Coding agents that run isolated — and under your control
Turn Jira and GitHub tickets into pull requests — fully autonomous, or steered ticket by ticket from a live dashboard.
Up and running in three commands
-
Install the CLI (Homebrew on macOS / Linux)
brew install takuto-team/tap/takuto -
Generate config with the interactive wizard
takuto setup -
Bring it up, then open the dashboard (default :8080)
takuto start
Prerequisites: just Docker or Podman. takuto setup creates a .takuto/ folder in the current directory (run it anywhere — not just a project folder), and takuto start pulls the Takuto Core image on first run and opens the dashboard, where you create your admin account and connect your AI provider and GitHub.
Bring your own coding agent — Takuto drives these four:
- Claude Code
- Cursor Agent
- Codex
- OpenCode self-hosted models only (LM Studio, Ollama, vLLM…)
Automation you stay in control of
Run the whole pipeline unattended, or take the wheel ticket by ticket. The choice is yours on every run.
Ticket-driven or standalone
Poll Jira or GitHub Issues for "To Do" tickets, or paste a description straight into the dashboard — no ticketing system required.
Autonomous, manual, or mixed
Let it run the full pipeline overnight, trigger each phase yourself, or auto-pick routine work while you curate the tricky tickets.
Parallel by design
Run multiple tickets at once — each gets its own git worktree and isolated environment, so nothing steps on anything else. Concurrency is yours to set (it starts at one).
A live dashboard
Stream terminal output per workflow, watch progress, and pause, resume, retry, or inspect any run from the browser.
Editor + terminal in the browser
Jump into any workflow with a VS Code editor and web terminal pointed at the exact worktree the agent is working on.
Pipelines you define
Chain steps — implement, address PR comments, merge the base branch — with dependencies, all edited in the dashboard’s Workflows tab.
Two ways to run it
Takuto CLI
The fastest path: takuto setup generates your config in a .takuto/ folder, then takuto start brings everything up and orchestrates Docker or Podman Compose for you — the dashboard is at :8080 by default. You finish setup (admin account, AI provider, GitHub) there.
Build your own from Takuto Core
Run the core engine directly — clone Takuto Core, configure config.toml, and bring it up with Docker Compose yourself. For teams that want full control of the image and deployment.
Isolation & privacy
Each agent runs in its own container
Work is isolated per workflow — separate container, separate git worktree, separate environment — which reduces the prompt-injection blast radius. Outbound traffic is locked down by a default-deny egress firewall. No tracking, no telemetry: your code and ticket content go only to the AI provider you configure.
The name
Takuto (タクト) — a conductor’s baton
In Japanese, takuto is the baton a conductor uses to lead an orchestra. The name fits what the tool does — keeping a section of AI agents in time, working from one score — and it’s a nod to Japan, where Takuto was built (in Tokyo).