lasagnastack 1.0.0

An AI pipeline that turns raw video clips into an editable CapCut project for short-form reel editing.

LasagnaStack

An AI pipeline that turns raw video clips into an editable CapCut project for short-form reel editing.

It is as simple as:

python -m lasagnastack make ./my_clips/ --out ./my_capcut_draft/

where...

./my_clips/: a folder of raw video clips in MP4/MOV format + one .txt creator brief.
./my_capcut_draft/: a CapCut draft folder, ready to open in CapCut Desktop.

The pipeline runs in six sequential stages: ingest (uses ffmpeg) → analyse (uses LLM) → direct (uses LLM) → critique loop (uses LLM) → enhance (uses LLM) → render (uses pyCapCut).

Each stage is a subclass of the Stage abstract base class (base.py). Adding, removing, or reordering stages requires only editing the stages list in ReelPipeline. See ARCHITECTURE.md for the full architecture guide.

Get started with development

1. Clone the repository.

git clone https://github.com/jyjulianwong/LasagnaStack.git

2. Verify that you have a compatible Python version installed on your machine.

python --version

3. Install uv (used as the package manager for this project).

4. Install the development dependencies.

cd LasagnaStack/
uv sync --all-groups
uv run pre-commit install

Set up environment

Copy .env.sample to .env and fill in your values:

cp .env.sample .env

.env is gitignored. Values set in the shell environment take precedence over .env.

Authentication

Get a Gemini API key at aistudio.google.com/apikey and add it to your .env:

LSNSTK_LLM_GEMINI_API_KEY=your-key-here

Run the pipeline

Prepare an input folder containing your MP4/MOV clips and exactly one .txt brief file, then run:

uv run python -m lasagnastack make ./my_clips/ --out ./drafts/reel_2025_05_05

The pipeline pauses for confirmation between each stage. To skip all prompts:

uv run python -m lasagnastack make ./my_clips/ --out ./drafts/reel_2025_05_05 --yes

Full CLI reference:

usage: lasagnastack make [-h] --out OUTPUT_DIR [--skill SKILL_FILE] [--yes]
                         [--critique-max-retries N] [--ingest-max-workers N]
                         [--analyse-max-workers N] INPUT_DIR

positional arguments:
  INPUT_DIR                     Folder containing clips and brief .txt

options:
  --out OUTPUT_DIR              Destination for the CapCut draft and working files
  --skill SKILL_FILE            Path to Markdown skill file injected into the direct, 
                                critique, and enhance prompt templates (optional)
  --yes, -y                     Auto-confirm all stage prompts
  --critique-max-retries N      Maximum # of critique loop retries (default: 2)
  --ingest-max-workers N        Maximum # of parallel worker processes for `ingest` stage (default: 2)
  --analyse-max-workers N       Maximum # of concurrent LLM calls for `analyse` stage (default: 4)

Open the draft in CapCut Desktop (macOS)

If CapCut Desktop is installed, the pipeline automatically:

1. Detects ~/Movies/CapCut/User Data/
2. Copies all .mp4/.mov files from your input folder into the CapCut draft folder — including clips not used on the timeline — so they are immediately available in CapCut's import panel
3. Rewrites the timeline clip paths in draft_info.json to point to the copied files
4. Registers the draft in root_meta_info.json so it appears on the CapCut home screen straight away

Open CapCut Desktop after the pipeline finishes — the draft will appear on the home screen under your local projects with all media already linked. Drafts are named LasagnaStack - Reel Name and use that same string as the folder name so they are easy to identify among existing projects.

If CapCut is not installed, the draft is written to <output_dir>/draft/LasagnaStack - {reel_name}/ and you can copy it manually.

This has been tested with CapCut Desktop 8.5.0 on macOS Sequoia 15.6.1. There may be issues with older versions or other operating systems.

Track LLM costs with MLflow

Every pipeline run is automatically traced with MLflow. Each Gemini API call is recorded as a span (prompt, response, token counts, latency, and estimated USD cost). Session-level totals are written to the run when the pipeline finishes.

1. Start the MLflow server (in a separate terminal, before running the pipeline):

mlflow server --host 127.0.0.1 --port 5001

macOS note: port 5000 is reserved by AirPlay Receiver. Use 5001 or higher.

2. Add the tracking variables to .env:

MLFLOW_TRACKING_URI=http://localhost:5001
MLFLOW_EXPERIMENT_NAME=lasagnastack

3. Run the pipeline as normal. Open http://localhost:5000 in your browser to watch live.

In Experiments -> lasagnastack -> Traces, spans appear in real time as stages progress. Each trace has three levels: the top-level pipeline span (ReelPipeline.run), a per-stage span (e.g. AnalyseStage.run), and individual LLM call spans (GeminiClient._call_api) nested inside.

Runs are named lasagnastack-{brief_stem}-{4-char-id} and tagged with the model, reel name, and critique_max_retries.

No server? Set MLFLOW_TRACKING_URI=mlruns to write results to a local folder instead, then view them with mlflow ui.

Configuration

Parameter	How to set	Default
LLM model	LSNSTK_LLM_MODEL env. var.	gemini/gemini-2.5-flash
Gemini API key	LSNSTK_LLM_GEMINI_API_KEY env. var. (required)	—
Path to skill file	--skill CLI flag	—
critique stage maximum # of retries	--critique-max-retries CLI flag	2
ingest stage maximum # of worker processes	--ingest-max-workers CLI flag	2
analyse stage maximum # of concurrent LLM calls	--analyse-max-workers CLI flag	4

Architecture

See ARCHITECTURE.md for four annotated diagrams covering the pipeline data flow, the Stage 4 critique loop, the Stage 6 render + CapCut export, and the extensibility model.

Get started with Jupyter notebooks

1. Once the above setup is complete, set up a Python kernel.

source .venv/bin/activate
python -m ipykernel install --user --name=lasagnastack

2. Refer to the following common commands.

jupyter kernelspec list
jupyter kernelspec uninstall lasagnastack

3. Start the Jupyter server.

jupyter lab

This repo is cool because...

• The pipeline is modularlised into stages, with each stage being responsible for transforming the global state of the pipeline run (similar to LangGraph). It is easy to add, remove, or reorder stages.
• The pipeline supports "skills" -- each user can write their own skill .md file to customise the pipeline to their own accounts' styles and branding, or use pre-written skills from marketplaces to cater for different types of reel content.
• Chain-of-thought reasoning is enabled via Gemini.
• Human-in-the-loop is deeply integrated in the design, with each stage prompting the user for confirmation before proceeding to the next stage.
• Prompt caching is enabled to avoid unnecessary LLM calls to reduce latency and cost.
• The tool is deeply integrated with its host machine. It auto-detects CapCut Desktop, copies all source media (timeline clips and unused footage) so the project opens in CapCut with no missing-media errors, no manual steps, all your raw clips already in the import panel, and the timeline editor populated and ready to go.