veotools 0.2.0

A Python toolkit for AI-powered video generation and seamless stitching using Google's Veo models.

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 to allow_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_DIR or pass a custom base path to StorageManager.

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 fluent Bridge API.

Contributing & tests

Pull requests are welcome! Please:

1. Install with pip install -e ".[dev,mcp]".
2. Run pytest (the suite relies on mocks; no live API calls).
3. Update docs/examples when behaviour changes.

MCP users can launch the server with veo-mcp or python -m veotools.server.mcp_server.