        memories = mem0.search(query, user_id=user_id)
         memory_list = memories['results']
 
         serialized_memories = ' '.join([mem["memory"] for mem in memory_list])
@@ -107,7 +107,7 @@ def save_interaction(user_id: str, user_input: str, assistant_response: str):
                 "content": assistant_response
             }
         ]
-        result = mem0.add(interaction, user_id=user_id, output_format='v1.1')
+        result = mem0.add(interaction, user_id=user_id)
         print(f"Memory saved successfully: {len(result.get('results', []))} memories added")
     except Exception as e:
         print(f"Error saving interaction: {e}")

docs/integrations/langgraph.mdx

@@ -67,7 +67,7 @@ def chatbot(state: State):
 
     try:
         # Retrieve relevant memories
-        memories = mem0.search(messages[-1].content, user_id=user_id, output_format='v1.1')
+        memories = mem0.search(messages[-1].content, user_id=user_id)
 
         # Handle dict response format
         memory_list = memories['results']
@@ -94,7 +94,7 @@ def chatbot(state: State):
                     "content": response.content
                 }
             ]
-            result = mem0.add(interaction, user_id=user_id, output_format='v1.1')
+            result = mem0.add(interaction, user_id=user_id)
             print(f"Memory saved: {len(result.get('results', []))} memories added")
         except Exception as e:
             print(f"Error saving memory: {e}")

docs/platform/features/direct-import.mdx

@@ -41,7 +41,7 @@ You can retrieve memories using the `search` method.
 <CodeGroup>
 
 ```python Python
-client.search("What is Alice's favorite sport?", user_id="alice", output_format="v1.1")
+client.search("What is Alice's favorite sport?", user_id="alice")
 ```
 
 ```json Output

examples/mem0-demo/app/api/chat/route.ts

@@ -70,7 +70,7 @@ const retrieveMemories = (memories: any) => {
 export async function POST(req: Request) {
   const { messages, system, tools, userId } = await req.json();
 
-  const memories = await getMemories(messages, { user_id: userId, rerank: true, threshold: 0.1, output_format: "v1.0" });
+  const memories = await getMemories(messages, { user_id: userId, rerank: true, threshold: 0.1 });
   const mem0Instructions = retrieveMemories(memories);
 
   const result = streamText({

examples/misc/fitness_checker.py

@@ -27,7 +27,7 @@
 # Store user preferences as memory
 def store_user_preferences(conversation: list, user_id: str = USER_ID):
     """Store user preferences from conversation history"""
-    memory_client.add(conversation, user_id=user_id, output_format="v1.1")
+    memory_client.add(conversation, user_id=user_id)
 
 
 # Memory-aware assistant function

examples/misc/multillm_memory.py

@@ -99,7 +99,6 @@ def research_with_specialist(task: str, specialist: str, project_id: str) -> str
         user_id=project_id,  # Project-level memory
         agent_id=specialist,  # Agent-specific memory
         metadata={"contributor": specialist, "task_type": "research", "model_used": spec_info["model"]},
-        output_format="v1.1",
     )
 
     return result

examples/misc/personal_assistant_agno.py

@@ -44,7 +44,7 @@ def chat_user(user_input: str = None, user_id: str = "user_123", image_path: str
         }
 
         # Send both as separate message objects
-        client.add([text_msg, image_msg], user_id=user_id, output_format="v1.1")
+        client.add([text_msg, image_msg], user_id=user_id)
         print("✅ Image uploaded and stored in memory.")
 
     if user_input:

examples/misc/personalized_search.py

@@ -66,7 +66,7 @@ def setup_user_history(user_id):
 
     logger.info(f"Setting up user history for {user_id}")
     for conversation in conversations:
-        mem0_client.add(conversation, user_id=user_id, output_format="v1.1")
+        mem0_client.add(conversation, user_id=user_id)
 
 
 def get_user_context(user_id, query):
@@ -209,7 +209,7 @@ def store_search_interaction(user_id, original_query, agent_response):
             {"role": "assistant", "content": f"Provided personalized results based on user preferences: {agent_response}"}
         ]
 
-        mem0_client.add(messages=interaction, user_id=user_id, output_format="v1.1")
+        mem0_client.add(messages=interaction, user_id=user_id)
 
         logger.info(f"Stored search interaction for user {user_id}")