Multi-Chat Feature

Overview

The Multi-Chat feature allows users to engage in conversations with multiple AI assistants simultaneously within a single interface. This feature enables comparative analysis, collaborative problem-solving, and diverse perspectives on complex topics.

Features

🔀 Multiple Assistant Conversations

Simultaneous Chats: Run multiple conversations in parallel
Assistant Switching: Quick navigation between different assistants
Context Preservation: Each conversation maintains its own context and history
Independent Configurations: Each assistant can have different tools and settings

📊 Comparative Analysis

Side-by-Side Responses: Compare answers from different assistants
Response Quality Assessment: Evaluate different AI approaches
Expertise Matching: Use specialized assistants for specific domains
Consensus Building: Aggregate insights from multiple AI perspectives

🎛️ Interface Controls

Chat Tabs: Tabbed interface for easy switching
Assistant Overview: Quick view of active conversations
Message History: Persistent history for each assistant
Unified Actions: Perform actions across multiple chats

User Interface

Multi-Chat Layout

The multi-chat interface is accessible at /multi-chat and provides:

Assistant Grid: Visual grid of active conversations
Quick Actions: Start new chats, close conversations
Message Sync: Optional synchronization of messages across chats
Export Options: Export conversations individually or combined

Tab System: Each assistant appears as a separate tab
Assistant Cards: Preview cards showing recent messages
Search: Find specific conversations or messages
Filtering: Filter by assistant type, tools used, or timeframe

Technical Implementation

Route Structure

// Multi-chat page component
// Location: src/app/multi-chat/page.tsx
export default function MultiChatPage() {
  // Manages multiple conversation states
  // Handles assistant switching and coordination
}

State Management

interface MultiChatState {
  activeChats: Map<string, ChatSession>;
  currentAssistant: string;
  sharedContext?: SharedContext;
  syncEnabled: boolean;
}

Component Architecture

MultiChatContainer: Main orchestration component
ChatTabPanel: Individual chat interface
AssistantSwitcher: Quick assistant selection
MessageSync: Cross-chat message coordination

Use Cases

🎯 Research and Analysis

Academic Research: Get perspectives from different specialized assistants
Problem Solving: Approach complex problems from multiple angles
Fact Checking: Cross-reference information across assistants
Creative Writing: Get diverse creative inputs and feedback

💼 Professional Applications

Code Review: Get different coding perspectives and approaches
Business Strategy: Evaluate decisions from multiple business viewpoints
Technical Documentation: Compare documentation styles and approaches
Project Planning: Get diverse project management insights

🧠 Learning and Education

Subject Tutoring: Learn from assistants with different teaching styles
Language Learning: Practice with assistants using different approaches
Skill Development: Get varied training methodologies
Knowledge Validation: Verify understanding across multiple sources

Configuration

Assistant Selection

// Configure assistants for multi-chat
const multiChatConfig = {
  assistants: [
    { id: 'technical-expert', name: 'Technical Expert' },
    { id: 'creative-writer', name: 'Creative Writer' },
    { id: 'business-analyst', name: 'Business Analyst' }
  ],
  maxConcurrent: 5,
  syncMessages: false
};

Message Synchronization

Users can optionally enable message synchronization:

Manual Sync: Manually send messages to multiple assistants
Auto Sync: Automatically broadcast messages to all active chats
Selective Sync: Choose which assistants receive synchronized messages

Workflow Examples

Research Workflow

Start Multi-Chat: Open the multi-chat interface
Select Assistants: Choose assistants with relevant expertise
Ask Question: Send the same question to multiple assistants
Compare Responses: Analyze different perspectives and approaches
Follow-Up: Ask targeted follow-up questions based on responses
Synthesize: Combine insights into a comprehensive understanding

Code Development Workflow

Problem Definition: Describe coding challenge to multiple assistants
Solution Approaches: Get different implementation strategies
Code Review: Have assistants review each other’s suggestions
Testing Strategies: Get varied testing approaches
Documentation: Generate different documentation styles
Best Practices: Consolidate best practices from all assistants

Performance Considerations

Resource Management

Connection Pooling: Efficient management of multiple WebSocket connections
Message Queuing: Handle high-volume message processing
Memory Usage: Optimize memory usage for multiple conversation contexts
Rate Limiting: Prevent overwhelming individual assistants

User Experience

Load Balancing: Distribute requests across available assistants
Response Timing: Handle varying response times gracefully
Error Handling: Graceful degradation when assistants are unavailable
Progress Indicators: Show processing status for each conversation

Limitations

Technical Constraints

Maximum Concurrent Chats: Limited by browser resources and API limits
Context Isolation: Each chat maintains separate context (no cross-chat memory)
Tool Limitations: Some tools may not work optimally in multi-chat mode
Performance Impact: Multiple simultaneous conversations may impact response time

Usage Guidelines

Focused Questions: Best results with clear, focused questions
Assistant Selection: Choose assistants with complementary expertise
Message Management: Monitor message volume to avoid overwhelming assistants
Context Awareness: Remember that assistants can’t see other conversations

Future Enhancements

Planned Features

Cross-Chat Context: Enable assistants to reference other conversations
Collaboration Mode: Allow assistants to collaborate on shared tasks
Advanced Analytics: Detailed analysis of response patterns and quality
Template Workflows: Pre-configured assistant combinations for common tasks

Integration Opportunities

Document Collaboration: Multi-assistant document editing and review
Project Management: Coordinated project planning across multiple assistants
Learning Pathways: Structured learning experiences with multiple instructors
Research Pipelines: Automated research workflows with specialized assistants

Troubleshooting

Common Issues

Performance Slowdown

Reduce number of concurrent chats
Clear conversation history for unused chats
Check network connection stability

Message Sync Issues

Verify sync settings are enabled
Check individual assistant availability
Restart conversations if sync fails

Context Confusion

Remember each chat is independent
Provide context in each conversation
Use assistant names when referencing responses

Security and Privacy

Data Isolation

Conversation Separation: Each chat maintains independent data
User Privacy: Messages not shared between assistants unless explicitly synced
Audit Trail: Complete tracking of which assistants received which messages
Data Retention: Individual conversation retention policies

Access Control

Assistant Permissions: Users can only access assistants they have permission for
Tool Restrictions: Tool availability based on user permissions and assistant configuration
Rate Limiting: Prevents abuse through excessive multi-chat usage
Session Management: Secure handling of multiple simultaneous sessions

Aitana Documentation