Updated integration docs (#3392)

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

@@ -24,8 +24,7 @@ Before you begin, make sure you have:
 
 Installed Google ADK and Mem0 SDK:
 ```bash
-pip install google-adk
-pip install mem0ai
+pip install google-adk mem0ai python-dotenv
 ```
 
 ## Code Breakdown
@@ -35,21 +34,25 @@ Let's get started and understand the different components required in building a
 ```python
 # Import dependencies
 import os
+import asyncio
 from google.adk.agents import Agent
-from google.adk.sessions import InMemorySessionService
 from google.adk.runners import Runner
+from google.adk.sessions import InMemorySessionService
 from google.genai import types
 from mem0 import MemoryClient
+from dotenv import load_dotenv
 
-# Set up API keys (replace with your actual keys)
-os.environ["GOOGLE_API_KEY"] = "your-google-api-key"
-os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+load_dotenv()
+
+# Set up environment variables
+# os.environ["GOOGLE_API_KEY"] = "your-google-api-key"
+# os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
 
 # Define a global user ID for simplicity
 USER_ID = "Alex"
 
 # Initialize Mem0 client
-mem0_client = MemoryClient()
+mem0 = MemoryClient()
 ```
 
 ## Define Memory Tools

docs/integrations/agentops.mdx

@@ -17,7 +17,7 @@ Before setting up Mem0 with AgentOps, ensure you have:
 
 1. Installed the required packages:
 ```bash
-pip install mem0ai agentops
+pip install mem0ai agentops python-dotenv
 ```
 
 2. Valid API keys:
@@ -37,7 +37,9 @@ import asyncio
 import logging
 from dotenv import load_dotenv
 import agentops
+import openai
 
+load_dotenv()
 #Set up environment variables for API keys
 os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY")
 os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")

docs/integrations/agno.mdx

@@ -18,7 +18,7 @@ Before setting up Mem0 with Agno, ensure you have:
 
 1. Installed the required packages:
 ```bash
-pip install agno mem0ai
+pip install agno mem0ai python-dotenv
 ```
 
 2. Valid API keys:
@@ -81,7 +81,7 @@ agent = Agent(
 
 def chat_user(
     user_input: Optional[str] = None,
-    user_id: str = "user_123",
+    user_id: str = "alex",
     image_path: Optional[str] = None
 ) -> str:
     """
@@ -120,13 +120,13 @@ def chat_user(
         })
 
         # Store messages in memory
-        client.add(messages, user_id=user_id)
+        client.add(messages, user_id=user_id, output_format='v1.1')
         print("✅ Image and text stored in memory.")
 
     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.get('results', []))
+        memories = client.search(user_input, user_id=user_id, output_format='v1.1')
+        memory_context = "\n".join(f"- {m['memory']}" for m in memories['results'])
 
         # Construct the prompt
         prompt = f"""
@@ -150,7 +150,8 @@ User question:
             response = agent.run(prompt)
 
         # Store the interaction in memory
-        client.add(f"User: {user_input}\nAssistant: {response.content}", user_id=user_id)
+        interaction_message = [{"role": "user", "content": f"User: {user_input}\nAssistant: {response.content}"}]
+        client.add(interaction_message, user_id=user_id, output_format='v1.1')
         return response.content
 
     return "No user input or image provided."
@@ -159,9 +160,9 @@ User question:
 # Example Usage
 if __name__ == "__main__":
     response = chat_user(
-        "This is the picture of what I brought with me in the trip to Bahamas",
+        "I like to travel and my favorite destination is London",
         image_path="travel_items.jpeg",
-        user_id="user_123"
+        user_id="alex"
     )
     print(response)
 ```

docs/integrations/autogen.mdx

@@ -1,3 +1,7 @@
+---
+title: AutoGen
+---
+
 Build conversational AI agents with memory capabilities. This integration combines AutoGen for creating AI agents with Mem0 for memory management, enabling context-aware and personalized interactions.
 
 ## Overview
@@ -10,7 +14,7 @@ In this guide, we'll explore an example of creating a conversational AI system w
 Install necessary libraries:
 
