---
title: ElevenLabs
---

Create voice-based conversational AI agents with memory capabilities by integrating ElevenLabs and Mem0. This integration enables 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:

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

Configure your environment variables:

<Note>You'll need both an ElevenLabs API key and a Mem0 API key to use this integration.</Note>

```bash
# 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

Let's break down the implementation into manageable parts:

### 1. Imports and Environment Setup

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

```python
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:

```python
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:

```python
    # 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:

```python
    # 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")

        # For Platform API, user_id goes in filters
        filters = {"user_id": USER_ID}

        # Search for relevant memories using the message as a query
        results = await mem0_client.search(
            query=message,
            filters=filters
        )

        # Extract and join the memory texts
        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:

```python
    # 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:

```python
    # Initialize the conversation
    conversation = Conversation(
        client,
        AGENT_ID,
        # Assume auth is required when API_KEY is set
        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}"),
        # callback_latency_measurement=lambda latency: print(f"Latency: {latency}ms"),
    )
```

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:
  - Displaying agent responses
  - Showing corrected responses (when the agent self-corrects)
  - Displaying user transcripts for debugging
  - (Commented out) Latency measurements

### 7. Conversation Management

Start and manage the conversation:

```python
    # 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.

#### How it works:
1. The agent identifies information that should be remembered
2. It formats the information as a message string
3. It calls the `addMemories` function with this message
4. The function stores the memory in Mem0 linked to the user's ID
5. Later conversations can retrieve this memory

#### Example usage in agent prompt:
```
When the user shares important information like preferences or personal details, 
use the addMemories function to store this information for future reference.
```

### 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

#### How it works:
1. The agent needs context for the current conversation
2. It calls `retrieveMemories` with the current conversation topic or question
3. The function performs a semantic search in Mem0
4. Relevant memories are returned to the agent
5. The agent incorporates these memories into its response

#### Example usage in agent prompt:
```
At the beginning of each conversation turn, use retrieveMemories to check if we've 
discussed this topic before or if the user has shared relevant preferences.
```

## 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 these specifications:

For `addMemories`:
```json
{
  "name": "addMemories",
  "description": "Stores important information from the conversation to remember for future interactions",
  "parameters": {
    "type": "object",
    "properties": {
      "message": {
        "type": "string",
        "description": "The important information to remember"
      }
    },
    "required": ["message"]
  }
}
```

For `retrieveMemories`:
```json
{
  "name": "retrieveMemories",
  "description": "Retrieves relevant information from past conversations",
  "parameters": {
    "type": "object",
    "properties": {
      "message": {
        "type": "string",
        "description": "The query to search for in past memories"
      }
    },
    "required": ["message"]
  }
}
```

3. Update your agent's prompt to instruct it to use these memory functions. For example:

```
You are a helpful voice assistant that remembers past conversations with the user.

You have access to memory tools that allow you to remember important information:
- Use retrieveMemories at the beginning of the conversation to recall relevant context from prior conversations
- Use addMemories to store new important information such as:
  * User preferences
  * Personal details the user shares
  * Important decisions made
  * Tasks or follow-ups promised to the user

Before responding to complex questions, always check for relevant memories first.
When the user shares important information, make sure to store it for future reference.
```

## 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**:
   ```python
   # 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**:
   ```python
   # 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."

## Example Use Cases

- **Personal Assistant** - Remember user preferences, past requests, and important dates
  ```
  User: "What restaurants did I say I liked last time?"
  Agent: *retrieves memories* "You mentioned enjoying Bella Italia and The Golden Dragon."
  ```

- **Customer Support** - Recall previous issues a customer has had
  ```
  User: "I'm having that same problem again!"
  Agent: *retrieves memories* "Is this related to the login issue you reported last week?"
  ```

- **Educational AI** - Track student progress and tailor teaching accordingly
  ```
  User: "Let's continue our math lesson."
  Agent: *retrieves memories* "Last time we were working on quadratic equations. Would you like to continue with that?"
  ```

- **Healthcare Assistant** - Remember symptoms, medications, and health concerns
  ```
  User: "Have I told you about my allergy medication?"
  Agent: *retrieves memories* "Yes, you mentioned you're taking Claritin for your pollen allergies."
  ```

## Troubleshooting

- **Missing API Keys**: 
  - Error: "API_KEY environment variable must be set"
  - Solution: Ensure all environment variables are set correctly in your .env file or system environment
  
- **Connection Issues**:
  - Error: "Failed to connect to API"
  - Solution: Check your network connection and API key permissions. Verify the API keys are valid and have the necessary permissions.
  
- **Empty Memory Results**:
  - Symptom: Agent always responds with "No memories found"
  - Solution: This is normal for new users. The memory database builds up over time as conversations occur. It's also possible your query isn't semantically similar to stored memories - try different phrasing.
  
- **Agent Not Using Memories**:
  - Symptom: The agent retrieves memories but doesn't incorporate them in responses
  - Solution: Update the agent's prompt to explicitly instruct it to use the retrieved memories in its responses

## 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

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