The open source software factory for expert engineers

AI coding agents are powerful but unpredictable. You either babysit every step or review a 50-file diff you don't trust. Fabro gives you a middle path: define the process as a graph, let agents execute it, and intervene only where it matters. Why Fabro?

curl -fsSL https://fabro.sh/install.sh | bash

---

Use Cases

• Extend disengagement time — Stop babysitting an agent REPL. Define a workflow with verification gates and walk away — Fabro keeps the process on track without you.
• Leverage ensemble intelligence — Seamlessly combine models from different vendors. Use one model to implement, another to cross-critique, and a third to summarize — all in a single workflow.
• Share best practices across your team — Collaborate on version-controlled workflows that encode your software processes as code. Review, iterate, and reuse them like any other source file.
• Reduce token bills — Route cheap tasks to fast, inexpensive models and reserve frontier models for the steps that need them. CSS-like stylesheets make this a one-line change.
• Improve agent security — Run agents in cloud sandboxes with full network and filesystem isolation. Keep untrusted code off your laptop and out of your production environment.
• Run agents 24/7 — Fabro's API server queues and executes runs continuously. Close your laptop — workflows keep running and results are waiting when you return.
• Scale infinitely — Move execution off your laptop and into cloud sandboxes. Run as many concurrent workflows as your infrastructure allows.
• Guarantee code quality — Layer deterministic verifications — test suites, linters, type checkers, LLM-as-judge — into your workflow graph. Failures trigger fix loops automatically.
• Achieve compounding engineering — Automatic retrospectives after every run feed a continuous improvement loop. Your workflows get better over time, not just your code.
• Specify in natural language — Define requirements as natural-language specs and let Fabro generate — and regenerate — implementations that conform to them.

---

Key Features

	Feature	Description
🔀	Deterministic workflow graphs	Define pipelines in Graphviz DOT with branching, loops, parallelism, and human gates. Diffable, reviewable, version-controlled
🙋	Human-in-the-loop	Approval gates pause for human decisions. Steer running agents mid-turn. Interview steps collect structured input
🎨	Multi-model routing	CSS-like stylesheets route each node to the right model and provider, with automatic fallback chains
☁️	Cloud sandboxes	Run agents in isolated Daytona cloud VMs with snapshot-based setup, network controls, and automatic cleanup
🔌	SSH access and preview links	Shell into running sandboxes with fabro ssh and expose ports with fabro preview for live debugging
🌲	Git checkpointing	Every stage commits code changes and execution metadata to Git branches. Resume, revert, or trace any change
📊	Automatic retros	Each run generates a retrospective with cost, duration, files touched, and an LLM-written narrative
⚡	Comprehensive API	REST API with SSE event streaming and a React web UI. Run workflows programmatically or as a service
🦀	Single binary, no runtime	One compiled Rust executable with zero dependencies. No Python, no Node, no Docker required
⚖️	Open source (MIT)	Full source code, no vendor lock-in. Self-host, fork, or extend to fit your workflow

---

Example Workflow

A plan-approve-implement workflow where a human reviews the plan before the agent writes code:

digraph PlanImplement {
    graph [
        goal="Plan, approve, implement, and simplify a change"
        model_stylesheet="
            *        { model: claude-haiku-4-5; reasoning_effort: low; }
            .coding  { model: claude-sonnet-4-5; reasoning_effort: high; }
        "
    ]

    start [shape=Mdiamond, label="Start"]
    exit  [shape=Msquare, label="Exit"]

    plan      [label="Plan", prompt="Analyze the goal and codebase. Write a step-by-step plan.", reasoning_effort="high"]
    approve   [shape=hexagon, label="Approve Plan"]
    implement [label="Implement", class="coding", prompt="Read plan.md and implement every step."]
    simplify  [label="Simplify", class="coding", prompt="Review the changes for clarity and correctness."]

    start -> plan -> approve
    approve -> implement [label="[A] Approve"]
    approve -> plan      [label="[R] Revise"]
    implement -> simplify -> exit
}

Agents run as multi-turn LLM sessions with tool access. Human gates (hexagon) pause for approval. The stylesheet routes planning to a cheap model and coding to a frontier model. See the DOT language reference for the full syntax.

---

📖 Documentation

Fabro ships with comprehensive documentation covering every feature in depth:

• Getting Started -- Installation, first workflow, and why Fabro exists
• Defining Workflows -- Node types, transitions, variables, stylesheets, and human gates
• Executing Workflows -- Run configuration, sandboxes, checkpoints, retros, and failure handling
• Tutorials -- Step-by-step guides from hello world to parallel multi-model ensembles
• API Reference -- Full OpenAPI spec with authentication, SSE events, and client SDKs

---

Quick Start

Install

curl -fsSL https://fabro.sh/install.sh | bash

# Initialize your project
cd my-repo/
fabro init

# Run your first workflow
fabro run hello

---

Help or Feedback

• Bug reports via GitHub Issues
• Feature requests via GitHub Issues
• Email bryan@qlty.sh for questions
• See CONTRIBUTING.md for build instructions and development workflow

---

License

Fabro is licensed under the MIT License.