Skip to main content

Mem0 home page

Search...

Ctrl KAsk AI

Search...

Navigation

Ops & Automations

Collaborative Task Assistant

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
• Full Code Example
• Usage
• Key Points
• Conclusion

​ Overview

Build a multi-user collaborative chat or task management system with Mem0. Each message is attributed to its author, and all messages are stored in a shared project space. Mem0 makes it easy to track contributions, sort and group messages, and collaborate in real time.

​ Setup

Install the required packages:

pip install openai mem0ai

​ Full Code Example

from openai import OpenAI
from mem0 import Memory
import os
from datetime import datetime
from collections import defaultdict

# Set your OpenAI API key
os.environ["OPENAI_API_KEY"] = "sk-your-key"

# Shared project context
RUN_ID = "project-demo"

# Initialize Mem0
mem = Memory()

class CollaborativeAgent:
    def __init__(self, run_id):
        self.run_id = run_id
        self.mem = mem

    def add_message(self, role, name, content):
        msg = {"role": role, "name": name, "content": content}
        self.mem.add([msg], run_id=self.run_id, infer=False)

    def brainstorm(self, prompt):
        # Get recent messages for context
        memories = self.mem.search(prompt, run_id=self.run_id, limit=5)["results"]
        context = "\n".join(f"- {m['memory']} (by {m.get('actor_id', 'Unknown')})" for m in memories)
        client = OpenAI()
        messages = [\
            {"role": "system", "content": "You are a helpful project assistant."},\
            {"role": "user", "content": f"Prompt: {prompt}\nContext:\n{context}"}\
        ]
        reply = client.chat.completions.create(
            model="gpt-4.1-nano-2025-04-14",
            messages=messages
        ).choices[0].message.content.strip()
        self.add_message("assistant", "assistant", reply)
        return reply

    def get_all_messages(self):
        return self.mem.get_all(run_id=self.run_id)["results"]

    def print_sorted_by_time(self):
        messages = self.get_all_messages()
        messages.sort(key=lambda m: m.get('created_at', ''))
        print("\n--- Messages (sorted by time) ---")
        for m in messages:
            who = m.get("actor_id") or "Unknown"
            ts = m.get('created_at', 'Timestamp N/A')
            try:
                dt = datetime.fromisoformat(ts.replace('Z', '+00:00'))
                ts_fmt = dt.strftime('%Y-%m-%d %H:%M:%S')
            except Exception:
                ts_fmt = ts
            print(f"[{ts_fmt}] [{who}] {m['memory']}")

    def print_grouped_by_actor(self):
        messages = self.get_all_messages()
        grouped = defaultdict(list)
        for m in messages:
            grouped[m.get("actor_id") or "Unknown"].append(m)
        print("\n--- Messages (grouped by actor) ---")
        for actor, mems in grouped.items():
            print(f"\n=== {actor} ===")
            for m in mems:
                ts = m.get('created_at', 'Timestamp N/A')
                try:
                    dt = datetime.fromisoformat(ts.replace('Z', '+00:00'))
                    ts_fmt = dt.strftime('%Y-%m-%d %H:%M:%S')
                except Exception:
                    ts_fmt = ts
                print(f"[{ts_fmt}] {m['memory']}")

​ Usage

# Example usage
agent = CollaborativeAgent(RUN_ID)
agent.add_message("user", "alice", "Let's list tasks for the new landing page.")
agent.add_message("user", "bob", "I'll own the hero section copy.")
agent.add_message("user", "carol", "I'll choose product screenshots.")

# Brainstorm with context
print("\nAssistant reply:\n", agent.brainstorm("What are the current open tasks?"))

# Print all messages sorted by time
agent.print_sorted_by_time()

# Print all messages grouped by actor
agent.print_grouped_by_actor()

​ Key Points

• Each message is attributed to a user or agent (actor)
• All messages are stored in a shared project space (run_id)
• You can sort messages by time, group by actor, and format timestamps for clarity
• Mem0 makes it easy to build collaborative, attributed chat/task systems

​ Conclusion

Mem0 enables fast, transparent collaboration for teams and agents, with full attribution, flexible memory search, and easy message organization.

---

Partition Memories by Entity \ \ Learn how to scope memories across users, agents, and runs for team workflows.

Support Inbox with Mem0 \ \ Apply collaborative memory patterns to customer support scenarios.

Was this page helpful?

YesNo

Suggest edits Raise issue

Multi-Session Research Agent\ \ Previous Memory-Powered Agent SDK\ \ Next

Ctrl+I

Assistant

Responses are generated using AI and may contain mistakes.

Suggestions

How do I retrieve stored messages?How do I add messages to memory?How do I set up Mem0?

Contact support