How to build a multi-user chatbot?

While both OpenAI and Anthropic offer powerful AI capabilities, OpenAI's architecture provides significant advantages for multi-user scenarios.

This guide will demonstrate why OpenAI is currently the better choice for building group chat applications, backed by technical examples and real-world implementations.

What are Multi-user chatbots?

A multi-user chatbot application is designed to interact with multiple users simultaneously in a shared conversation space.

Unlike traditional chatbots that handle one-on-one conversations, multi-user chatbots must manage group dynamics, track multiple conversation threads, and maintain context across different users.

Why OpenAI is better for Multi-User Scenarios

OpenAI's advantage lies in its flexible message handling:

• Allows multiple consecutive messages from the same role
• Supports unique user identification through the 'name' property
• Maintains separate message threads without forced merging
• Enables true multi-user conversations

messages = [
    {"role": "system", "content": "You're a helpful assistant"},
    {"role": "user", "name": "Sarah", "content": "What's the weather?"},
    {"role": "user", "name": "Mike", "content": "What time is it?"},
    {"role": "assistant", "content": "Sarah, it's sunny today!"},
    {"role": "user", "name": "Sarah", "content": "Thanks!"}
]

Anthropic's Limitations

In contrast, Anthropic's platform:

• Automatically merges consecutive user messages
• Requires strict alternation between user and assistant messages
• Makes it difficult to maintain separate user identities
• It forces a one-on-one conversation model

This means if Sarah and Mike try to chat simultaneously:

# In Anthropic's system, these would merge into one message
message1 = "What's the weather?" # From Sarah
message2 = "What time is it?"    # From Mike
# Results in losing the individual user context

This limitation makes Anthropic unsuitable for true multi-user applications without significant workarounds.

Building Your Multi-User ChatBot with OpenAI

Now that we understand why OpenAI is the a better choice, let's walk through building a chatbot that can handle multiple conversations.

1. Initial Setup

First, install the necessary dependencies:

pip install openai python-dotenv

Add your OPENAI_API_KEY to your environment

export OPENAI_API_KEY=YOUR_KEY

2. Set messages based on roles and names

Set up your system message to establish the chatbot's behavior:

system_message = {
    "role": "system",
    "content": "You are a helpful assistant in a multi-user chat. Address each user by their name when responding."
}

messages = [
    system_message,
    {"role": "user", "name": "User1", "content": "Hi, what's the weather today?"},
    {"role": "user", "name": "User2", "content": "Can you recommend a good book?"}
]

3. Message Processing

Implement the core message handling functionality:

from openai import OpenAI
from dotenv import load_dotenv

import os

load_dotenv()

OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')

client = OpenAI(api_key=OPENAI_API_KEY)
response = client.chat.completions.create(
    model="gpt-4",
    messages=messages
)

print(response.choices[0].message.content)

Common Challenges and Solutions

Message Ordering

• Implement timestamps for message sequencing
• Use message IDs for tracking
• Handle concurrent messages appropriately

Context Management

• Regular context cleanup to prevent memory bloat
• Implement context expiration
• Balance context retention with performance

Conclusion

The technical architecture of OpenAI's API provides a foundation that scales more easily for multi-user applications, reducing development overhead.

While workarounds exist for other platforms, the native support for discrete user messages in OpenAI significantly reduces technical debt and implementation complexity in production environments.