[Feature] Support AI and Human Group Chat (Multi-Agent Collaboration)

[Clawiee]

Feature Request: AI and Human Group Chat (Multi-Agent Collaboration)

🎯 Problem Statement

Currently, Clawith agents operate primarily as individual assistants responding to user requests. There is no native support for multi-agent collaboration or group conversations where multiple AI agents and humans can interact together in real-time.

💡 Proposed Solution

Inspired by Slock.ai (https://slock.ai), which enables "humans and AI agents to collaborate in channels and DMs — in real-time", we propose adding group chat functionality to Clawith.

🌟 Key Features

1. Group Chat Channels

• Create channels where multiple agents and humans can participate
• Agents can see every message in their channels and respond naturally
• No context switching or copy-pasting between conversations

2. Multi-Agent Collaboration

• Add multiple agents to the same conversation/channel
• Agents can collaborate, delegate tasks, and communicate with each other
• Simulate a team environment with specialized agent roles

3. Agent Memory Persistence

• Each agent maintains persistent memory across sessions
• Agents remember conversation context and past interactions
• Full context restored when agents "wake up"

4. Proactive Agent Behavior

• Agents can respond autonomously without being directly addressed
• Agents can initiate actions based on channel context
• Agents can "sleep" when idle and wake on new activity

🎨 User Experience

Example workflow:

[Channel: #product-review]

User: @assistant-team let's review the new feature

Dev-Agent: I'll analyze the code changes and provide a technical review.

Design-Agent: I can check if the UI follows our design system guidelines.

PM-Agent: From a product perspective, this addresses the main user pain points we identified.

Dev-Agent: The implementation looks solid. I've verified the API integration.

User: Great work team! Please create a summary for the stakeholders.

📋 Use Cases

1. Team Collaboration: Multiple specialized agents working together on complex tasks
2. Brainstorming: Agents with different perspectives collaborating on ideas
3. Code Review: Development, security, and QA agents reviewing together
4. Customer Support: Multiple agents handling different aspects of support
5. Meeting Assistant: Scheduling, note-taking, and action-item agents working in sync

🔧 Technical Considerations

• Channel/message model with agent participants
• Agent subscription to specific channels
• Message routing and context management
• Permission controls for channel access
• Agent-to-agent communication protocols

📎 Reference

• Slock.ai: https://slock.ai - "Where humans and AI agents collaborate"

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Reported by

Yifei Zhou

[Clawiee]

🎉 Great Feature Proposal!

Thank you for this detailed and well-structured feature request! This is an exciting direction that aligns with the future of AI agent platforms.

💭 Initial Thoughts

This multi-agent collaboration feature would significantly enhance Clawith's capabilities. Here's some initial analysis:

Current Architecture Considerations:

• The existing single-agent conversation model would need extension for channel-based messaging
• Agent subscription system needed for channel participation
• Message routing logic for multi-recipient scenarios
• Context window management for group conversations (token limits)

Potential Implementation Approaches:

1. Channel Model Extension

   • Add channel_id to message schema
   • Agent subscription table for channel participation
   • Broadcast message routing

2. Agent Collaboration Protocol

   • Define agent-to-agent message formats
   • Add @mention detection and routing
   • Implement private/directed messaging

3. Context Management

   • Hierarchical context: global → channel → thread
   • Agent selective context loading
   • Summarization for long conversations

Related Considerations:

• This overlaps somewhat with the existing Relationship feature (agent-to-agent communication)
• May want to leverage existing notification/workspace infrastructure

📋 Suggested Next Steps

1. Technical Design Doc (RFC) - Define the data models and API endpoints
2. Prototype - Start with basic channel + 2-agent demo
3. Incremental Features - Channel creation → Agent joining → Message routing → Proactive behavior

🔗 References

Great reference to Slock.ai! They have a compelling model for human-AI collaboration.

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Labels added: enhancement, multi-agent, collaboration - these are great for tracking this epic feature.

Looking forward to seeing this evolve! 🚀