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## Summary Removes Fabro's automatic retro generation stage so workflow runs go directly from execution to finalization and optional PR creation. This drops the retro-specific crate, events, projection fields, config/API knobs, and user-facing docs in favor of the existing durable run observability surfaces. ## What Changed - Deleted the `fabro-retro` crate and the workflow `retro` pipeline phase, with finalization now consuming `Executed` state directly. - Removed retro configuration and API surface area, including `--no-retro`, `[run.execution].retros`, manifest `no_retro`, `features.retros`, and run projection `retro*` fields. - Retired typed `retro.*` events while keeping historical event logs readable by deserializing retired retro event names as `Unknown`. - Stopped appending retro sections to generated PR bodies and updated docs, marketing copy, screenshots, and navigation to point users toward observability/event-stream inspection. ## Testing Not run during PR creation; this branch already contained the implementation commit. --- [](https://github.com/EveryInc/compound-engineering-plugin) 🤖 Generated with GPT-5 (unknown context, reasoning unspecified) via [Codex](https://openai.com/codex)
51 lines
2.8 KiB
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51 lines
2.8 KiB
Text
---
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title: "Introduction"
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description: "Fabro is the open source dark software factory for small teams of expert engineers"
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---
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Fabro replaces the prompt-wait-review loop with version-controlled workflow graphs that orchestrate AI agents, shell commands, and human decisions into repeatable, long-horizon coding processes.
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<img src="/images/runs-board.png" alt="Fabro Runs board showing workflows across Working, Pending, Verify, and Merge stages" />
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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 at scale** — Fabro's API server queues and executes runs continuously in cloud sandboxes. Close your laptop — workflows keep running across as many concurrent runs 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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- **Inspect every run** — Query durable event streams, checkpoints, conclusions, and stage outputs to understand what happened and improve the workflow.
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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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<Columns cols={2}>
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<Card
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title="Why Fabro?"
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icon="lightbulb"
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href="/getting-started/why-fabro"
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>
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The problems Fabro solves for AI-assisted software teams.
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</Card>
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<Card
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title="Quick Start"
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icon="rocket"
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href="/getting-started/quick-start"
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>
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Install Fabro and run your first workflow in minutes.
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</Card>
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<Card
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title="Workflows"
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icon="diagram-project"
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href="/core-concepts/workflows"
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>
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Learn how workflow graphs orchestrate agents, commands, and human gates.
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</Card>
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<Card
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title="Agents"
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icon="robot"
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href="/core-concepts/agents"
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>
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Understand how Fabro configures and runs LLM agents within workflows.
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</Card>
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</Columns>
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