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This guide demonstrates how to create a memory-enabled voice assistant using LiveKit, Deepgram, OpenAI, and Mem0, focusing on creating an intelligent, context-aware travel planning agent.

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Prerequisites

Before you begin, make sure you have:

  1. Installed Livekit Agents SDK with voice dependencies of silero and deepgram:
pip install livekit-agents[voice] \
livekit-plugins-silero \
livekit-plugins-deepgram \
livekit-plugins-openai
  1. Installed Mem0 SDK:
pip install mem0ai
  1. Set up your API keys in a .env file:
LIVEKIT_URL=your_livekit_url
LIVEKIT_API_KEY=your_livekit_api_key
LIVEKIT_API_SECRET=your_livekit_api_secret
DEEPGRAM_API_KEY=your_deepgram_api_key
MEM0_API_KEY=your_mem0_api_key
OPENAI_API_KEY=your_openai_api_key

Note: Make sure to have a Livekit and Deepgram account. You can find these variables LIVEKIT_URL , LIVEKIT_API_KEY and LIVEKIT_API_SECRET from LiveKit Cloud Console and for more information you can refer this website LiveKit Documentation. For DEEPGRAM_API_KEY you can get from Deepgram Console refer this website Deepgram Documentation for more details.

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Code Breakdown

Let’s break down the key components of this implementation using LiveKit Agents:

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1. Setting Up Dependencies and Environment

import asyncio
import logging
import os
from typing import List, Dict, Any, Annotated

import aiohttp
from dotenv import load_dotenv
from livekit.agents import (
    Agent,
    AgentSession,
    AutoSubscribe,
    JobContext,
    llm,
    function_tool,
    RunContext,
    cli,
    WorkerOptions,
    ModelSettings,
)
from livekit.plugins import deepgram, openai, silero
from livekit.plugins.turn_detector.multilingual import MultilingualModel
from mem0 import AsyncMemoryClient

# Load environment variables
load_dotenv()

# Configure logging
logger = logging.getLogger("memory-assistant")
logger.setLevel(logging.INFO)

# Define a global user ID for simplicity
USER_ID = "voice_user"

# Initialize Mem0 client
mem0 = AsyncMemoryClient()

This section handles:

  • Importing required modules
  • Loading environment variables
  • Setting up logging
  • Extracting user identification
  • Initializing the Mem0 client

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2. Memory Enrichment Function

async def _enrich_with_memory(chat_ctx: llm.ChatContext):
    """Add memories and augment chat context with relevant memories"""
    if not chat_ctx.messages:
        return

    # Get the latest user message
    user_msg = chat_ctx.messages[-1]
    if user_msg.role != "user":
        return

    user_content = user_msg.text_content()
    if not user_content:
        return

    # Store user message in Mem0
    await mem0.add(
        [{"role": "user", "content": user_content}],
        user_id=USER_ID
    )

    # Search for relevant memories
    results = await mem0.search(
        user_content,
        user_id=USER_ID,
    )

    # Augment context with retrieved memories
    if results:
        memories = ' '.join([result["memory"] for result in results])
        logger.info(f"Enriching with memory: {memories}")

        # Add memory context as a assistant message
        memory_msg = llm.ChatMessage.create(
            text=f"Relevant Memory: {memories}\n",
            role="assistant",
        )

        # Modify chat context with retrieved memories
        chat_ctx.messages[-1] = memory_msg
        chat_ctx.messages.append(user_msg)

This function:

  • Stores user messages in Mem0
  • Performs semantic search for relevant memories
  • Augments the chat context with retrieved memories
  • Enables contextually aware responses

​
3. Prewarm and Entrypoint Functions

def prewarm_process(proc):
    """Preload components to speed up session start"""
    proc.userdata["vad"] = silero.VAD.load()

async def entrypoint(ctx: JobContext):
    """Main entrypoint for the memory-enabled voice agent"""

    # Connect to LiveKit room
    await ctx.connect(auto_subscribe=AutoSubscribe.AUDIO_ONLY)

    # Create agent session with modern 1.0 architecture
    session = AgentSession(
        stt=deepgram.STT(),
        llm=openai.LLM(model="gpt-4o-mini"),
        tts=openai.TTS(),
        vad=silero.VAD.load(),
        turn_detection=MultilingualModel(),
    )

    # Create memory-enabled agent
    agent = MemoryEnabledAgent()

    # Start the session
    await session.start(
        room=ctx.room,
        agent=agent,
    )

    # Initial greeting
    await session.generate_reply(
        instructions="Greet the user warmly as George the travel guide and ask how you can help them plan their next adventure."
    )

The entrypoint function:

  • Connects to LiveKit room
  • Initializes Mem0 memory client
  • Create agent session using AgentSession orchestrator with memory enrichment
  • Uses modern turn detection with MultilingualModel()
  • Starts the agent with an initial greeting

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Create a Memory-Enabled Voice Agent

Now that we’ve explained each component, here’s the complete implementation that combines OpenAI Agents SDK for voice with Mem0’s memory capabilities:

import asyncio
import logging
import os
from typing import AsyncIterable, Any

from dotenv import load_dotenv
from livekit.agents import (
    Agent,
    AgentSession,
    JobContext,
    llm,
    function_tool,
    RunContext,
    cli,
    WorkerOptions,
    ModelSettings,
)
from livekit.plugins import deepgram, openai, silero
from livekit.plugins.turn_detector.multilingual import MultilingualModel
from mem0 import AsyncMemoryClient

