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YYLO
YYLO CLI and YYLO Ledger bring controlled AI coding-agent orchestration and Git-native task management into one modular workflow. Run Claude Code, Codex, Gemini, or Pi with bounded loops, durable project records, validation evidence, and guarded Git delivery.
Launched on Sep 7, 2026
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Product Overview
YYLO (why-lo) coordinates coding agents from engineering intent through validated code, while preserving durable task, session, review, and evaluation evidence that explains what happened. It is designed for teams that want agent-assisted development to remain inspectable and tied to Git-based work.
YYLO includes two open-source tools: YYLO CLI, a TypeScript command-line orchestrator that runs coding agents with bounded loops and validation evidence, and YYLO Ledger, a Python CLI with Git-native task state and a hash-chained record history. Together, they support repeatable test-and-fix cycles and auditable delivery without replacing Git.
Key features
- Bounded agent work loops with iteration limits to avoid unbounded execution.
- Reusable command or YAML workflows that keep session continuity and provenance.
- Execution isolation using exact-base Git worktrees, with guarded merge operations in configured repositories.
- Evidence-first validation: tests, evidence receipts, and policy-bound review make acceptance inspectable.
- Ledger task truth in reviewable repository files, plus opt-in hash-chained history for durable progress records.
- Integrity checks, commit-linked completion, and typed Records with structured output for traceable results.
How YYLO works
- 1
Define intent in a task
Create a task record with a bounded outcome, constraints, and dependencies.
- 2
Run bounded agent execution
Use YYLO CLI to delegate work to an agent while preserving session provenance and using isolated worktrees.
- 3
Validate and record evidence
Run tests and validation, then store responses, commits, review evidence, and merge outcomes for future reference.
Use cases
- A developer defines a single bounded change (e.g., “Add keyboard navigation”) with constraints and dependencies, delegates execution to a chosen agent, then validates the result with evidence receipts and tests before proceeding.
- A team runs multi-model debugging where each agent’s work is recorded with session provenance, commit evidence, and review artifacts so later iterations can reference what happened.
- An engineering workflow manager keeps task intent, blockers, responses, and merge outcomes in Git-native records, enabling reviewable progress and integrity-checked histories over time.
Who is it for?
YYLO is for software engineers and teams using coding agents who need controlled loops, durable task records, and evidence-based validation tied to Git. It also fits maintainers building repeatable, auditable AI-assisted feature development workflows.