## Create voice-based conversational AI agents with memory capabilities

Integrate ElevenLabs and Mem0 to enable persistent, context-aware voice interactions that remember past conversations.

## Overview

In this guide, we’ll build a voice agent that:

1. Uses ElevenLabs Conversational AI for voice interaction
2. Leverages Mem0 to store and retrieve memories from past conversations
3. Provides personalized responses based on user history

## Setup and Configuration

Install necessary libraries:

```
pip install elevenlabs mem0ai python-dotenv
```

Configure your environment variables:

You’ll need both an ElevenLabs API key and a Mem0 API key to use this integration.

```
# Create a .env file with these variables
AGENT_ID=your-agent-id
USER_ID=unique-user-identifier
ELEVENLABS_API_KEY=your-elevenlabs-api-key
MEM0_API_KEY=your-mem0-api-key
```

## Integration Code Breakdown

### 1. Imports and Environment Setup

First, we import required libraries and set up the environment:

```
import os
import signal
import sys
from mem0 import AsyncMemoryClient

from elevenlabs.client import ElevenLabs
from elevenlabs.conversational_ai.conversation import Conversation
from elevenlabs.conversational_ai.default_audio_interface import DefaultAudioInterface
from elevenlabs.conversational_ai.conversation import ClientTools
```

These imports provide:

- Standard Python libraries for system operations and signal handling
- `AsyncMemoryClient` from Mem0 for memory operations
- ElevenLabs components for voice interaction

### 2. Environment Variables and Validation

Next, we validate the required environment variables:

```
def main():
    # Required environment variables
    AGENT_ID = os.environ.get('AGENT_ID')
    USER_ID = os.environ.get('USER_ID')
    API_KEY = os.environ.get('ELEVENLABS_API_KEY')
    MEM0_API_KEY = os.environ.get('MEM0_API_KEY')

# Validate required environment variables
    if not AGENT_ID:
        sys.stderr.write("AGENT_ID environment variable must be set\n")
        sys.exit(1)

if not USER_ID:
        sys.stderr.write("USER_ID environment variable must be set\n")
        sys.exit(1)

if not API_KEY:
        sys.stderr.write("ELEVENLABS_API_KEY not set, assuming the agent is public\n")

if not MEM0_API_KEY:
        sys.stderr.write("MEM0_API_KEY environment variable must be set\n")
        sys.exit(1)

# Set up Mem0 API key in the environment
    os.environ['MEM0_API_KEY'] = MEM0_API_KEY
```

This section:

- Retrieves required environment variables
- Performs validation to ensure required variables are present
- Exits the application with an error message if required variables are missing
- Sets the Mem0 API key in the environment for the Mem0 client to use

### 3. Client Initialization

Initialize both the ElevenLabs and Mem0 clients:

```
# Initialize ElevenLabs client
client = ElevenLabs(api_key=API_KEY)

# Initialize memory client and tools
client_tools = ClientTools()
mem0_client = AsyncMemoryClient()
```

Here we:

- Create an ElevenLabs client with the API key
- Initialize a ClientTools object for registering function tools
- Create an AsyncMemoryClient instance for Mem0 interactions

### 4. Memory Function Definitions

Define the two key memory functions that will be registered as tools:

```
# Define memory-related functions for the agent
async def add_memories(parameters):
    """Add a message to the memory store"""
    message = parameters.get("message")
    await mem0_client.add(
        messages=message,
        user_id=USER_ID
    )
    return "Memory added successfully"

async def retrieve_memories(parameters):
    """Retrieve relevant memories based on the input message"""
    message = parameters.get("message")

filters = {"user_id": USER_ID}

results = await mem0_client.search(
        query=message,
        filters=filters
    )

memories = ' '.join([result["memory"] for result in results.get('results', [])])
    print("[ Memories ]", memories)

if memories:
        return memories
    return "No memories found"
```

These functions:

#### `add_memories`:

- Takes a message parameter containing information to remember
- Stores the message in Mem0 using the `add` method
- Associates the memory with the specific USER_ID
- Returns a success message to the agent

#### `retrieve_memories`:

- Takes a message parameter as the search query
- Sets up filters to only retrieve memories for the current user
- Uses semantic search to find relevant memories
- Joins all retrieved memories into a single text
- Prints retrieved memories to the console for debugging
- Returns the memories or a “No memories found” message if none are found

