Mem0:Long-Term Memory and Personalization for Agents

Mem0 Platform provides a smart, self-improving memory layer for Large Language Models (LLMs), enabling developers to create personalized AI experiences that evolve with each user interaction.

At a high level, Mem0 Platform offers comprehensive memory management, self-improving memory capabilities, cross-platform consistency, and centralized memory control for AI applications. For more info, check out the Mem0 Platform Documentation.

🧠 Comprehensive Memory Management	Manage long-term, short-term, semantic, and episodic memories
🔄 Self-Improving Memory	Adaptive system that learns from user interactions
🌐 Cross-Platform Consistency	Unified user experience across various AI platforms
🎛️ Centralized Memory Control	Effortless storage, updating, and deletion of memories
🚀 Simplified Development	API-first approach for streamlined integration

Activity Dashboard

Installation

Mem0 Platform works seamlessly with various AI applications.

1. Sign Up: Create an account at Mem0 Platform

2. Generate API Key: Create an API key in your Mem0 dashboard

3. Install Mem0 SDK:

pip install mem0ai

4. Configure Your Environment: Add your API key to your environment variables

MEM0_API_KEY=<YOUR_MEM0_API_KEY>

5. Initialize Mem0:

from mem0ai import MemoryClient
memory = MemoryClient(api_key=os.getenv("MEM0_API_KEY"))

After initializing Mem0, you can start using its memory management features in your AI application.

Features

• Long-term Memory: Store and retrieve information persistently across sessions
• Short-term Memory: Manage temporary information within a single interaction
• Semantic Memory: Organize and retrieve conceptual knowledge
• Episodic Memory: Store and recall specific events or experiences
• Self-Improving System: Continuously refine understanding based on user interactions

Common Use Cases

• Personalized Learning Assistants
• Customer Support AI Agents
• Healthcare Assistants
• Virtual Companions

Mem0 Platform Examples

AG2 with Mem0 Example

This example demonstrates how to use Mem0 with AG2 to create a conversational AI system with memory capabilities.

import os
from autogen import ConversableAgent, LLMConfig
from mem0 import MemoryClient

# Set up environment variables
os.environ["OPENAI_API_KEY"] = "your_openai_api_key"
os.environ["MEM0_API_KEY"] = "your_mem0_api_key"

# Initialize Agent and Memory
agent = ConversableAgent(
    "chatbot",
    llm_config=LLMConfig(
        api_type="openai",
        model="gpt-4",
        api_key=os.environ.get("OPENAI_API_KEY")
    ),
    code_execution_config=False,
    function_map=None,
    human_input_mode="NEVER",
)

memory = MemoryClient(api_key=os.environ.get("MEM0_API_KEY"))

# Insert a conversation into memory
conversation = [
   {
        "role": "assistant",
        "content": "Hi, I'm Best Buy's chatbot!\n\nThanks for being a My Best Buy TotalTM member.\n\nWhat can I help you with?"
    },
    {
        "role": "user",
        "content": "Seeing horizontal lines on our tv. TV model: Sony - 77\" Class BRAVIA XR A80K OLED 4K UHD Smart Google TV"
    },
]

memory.add(messages=conversation, user_id="customer_service_bot")

# Agent Inference
data = "Which TV am I using?"

relevant_memories = memory.search(data, user_id="customer_service_bot")
flatten_relevant_memories = "\n".join([m["memory"] for m in relevant_memories])

prompt = f"""Answer the user question considering the memories.
Memories:
{flatten_relevant_memories}
\n\n
Question: {data}
"""

reply = agent.generate_reply(messages=[{"content": prompt, "role": "user"}])
print("Reply :", reply)

# Multi Agent Conversation
manager = ConversableAgent(
    "manager",
    system_message="You are a manager who helps in resolving customer issues.",
    llm_config=LLMConfig(
        api_type="openai",
        model="gpt-4",
        temperature=0,
        api_key=os.environ.get("OPENAI_API_KEY")
    ),
    human_input_mode="NEVER"
)

customer_bot = ConversableAgent(
    "customer_bot",
    system_message="You are a customer service bot who gathers information on issues customers are facing.",
    llm_config=LLMConfig(
        api_type="openai",
        model="gpt-4",
        temperature=0,
        api_key=os.environ.get("OPENAI_API_KEY")
    ),
    human_input_mode="NEVER"
)

data = "What appointment is booked?"

relevant_memories = memory.search(data, user_id="customer_service_bot")
flatten_relevant_memories = "\n".join([m["memory"] for m in relevant_memories])

prompt = f"""
Context:
{flatten_relevant_memories}
\n\n
Question: {data}
"""

result = manager.send(prompt, customer_bot, request_reply=True)

Access the complete code from this notebook: Mem0 with AG2

This example showcases:

1. Setting up AG2 agents and Mem0 memory
2. Adding a conversation to Mem0 memory
3. Using Mem0 to retrieve relevant memories for agent inference
4. Implementing a multi-agent conversation with memory-augmented context

For more Mem0 examples, visit our documentation.

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