# Load environment variables
load_dotenv()

# Configure logging
logger = logging.getLogger("memory-assistant")
logger.setLevel(logging.INFO)

# Define a global user ID for simplicity
USER_ID = "voice_user"

# Initialize Mem0 memory client
mem0 = AsyncMemoryClient()

class MemoryEnabledAgent(Agent):
    """Travel guide agent with Mem0 memory integration"""

    def __init__(self):
        super().__init__(
            instructions="""
            You are a helpful voice assistant.
            You are a travel guide named George and will help the user to plan a travel trip of their dreams.
            You should help the user plan for various adventures like work retreats, family vacations or solo backpacking trips.
            You should be careful to not suggest anything that would be dangerous, illegal or inappropriate.
            You can remember past interactions and use them to inform your answers.
            Use semantic memory retrieval to provide contextually relevant responses.
            """
        )

    async def llm_node(
        self,
        chat_ctx: llm.ChatContext,
        tools: list[llm.FunctionTool],
        model_settings: ModelSettings,
    ) -> AsyncIterable[llm.ChatChunk]:
        """Override LLM node to add memory enrichment before inference"""

        # Enrich context with memory before LLM inference
        await self._enrich_with_memory(chat_ctx)

        # Call default LLM node with enriched context
        async for chunk in Agent.default.llm_node(self, chat_ctx, tools, model_settings):
            yield chunk

    async def _enrich_with_memory(self, chat_ctx: llm.ChatContext):
        """Add memories and augment chat context with relevant memories"""
        if not chat_ctx.messages:
            return

        # Get the latest user message
        user_msg = chat_ctx.messages[-1]
        if user_msg.role != "user":
            return

        user_content = user_msg.text_content()
        if not user_content:
            return

        # Store user message in Mem0
        await mem0.add(
            [{"role": "user", "content": user_content}],
            user_id=USER_ID
        )

        # Search for relevant memories
        results = await mem0.search(
            user_content,
            user_id=USER_ID,
        )

        # Augment context with retrieved memories
        if results:
            memories = ' '.join([result["memory"] for result in results])
            logger.info(f"Enriching with memory: {memories}")

            # Add memory context as a assistant message
            memory_msg = llm.ChatMessage.create(
                text=f"Relevant Memory: {memories}\n",
                role="assistant",
            )

            # Modify chat context with retrieved memories
            chat_ctx.messages[-1] = memory_msg
            chat_ctx.messages.append(user_msg)

def prewarm_process(proc):
    """Preload components to speed up session start"""
    proc.userdata["vad"] = silero.VAD.load()

async def entrypoint(ctx: JobContext):
    """Main entrypoint for the memory-enabled voice agent"""

    # Connect to LiveKit room
    await ctx.connect(auto_subscribe=AutoSubscribe.AUDIO_ONLY)

    # Initialize Mem0 client
    mem0 = AsyncMemoryClient()

    # Create agent session with modern 1.0 architecture
    session = AgentSession(
        stt=deepgram.STT(),
        llm=openai.LLM(model="gpt-4o-mini"),
        tts=openai.TTS(),
        vad=silero.VAD.load(),
        turn_detection=MultilingualModel(),
    )

    # Create memory-enabled agent
    agent = MemoryEnabledAgent()

    # Start the session
    await session.start(
        room=ctx.room,
        agent=agent,
    )

    # Initial greeting
    await session.generate_reply(
        instructions="Greet the user warmly as George the travel guide and ask how you can help them plan their next adventure.",
        allow_interruptions=True
    )

# Run the application
if __name__ == "__main__":
    cli.run_app(WorkerOptions(
        entrypoint_fnc=entrypoint,
        prewarm_fnc=prewarm_process
    ))

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Key Features of This Implementation

  1. Semantic Memory Retrieval: Uses Mem0 to store and retrieve contextually relevant memories
  2. Voice Interaction: Leverages LiveKit for voice communication with proper turn detection
  3. Intelligent Context Management: Augments conversations with past interactions
  4. Travel Planning Specialization: Focused on creating a helpful travel guide assistant
  5. Function Tools: Modern tool definition for enhanced capabilities

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Running the Example

To run this example:

  1. Install all required dependencies
  2. Set up your .env file with the necessary API keys
  3. Ensure your microphone and audio setup are configured
  4. Run the script with Python 3.11 or newer and with the following command:
python mem0-livekit-voice-agent.py start
  1. After the script starts, you can interact with the voice agent using Livekit’s Agent Platform and connect to the agent inorder to start conversations.

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Best Practices for Voice Agents with Memory

  1. Context Preservation: Store enough context with each memory for effective retrieval
  2. Privacy Considerations: Implement secure memory management
  3. Relevant Memory Filtering: Use semantic search to retrieve only the most relevant memories
  4. Error Handling: Implement robust error handling for memory operations

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Debugging Function Tools

  • To run the script in debug mode simply start the assistant with dev mode:
python mem0-livekit-voice-agent.py dev
  • When working with memory-enabled voice agents, use Python’s logging module for effective debugging:
import logging

# Set up logging
logging.basicConfig(
    level=logging.DEBUG,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger("memory_voice_agent")