genblaze-core 0.1.1

Core SDK for genblaze media generation orchestration

genblaze-core

Python SDK for building generative AI pipelines across video, image, and audio — with built-in SHA-256 provenance.

genblaze-core is the core of genblaze, an open-source orchestration framework by Backblaze for composing multi-step AI media generation workflows. It gives you a single, provider-agnostic Pipeline API for text-to-video, text-to-image, text-to-speech, image-to-video, and audio generation — so you can swap models (Sora, Veo, Runway, Luma, Flux, DALL·E, ElevenLabs, Stable Audio, LMNT, GMICloud) without rewriting pipeline logic.

Every pipeline run emits a canonical, hash-verified provenance manifest — a tamper-evident JSON document capturing the provider, model, prompt, parameters, timestamps, and SHA-256 hash of every generated asset. Manifests can be embedded directly into PNG, JPEG, WebP, MP4, MP3, and WAV files, uploaded alongside assets to S3-compatible storage, or exported to Parquet for analytics.

Why genblaze-core

• One API for every generative AI provider — Pipelines, not per-vendor SDK glue. Fluent, composable, chainable.
• Built-in provenance — Every asset gets a SHA-256–verified manifest. Prove how media was made; detect tampering.
• Production-ready — Retries, timeouts, progress streaming, moderation hooks, OpenTelemetry tracing, step caching.
• Storage-agnostic sinks — Drop into Backblaze B2, AWS S3, Cloudflare R2, MinIO, Parquet, or local disk.
• Policy + privacy controls — Redact prompts, strip params, pointer-mode for sensitive content.
• Agent loops + templates — Evaluator-driven iteration, reusable pipeline and step templates.
• Zero lock-in — MIT licensed, typed, lazy imports, provider adapters are separate packages.

Features

Capability	What you get
Pipeline API	Fluent multi-step generation, fan-in (input_from), AV compositing via FFmpeg
Provider discovery	Entry-point–based registry — pip install genblaze-<provider> and it's available
Manifest (Pydantic)	Run, Step, Asset models with canonical JSON hashing and .verify()
Media embedding	PngHandler, Mp4Handler, Mp3Handler, etc. — embed + extract manifests in-file
Storage sink	ObjectStorageSink with hierarchical or content-addressable key layout
Parquet sink	Partitioned run/step/asset tables for downstream analytics
Observability	OTelTracer, LoggingTracer, CompositeTracer, structured events
Agents	AgentLoop with pluggable Evaluator for iterative refinement
Moderation	Pre/post moderation hooks, configurable embed policies
Testing	MockProvider, MockVideoProvider, MockAudioProvider for offline tests

Install

pip install genblaze-core

Optional extras:

pip install "genblaze-core[parquet]"   # ParquetSink for analytics
pip install "genblaze-core[audio]"     # Audio metadata embedding (mutagen)

Add provider adapters separately:

pip install genblaze-openai genblaze-google genblaze-runway genblaze-luma \
            genblaze-decart genblaze-replicate genblaze-elevenlabs \
            genblaze-stability-audio genblaze-lmnt genblaze-gmicloud

pip install genblaze-s3    # Storage backend for Backblaze B2 / AWS S3 / R2 / MinIO
pip install genblaze-cli   # Extract / verify / replay / index manifests

Quickstart — local, zero API keys

from genblaze_core import Modality, Pipeline
from genblaze_core.testing import MockVideoProvider

run, manifest = (
    Pipeline("hello-genblaze")
    .step(MockVideoProvider(), model="mock-v1",
          prompt="A drone shot over a coastal city at golden hour",
          modality=Modality.VIDEO)
    .run()
)

print(manifest.canonical_hash)   # deterministic SHA-256 of the run
print(manifest.verify())         # True

Quickstart — Sora + Backblaze B2 storage

Generate a video, upload it + its manifest to Backblaze B2, verify the hash:

pip install genblaze-core genblaze-openai genblaze-s3
export OPENAI_API_KEY="sk-..."
export B2_KEY_ID="..."
export B2_APP_KEY="..."

from genblaze_core import KeyStrategy, Modality, ObjectStorageSink, Pipeline
from genblaze_openai import SoraProvider
from genblaze_s3 import S3StorageBackend

storage = ObjectStorageSink(
    S3StorageBackend.for_backblaze("my-bucket"),
    key_strategy=KeyStrategy.HIERARCHICAL,
)

result = (
    Pipeline("hero-reel")
    .step(SoraProvider(), model="sora-2",
          prompt="Aerial flyover of a mountain lake at sunrise",
          modality=Modality.VIDEO, seconds=4, size="1280x720")
    .run(sink=storage, timeout=300)
)

print(result.run.steps[0].assets[0].url)   # durable B2 URL
print(result.manifest.canonical_hash)      # SHA-256 of the full run
assert result.manifest.verify()

Storage — Backblaze B2 recommended

Backblaze B2 is the recommended default sink for genblaze — purpose-built for large AI-generated media with S3-compatible APIs, resilient multipart uploads, Object Lock for immutable manifests, and strong cost economics at scale. One-liner credentials from B2_KEY_ID / B2_APP_KEY. See the genblaze-s3 backend for the full recipe plus AWS S3, Cloudflare R2, and MinIO variants.

Documentation

• Main repo: https://github.com/backblaze-labs/genblaze
• Architecture: https://github.com/backblaze-labs/genblaze/blob/main/ARCHITECTURE.md
• Feature docs: https://github.com/backblaze-labs/genblaze/tree/main/docs/features
• Runnable examples: https://github.com/backblaze-labs/genblaze/tree/main/examples

Related packages

Provider adapters: genblaze-openai · genblaze-google · genblaze-runway · genblaze-luma · genblaze-decart · genblaze-replicate · genblaze-elevenlabs · genblaze-stability-audio · genblaze-lmnt · genblaze-gmicloud

Storage + tooling: genblaze-s3 · genblaze-cli · genblaze-langsmith

License

MIT