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This tutorial builds a plan-approve-implement workflow where a human reviews the plan before the agent writes code. If the plan isn’t right, the human sends it back for revision — creating a loop.

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The workflow

Plan-Implement workflow: Start → Plan → Approve Plan → Implement → Simplify → Exit, with Revise loop back to Plan
plan-implement.fabro
digraph PlanImplement {
    graph [goal="Plan, approve, implement, and simplify a change"]
    rankdir=LR

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

    plan      [label="Plan", prompt="Analyze the goal and codebase. Write a clear, step-by-step implementation plan to a Markdown file called plan.md. Include what files will change and why.", reasoning_effort="high"]
    approve   [shape=hexagon, label="Approve Plan"]
    implement [label="Implement", prompt="Read plan.md and implement every step. Make all the code changes described in the plan."]
    simplify  [label="Simplify", prompt="@files-internal/prompts/simplify.md"]

    start -> plan -> approve
    approve -> implement [label="[A] Approve"]
    approve -> plan      [label="[R] Revise"]
    implement -> simplify -> exit
}

fabro run files-internal/demo/10-plan-implement.fabro

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Human gates

The approve node has shape=hexagon, which makes it a human gate — the workflow pauses and waits for a person to choose a path. 
approve [shape=hexagon, label="Approve Plan"]

approve -> implement [label="[A] Approve"]
approve -> plan      [label="[R] Revise"]
The outgoing edge labels define the options. In the CLI, you’ll see: 
? Approve Plan
  [1] A - [A] Approve
  [2] R - [R] Revise
Select:
The [A] and [R] prefixes are keyboard accelerators — type the letter to select.

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The revision loop

If you choose Revise, execution goes back to the plan node. The agent runs again with context about what happened — it knows its previous plan was rejected and can improve it. This cycle repeats until you approve. 
start → plan → approve → [Revise] → plan → approve → [Approve] → implement → simplify → exit
Loops are natural in Fabro — just point an edge back to an earlier node. For safety, you can set max_visits on a node to prevent infinite loops: 
plan [label="Plan", max_visits=5, ...]

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Reasoning effort

The plan node sets reasoning_effort="high". This tells the model to think harder — useful for planning tasks that require careful analysis. The default is high, but you can set it to low or medium for simpler tasks to save cost and time.

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Prompt file references

The simplify node uses @files-internal/prompts/simplify.md instead of an inline prompt string: 
simplify [label="Simplify", prompt="@files-internal/prompts/simplify.md"]
The @ prefix tells Fabro to load the prompt from a Markdown file, resolved relative to the DOT file’s location. This keeps DOT files concise and lets you version prompts as standalone files. See Prompts for details.

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Context flow between nodes

Each node receives a preamble summarizing what happened in prior stages. When the implement node runs, it knows that a plan was written and approved. The preamble includes:
  • The workflow goal
  • A summary of completed stages with their outcomes
  • Files touched by prior stages
  • Run context values
The agent reads plan.md (as instructed by its prompt), but the preamble gives it additional context about the overall workflow state. See Context for the full reference.

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What you’ve learned

  • Human gates (shape=hexagon) pause for human input with edge labels as options
  • Revision loops are just edges that point back to earlier nodes
  • Prompt file references (@path/to/file.md) keep DOT files clean
  • reasoning_effort controls how hard the model thinks
  • Nodes receive preambles summarizing prior stages

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Next

Branch & Loop

Add conditional branching and automated test validation loops.