fabro/lib/crates/fabro-workflows
brynary-fabro[bot] d6f1bce7ef fabro fork subcommand (#9)
This PR adds a new `fabro fork` subcommand that creates a new run
branching from an existing run at a specific checkpoint, without
modifying the original run. This is a non-destructive alternative to
`fabro rewind` — instead of moving branch refs backward and losing later
checkpoint history, fork preserves the source run entirely and creates
fresh run and metadata branches for the new run.

The implementation heavily reuses existing infrastructure from
`rewind.rs` (timeline building, target resolution, parallel map loading,
prefix-based run ID lookup) and follows the same CLI patterns. The core
`execute_fork` function generates a new ULID, creates a run branch ref
pointing at the target checkpoint's commit, then builds a new metadata
branch containing an updated manifest (with new run ID and branch name),
the original graph, and the checkpoint state from the target commit. It
supports the same target syntax as rewind (`@N`, `node_name`,
`node_name@N`), defaults to the latest checkpoint when no target is
specified, and optionally pushes new branches to the remote.

The PR also makes `load_parallel_map` public in `rewind.rs` so fork can
reuse it, and includes five tests covering run branch creation, metadata
branch correctness, preservation of the original run, default-to-latest
behavior, and forking at a specific ordinal.

### Fabro Details

<details>
<summary>Ran 7 stages in 15m 15s for $4.39</summary>

| Stage | Duration | Cost | Retries |
|---|---|---|---|
| start | 0s | – | 0 |
| toolchain | 0s | – | 0 |
| preflight_compile | 0s | – | 0 |
| preflight_lint | 0s | – | 0 |
| implement | 0s | $1.94 | 0 |
| simplify | 0s | $2.44 | 0 |
| verify | 0s | – | 0 |
| **Total** | **15m 15s** | **$4.39** | **0** |

</details>

<details>
<summary>Ran <code>ImplementAndSimplify.fabro</code> (10 nodes and 13
edges)</summary>

```dot
digraph ImplementAndSimplify {
    graph [
        goal="Implement and simplify",
        model_stylesheet="
            * { backend: api; model: claude-opus-4-6;}
        "
    ]
    rankdir=LR

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

    toolchain         [label="Toolchain", shape=parallelogram, script="command -v cargo >/dev/null || { curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y && sudo ln -sf $HOME/.cargo/bin/* /usr/local/bin/; }; cargo --version 2>&1", max_retries=0]
    preflight_compile [label="Preflight Compile", shape=parallelogram, script="cargo check 2>&1", max_retries=0]
    preflight_lint    [label="Preflight Lint", shape=parallelogram, script="cargo clippy -- -D warnings 2>&1", max_retries=0]
    fix_lints         [label="Fix Lints", prompt="The preflight lint step failed. Read the build output from context and fix all clippy lint warnings.", max_visits=3]
    implement         [label="Implement", prompt="Read the plan file referenced in the goal and implement every step. Make all the code changes described in the plan."]
    simplify          [label="Simplify", prompt="@prompts/simplify.md"]
    verify            [label="Verify", shape=parallelogram, script="cargo clippy -- -D warnings 2>&1 && cargo test 2>&1", goal_gate=true, retry_target="fixup"]
    fixup             [label="Fixup", prompt="The verify step failed. Read the build output from context and fix all clippy lint warnings and test failures.", max_visits=3]

    start -> toolchain
    toolchain -> preflight_compile [condition="outcome=success"]
    toolchain -> exit
    preflight_compile -> preflight_lint [condition="outcome=success"]
    preflight_compile -> exit
    preflight_lint -> implement [condition="outcome=success"]
    preflight_lint -> fix_lints
    fix_lints -> preflight_lint
    implement -> simplify -> verify
    verify -> exit  [condition="outcome=success"]
    verify -> fixup
    fixup -> verify
}

```

</details>

⚒️ Generated with [Fabro](https://fabro.sh)

---------

Co-authored-by: Fabro <noreply@fabro.sh>
Co-authored-by: Bryan Helmkamp <bryan@brynary.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-15 19:54:24 -04:00
..
src fabro fork subcommand (#9) 2026-03-15 19:54:24 -04:00
tests Fold CheckpointSaved into CheckpointCompleted 2026-03-15 14:43:09 -04:00
Cargo.toml Adopt cli-table for ANSI-aware table rendering and fix fabro ps bugs 2026-03-15 17:27:10 -04:00
README.md simplify model stylesheet: rename llm_model/llm_provider, add provider inference 2026-03-12 22:27:18 -04:00

fabro-workflows

A DOT-based pipeline runner for multi-stage AI workflows. Define workflows as Graphviz digraph files and execute them with pluggable handlers, conditional routing, human-in-the-loop gates, parallel branching, retry policies, and checkpoint-based recovery.

