Veotools
A Python toolkit for orchestrating multi-scene videos with Google Veo. Handles scene planning, video rendering, overlap-aware stitching, and progress tracking.
Features
- One-command stories – Turn ideas into storyboards, render clips, and deliver stitched videos
- Gemini planning – Generate cinematic shot lists with consistent characters and dialogue
- Veo 3.1 support – Full SDK access with latest Veo 3.1 features including reference images, video extension, and frame interpolation
- Audio-preserving stitching – FFmpeg pipeline maintains perfect audio alignment
- Python-first – Clean API for programmatic video generation workflows
Installation
pip install veotools # CLI and SDK
pip install "veotools[mcp]" # Add MCP server support
pip install -e ".[dev,mcp]" # Contribute / run tests locally
# Environment (choose your models / region access)
export GEMINI_API_KEY="your-gemini-key"
# Daydreams Router support (optional)
export VEO_PROVIDER="daydreams"
export DAYDREAMS_API_KEY="sk-router-..."
# export DAYDREAMS_BASE_URL="https://api-beta.daydreams.systems/v1" # override if needed
Set VEO_PROVIDER=daydreams to proxy prompts and video jobs through the Daydreams Router. Omit the flag (or set it to google) to call the Google GenAI SDK directly.
Quick start (CLI)
# Plan + render + stitch in one command
veo plan-run \
--idea "N64 Japanese retro explainer about the x402 protocol" \
--save-plan output-plans/x402.json \
--execute-model veo-3.0-generate-001 \
--seed-last-frame --seed-offset -0.25
Outputs land under output/:
output-plans/x402.json– Gemini storyboard (optional if you omit--save-plan).output/videos/video_*.mp4– Individual clips.output/videos/stitched_*.mp4– Final stitched master with audio.
You can also execute an existing plan:
veo plan-execute --plan output-plans/x402.json --model veo-3.0-generate-001 --seed-last-frame
…and generate only the storyboard:
veo plan --idea "Retro travel vlog" --scenes 4 --save output-plans/vlog.json --json
Quick start (Python)
import veotools as veo
veo.init() # sets up logging + validates GEMINI_API_KEY
plan = veo.generate_scene_plan(
"N64 Japanese retro explainer about the x402 protocol",
number_of_scenes=4,
additional_context="Keep the tone energetic and educational",
)
result = veo.execute_scene_plan(
plan,
model="veo-3.0-generate-001",
auto_seed_last_frame=True, # feed each clip the previous clip's final frame
seed_frame_offset=-0.25, # extract the seed frame 0.25s before the end
)
print("Rendered clip files:")
for clip in result.clip_results:
print(" -", clip.path)
if result.final_result:
print("Final stitched video:", result.final_result.path)
CLI overview
veo plan– Generate / save a Gemini storyboard (structured JSON).veo plan-execute– Render clips + stitch an existing plan.veo plan-run– Plan and execute in one shot (see quick start).veo generate/veo continue– Low-level Veo helpers for ad hoc clips.veo list-models,veo preflight– Environment + model diagnostics.
Run veo <command> --help for full flag descriptions.
Python API map
| Task | Function(s) |
|---|---|
| Scene planning | generate_scene_plan, SceneWriter |
| Plan execution | execute_scene_plan, PlanExecutionResult |
| Single clip generation | generate_from_text, generate_from_image, generate_from_video |
| Veo 3.1 features | extend_video, generate_with_reference_images, generate_with_interpolation |
| Media analysis | extract_frame, extract_frames, get_video_info |
| Stitching | stitch_videos, stitch_with_transitions, create_transition_points |
| Workflow chaining | Bridge (fluent API for multi-step stories) |
Veo 3.1 features
from veotools import extend_video, generate_with_reference_images, generate_with_interpolation
from pathlib import Path
# Extend an existing video (7-second extensions, up to 20x)
result = extend_video(
video_path=Path("input.mp4"),
prompt="Continue with a dramatic reveal",
model="veo-3.1-generate-preview"
)
# Generate with reference images for character/scene consistency
result = generate_with_reference_images(
prompt="A character walking through the forest",
reference_images=["char_ref.jpg", "forest_ref.jpg"],
model="veo-3.1-generate-preview"
)
# Interpolate between first and last frames
result = generate_with_interpolation(
first_frame=Path("start.jpg"),
last_frame=Path("end.jpg"),
prompt="Smooth transition showing time passing",
model="veo-3.1-generate-preview"
)
Model / safety notes
- Veo 3.x text-to-video clips default to
person_generation="allow_all"; image/video-seeded clips default toallow_adult. Override via keyword arguments if you need a different policy. - Use
list_models(include_remote=True)to discover the Veo variants your account can access (stable IDs:veo-3.1-generate-001,veo-3.0-generate-001,veo-3.0-fast-generate-001, etc.).
Supported models
| Model | Features |
|---|---|
veo-3.1-generate-preview |
Reference images, video extension, frame interpolation, audio, 1080p |
veo-3.1-fast-generate-preview |
Same as above, optimized for speed |
veo-3.0-generate-001 |
Audio, resolution control, seed support |
veo-3.0-fast-generate-001 |
Same as above, optimized for speed |
veo-2.0-generate-001 |
Duration control, enhance prompt, FPS control |
Storage layout
output/videos/– All rendered clips, stitched deliverables, and intermediate assets.output/frames/– Extracted stills (including auto-seed frames when enabled).output/ops/– MCP job records, cached model lists, etc.- Override with
VEO_OUTPUT_DIRor pass a custom base path toStorageManager.
Examples
examples/plan_run.py– Full idea → plan → render workflow in ~30 lines.examples/text_to_video.py– Minimal text-to-video clip generator.examples/bridge_story.py– Build a three-scene story with the fluentBridgeAPI.
Contributing & tests
Pull requests are welcome! Please:
- Install with
pip install -e ".[dev,mcp]". - Run
pytest(the suite relies on mocks; no live API calls). - Update docs/examples when behaviour changes.
MCP users can launch the server with veo-mcp or python -m veotools.server.mcp_server.
Metadata
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