TAKT

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Stop babysitting AI coding agents.

TAKT is an open-source CLI that turns AI coding agents into repeatable development workflows. Define planning, implementation, review, fix loops, human checkpoints, permissions, and output contracts in YAML, then run tasks with isolated worktrees and traceable logs.

Instead of asking one agent to remember the whole process, TAKT gives each step its own role, context, and transition rules. Agents can code, but the workflow decides what happens next.

• Run plan → implement → review → fix loops as explicit workflow steps
• Keep context focused with step-specific personas, policies, knowledge, instructions, and output contracts
• Execute queued tasks in isolated worktrees and inspect logs and reports afterward
• Use Claude Code, Claude SDK, Codex SDK, OpenCode SDK, Pi SDK, the official DeepSeek Harness SDK, Cursor, GitHub Copilot CLI, or Kiro as providers

TAKT Agent Koordination Topology orchestrates multiple AI agents with review loops, managed prompts, and per-step permissions.

Talk to AI to define what you want, queue it as a task, and run it with takt run. Planning, implementation, review, and fix loops are defined in YAML workflow files, so the process is not left to the agent's discretion. TAKT coordinates Claude Code, Codex, OpenCode, Pi, the official DeepSeek Harness SDK, Cursor, GitHub Copilot CLI, and Kiro CLI as agents with different roles, permissions, and context.

TAKT is built primarily for AI coding workflows, but the same model applies beyond coding: any task where multiple AI agents need to coordinate, or where review, judgment, and feedback loops can improve task quality.

TAKT is built with TAKT itself (dogfooding).

Why TAKT

AI coding agents do not automatically create a stable development process. In long-running work, they forget instructions, accumulate polluted context, blur implementation and review responsibilities, and often force humans to repeat the same feedback again and again.

Adding more rules to prompts, CLAUDE.md, or skills can help, but it cannot enforce the process. Whether the rules are followed is still left to the agent's behavior.

TAKT treats AI agents as something to be controlled from the outside, not simply trusted.

Workflows define the phases, and each step receives its own persona, policy, knowledge, instruction, and output contract. TAKT manages implementation, review, fix, and re-review flows declaratively. By separating responsibilities, knowledge, and constraints, then giving each agent only what it needs for the current step, TAKT improves task quality without bloating context.

Reviews cannot be silently skipped. Findings route work back to fix steps, and human judgment can be requested when needed. Tasks run in isolated worktrees, and each step leaves logs and reports so the path from task to PR remains traceable.

TAKT runs all of this as a reusable agent process built from roles, phases, judgments, and feedback loops, so the development process stays reviewable and reproducible without constant human intervention.

Try It in 5 Minutes

From a Git repository with at least one commit:

npm install -g takt

# Talk to AI, describe a task, use /go, then choose "Save as Task"
takt

# Execute queued tasks in isolated worktrees
takt run

# Review diffs, merge, retry, requeue, or delete task branches
takt list