Key Concepts

  • Graph -- A directed graph parsed from DOT syntax containing nodes, edges, and attributes. The graph carries a goal describing the pipeline's purpose.
  • Node -- A workflow step. Graphviz shapes map to handler types (e.g., Mdiamond = start, Msquare = exit, box = agent, tab = prompt, diamond = conditional, hexagon = human gate, component = parallel).
  • Edge -- A connection between nodes with optional condition, label, weight, and fidelity attributes that control routing.
  • Handler -- An async trait implementation that executes a node and returns an Outcome. Built-in handlers include StartHandler, ExitHandler, AgentHandler, PromptHandler, ConditionalHandler, HumanHandler, ParallelHandler, FanInHandler, CommandHandler, and SubWorkflowHandler.
  • Outcome -- The result of executing a handler, carrying a StageStatus (Success, Fail, PartialSuccess, Retry, Skipped), optional routing hints (preferred_label, suggested_next_ids), and context updates.
  • Context -- A thread-safe key-value store shared across pipeline stages, supporting snapshots and isolated cloning for parallel branches.
  • Interviewer -- A trait for human-in-the-loop interactions. Implementations include AutoApproveInterviewer, QueueInterviewer, CallbackInterviewer, ConsoleInterviewer, and RecordingInterviewer.
  • Checkpoint -- A serializable snapshot of execution state (completed nodes, context values, logs) for crash recovery and resume.

Pipeline Definition

Pipelines are defined using Graphviz DOT syntax:

digraph MyPipeline {
    graph [goal="Implement and validate a feature"]
    rankdir=LR
    node [shape=box, timeout="900s"]

    start     [shape=Mdiamond, label="Start"]
    exit      [shape=Msquare, label="Exit"]
    plan      [label="Plan", prompt="Plan the implementation"]
    implement [label="Implement", prompt="Implement the plan"]
    validate  [label="Validate", prompt="Run tests"]
    gate      [shape=diamond, label="Tests passing?"]

    start -> plan -> implement -> validate -> gate
    gate -> exit      [label="Yes", condition="outcome=success"]
    gate -> implement [label="No", condition="outcome!=success"]
}

Usage

Parsing and Validating a Pipeline

use arc_workflows::pipeline::prepare_pipeline;

let dot_source = r#"digraph Simple {
    graph [goal="Run tests"]
    start [shape=Mdiamond]
    exit  [shape=Msquare]
    work  [shape=box, prompt="Run the test suite"]
    start -> work -> exit
}"#;

let graph = prepare_pipeline(dot_source)
    .expect("pipeline should parse and validate");
assert_eq!(graph.name, "Simple");
assert_eq!(graph.goal(), "Run tests");

prepare_pipeline parses the DOT source, applies built-in transforms (variable expansion, stylesheet application, preamble injection), and validates the graph against 14 built-in lint rules.

Running a Pipeline

use arc_workflows::engine::{PipelineEngine, RunConfig};
use arc_workflows::event::EventEmitter;
use arc_workflows::handler::HandlerRegistry;
use arc_workflows::handler::start::StartHandler;
use arc_workflows::handler::exit::ExitHandler;
use arc_workflows::handler::agent::AgentHandler;
use arc_workflows::pipeline::prepare_pipeline;

let graph = prepare_pipeline(dot_source).unwrap();

let mut registry = HandlerRegistry::new(Box::new(AgentHandler::new(None)));
registry.register("start", Box::new(StartHandler));
registry.register("exit", Box::new(ExitHandler));
registry.register("agent", Box::new(AgentHandler::new(None)));

let engine = PipelineEngine::new(registry, EventEmitter::new());
let config = RunConfig {
    run_dir: "/tmp/pipeline-run".into(),
};

// engine.run(&graph, &config).await

Custom Handlers

Implement the Handler trait to add custom node behavior:

use arc_workflows::handler::Handler;
use arc_workflows::context::Context;
use arc_workflows::graph::{Graph, Node};
use arc_workflows::outcome::Outcome;
use arc_workflows::error::ArcError;
use async_trait::async_trait;
use std::path::Path;

struct MyHandler;

#[async_trait]
impl Handler for MyHandler {
    async fn execute(
        &self,
        node: &Node,
        context: &Context,
        graph: &Graph,
        run_dir: &Path,
    ) -> Result<Outcome, ArcError> {
        // Custom logic here
        Ok(Outcome::success())
    }
}

Model Stylesheets

CSS-like stylesheets control LLM model assignment with specificity-based cascading:

digraph Styled {
    graph [
        goal="Build feature",
        model_stylesheet="
            * { model: claude-sonnet-4-5;}
            .code { model: claude-opus-4-6; }
            #critical_review { model: gpt-5.2;}
        "
    ]
    // ...
}

Selectors by specificity: * (universal, 0) < shape (1) < .class (2) < #id (3). Explicit node attributes are never overridden.

Condition Expressions

Edge conditions use a simple expression syntax for routing:

outcome=success
outcome!=fail
outcome=success && context.tests_passed=true
my_flag

Clauses support =, !=, and bare key truthiness checks, joined with &&.

Human-in-the-Loop Gates

Nodes with shape=hexagon or type="human" pause execution for human input. Outgoing edge labels become selectable options, with accelerator key parsing for patterns like [A] Approve and F) Fix.

Parallel Execution

Nodes with shape=component fan out to branches concurrently. Configurable join policies: wait_all (default), first_success, k_of_n(N), quorum(0.5). Error policies: continue, fail_fast, ignore.

Checkpoints and Resume

The engine saves a checkpoint after each node. Resume from a checkpoint with engine.run_from_checkpoint(&graph, &config, &checkpoint).

Architecture

parser (DOT -> AST -> Graph)
  -> transform (variable expansion, stylesheet, preamble)
    -> validation (14 lint rules)
      -> engine (execution loop with retry, edge selection, goal gates)
        -> handler (pluggable node executors)
          -> interviewer (human-in-the-loop I/O)