 ```bash
-pip install pyautogen mem0ai openai
+pip install autogen mem0ai openai python-dotenv
 ```
 
 First, we'll import the necessary libraries and set up our configurations.
@@ -22,15 +26,18 @@ import os
 from autogen import ConversableAgent
 from mem0 import MemoryClient
 from openai import OpenAI
+from dotenv import load_dotenv
+
+load_dotenv()
 
 # Configuration
-OPENAI_API_KEY = 'sk-xxx'  # Replace with your actual OpenAI API key
-MEM0_API_KEY = 'your-mem0-key'  # Replace with your actual Mem0 API key from https://app.mem0.ai
-USER_ID = "customer_service_bot"
+# OPENAI_API_KEY = 'sk-xxx'  # Replace with your actual OpenAI API key
+# MEM0_API_KEY = 'your-mem0-key'  # Replace with your actual Mem0 API key from https://app.mem0.ai
+USER_ID = "alice"
 
 # Set up OpenAI API key
-os.environ['OPENAI_API_KEY'] = OPENAI_API_KEY
-os.environ['MEM0_API_KEY'] = MEM0_API_KEY
+OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
+# os.environ['MEM0_API_KEY'] = MEM0_API_KEY
 
 # Initialize Mem0 and AutoGen agents
 memory_client = MemoryClient()
@@ -55,7 +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)
+memory_client.add(messages=conversation, user_id=USER_ID, output_format="v1.1")
 print("Conversation added to memory.")
 ```
 
@@ -65,7 +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)
+    relevant_memories = memory_client.search(question, user_id=USER_ID, output_format='v1.1')
     context = "\n".join([m["memory"] for m in relevant_memories.get('results', [])])
 
     prompt = f"""Answer the user question considering the previous interactions:
@@ -97,7 +104,7 @@ manager = ConversableAgent(
 )
 
 def escalate_to_manager(question):
-    relevant_memories = memory_client.search(question, user_id=USER_ID)
+    relevant_memories = memory_client.search(question, user_id=USER_ID, output_format='v1.1')
     context = "\n".join([m["memory"] for m in relevant_memories.get('results', [])])
 
     prompt = f"""

docs/integrations/elevenlabs.mdx

@@ -16,7 +16,7 @@ In this guide, we'll build a voice agent that:
 Install necessary libraries:
 
 ```bash
-pip install elevenlabs mem0 python-dotenv
+pip install elevenlabs mem0ai python-dotenv
 ```
 
 Configure your environment variables:

docs/integrations/google-ai-adk.mdx

@@ -17,7 +17,7 @@ Before setting up Mem0 with Google ADK, ensure you have:
 
 1. Installed the required packages:
 ```bash
-pip install google-adk mem0ai
+pip install google-adk mem0ai python-dotenv
 ```
 