### 5. Registering Memory Functions as Tools

Register the memory functions with the ElevenLabs ClientTools system:

```
# Register the memory functions as tools for the agent
client_tools.register("addMemories", add_memories, is_async=True)
client_tools.register("retrieveMemories", retrieve_memories, is_async=True)
```

This allows the ElevenLabs agent to:

- Access these functions through function calling
- Wait for asynchronous results (is_async=True)
- Call these functions by name (“addMemories” and “retrieveMemories”)

### 6. Conversation Setup

Configure the conversation with ElevenLabs:

```
# Initialize the conversation
conversation = Conversation(
    client,
    AGENT_ID,
    requires_auth=bool(API_KEY),
    audio_interface=DefaultAudioInterface(),
    client_tools=client_tools,
    callback_agent_response=lambda response: print(f"Agent: {response}"),
    callback_agent_response_correction=lambda original, corrected: print(f"Agent: {original} -> {corrected}"),
    callback_user_transcript=lambda transcript: print(f"User: {transcript}"),
)
```

This sets up the conversation with:

- The ElevenLabs client and Agent ID
- Authentication requirements based on API key presence
- DefaultAudioInterface for handling audio I/O
- The client_tools with our memory functions
- Callback functions for various interactions.

### 7. Conversation Management

Start and manage the conversation:

```
# Start the conversation
print(f"Starting conversation with user_id: {USER_ID}")
conversation.start_session()

# Handle Ctrl+C to gracefully end the session
signal.signal(signal.SIGINT, lambda sig, frame: conversation.end_session())

# Wait for the conversation to end and get the conversation ID
conversation_id = conversation.wait_for_session_end()
print(f"Conversation ID: {conversation_id}")

if __name__ == '__main__':
    main()
```

This final section:

- Prints a message indicating the conversation has started
- Starts the conversation session
- Sets up a signal handler to gracefully end the session on Ctrl+C
- Waits for the session to end and gets the conversation ID
- Prints the conversation ID for reference

## Memory Tools Overview

This integration provides two key memory functions to your conversational AI agent:

### 1. Adding Memories (`addMemories`)

The `addMemories` tool allows your agent to store important information during a conversation, including:

- User preferences
- Important facts shared by the user
- Decisions or commitments made during the conversation
- Action items to follow up on

When the agent identifies information worth remembering, it calls this function to store it in the Mem0 database with the appropriate user ID.

### 2. Retrieving Memories (`retrieveMemories`)

The `retrieveMemories` tool allows your agent to search for and retrieve relevant memories from previous conversations. The agent can:

- Search for context related to the current topic
- Recall user preferences
- Remember previous interactions on similar topics
- Create continuity across multiple sessions

## Configuring Your ElevenLabs Agent

To enable your agent to effectively use memory:

1. Add function calling capabilities to your agent in the ElevenLabs platform:
   - Go to your agent settings in the ElevenLabs platform
   - Navigate to the “Tools” section
   - Enable function calling for your agent
   - Add the memory tools as described below
2. Add the `addMemories` and `retrieveMemories` tools to your agent with the provided specifications.
3. Update your agent’s prompt to instruct it to use these memory functions.

## Example Conversation Flow

Here’s how a typical conversation with memory might flow:

1. **User speaks**: “Hi, do you remember my favorite color?”
2. **Agent retrieves memories**:

```
# Agent calls retrieve_memories
memories = retrieve_memories({"message": "user's favorite color"})
# If found: "The user's favorite color is blue"
```

3. **Agent processes with context**:  
   - If memories found: Prepares a personalized response  
   - If no memories: Prepares to ask and store the information  
4. **Agent responds**:  
   - With memory: “Yes, your favorite color is blue!”  
   - Without memory: “I don’t think you’ve told me your favorite color before. What is it?”  
5. **User responds**: “It’s actually green.”  
6. **Agent stores new information**:

```
# Agent calls add_memories
add_memories({"message": "The user's favorite color is green"})
```

7. **Agent confirms**: “Thanks, I’ll remember that your favorite color is green.”

## Conclusion

By integrating ElevenLabs Conversational AI with Mem0, you can create voice agents that maintain context across conversations and provide personalized responses based on user history. This powerful combination enables:

- More natural, context-aware conversations
- Personalized user experiences that improve over time
- Reduced need for users to repeat information
- Long-term relationship building between users and AI agents