This PR fixes a bug where users with only OpenAI or Gemini API keys
configured would hit errors when running commands without an explicit
`--provider` flag, because the system hardcoded `Provider::Anthropic` as
the fallback in multiple places.
The core change introduces `Provider::default_from_env()`, which checks
which providers have API keys configured and selects the best available
one using a precedence order (Anthropic → OpenAI → Gemini), falling back
to Anthropic if none are found. A testable `default_with()` helper
enables thorough unit testing of the selection logic without relying on
environment variables. A companion `catalog::default_model_from_env()`
function pairs the provider selection with the correct default model
lookup.
All six call sites across the CLI, API server, workflow handler, and
agent that previously hardcoded `Provider::Anthropic` or used
`catalog::default_model()` without considering available keys have been
updated to use the new environment-aware defaults. The catalog functions
also now normalize provider aliases (e.g. `"open_ai"` → `"openai"`) to
prevent silent lookup misses.
### Fabro Details
<details>
<summary>Ran 10 stages in 28m 34s for $6.23</summary>
| Stage | Duration | Cost | Retries |
|---|---|---|---|
| start | 0s | – | 0 |
| toolchain | 0s | – | 0 |
| preflight_compile | 1m 8s | – | 0 |
| preflight_lint | 13s | – | 0 |
| implement | 4m 40s | $0.79 | 0 |
| simplify_opus | 7m 23s | $1.84 | 0 |
| simplify_gemini | 6m 24s | $1.44 | 0 |
| simplify_gpt | 7m 56s | $2.16 | 0 |
| verify | 14s | – | 0 |
| fmt | 1s | – | 0 |
| **Total** | **28m 34s** | **$6.23** | **0** |
</details>
<details>
<summary>Ran <code>ImplementAndSimplify.fabro</code> (13 nodes and 16
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 -q --workspace 2>&1", max_retries=0]
preflight_lint [label="Preflight Lint", shape=parallelogram, script="cargo clippy -q --workspace -- -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. Use red/green TDD."]
simplify_opus [label="Simplify (Opus)", prompt="@prompts/simplify.md"]
simplify_gemini [label="Simplify (Gemini)", prompt="@prompts/simplify.md", model="gemini-3.1-pro-preview-customtools"]
simplify_gpt [label="Simplify (GPT-54)", prompt="@prompts/simplify.md", model="gpt-54"]
verify [label="Verify", shape=parallelogram, script="cargo clippy -q --workspace -- -D warnings 2>&1 && cargo nextest run --cargo-quiet --workspace --status-level fail 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]
fmt [label="Format", shape=parallelogram, script="cargo fmt --all 2>&1", max_retries=0]
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_opus -> simplify_gemini -> simplify_gpt -> verify
verify -> fmt [condition="outcome=success"]
verify -> fixup
fixup -> verify
fmt -> exit
}
```
</details>
⚒️ Generated with [Fabro](https://fabro.sh)
---------
Co-authored-by: Fabro <noreply@fabro.sh>
Co-authored-by: Fabro <fabro@fabro.sh>
|
||
|---|---|---|
| .. | ||
| src | ||
| tests | ||
| Cargo.toml | ||
| README.md | ||
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
goaldescribing 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, andfidelityattributes that control routing. - Handler -- An async trait implementation that executes a node and returns an
Outcome. Built-in handlers includeStartHandler,ExitHandler,AgentHandler,PromptHandler,ConditionalHandler,HumanHandler,ParallelHandler,FanInHandler,CommandHandler, andSubWorkflowHandler. - 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, andRecordingInterviewer. - 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)