 2. Valid API keys:
@@ -30,15 +30,19 @@ The following example demonstrates how to create a Google ADK agent with Mem0 me
 
 ```python
 import os
+import asyncio
 from google.adk.agents import Agent
 from google.adk.runners import Runner
 from google.adk.sessions import InMemorySessionService
 from google.genai import types
 from mem0 import MemoryClient
+from dotenv import load_dotenv
+
+load_dotenv()
 
 # Set up environment variables
-os.environ["GOOGLE_API_KEY"] = "your-google-api-key"
-os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+# os.environ["GOOGLE_API_KEY"] = "your-google-api-key"
+# os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
 
 # Initialize Mem0 client
 mem0 = MemoryClient()
@@ -46,17 +50,18 @@ 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)
+    memories = mem0.search(query, user_id=user_id, output_format='v1.1')
     if memories.get('results', []):
-        memory_context = "\n".join([f"- {mem['memory']}" for mem in memories.get('results', [])])
+        memory_list = memories['results']
+        memory_context = "\n".join([f"- {mem['memory']}" for mem in memory_list])
         return {"status": "success", "memories": memory_context}
     return {"status": "no_memories", "message": "No relevant memories found"}
 
 def save_memory(content: str, user_id: str) -> dict:
     """Save important information to memory"""
     try:
-        mem0.add([{"role": "user", "content": content}], user_id=user_id)
-        return {"status": "success", "message": "Information saved to memory"}
+        result = mem0.add([{"role": "user", "content": content}], user_id=user_id, output_format='v1.1')
+        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)}"}
 
@@ -72,7 +77,7 @@ personal_assistant = Agent(
     tools=[search_memory, save_memory]
 )
 
-def chat_with_agent(user_input: str, user_id: str) -> str:
+async def chat_with_agent(user_input: str, user_id: str) -> str:
     """
     Handle user input with automatic memory integration.
 
@@ -85,7 +90,7 @@ def chat_with_agent(user_input: str, user_id: str) -> str:
     """
     # Set up session and runner
     session_service = InMemorySessionService()
-    session = session_service.create_session(
+    session = await session_service.create_session(
         app_name="memory_assistant",
         user_id=user_id,
         session_id=f"session_{user_id}"
@@ -107,10 +112,10 @@ def chat_with_agent(user_input: str, user_id: str) -> str:
 
 # Example usage
 if __name__ == "__main__":
-    response = chat_with_agent(
+    response = asyncio.run(chat_with_agent(
         "I love Italian food and I'm planning a trip to Rome next month",
         user_id="alice"
-    )
+    ))
     print(response)
 ```
 

docs/integrations/langchain.mdx

@@ -16,7 +16,7 @@ In this guide, we'll create a Travel Agent AI that:
 Install necessary libraries:
 
 ```bash
-pip install langchain langchain_openai mem0ai
+pip install langchain langchain_openai mem0ai python-dotenv
 ```
 
 Import required modules and set up configurations:
@@ -30,10 +30,13 @@ from langchain_openai import ChatOpenAI
 from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from mem0 import MemoryClient
+from dotenv import load_dotenv
+
+load_dotenv()
 
 # Configuration
-os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
-os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
+# os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
+# os.environ["MEM0_API_KEY"] = "your-mem0-api-key"
 
 # Initialize LangChain and Mem0
 llm = ChatOpenAI(model="gpt-4o-mini")
@@ -61,19 +64,26 @@ Create functions to handle context retrieval, response generation, and addition
 ```python
 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.get('results', [])])
-    context = [
-        {
-            "role": "system", 
-            "content": f"Relevant information: {serialized_memories}"
-        },
-        {
-            "role": "user",
-            "content": query
-        }
-    ]
-    return context
+    try:
+        memories = mem0.search(query, user_id=user_id, output_format='v1.1')
+        memory_list = memories['results']
+        
+        serialized_memories = ' '.join([mem["memory"] for mem in memory_list])
+        context = [
+            {
+                "role": "system", 
+                "content": f"Relevant information: {serialized_memories}"
+            },
+            {
+                "role": "user",
+                "content": query
+            }
+        ]
+        return context
+    except Exception as e:
+        print(f"Error retrieving memories: {e}")
+        # Return empty context if there's an error
+        return [{"role": "user", "content": query}]
 
 def generate_response(input: str, context: List[Dict]) -> str:
     """Generate a response using the language model"""
@@ -86,17 +96,21 @@ def generate_response(input: str, context: List[Dict]) -> str:
 
 def save_interaction(user_id: str, user_input: str, assistant_response: str):
     """Save the interaction to Mem0"""
-    interaction = [
-        {
-          "role": "user",
-          "content": user_input
-        },
-        {
-            "role": "assistant",
-            "content": assistant_response
-        }
-    ]
-    mem0.add(interaction, user_id=user_id)
+    try:
+        interaction = [
+            {
+              "role": "user",
+              "content": user_input
+            },
+            {
+                "role": "assistant",
+                "content": assistant_response
+            }
+        ]
+        result = mem0.add(interaction, user_id=user_id, output_format='v1.1')
+        print(f"Memory saved successfully: {len(result.get('results', []))} memories added")
+    except Exception as e:
+        print(f"Error saving interaction: {e}")
 ```
 
