* feat: replace agent_type with agent_profile_id - Replace agent_type field with agent_profile_id across backend and frontend - Add ProfileResolver interface and StoreProfileResolver implementation - Update task creation/update to use agent_profile_id - Change default agent runtime from docker to standalone - Fix workspace state types to include all required fields - Fix websocket handlers type mismatches for environments, executors, workspaces - Update integration tests to use agent_profile_id * feat: pass model as CLI argument to agents - Add ModelFlag field to AgentTypeConfig for specifying the CLI flag - Set ModelFlag to '--model' for all agents (auggie, claude, codex, gemini, opencode, copilot) - Update launchStandalone to append model flag to agent command - Update launchDocker/buildContainerConfig to append model flag to container command - Model is passed when both agent supports ModelFlag and profile has Model configured * fix: make WebSocket message handling async to prevent blocking When a long-running handler like orchestrator.prompt was executing, it blocked the ReadPump loop, preventing other requests (like workspace.tree.get) from being processed until the handler completed. Now each message is handled in its own goroutine, allowing concurrent message handling so file tree requests work while the agent is working. * feat: show task description as first message in chat - Add 'Task' label with distinct styling (lighter background, border) - Pass taskDescription from page to TaskChatPanel component - Differentiate from regular user messages visually
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 ./...
Pre-commit Hooks
We use pre-commit to run the backend tests, web tests, and web lint before commits.
Install
# Install pre-commit (https://pre-commit.com/#install)
pipx install pre-commit
# Install git hooks
pre-commit install
Run Manually
# Run all hooks once
pre-commit run --all-files
Hooks Config
The hooks are defined in .pre-commit-config.yaml and run these Makefile targets:
make test-backendmake test-webmake lint-web
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.