Key takeaways
- Deterministic, event-sourced orchestration framework for Claude Code with resumable runs and complete audit trails
- Quality convergence — agents iterate until quality targets are met, not just run once and hope
- Human-in-the-loop breakpoints for structured approval gates during complex workflows
- 2,000+ pre-built process definitions covering common development workflows
FAQ
What is Babysitter?
Babysitter is an orchestration framework for Claude Code that enables deterministic, event-sourced workflow management with quality gates, human approval checkpoints, and automatic iteration until quality targets are met.
How does Babysitter work?
Babysitter runs an iterate-execute-record loop: advance process, get pending effects, execute tasks, post results, repeat until complete. Everything is event-sourced in .a5c/runs/ for full resumability and audit trails.
How much does Babysitter cost?
Babysitter is free and open-source under the MIT license. It requires Claude Code as the underlying agent.
Who competes with Babysitter?
Claude-Flow for swarm orchestration, wshobson/agents for plugin-based multi-agent coordination, and BMAD Method for full agile lifecycle methodology.
Executive Summary
Babysitter is an orchestration framework for Claude Code that brings deterministic, event-sourced workflow management to AI coding agents. Unlike frameworks that provide static skills or methodology guidance, Babysitter actively manages multi-step workflows with quality gates, human approval breakpoints, and automatic iteration until quality targets are met. [1]
| Attribute | Value |
|---|---|
| Company | A5C AI |
| Founded | 2026 |
| Funding | Not disclosed |
| License | MIT |
| GitHub Stars | 317 |
Product Overview
Babysitter sits between methodology frameworks (which tell agents what to do) and runtime orchestrators (which manage how agents execute). It provides an event-sourced execution loop where every step is recorded, resumable, and auditable. [1]
The core innovation is quality convergence: instead of running a workflow once, Babysitter iterates until quality targets are met, with agent scoring and parallel execution support. Human-in-the-loop breakpoints provide structured approval gates — not just "approve/reject" but context-rich checkpoints.
Key Capabilities
| Capability | Description |
|---|---|
| Event-Sourced Execution | Complete journal of all events in .a5c/runs/ |
| Quality Convergence | Iterate until quality targets met, not one-shot |
| Human Breakpoints | Structured approval gates with full context |
| Parallel Execution | Run tasks concurrently with dependency management |
| Deterministic Replay | Resume or replay any run from any point |
| Process Library | 2,000+ pre-built process definitions |
| Agent Scoring | Evaluate agent output quality programmatically |
Installation
Babysitter installs as a Claude Code plugin: [2]
npm install -g @a5c-ai/babysitter@latest @a5c-ai/babysitter-sdk@latest @a5c-ai/babysitter-breakpoints@latest
claude plugin marketplace add a5c-ai/babysitter
claude plugin install --scope user babysitter@a5c.ai
claude plugin enable --scope user babysitter@a5c.ai
Invoke via /babysitter:call or natural language in Claude Code.
