- Add comment handlers for WebSocket API (comment.add, comment.list) - Update e2e-test.sh to focus on comment system testing - Test multi-turn conversation flow (3 turns) - Display comment content and metadata in test output - Reduce test timeouts for faster execution - Fix various orchestrator and lifecycle manager issues - Add CODE_REVIEW_ISSUES.md documenting known issues
Kandev - Kanban Board with AI Agent Orchestration
A Kanban board system that integrates AI agent orchestration capabilities with real-time Agent Communication Protocol (ACP) streaming, allowing tasks to be automatically executed by AI agents running in isolated Docker containers.
Overview
Kandev combines traditional Kanban task management with automated AI agent execution and real-time feedback streaming. Users create tasks on a Kanban board, and when configured with an AI agent type, the system automatically launches Docker containers to execute those tasks against specified code repositories. Real-time progress, logs, and results are streamed from agents to the frontend via WebSocket connections.
Key Features
- Kanban Board Management: Create and manage tasks across multiple boards with columns
- AI Agent Orchestration: Launch AI agents (Augment Agent) to execute tasks
- Real-Time ACP Streaming: Live progress updates, logs, and results streamed from agents
- Session Resumption: Agents can resume previous conversations using session IDs
- Docker Isolation: Each agent runs in an isolated Docker container with resource limits
- Repository Integration: Agents can work on code repositories with host credentials
- Host Credential Mounting: Secure read-only mounting of SSH keys and Git credentials
- SQLite Persistence: Lightweight file-based database for task and board storage
- Local Deployment: Designed for client machines with minimal dependencies
Architecture
The system runs as a unified Go binary that includes:
- Task Service: Manages tasks, boards, and columns with SQLite persistence
- Agent Manager: Manages Docker container lifecycle and ACP message streaming
- Orchestrator: Coordinates agent launches and monitors execution
- WebSocket API: Real-time bidirectional communication for all operations (Port 8080)
Agent Communication Protocol (ACP)
ACP is a JSON-based streaming protocol that enables real-time communication between AI agents and the backend:
Agent Container (stdout) → Agent Manager → Event Bus → API
Message Types: progress, log, result, error, status, heartbeat
See docs/ARCHITECTURE.md for detailed architecture documentation.
Supported Agent Types
- Augment Agent (
augment-agent)- Uses Auggie CLI for code analysis, generation, debugging, refactoring
- Native ACP support via stdout JSON streaming
- Session resumption for multi-turn conversations
Technology Stack
- Backend: Go 1.21+
- Database: SQLite (with WAL mode)
- Container Runtime: Docker
- Web Framework: Gin
- Logging: Zap (structured logging)
- Deployment: Single binary for client machines
Quick Start
Prerequisites
- Go 1.21 or higher
- Docker
- Augment CLI credentials (
~/.augment/session.json)
Build and Run
cd apps/backend
# Build the binary
make build
# Run the server (creates kandev.db in current directory)
./bin/kandev
# Or specify a custom database path
KANDEV_DB_PATH=/path/to/kandev.db ./bin/kandev
WebSocket API Usage
Connection: ws://localhost:8080/ws
Message Envelope
{
"id": "correlation-uuid",
"type": "request|response|notification|error",
"action": "action.name",
"payload": {},
"timestamp": "2026-01-10T10:30:00Z"
}
Key Message Actions
board.create- Create boardboard.list- List boardscolumn.create- Create columntask.create- Create taskagent.launch- Launch agentagent.status- Get agent statusorchestrator.start- Start task execution
Example Usage (JavaScript)
const ws = new WebSocket('ws://localhost:8080/ws');
// Create a board
ws.send(JSON.stringify({
id: crypto.randomUUID(),
type: 'request',
action: 'board.create',
payload: { name: 'My Project', description: 'Project tasks' }
}));
// Create a task
ws.send(JSON.stringify({
id: crypto.randomUUID(),
type: 'request',
action: 'task.create',
payload: {
board_id: '{board_id}',
column_id: '{column_id}',
title: 'Fix login bug',
description: 'Users cannot login'
}
}));
// Launch an agent
ws.send(JSON.stringify({
id: crypto.randomUUID(),
type: 'request',
action: 'agent.launch',
payload: {
task_id: '{task_id}',
agent_type: 'augment-agent',
workspace_path: '/path/to/project',
env: {
AUGMENT_SESSION_AUTH: '...',
TASK_DESCRIPTION: 'What is 2+2?'
}
}
}));
// Handle responses and notifications
ws.onmessage = (event) => {
const msg = JSON.parse(event.data);
if (msg.type === 'response') {
console.log('Response:', msg.payload);
} else if (msg.type === 'notification') {
// Real-time updates: acp.progress, acp.log, task.updated, etc.
console.log('Notification:', msg.action, msg.payload);
}
};
See apps/backend/NEXT_STEPS.md for detailed API reference and next steps.
Project Structure
kandev/
├── apps/
│ ├── backend/ # Go backend services
│ │ ├── cmd/kandev/ # Unified binary entry point
│ │ ├── internal/
│ │ │ ├── agent/ # Agent manager (docker, lifecycle, registry, api)
│ │ │ ├── task/ # Task service (models, repository, service, api)
│ │ │ ├── orchestrator/ # Orchestrator service
│ │ │ ├── events/ # In-memory event bus
│ │ │ └── common/ # Shared utilities (logger, config)
│ │ ├── pkg/acp/ # ACP protocol types
│ │ ├── dockerfiles/ # Agent container Dockerfiles
│ │ └── Makefile
│ └── web/ # Frontend application
├── docs/ # Architecture and planning docs
├── migrations/ # Database migrations (future)
├── deployments/ # Deployment configs
└── scripts/ # Utility scripts (including e2e-test.sh)
Configuration
| Environment Variable | Default | Description |
|---|---|---|
KANDEV_DB_PATH |
./kandev.db |
SQLite database file path |
PORT |
8080 |
HTTP server port |
Development
cd apps/backend
# Build
make build
# Run tests
make test
# Format code
go fmt ./...
# Lint
go vet ./...
Project Status
✅ Core Features Working
- SQLite persistence for boards, columns, and tasks
- Agent lifecycle management with Docker
- ACP message streaming from containers
- Session resumption for multi-turn agent conversations
- WebSocket-first API for all operations
- Real-time bidirectional communication
🚧 In Progress
- Authentication and authorization
- Frontend UI
See apps/backend/NEXT_STEPS.md for the complete roadmap.
License
[To be determined]
Acknowledgments
Built with Go and inspired by modern DevOps practices and AI-assisted development workflows.