m decompose

Break complex tasks into ordered, executable subtasks with the m decompose CLI.

m decompose takes a complex task description and uses an LLM to:

1. Extract the constraints the output must satisfy
2. Identify the subtasks needed to complete the goal, with dependency ordering
3. Generate a prompt template for each subtask
4. Output a ready-to-run Python script that executes each subtask in order

Prerequisites: Mellea installed (uv add mellea), Ollama running locally (or an OpenAI-compatible endpoint).

Basic usage

Write your task description to a text file, then run:

mkdir -p ./output
m decompose run --prompt-file task.txt --out-dir ./output/

Note: The output directory must already exist — the command will error if it does not. On first run with Ollama, the default model will be downloaded automatically (~15 GB for the full model). Use --model-id with a smaller model (e.g. granite4:micro) to avoid the large download.

This produces two files in ./output/:

• m_decomp_result.json — the full decomposition: subtask list, constraints, dependency graph, and prompt templates
• m_decomp_result.py — a runnable Python script that calls m.instruct() for each subtask in dependency order

Example

Given a task.txt:

Write a short blog post about the benefits of morning exercise.
Include a catchy title, an introduction paragraph, three main benefits
with explanations, and a conclusion that encourages readers to start
their morning exercise routine.

Run:

m decompose run --prompt-file task.txt --out-dir ./output/

Then execute the generated script:

python output/m_decomp_result.py

Backend options

m decompose defaults to Ollama with granite4:micro. Pass --backend and --model-id to use a different inference engine:

m decompose run \
  --prompt-file task.txt \
  --out-dir ./output/ \
  --backend openai \
  --model-id gpt-4o-mini

To see all options:

m decompose --help
m decompose run --help

Python API

Use the decompose pipeline directly from Python:

from cli.decompose.pipeline import DecompBackend, decompose

result = decompose(
    task_prompt="Write a short blog post about morning exercise.",
    model_id="granite4:micro",
    backend=DecompBackend.ollama,
)

# result["subtask_list"]       — ordered list of subtask descriptions
# result["identified_constraints"] — constraints extracted from the prompt
# result["subtasks"]           — detailed subtask objects with prompt templates

Each subtask in result["subtasks"] has:

Field	Description
subtask	Description of the subtask
tag	Short identifier used for dependency references
depends_on	List of tag values this subtask depends on
prompt_template	Ready-to-use prompt string for m.instruct()
input_vars_required	Variables that must be filled in the template
constraints	Constraints from the original prompt that apply here

When to use m decompose

m decompose is useful when:

• A task prompt is too large or complex for a single LLM call
• The work can be broken into sequential or parallel subtasks
• You want a first-pass structure you can then edit by hand
• You are exploring how to decompose a problem before writing code

For tasks that fit comfortably in a single prompt, use m.instruct() directly.

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Full example: docs/examples/m_decompose/

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See also: Tools and Agents | Refactor Prompts with CLI