Technical Architecture
Babysitter runs an iterate-execute-record loop:
- Advance process — Determine next steps from process definition
- Get pending effects — Identify tasks that need execution
- Execute tasks — Run tasks (potentially in parallel) via Claude Code subagents
- Post results — Record outcomes to event journal
- Repeat — Continue until all tasks complete or quality gates pass
Key Technical Details
| Aspect | Detail |
|---|---|
| Runtime | Node.js 20+ (22.x LTS recommended) |
| Agent | Claude Code (required) |
| State | Event-sourced journal in .a5c/runs/<runId>/ |
| Execution | Sequential and parallel task execution |
| Language | JavaScript |
| Open Source | Yes (MIT) |
Strengths
- Deterministic workflows — Event-sourced execution means runs are reproducible and resumable
- Quality convergence — Iterates until quality targets are met, unlike one-shot frameworks
- Human-in-the-loop — Structured breakpoints with rich context for meaningful approval gates
- Audit trail — Complete journal of every event, task, and decision
- Large process library — 2,000+ pre-built processes cover common development workflows
- Composable — Works with existing Claude Code subagents, skills, and tools
- Active development — Pushed today (Feb 23), rapidly evolving
Cautions
- Claude Code only — Locked to a single agent runtime; no Codex, OpenCode, or Gemini support
- Early stage — 317 stars, 7 weeks old; limited production validation
- Complexity — Event-sourced architecture adds overhead vs. simpler skill-based approaches
- Process library quality — 2,000+ processes is a bold claim for a 7-week-old project; quality unverified
- Unknown company — A5C AI has no disclosed funding, team, or track record
- Node.js dependency — Requires global npm install and Claude Code plugin system
Pricing & Licensing
| Tier | Price | Includes |
|---|---|---|
| Open Source | Free | Full framework, MIT license |
Hidden costs: Requires Claude Code subscription (Anthropic API costs)
Competitive Positioning
Direct Competitors
| Competitor | Differentiation |
|---|---|
| Claude-Flow | Claude-Flow does swarm orchestration with consensus protocols; Babysitter does deterministic event-sourced workflows with quality convergence |
| wshobson/agents | wshobson/agents is plugin-based multi-agent coordination; Babysitter is process-driven single-agent orchestration |
| BMAD Method | BMAD provides agile lifecycle methodology; Babysitter provides runtime workflow execution with quality gates |
| Superpowers | Superpowers enforces methodology via skills; Babysitter enforces via deterministic process execution |
When to Choose Babysitter Over Alternatives
- Choose Babysitter when: You need deterministic, resumable workflows with quality convergence and audit trails in Claude Code
- Choose Claude-Flow when: You need distributed multi-agent coordination with formal consensus
- Choose wshobson/agents when: You want composable plugin-based agents with broad tool coverage
- Choose Superpowers when: You want methodology enforcement without runtime orchestration overhead
Ideal Customer Profile
Best fit:
- Teams using Claude Code who need structured, multi-step development workflows
- Organizations requiring audit trails and approval gates for AI-generated code
- Developers working on complex features that benefit from iterative quality improvement
- Teams that want "iterate until right" instead of "run once and fix manually"
Poor fit:
- Teams not using Claude Code (no multi-agent support)
- Developers wanting lightweight skills without orchestration overhead
- Organizations needing multi-model or multi-agent flexibility
- Quick one-off tasks where orchestration is overkill
Viability Assessment
| Factor | Assessment |
|---|---|
| Financial Health | Unknown — no disclosed funding |
| Market Position | Niche — Claude Code-specific orchestration |
| Innovation Pace | Rapid — active daily commits, 7 weeks old |
| Community/Ecosystem | Nascent — 317 stars, 13 forks |
| Long-term Outlook | Uncertain — depends on Claude Code ecosystem growth and A5C AI viability |
The event-sourced, quality-convergence approach is genuinely novel in the skills framework space. Most competitors either provide static methodology (Superpowers, BMAD) or distributed coordination (Claude-Flow, wshobson). Babysitter's niche — deterministic workflow execution with iterative quality — is underserved. The risk is Claude Code lock-in and the unknown viability of A5C AI.
Bottom Line
Babysitter brings software engineering rigor — event sourcing, deterministic replay, quality convergence — to AI agent orchestration. The approach is more sophisticated than static skills frameworks and more focused than distributed multi-agent platforms.
Recommended for: Claude Code users who need structured, auditable, iterative workflows for complex development tasks.
Not recommended for: Teams wanting agent flexibility, lightweight skill catalogs, or multi-model support.
Outlook: If Babysitter proves the quality-convergence model works in practice, expect the pattern to be adopted by larger orchestration platforms. The Claude Code lock-in limits addressable market, but the core innovation — iterate until quality targets are met — is platform-agnostic in principle.
Research by Ry Walker Research • methodology