AgentHub - Multi-Agent Collaboration Framework

Orchestrate multiple AI Agents to work together like a human team Complete complex software development tasks through reliable messaging protocols

Features | Quick Start | Architecture | Examples | Contributing

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What is AgentHub?

AgentHub is an open-source Multi-Agent Orchestration Framework that enables reliable communication and coordinated work between multiple AI Agents through a message routing architecture.

With a single command, launch a complete AI development team with:

• 1 Coordinator Agent (MAIN) - task planning, coordination, and review
• 4 Executor Agents (A/B/C/D) - parallel task execution

Manage AI collaborative development just like managing a human team.

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Why AgentHub?

Traditional AI Development	AgentHub
Single AI working alone	Multi-Agent Parallel Collaboration
No division of labor	Automatic Task Breakdown & Assignment
High communication overhead	Standardized Messaging Protocol
State easily lost	Persistence + Crash Recovery
Hard to track progress	Complete Task State Management

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Features

🚀 One-Command Team Launch

./scripts/start_team.sh

• Auto-start Router (message hub)
• Open 5 independent terminal windows
• Generate standard documentation templates
• Inject AI role prompts

🔄 Reliable Message Delivery

• ACK Confirmation - Dual acknowledgment at delivery and application layer
• Auto Retry - Exponential backoff retry strategy
• Timeout Handling - Automatic timeout detection and handling
• Idempotency - Message deduplication to avoid duplicate execution

📋 Complete Collaboration Protocol

review (review) -> assign (assign) -> execute (execute) -> verify (verify)

Standardized AI-to-AI communication protocol supporting:

• Document/code review
• Task assignment
• Q&A coordination
• Result verification

💾 State Persistence & Recovery

• Message logs (JSONL format)
• Inbox state persistence
• Auto-recovery after crash
• Session/epoch management support

🔧 Flexible Configuration

• Customizable agent count
• Support for different AI CLI tools (Codex, Claude Code, etc.)
• Environment variable configuration

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Demo

Watch how AgentHub orchestrates 5 AI agents working together

Coming soon: Video demonstration of multi-agent collaboration

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Architecture

                    Router Server
        (Message Routing / State Management / Delivery)

                    │
    ┌───────┬───────┼───────┬───────┐
    │       │       │       │       │
┌───▼───┐ ┌─▼───┐ ┌─▼───┐ ┌─▼───┐ ┌─▼───┐
│ MAIN  │ │  A  │ │  B  │ │  C  │ │  D  │
│Coord. │ │Exec │ │Exec │ │Exec │ │Exec │
│Agent  │ │Agent│ │Agent│ │Agent│ │Agent│
└───────┘ └─────┘ └─────┘ └─────┘ └─────┘

Role Responsibilities:

Agent	Role	Responsibilities
MAIN	Coordinator	Task planning, document writing, problem solving, final review
A/B/C/D	Executors	Task execution, document review, feedback collection

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Message Protocol

AgentHub defines a complete AI-to-AI communication protocol:

Message Type	Direction	Purpose
review	MAIN->Members	Review documents/code
report	Members->MAIN	Feedback review results
assign	MAIN->Members	Assign tasks
clarify	Members->MAIN	Ask questions
answer	MAIN->Members	Answer questions
verify	MAIN->Members	Verify changes
done	Members->MAIN	Task complete
fail	Members->MAIN	Task failed

See docs/main-members-workflow.md for complete protocol specification.

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Quick Start

Prerequisites

• macOS (Linux support planned)
• Python 3.8+
• Terminal.app or iTerm2
• AI CLI tool (Codex, Claude Code, or compatible)

Installation

# Clone repository
git clone https://github.com/Dmatut7/AgentHub.git
cd AgentHub

Launch AI Team

# Start in your project directory
./scripts/start_team.sh

The system will automatically:

1. Start Router (default port 8765)
2. Generate standard documentation templates
3. Open 5 terminal windows for each agent

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Use Cases

1. Code Review Pipeline

# MAIN writes code -> A/B/C/D review in parallel -> MAIN consolidates feedback

2. Parallel Feature Development

# MAIN breaks down feature -> A/B/C/D implement components -> MAIN integrates

3. Documentation Generation

# MAIN outlines -> A/B/C/D write sections -> MAIN finalizes

4. Bug Hunt & Fix

# MAIN describes bug -> A/B/C/D investigate & propose fixes -> MAIN verifies

See EXAMPLES.md for detailed use cases.

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Directory Structure

AgentHub/
├── scripts/               # Launch scripts
│   ├── start_team.sh     # One-command launch
│   ├── stop_team.sh      # Stop system
│   └── status_team.sh    # Check status
├── src/
│   ├── api/              # HTTP server
│   ├── cli/              # CLI tools
│   ├── router/           # Message routing core
│   ├── protocol/         # Protocol definitions
│   ├── state/            # State management
│   ├── storage/          # Persistent storage
│   └── launcher/         # Terminal launcher
├── prompts/              # AI prompt templates
├── doc/                  # Documentation templates
├── docs/                 # Design documents
│   ├── design.md         # System architecture
│   └── main-members-workflow.md  # Protocol spec
└── README.md

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Common Commands

# Start system
./scripts/start_team.sh

# Check status
./scripts/status_team.sh

# Send message
python3 src/cli/team.py say --from MAIN --to A --text "Start task"

# View message queue
curl http://127.0.0.1:8765/status | python3 -m json.tool

# Stop system
./scripts/stop_team.sh

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Configuration

Environment Variable	Description	Default
TERMINAL_ADAPTER	Terminal type (terminal/iterm)	terminal
CODEX_PATH	AI CLI executable path	codex

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Contributing

We welcome contributions! Please see CONTRIBUTING.md for details.

Quick steps:

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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Documentation

• Design Doc - System architecture
• Protocol Spec - Message protocol details
• Examples - Use cases and examples
• Contributing - Contribution guide
• Support - Help & troubleshooting
• Changelog - Version history

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Roadmap

• Linux support
• Windows support
• Web dashboard for monitoring
• More AI model integrations (GPT-4, Claude, Gemini)
• Plugin system for custom protocols
• Distributed agent support (across machines)

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License

MIT License © 2026 Dmatut7

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AgentHub - Making AI team collaboration simpler.

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