Kagan - AI Orchestration Layer
Kagan is an AI orchestration layer providing a Kanban-style interface for managing coding tasks, automating development workflows with AI agents, and integrating with external systems.
Task Management — Create (individually or in batch), list, get, update, delete, search, and add notes to tasks. Tasks support properties like title, description, priority, base branch, acceptance criteria, and agent backend. Also view task events, wait for status changes, and get task counts.
Agent Run Management — Start, cancel, kill, and manage interactive sessions for AI agents working on tasks. Retrieve run status/summaries, check for active sessions, and detach from sessions.
Project & Repository Management — Create, list, delete, and activate projects. Add repositories to projects and configure default branches.
Code Review Workflow — Approve, reject (with feedback), and merge review-ready tasks. Perform Git operations like rebasing, conflict handling, and rebase progression. Set or clear AI review verdicts for individual acceptance criteria.
Settings & Audit — Retrieve and modify system configuration settings; access audit logs.
Persona Preset Management — Audit, import, and export AI persona presets from/to repositories; manage a whitelist of trusted persona sources.
Plugin Integrations — Sync external items (e.g., GitHub issues) into Kagan tasks, and preflight-check plugin dependencies.
Interfaces & Agent Modes — Interact via a terminal Kanban board (TUI) or web dashboard. Supports both autonomous and pair programming modes across 14 AI coding agents.
Provides comprehensive management of Git repositories through task-based branches, supporting operations such as rebasing, merging approved changes, and tracking repository-specific branches.
Enables synchronization of GitHub issues into the Kanban board, mapping labels like priority and status to task properties while providing tools to verify GitHub CLI authentication.
Kagan is a Kanban TUI for AI coding agents with a structural human review gate. No agent-authored task reaches your main branch without an explicit approval — the state machine enforces it.
The agent runs in an isolated git worktree. When it finishes, the task card moves to REVIEW. You read the diff, check the acceptance criteria, and press approve. Then merge fires. That transition — REVIEW to DONE — cannot be automated away. It is not a setting.
Install
uv tool install kagan # or: uvx kagancurl -fsSL https://uvget.me/install.sh | bash -s -- kaganiwr -useb uvget.me/install.ps1 -OutFile install.ps1; .\install.ps1 kaganRelated MCP server: mcp-github
What you get
Kanban board (BACKLOG → IN_PROGRESS → REVIEW → DONE) enforced by a state machine
Each task runs in its own git worktree — your working copy stays untouched
Managed runs (background agent) or interactive attach (you + agent in tmux/editor)
REVIEW stage requires explicit human approval before merge; no path around it
MCP server so Claude Code, Codex, or any MCP-capable client can drive the board
kagan doctorpreflight checks all required tools before first run
Tested agents: Claude Code · Codex · Gemini CLI · 11 more — see docs/backends.
Full docs: docs.kagan.sh
Companion surfaces
The TUI (kagan) is the primary operator surface. Two companion surfaces exist for specific workflows:
Web dashboard (
kagan web) — browser-based board, useful for remote access or a second monitorVS Code extension — sidebar panel and
@kaganchat participant inside VS Code
Both companions share the same state as the TUI via the same API server. Neither is required.
License
Maintenance
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