docs: fix search method return value handling in integration and example docs (#3208)

docs/integrations/agentops.mdx

@@ -104,7 +104,7 @@ def demonstrate_sync_memory(local_config, sample_messages, sample_preferences, u
             results = memory.search(query, user_id=user_id)
         
             if results and "results" in results:
-                for j, result in enumerate(results): 
+                for j, result in enumerate(results['results']): 
                     print(f"Result {j+1}: {result.get('memory', 'N/A')}")
             else:
                 print("No results found")

docs/integrations/agno.mdx

@@ -128,7 +128,7 @@ def chat_user(
     if user_input:
         # Search for relevant memories
         memories = client.search(user_input, user_id=user_id)
-        memory_context = "\n".join(f"- {m['memory']}" for m in memories)
+        memory_context = "\n".join(f"- {m['memory']}" for m in memories.get('results', []))
 
         # Construct the prompt
         prompt = f"""

docs/integrations/autogen.mdx

@@ -68,7 +68,7 @@ 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)
-    context = "\n".join([m["memory"] for m in relevant_memories])
+    context = "\n".join([m["memory"] for m in relevant_memories.get('results', [])])
 
     prompt = f"""Answer the user question considering the previous interactions:
     Previous interactions:
@@ -100,7 +100,7 @@ manager = ConversableAgent(
 
 def escalate_to_manager(question):
     relevant_memories = memory_client.search(question, user_id=USER_ID)
-    context = "\n".join([m["memory"] for m in relevant_memories])
+    context = "\n".join([m["memory"] for m in relevant_memories.get('results', [])])
 
     prompt = f"""
     Context from previous interactions:

docs/integrations/elevenlabs.mdx

@@ -152,7 +152,7 @@ Define the two key memory functions that will be registered as tools:
         )
 
         # Extract and join the memory texts
-        memories = ' '.join([result["memory"] for result in results])
+        memories = ' '.join([result["memory"] for result in results.get('results', [])])
         print("[ Memories ]", memories)
 
         if memories:

docs/integrations/google-ai-adk.mdx

@@ -49,8 +49,8 @@ mem0 = MemoryClient()
 def search_memory(query: str, user_id: str) -> dict:
     """Search through past conversations and memories"""
     memories = mem0.search(query, user_id=user_id)
-    if memories:
-        memory_context = "\n".join([f"- {mem['memory']}" for mem in memories])
+    if memories.get('results', []):
+        memory_context = "\n".join([f"- {mem['memory']}" for mem in memories.get('results', [])])
         return {"status": "success", "memories": memory_context}
     return {"status": "no_memories", "message": "No relevant memories found"}
 

docs/integrations/langchain.mdx

@@ -64,7 +64,7 @@ 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"""
     memories = mem0.search(query, user_id=user_id)
-    serialized_memories = ' '.join([mem["memory"] for mem in memories])
+    serialized_memories = ' '.join([mem["memory"] for mem in memories.get('results', [])])
     context = [
         {
             "role": "system", 

docs/integrations/langgraph.mdx

@@ -68,7 +68,7 @@ def chatbot(state: State):
     memories = mem0.search(messages[-1].content, user_id=user_id)
 
     context = "Relevant information from previous conversations:\n"
-    for memory in memories:
+    for memory in memories.get('results', []):
         context += f"- {memory['memory']}\n"
 
     system_message = SystemMessage(content=f"""You are a helpful customer support assistant. Use the provided context to personalize your responses and remember user preferences and past interactions.

docs/integrations/livekit.mdx

@@ -119,9 +119,9 @@ class MemoryEnabledAgent(Agent):
                 user_id=RAG_USER_ID,
             )
             logger.info(f"mem0_client.search returned: {search_results}")
-            if search_results and isinstance(search_results, list):
+            if search_results and search_results.get('results', []):
                 context_parts = []
-                for result in search_results:
+                for result in search_results.get('results', []):
                     paragraph = result.get("memory") or result.get("text")
                     if paragraph:
                         source = "mem0 Memories"

docs/integrations/multion.mdx

@@ -71,7 +71,7 @@ Define search command and retrieve relevant memories from Mem0.
 command = "Find papers on arxiv that I should read based on my interests."
 
 relevant_memories = memory.search(command, user_id=USER_ID, limit=3)
-relevant_memories_text = '\n'.join(mem['text'] for mem in relevant_memories)
+relevant_memories_text = '\n'.join(mem['memory'] for mem in relevant_memories['results'])
 print(f"Relevant memories:")
 print(relevant_memories_text)
 ```
@@ -98,9 +98,9 @@ def get_travel_info(question, use_memory=True):
     if use_memory:
         previous_memories = memory_client.search(question, user_id=USER_ID)
         relevant_memories_text = ""
-        if previous_memories:
+        if previous_memories and previous_memories.get('results'):
             print("Using previous memories to enhance the search...")
-            relevant_memories_text = '\n'.join(mem["memory"] for mem in previous_memories)
+            relevant_memories_text = '\n'.join(mem["memory"] for mem in previous_memories['results'])
         
         command = "Find travel information based on my interests:"
         prompt = f"{command}\n Question: {question} \n My preferences: {relevant_memories_text}"

docs/integrations/openai-agents-sdk.mdx

@@ -47,8 +47,8 @@ mem0 = MemoryClient()
 def search_memory(query: str, user_id: str) -> str:
     """Search through past conversations and memories"""
     memories = mem0.search(query, user_id=user_id, limit=3)
-    if memories:
-        return "\n".join([f"- {mem['memory']}" for mem in memories])
+    if memories and memories.get('results'):
+        return "\n".join([f"- {mem['memory']}" for mem in memories['results']])
     return "No relevant memories found."
 
 @function_tool