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d50b3187e5
Users should use `fabro repo init` instead. The deprecation shim has been in place long enough; remove it and update all docs references. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
163 lines
8.8 KiB
Markdown
163 lines
8.8 KiB
Markdown
<div align="left" id="top">
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<a href="https://docs.fabro.sh"><img alt="Fabro" src="docs/logo/dark.svg" height="75"></a>
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</div>
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## The open source dark software factory for expert engineers
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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?](https://docs.fabro.sh/getting-started/why-fabro)
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[](LICENSE.md)
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[](https://docs.fabro.sh)
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```bash
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# With Claude Code
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curl -fsSL https://fabro.sh/install.md | claude
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# With Codex
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codex "$(curl -fsSL https://fabro.sh/install.md)"
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# With Bash
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curl -fsSL https://fabro.sh/install.sh | bash
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```
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<img src="docs/images/runs-board.png" alt="Fabro Runs board showing workflows across Working, Pending, Verify, and Merge stages" />
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---
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## Use Cases
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- **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.
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- **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.
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- **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.
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- **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.
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- **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.
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- **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.
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- **Scale infinitely** — Move execution off your laptop and into cloud sandboxes. Run as many concurrent workflows as your infrastructure allows.
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- **Guarantee code quality** — Layer deterministic verifications — test suites, linters, type checkers, LLM-as-judge — into your workflow graph. Failures trigger fix loops automatically.
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- **Achieve compounding engineering** — Automatic retrospectives after every run feed a continuous improvement loop. Your workflows get better over time, not just your code.
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- **Specify in natural language** — Define requirements as natural-language specs and let Fabro generate — and regenerate — implementations that conform to them.
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---
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## Key Features
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| | Feature | Description |
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| --- | ------------------------------ | ----------------------------------------------------------------------------------------------------- |
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| 🔀 | Deterministic workflow graphs | Define pipelines in Graphviz DOT with branching, loops, parallelism, and human gates. Diffable, reviewable, version-controlled |
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| 🙋 | Human-in-the-loop | Approval gates pause for human decisions. Steer running agents mid-turn. Interview steps collect structured input |
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| 🎨 | Multi-model routing | CSS-like stylesheets route each node to the right model and provider, with automatic fallback chains |
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| ☁️ | Cloud sandboxes | Run agents in isolated Daytona cloud VMs with snapshot-based setup, network controls, and automatic cleanup |
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| 🔌 | SSH access and preview links | Shell into running sandboxes with `fabro sandbox ssh` and expose ports with `fabro sandbox preview` for live debugging |
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| 🌲 | Git checkpointing | Every stage commits code changes and execution metadata to Git branches. Resume, revert, or trace any change |
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| 📊 | Automatic retros | Each run generates a retrospective with cost, duration, files touched, and an LLM-written narrative |
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| ⚡ | Comprehensive API | REST API with SSE event streaming and a React web UI. Run workflows programmatically or as a service |
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| 🦀 | Single binary, no runtime | One compiled Rust executable with zero dependencies. No Python, no Node, no Docker required |
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| ⚖️ | Open source (MIT) | Full source code, no vendor lock-in. Self-host, fork, or extend to fit your workflow |
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---
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## Example Workflow
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A plan-approve-implement workflow where a human reviews the plan before the agent writes code:
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<img src="docs/images/plan-implement-readme.svg" alt="Plan-Implement workflow graph showing Start → Plan → Approve Plan → Implement → Simplify → Exit with a Revise loop" />
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```dot
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digraph PlanImplement {
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graph [
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goal="Plan, approve, implement, and simplify a change"
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model_stylesheet="
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* { model: claude-haiku-4-5; reasoning_effort: low; }
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.coding { model: claude-sonnet-4-5; reasoning_effort: high; }
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"
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]
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start [shape=Mdiamond, label="Start"]
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exit [shape=Msquare, label="Exit"]
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plan [label="Plan", prompt="Analyze the goal and codebase. Write a step-by-step plan.", reasoning_effort="high"]
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approve [shape=hexagon, label="Approve Plan"]
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implement [label="Implement", class="coding", prompt="Read plan.md and implement every step."]
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simplify [label="Simplify", class="coding", prompt="Review the changes for clarity and correctness."]
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start -> plan -> approve
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approve -> implement [label="[A] Approve"]
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approve -> plan [label="[R] Revise"]
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implement -> simplify -> exit
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}
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```
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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 [Graphviz DOT language reference](https://docs.fabro.sh/reference/dot-language) for the full syntax.
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---
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## 📖 Documentation
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Fabro ships with [comprehensive documentation](https://docs.fabro.sh) covering every feature in depth:
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- [**Getting Started**](https://docs.fabro.sh/getting-started/introduction) -- Installation, first workflow, and why Fabro exists
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- [**Defining Workflows**](https://docs.fabro.sh/workflows/stages-and-nodes) -- Node types, transitions, variables, stylesheets, and human gates
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- [**Executing Workflows**](https://docs.fabro.sh/execution/run-configuration) -- Run configuration, sandboxes, checkpoints, retros, and failure handling
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- [**Tutorials**](https://docs.fabro.sh/tutorials/hello-world) -- Step-by-step guides from hello world to parallel multi-model ensembles
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- [**API Reference**](https://docs.fabro.sh/api-reference/overview) -- Full OpenAPI spec with authentication, SSE events, and client SDKs
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---
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## Quick Start
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### Install
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```bash
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# With Claude Code
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curl -fsSL https://fabro.sh/install.md | claude
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# With Codex
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codex "$(curl -fsSL https://fabro.sh/install.md)"
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# With Bash
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curl -fsSL https://fabro.sh/install.sh | bash
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```
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Then initialize Fabro in your project:
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```bash
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fabro install # one-time setup
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cd my-project
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fabro repo init # per project
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```
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---
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## Contributing to Fabro
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Fabro uses an **issue-based contribution model**. Instead of accepting outside pull requests, we accept bug reports and feature requests as GitHub Issues.
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AI can rapidly write or edit large amounts of plausible-looking code. Accepting these patches from external sources opens up risks to security and quality. To mitigate these risks, we are tightly controlling the inputs into the software development process.
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Contributions follow these steps:
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1. **Open an issue** -- File an issue with a bug report or feature request. The more detail your issue contains, the easier it will be for us to address it quickly and successfully.
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2. **We build it** -- A Fabro maintainer will follow our software development process to create a patch, supervising AI coding agents and workflows.
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3. **You get credit** -- We will include you as a co-author on the commit which lands the change.
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As a result, you get the feature you need, without needing to keep a fork in sync.
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If you need a capability which is not in-scope for Fabro, you always have the option to maintain a fork of Fabro as it is distributed under the MIT license.
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---
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## Help or Feedback
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- [Bug reports](https://github.com/fabro-sh/fabro/issues) via GitHub Issues
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- [Feature requests](https://github.com/fabro-sh/fabro/discussions) via GitHub Discussions
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- Email [bryan@qlty.sh](mailto:bryan@qlty.sh) for questions
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- See [CONTRIBUTING.md](CONTRIBUTING.md) for build instructions and development workflow
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---
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## License
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Fabro is licensed under the [MIT License](LICENSE.md).
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