parallel

name	parallel
description	Parallel execution patterns for montaj — load when workflow has multiple clips or foreach steps
step	false

Parallel Execution

Important: Claude Code's Bash tool executes sequentially — multiple Bash calls in the same turn queue one after another. They do NOT run in parallel. Use the patterns below for true parallelism.

Step-level — same step, multiple clips

For foreach steps on independent clips, use a single Bash call with background jobs:

out0=$(curl -s -X POST http://localhost:3000/api/steps/transcribe \
  -H "Content-Type: application/json" \
  -d '{"input": "/path/clip0.MOV", "model": "base.en"}') &
out1=$(curl -s -X POST http://localhost:3000/api/steps/transcribe \
  -H "Content-Type: application/json" \
  -d '{"input": "/path/clip1.MOV", "model": "base.en"}') &
# ... repeat for each clip
wait
echo "$out0"; echo "$out1"  # parse results after all complete

Use for any independent foreach step: waveform_trim, transcribe, rm_fillers, probe, snapshot.

Do NOT use background jobs for encoding steps. See Encoding steps below.

Encoding steps — materialize_cut, remove_bg

Encoding steps are memory-intensive per instance (full libx264 encode + frame buffers). Fanning them out unbounded across N clips will exhaust memory on 4K footage. Use batch mode with the built-in concurrency cap instead of background jobs:

# materialize_cut — batch mode, 2 concurrent encodes (default)
montaj materialize-cut --inputs clip0_spec.json clip1_spec.json clip2_spec.json
# → JSON array of output paths

# raise to 3 only on machines with 32GB+ RAM and 1080p or smaller footage
montaj materialize-cut --inputs clip0.MOV clip1.MOV --workers 3

# remove_bg — always use --inputs; GPU processes sequentially, CPU parallelises with workers
montaj remove-bg --inputs clip0_cut.mp4 clip1_cut.mp4 clip2_cut.mp4
# → JSON array of {nobg_src, nobg_preview_src} objects

remove_bg is long-running (minutes per clip on MPS/GPU). Always run it in the background when using the Agent tool or HTTP API so the agent remains responsive.

waveform_trim native batch

Preferred over background jobs — pass all clips in a single call:

# HTTP
curl -s -X POST http://localhost:3000/api/steps/waveform_trim \
  -H "Content-Type: application/json" \
  -d '{"inputs": ["/path/clip0.mp4", "/path/clip1.mp4", "/path/clip2.mp4"]}'
# → returns JSON array of trim specs: [{"input": "...", "keeps": [...]}, ...]

# CLI
montaj waveform-trim clip0.mp4 clip1.mp4 clip2.mp4
# → JSON array of trim specs printed to stdout

Clip-level — multiple steps per clip (swarm)

When each clip needs 3+ sequential steps before concat, background jobs get unwieldy. Use one subagent per clip:

1. Identify fan-out point: all per-clip steps (before concat)
2. Identify fan-in point: concat and everything after
3. Spawn one subagent per clip — each receives: clip path, steps to run, project id, editing prompt
4. Cap at 4 concurrent clip agents to avoid resource contention
5. Wait for all subagents to complete, collect output paths
6. Fan in: run concat with all paths, then continue

Use when a clip needs 3+ sequential steps — subagent coordination overhead is worth it beyond that threshold.

Dependency waves

Workflow steps declare needs: [step_ids]. Identify waves of ready steps (all needs met) and execute each wave with the appropriate parallel pattern before moving to the next.

Example — default workflow:

Wave	Steps	Pattern
1	probe, snapshot, waveform_trim (per clip)	Bash & or waveform_trim batch
2	transcribe (per clip)	Bash &
3	rm_fillers (per clip)	Bash &
4	select-takes	Sequential
5	caption, overlays	Sequential
6	concat	Sequential (encode boundary)