 ## Create Chat Turn Function
@@ -124,7 +138,7 @@ Set up the main program loop for user interaction:
 ```python
 if __name__ == "__main__":
     print("Welcome to your personal Travel Agent Planner! How can I assist you with your travel plans today?")
-    user_id = "john"
+    user_id = "alice"
     
     while True:
         user_input = input("You: ")

docs/integrations/langgraph.mdx

@@ -16,7 +16,7 @@ In this guide, we'll create a Customer Support AI Agent that:
 Install necessary libraries:
 
 ```bash
-pip install langgraph langchain-openai mem0ai
+pip install langgraph langchain-openai mem0ai python-dotenv
 ```
 
 
@@ -31,14 +31,17 @@ from langgraph.graph.message import add_messages
 from langchain_openai import ChatOpenAI
 from mem0 import MemoryClient
 from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
+from dotenv import load_dotenv
+
+load_dotenv()
 
 # Configuration
-OPENAI_API_KEY = 'sk-xxx'  # Replace with your actual OpenAI API key
-MEM0_API_KEY = 'your-mem0-key'  # Replace with your actual Mem0 API key
+# OPENAI_API_KEY = 'sk-xxx'  # Replace with your actual OpenAI API key
+# MEM0_API_KEY = 'your-mem0-key'  # Replace with your actual Mem0 API key
 
 # Initialize LangChain and Mem0
-llm = ChatOpenAI(model="gpt-4", api_key=OPENAI_API_KEY)
-mem0 = MemoryClient(api_key=MEM0_API_KEY)
+llm = ChatOpenAI(model="gpt-4")
+mem0 = MemoryClient()
 ```
 
 ## Define State and Graph
@@ -62,22 +65,47 @@ def chatbot(state: State):
     messages = state["messages"]
     user_id = state["mem0_user_id"]
 
-    # Retrieve relevant memories
-    memories = mem0.search(messages[-1].content, user_id=user_id)
+    try:
+        # Retrieve relevant memories
+        memories = mem0.search(messages[-1].content, user_id=user_id, output_format='v1.1')
+        
+        # Handle dict response format
+        memory_list = memories['results']
 
-    context = "Relevant information from previous conversations:\n"
-    for memory in memories.get('results', []):
-        context += f"- {memory['memory']}\n"
+        context = "Relevant information from previous conversations:\n"
+        for memory in memory_list:
+            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.
+        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.
 {context}""")
 
-    full_messages = [system_message] + messages
-    response = llm.invoke(full_messages)
+        full_messages = [system_message] + messages
+        response = llm.invoke(full_messages)
 
-    # Store the interaction in Mem0
-    mem0.add(f"User: {messages[-1].content}\nAssistant: {response.content}", user_id=user_id)
-    return {"messages": [response]}
+        # Store the interaction in Mem0
+        try:
+            interaction = [
+                {
+                    "role": "user",
+                    "content": messages[-1].content
+                },
+                {
+                    "role": "assistant", 
+                    "content": response.content
+                }
+            ]
+            result = mem0.add(interaction, user_id=user_id, output_format='v1.1')
+            print(f"Memory saved: {len(result.get('results', []))} memories added")
+        except Exception as e:
+            print(f"Error saving memory: {e}")
+            
+        return {"messages": [response]}
+        
+    except Exception as e:
+        print(f"Error in chatbot: {e}")
+        # Fallback response without memory context
+        response = llm.invoke(messages)
+        return {"messages": [response]}
 ```
 
 ## Set Up Graph Structure
@@ -115,7 +143,7 @@ Set up the main program loop for user interaction:
 ```python
 if __name__ == "__main__":
     print("Welcome to Customer Support! How can I assist you today?")
-    mem0_user_id = "customer_123"  # You can generate or retrieve this based on your user management system
+    mem0_user_id = "alice"  # You can generate or retrieve this based on your user management system
     while True:
         user_input = input("You: ")
         if user_input.lower() in ['quit', 'exit', 'bye']:

