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Frameworks & Multimodal

ReAct Agents with Memory

Welcome Mem0 Platform Open Source Cookbooks Integrations API Reference Release Notes

• Documentation

Getting Started

• Overview

Essentials

• Build a Companion with Mem0
• Partition Memories by Entity
• Control Memory Ingestion
• Set Memory Expiration
• Tag and Organize Memories
• Export Stored Memories
• Choose Vector vs Graph Memory

Companion Playbooks

• Interactive Memory Demo
• Build a Node.js Companion
• Personalized AI Tutor
• Smart Travel Assistant
• Research Assistant for YouTube
• Voice-First AI Companion
• Self-Hosted AI Companion

Ops & Automations

• Memory-Powered Support Agent
• Automated Email Intelligence
• Content Creation Workflow
• Multi-Session Research Agent
• Collaborative Task Assistant

Integrations & Platforms

• Memory-Powered Agent SDK
• Memory as OpenAI Tool
• Persistent Mastra Agents
• Healthcare Coach with ADK
• Bedrock with Persistent Memory
• Graph Memory on Neptune
• Search with Personal Context

Frameworks & Multimodal

• ReAct Agents with Memory
• Multi-Agent Collaboration
• Visual Memory Retrieval
• Persistent Eliza Characters
• Browser Extension Memory
• Gemini 3 with Mem0 MCP
• MiroFish Swarm Memory

On this page

• Overview
• Setup
• Using the Agent Without Memory
• Using the Agent With Memory

Create a ReAct Agent with LlamaIndex which uses Mem0 as the memory store.

​ Overview

A ReAct agent combines reasoning and action capabilities, making it versatile for tasks requiring both thought processes (reasoning) and interaction with tools or APIs (acting). Mem0 as memory enhances these capabilities by allowing the agent to store and retrieve contextual information from past interactions.

​ Setup

pip install llama-index-core llama-index-memory-mem0

Initialize the LLM.

import os
from llama_index.llms.openai import OpenAI

os.environ["OPENAI_API_KEY"] = "<your-openai-api-key>"
llm = OpenAI(model="gpt-4.1-nano-2025-04-14")

Initialize the Mem0 client. You can find your API key here. Read about Mem0 Open Source.

os.environ["MEM0_API_KEY"] = "<your-mem0-api-key>"

from llama_index.memory.mem0 import Mem0Memory

context = {"user_id": "david"}
memory_from_client = Mem0Memory.from_client(
    context=context,
    api_key=os.environ["MEM0_API_KEY"],
    search_msg_limit=4,  # optional, default is 5
)

Create the tools. These tools will be used by the agent to perform actions.

from llama_index.core.tools import FunctionTool

def call_fn(name: str):
    """Call the provided name.
    Args:
        name: str (Name of the person)
    """
    return f"Calling... {name}"

def email_fn(name: str):
    """Email the provided name.
    Args:
        name: str (Name of the person)
    """
    return f"Emailing... {name}"

def order_food(name: str, dish: str):
    """Order food for the provided name.
    Args:
        name: str (Name of the person)
        dish: str (Name of the dish)
    """
    return f"Ordering {dish} for {name}"

call_tool = FunctionTool.from_defaults(fn=call_fn)
email_tool = FunctionTool.from_defaults(fn=email_fn)
order_food_tool = FunctionTool.from_defaults(fn=order_food)

Initialize the agent with tools and memory.

from llama_index.core.agent import FunctionCallingAgent

agent = FunctionCallingAgent.from_tools(
    [call_tool, email_tool, order_food_tool],
    llm=llm,
    memory=memory_from_client,  # or memory_from_config
    verbose=True,
)

Start the chat.

The agent will use Mem0 to store the relevant memories from the chat.

Input

response = agent.chat("Hi, My name is David")
print(response)

Output

> Running step bf44a75a-a920-4cf3-944e-b6e6b5695043. Step input: Hi, My name is David
Added user message to memory: Hi, My name is David
=== LLM Response ===
Hello, David! How can I assist you today?

Input

response = agent.chat("I love to eat pizza on weekends")
print(response)

Output

> Running step 845783b0-b85b-487c-baee-8460ebe8b38d. Step input: I love to eat pizza on weekends
Added user message to memory: I love to eat pizza on weekends
=== LLM Response ===
Pizza is a great choice for the weekend! If you'd like, I can help you order some. Just let me know what kind of pizza you prefer!

Input

response = agent.chat("My preferred way of communication is email")
print(response)

Output

> Running step 345842f0-f8a0-42ea-a1b7-612265d72a92. Step input: My preferred way of communication is email
Added user message to memory: My preferred way of communication is email
=== LLM Response ===
Got it! If you need any assistance or have any requests, feel free to let me know, and I can communicate with you via email.

​ Using the Agent Without Memory

Input

agent = FunctionCallingAgent.from_tools(
    [call_tool, email_tool, order_food_tool],
    # memory is not provided
    llm=llm,
    verbose=True,
)
response = agent.chat("I am feeling hungry, order me something and send me the bill")
print(response)

Output

> Running step e89eb75d-75e1-4dea-a8c8-5c3d4b77882d. Step input: I am feeling hungry, order me something and send me the bill
Added user message to memory: I am feeling hungry, order me something and send me the bill
=== LLM Response ===
Please let me know your name and the dish you'd like to order, and I'll take care of it for you!

The agent is not able to remember the past preferences the user shared in previous chats.

​ Using the Agent With Memory

Input

agent = FunctionCallingAgent.from_tools(
    [call_tool, email_tool, order_food_tool],
    llm=llm,
    # memory is provided
    memory=memory_from_client,  # or memory_from_config
    verbose=True,
)
response = agent.chat("I am feeling hungry, order me something and send me the bill")
print(response)

Output

> Running step 5e473db9-3973-4cb1-a5fd-860be0ab0006. Step input: I am feeling hungry, order me something and send me the bill
Added user message to memory: I am feeling hungry, order me something and send me the bill
=== Calling Function ===
Calling function: order_food with args: {"name": "David", "dish": "pizza"}
=== Function Output ===
Ordering pizza for David
=== Calling Function ===
Calling function: email_fn with args: {"name": "David"}
=== Function Output ===
Emailing... David
> Running step 38080544-6b37-4bb2-aab2-7670100d926e. Step input: None
=== LLM Response ===
I've ordered a pizza for you, and the bill has been sent to your email. Enjoy your meal! If there's anything else you need, feel free to let me know.

The agent is able to remember the past preferences the user shared and use them to perform actions.

---

LlamaIndex Multiagent with Mem0 \ \ Scale to multi-agent workflows with shared memory coordination.

Build a Mem0 Companion \ \ Master the core patterns for memory-powered agents across frameworks.

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