Mem0

Long-term conversation memory service powered by Mem0

Overview

Mem0MemoryService provides long-term memory capabilities for conversational agents by integrating with Mem0’s API. It automatically stores conversation history and retrieves relevant past context based on the current conversation, enhancing LLM responses with persistent memory across sessions.

Mem0 Memory API Reference

Pipecat’s API methods for Mem0 memory integration

Example Implementation

Browse examples using Mem0 memory

Mem0 Documentation

Official Mem0 API documentation and guides

Mem0 Platform

Access memory services and manage API keys

Installation

To use Mem0 memory services, install the required dependencies:

pip install "pipecat-ai[mem0]"

Prerequisites

Mem0 Account Setup

Before using Mem0 memory services, you need:

1. Mem0 Account: Sign up at Mem0 Platform
2. API Key: Generate an API key from your account dashboard
3. Host Configuration: Set up your Mem0 host endpoint
4. User Management: Configure user IDs for memory association

Required Environment Variables

• MEM0_API_KEY: Your Mem0 API key for authentication

Configuration Options

• User ID: Unique identifier for associating memories with specific users
• Agent ID: Identifier for the agent using the memory service
• Run ID: Identifier for specific conversation sessions
• Memory Retrieval: Configure how past context is retrieved and used

Key Features

• Persistent Memory: Long-term conversation history across sessions
• Context Retrieval: Automatic retrieval of relevant past conversations
• User Association: Memory tied to specific users for personalization
• Session Management: Track conversations across different runs and agents

Configuration

api_key · str · default: "None"

The API key for accessing Mem0’s cloud API.

local_config · dict · default: "None"

Local configuration for Mem0 client as an alternative to the cloud API.

user_id · str · default: "None"

The user ID to associate with memories in Mem0. At least one of user_id, agent_id, or run_id must be provided.

agent_id · str · default: "None"

The agent ID to associate with memories in Mem0.

run_id · str · default: "None"

The run ID to associate with memories in Mem0.

params · InputParams · default: "None"

Configuration parameters for memory retrieval and storage. See InputParams below.

host · str · default: "None"

The host of the Mem0 server.

InputParams

Parameter	Type	Default	Description
search_limit	int	10	Maximum number of memories to retrieve per query (min: 1).
search_threshold	float	0.1	Minimum similarity threshold for memory retrieval (0.0-1.0).
api_version	str	"v2"	API version to use for Mem0 client operations.
system_prompt	str	"Based on previous conversations, I recall: \n\n"	Prefix text for memory context messages.
add_as_system_message	bool	True	Whether to add memories as system messages. When False, adds as user messages.
position	int	1	Position to insert memory messages in context.

Usage

Cloud API Setup

from pipecat.services.mem0 import Mem0MemoryService

memory = Mem0MemoryService(
    api_key=os.getenv("MEM0_API_KEY"),
    user_id="user-123",
)

With Custom Parameters

memory = Mem0MemoryService(
    api_key=os.getenv("MEM0_API_KEY"),
    user_id="user-123",
    agent_id="assistant-1",
    params=Mem0MemoryService.InputParams(
        search_limit=5,
        search_threshold=0.3,
        add_as_system_message=True,
    ),
)

Local Configuration

memory = Mem0MemoryService(
    local_config={
        "vector_store": {
            "provider": "chroma",
            "config": {"collection_name": "memories"},
        },
    },
    user_id="user-123",
)

Retrieving Memories Outside the Pipeline

The get_memories() method allows you to access stored memories outside the normal pipeline flow, such as when creating a personalized greeting at connection time:

# Get all stored memories for the configured user/agent/run IDs
memories = await memory.get_memories()

# Create a personalized greeting
if memories:
    greeting = "Hello! Based on our previous conversations, I remember: "
    for mem in memories[:3]:
        greeting += f"{mem['memory']} "
else:
    greeting = "Hello! It's nice to meet you."

Notes

• At least one ID required: You must provide at least one of user_id, agent_id, or run_id. A ValueError is raised if none are provided.
• Cloud vs local: Use api_key for Mem0’s cloud API, or local_config for a self-hosted Mem0 instance using Memory.from_config().
• Pipeline placement: Place the Mem0MemoryService before your LLM service in the pipeline. It intercepts LLMContextFrame, OpenAILLMContextFrame, and LLMMessagesFrame to enhance context with relevant memories before passing them downstream.
• Non-blocking operation: All Mem0 API calls (storage, retrieval, search) run in background threads to avoid blocking the event loop. Message storage is fire-and-forget, so it doesn’t delay downstream processing.
• Message role filtering: Only messages with user or assistant roles are stored in Mem0. Messages with other roles (such as system or developer) are automatically filtered out, as the Mem0 API does not accept them.