docs/integrations/llama-index.mdx

@@ -13,7 +13,7 @@ LlamaIndex supports Mem0 as a [memory store](https://llamahub.ai/l/memory/llama-
 To install the required package, run:
 
 ```bash
-pip install llama-index-core llama-index-memory-mem0
+pip install llama-index-core llama-index-memory-mem0 python-dotenv
 ```
 
 ### Setup with Mem0 Platform
@@ -25,18 +25,23 @@ Set your Mem0 Platform API key as an environment variable. You can replace `<you
 </Note>
 
 ```python
-os.environ["MEM0_API_KEY"] = "<your-mem0-api-key>"
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
+# os.environ["MEM0_API_KEY"] = "<your-mem0-api-key>"
 ```
 
 Import the necessary modules and create a Mem0Memory instance:
 ```python
 from llama_index.memory.mem0 import Mem0Memory
 
-context = {"user_id": "user_1"}
+context = {"user_id": "alice"}
 memory_from_client = Mem0Memory.from_client(
     context=context,
-    api_key="<your-mem0-api-key>",
     search_msg_limit=4,  # optional, default is 5
+    output_format='v1.1',  # Remove deprecation warnings
 )
 ```
 
@@ -44,8 +49,8 @@ Context is used to identify the user, agent or the conversation in the Mem0. It
 
 ```python
 context = {
-    "user_id": "user_1",
-    "agent_id": "agent_1",
+    "user_id": "alice", 
+    "agent_id": "llama_agent_1",
     "run_id": "run_1",
 }
 ```
@@ -98,17 +103,20 @@ memory_from_config = Mem0Memory.from_config(
     context=context,
     config=config,
     search_msg_limit=4,  # optional, default is 5
+    output_format='v1.1',  # Remove deprecation warnings
 )
 ```
 
 Initialize the LLM
 
 ```python
-import os
 from llama_index.llms.openai import OpenAI
+from dotenv import load_dotenv
 
-os.environ["OPENAI_API_KEY"] = "<your-openai-api-key>"
-llm = OpenAI(model="gpt-4o")
+load_dotenv()
+
+# os.environ["OPENAI_API_KEY"] = "<your-openai-api-key>"
+llm = OpenAI(model="gpt-4o-mini")
 ```
 
 ### SimpleChatEngine
@@ -122,7 +130,7 @@ agent = SimpleChatEngine.from_defaults(
 )
 
 # Start the chat
-response = agent.chat("Hi, My name is Mayank")
+response = agent.chat("Hi, My name is Alice")
 print(response)
 ```
 Now we will learn how to use Mem0 with FunctionCalling and ReAct agents.
@@ -165,7 +173,7 @@ agent = FunctionCallingAgent.from_tools(
 )
 
 # Start the chat
-response = agent.chat("Hi, My name is Mayank")
+response = agent.chat("Hi, My name is Alice")
 print(response)
 ```
 
@@ -182,7 +190,7 @@ agent = ReActAgent.from_tools(
 )
 
 # Start the chat
-response = agent.chat("Hi, My name is Mayank")
+response = agent.chat("Hi, My name is Alice")
 print(response)
 ```
 

docs/integrations/pipecat.mdx

@@ -92,7 +92,7 @@ async def websocket_endpoint(websocket: WebSocket):
     await websocket.accept()
     
     # Basic setup with minimal configuration
-    user_id = "user123"
+    user_id = "alice"
     
     # WebSocket transport
     transport = FastAPIWebsocketTransport(