Let a local LLM edit for you

AutoEditor turns source videos plus a one-line brief into a finished cut. A local vision-language model served by Ollama makes the editorial selection — no cloud keys, no timestamps to hand-author.

Needs pip install "videopython[ai]" and ollama pull qwen3.6:27b.

Cut a teaser from three clips

from videopython.ai import AutoEditor, OllamaVisionLLM

editor = AutoEditor(planner=OllamaVisionLLM(model="qwen3.6:27b"))

edit = editor.edit(
    ["clip_a.mp4", "clip_b.mp4", "clip_c.mp4"],
    brief="A punchy 15-second teaser; lead with the most dynamic shot.",
)
edit.run_to_file("teaser.mp4")

edit(...) returns a validated VideoEdit — the same plan object you would author by hand — so you can inspect it, tweak a segment, or re-render it without re-running the model.

What it does under the hood

1. Analyze — each source goes through VideoAnalyzer: scene boundaries, a per-scene caption, a transcript.
2. Catalog — scenes are flattened into candidate clips, each with a stable scene_id, exact bounds, caption, transcript, and a keyframe image.
3. Plan — the planner sees the catalog (text + keyframes) and your brief, and authors a plan that references scenes by id plus operations.
4. Resolve and run — ids map back to exact bounds, and the plan is repaired, dimension-normalized and validated before rendering.

The model never authors timestamps, so its temporal imprecision cannot reach the render. Precise bounds come from scene detection; editorial judgment comes from the model. See LLM-first design.

Tune the refine loop

If a plan references an unknown id or violates a bound, the structured error goes back to the planner. AutoEditError is raised if no valid edit emerges within the budget.

editor = AutoEditor(
    planner=OllamaVisionLLM(model="qwen3.6:27b"),
    max_rounds=3,                  # default
    normalize_target="largest",    # unify segment dimensions to the largest source
)

Analyze once, try several briefs

Analysis is the expensive step — reuse it:

from videopython.ai import VideoAnalyzer

analyses = [VideoAnalyzer().analyze_path(p) for p in ["a.mp4", "b.mp4"]]

edit = editor.edit_from_analyses(analyses, brief="A calm, scenic 20s intro.")
alt  = editor.edit_from_analyses(analyses, brief="Fast, punchy, 8 seconds.")

Choose a planner model

The planner must be vision-capable (it receives keyframes) and support Ollama's structured-output format. qwen3.6:27b is the default and is Apache-2.0.

Some builds — certain MLX vision models, for instance — accept images but ignore format; planning then fails because the model returns prose instead of JSON. If that happens, pick another tag. OllamaVisionLLM is model-agnostic via model=, and StructuredVisionLLM is the seam for backing the planner with something other than Ollama.

Drive the catalog yourself

For custom planning logic, the pieces are public:

from videopython.ai import VideoAnalyzer, build_catalog
from videopython.ai.auto_edit import EditPlan, resolve_plan

analyses = [VideoAnalyzer().analyze_path("a.mp4")]
bundle = build_catalog(analyses)                 # bundle.catalog + bundle.keyframes

plan = EditPlan.model_validate({"segments": [{"scene_id": bundle.catalog.scenes[0].id}]})
edit = resolve_plan(plan, bundle.catalog)        # -> VideoEdit
edit.run_to_file("out.mp4")

Full signatures: AI auto-editing reference.