nrslib/takt

TAKT Agent Koordination Topology — Give your AI coding agents structured review loops, managed prompts, and guardrails — so they deliver quality code, not just code.

TAKT runs AI agents (Claude Code, Codex, OpenCode, Cursor, GitHub Copilot CLI) through YAML-defined workflows with built-in review cycles. You talk to AI to define what you want, queue tasks, and let TAKT handle the execution — planning, implementation, multi-stage review, and fix loops — all governed by declarative piece files.

TAKT is built with TAKT itself (dogfooding).

Batteries included — Architecture, security, and AI antipattern review criteria are built in. Ship code that meets a quality bar from day one.

Practical — A tool for daily development, not demos. Talk to AI to refine requirements, queue tasks, and run them. Worktree isolation on task execution, PR creation, and retry on failure.

Reproducible — Execution paths are declared in YAML, keeping results consistent. Pieces are shareable — a workflow built by one team member can be used by anyone else to run the same quality process. Every step is logged in NDJSON for full traceability from task to PR.

Multi-agent — Orchestrate multiple agents with different personas, permissions, and review criteria. Run parallel reviewers, route failures back to implementers, aggregate results with declarative rules. Prompts are managed as independent facets (persona, policy, knowledge, instruction) that compose freely across workflows (Faceted Prompting).