fabro/docs/workflows/transitions.mdx

---
title: "Transitions"
description: "How Fabro decides which node to execute next"
---

After each node finishes, Fabro must decide which edge to follow to the next node. This decision is deterministic by default — given the same outcome and context, Fabro always picks the same edge. Nodes can opt into [random selection](#random-selection) for weighted-random tiebreaking instead. Understanding the transition logic helps you design workflows that route reliably.

## How transitions work

When a node completes, it produces an **outcome** with a status (`success`, `fail`, `partial_success`) and optional signals like a preferred label or suggested next node. Fabro evaluates the outgoing edges in a fixed priority order:

1. **Condition match** — Edges with a `condition` attribute are evaluated first. If one or more conditions match, the edge with the highest `weight` wins (lexical tiebreak on target node ID).
2. **Preferred label** — If the node's outcome includes a preferred label (e.g. from a human gate selection), the edge whose `label` matches is chosen.
3. **Suggested next** — If the node suggests a specific next node ID, the edge pointing to that node is chosen.
4. **Unconditional fallback** — Edges without conditions are considered last, again using `weight` then lexical tiebreak.

If no edge matches at all, the workflow halts with an error.

## Edge attributes

| Attribute | Description |
|---|---|
| `label` | Display text on the edge; also used for human gate option matching |
| `condition` | Boolean expression that must evaluate to true for this edge (see below) |
| `weight` | Numeric priority for tiebreaking (higher wins, default: 0) |

## Conditions

Edge conditions are boolean expressions evaluated against the stage outcome and run context. Conditions go in the `condition` attribute on an edge:

```dot
gate -> exit      [label="Pass", condition="outcome=success"]
gate -> implement [label="Fix", condition="outcome=fail"]
```

### Available keys

| Key | Resolves to |
|---|---|
| `outcome` | The stage status: `success`, `fail`, or `partial_success` |
| `preferred_label` | The label selected by a human gate |
| `context.KEY` | A value from the run context (e.g. `context.tests_passed`) |
| `KEY` | Shorthand for context lookup (without the `context.` prefix) |

### Operators

| Operator | Example | Description |
|---|---|---|
| `=` | `outcome=success` | Equality |
| `!=` | `outcome!=fail` | Inequality |
| `>` | `context.score > 80` | Greater than (numeric) |
| `<` | `context.count < 5` | Less than (numeric) |
| `>=` | `context.score >= 80` | Greater than or equal (numeric) |
| `<=` | `context.count <= 10` | Less than or equal (numeric) |
| `contains` | `context.message contains error` | Substring match, or array membership |
| `matches` | `context.version matches ^v\d+` | Regular expression match |

A bare key with no operator is a **truthiness check** — it passes if the value is non-empty, not `"false"`, and not `"0"`:

```dot
gate -> next [condition="my_flag"]
```

### Combining conditions

Use `&&` (AND), `||` (OR), and `!` (NOT) to build compound expressions. `&&` binds tighter than `||`:

```dot
// Both must be true
gate -> deploy [condition="outcome=success && context.tests_passed=true"]

// Either can be true
gate -> proceed [condition="outcome=success || outcome=partial_success"]

// Negation
gate -> retry [condition="!outcome=success"]

// Mixed precedence: (a AND b) OR c
gate -> next [condition="outcome=success && context.ready=true || context.override"]
```

## Agent transitions

Agent and prompt nodes can influence which edge is taken by including a JSON object in their response with routing directives. Fabro scans the LLM output for the last JSON object containing any of these fields:

```json
{
  "preferred_next_label": "fix",
  "suggested_next_ids": ["implement", "review"],
  "context_updates": { "tests_passed": true }
}
```

| Field | Effect |
|---|---|
| `preferred_next_label` | Matched against edge labels (same as human gate selection) |
| `suggested_next_ids` | Ordered list of preferred target node IDs |
| `context_updates` | Key-value pairs merged into the run context for downstream conditions |

Fabro automatically scans LLM output for these JSON objects — no special configuration is needed. However, you do need to instruct the LLM to emit the JSON in your prompt. For example:

```dot
review [
    label="Review",
    shape=tab,
    prompt="Review the implementation for correctness and \
        code quality. If changes are needed, respond with: \
        {\"preferred_next_label\": \"fix\"}. If everything \
        looks good, respond with: \
        {\"preferred_next_label\": \"approve\"}."
]

review -> fix     [label="Fix"]
review -> approve [label="Approve"]
```

The LLM's natural language response can contain other text — Fabro finds the last JSON object with a recognized routing field and extracts the directives from it.

## Human gate transitions

Human gates use edge labels to present options to the user. The selected label becomes the `preferred_label` in the outcome, and Fabro matches it to the corresponding edge:

```dot
approve [shape=hexagon, label="Approve Plan"]

approve -> implement [label="[A] Approve"]
approve -> plan      [label="[R] Revise"]
approve -> skip      [label="[S] Skip"]
```

The `[A]`, `[R]`, `[S]` prefixes are keyboard accelerators — Fabro strips them when matching, so the user can type just the letter.

## Unconditional edges

An edge without a `condition` attribute always matches. When a node has a single outgoing edge, it doesn't need a condition:

```dot
start -> plan -> implement -> exit
```

When mixing conditional and unconditional edges, conditional matches take priority. An unconditional edge acts as the default fallback:

```dot
gate -> fast_path [condition="outcome=success"]
gate -> slow_path
```

## Weight tiebreaking

When multiple edges match (e.g. two unconditional edges), `weight` determines the winner. Higher weight wins:

```dot
node -> preferred [weight=10]
node -> fallback  [weight=1]
```

If weights are equal, the edge with the lexicographically first target node ID is chosen. This makes the behavior fully deterministic.

## Random selection

By default, tiebreaking between candidate edges is deterministic (highest weight, then lexical node ID). Setting `selection="random"` on a node switches to weighted-random tiebreaking for its outgoing edges:

```dot
picker [label="Pick path", selection="random"]

picker -> path_a [weight=3]
picker -> path_b [weight=1]
```

In this example, `path_a` is chosen ~75% of the time and `path_b` ~25%. Edges with weight ≤ 0 are treated as weight 1. The cascade priority (conditions → preferred label → suggested next → unconditional → fallback) is unchanged — randomness only affects the pick-one-from-candidates step within each tier.

<Note>
`selection="random"` cannot be combined with conditional edges on the same node. Validation rejects this combination because condition evaluation order would conflict with random selection. Use unconditional edges with weights instead.
</Note>