genblaze is a production-grade Python monorepo SDK for orchestrating generative AI media pipelines — image, video, audio, text-to-speech, and chat — across 16+ provider integrations (OpenAI, Google Gemini/Imagen/Veo, NVIDIA NIM, GMICloud, Runway, Luma, Replicate, FAL, ElevenLabs, AssemblyAI, Hume, LMNT, Stability Audio, Decart, AtlasCloud). The public API is a chainable Pipeline/Step abstraction with batch, streaming, ingest, and agent-loop modes, plus a Click-based CLI (extract/verify/replay/index) for manifest inspection, a schema-first spec package generating TypeScript types from JSON Schemas, and ~30 runnable examples. Architecturally it is a monorepo: libs/core (genblaze-core) contains the framework (pipeline, providers, model registry, storage, media handlers, canonical hashing, signing, observability, webhooks), while each connector under libs/connectors/* is an independently versioned package registering itself via plugin entry points. Key entry points are Pipeline.run()/batch_run()/stream() for programmatic use and the genblaze command line; data flows through provider submit/poll/fetch_output lifecycles, optional caching, S3/B2 object-storage sinks with URL policies, and manifest generation that can be embedded directly into output media files. Two capabilities set this apart. First, provenance: every run produces a canonical, hash-verified Manifest (SHA-256 over deterministically serialized JSON), optionally Ed25519-signed, and byte-preservingly embedded into JPEG/PNG/WebP/MP4/audio files via XMP/atom splicing without re-encoding. Second, security and reliability engineering: SSRF protection with DNS pinning, credential redaction, ReDoS-safe pattern heuristics, ffmpeg injection escaping, configurable retry policies, model liveness probes, and discovery-aware validation (NATIVE/PARTIAL/NONE support levels) that reduces hardcoded slug rot. The project is actively maintained with unusually thorough tooling: golden hash-stability tests, a provider conformance test base class (~15 contract checks per connector), pin-parity and PyPI-metadata release gates, zizmor/dependabot CI, and live probe scripts that fail on API drift. Main risks are the inherent churn of 16+ third-party APIs (mitigated but not eliminated by mock-heavy tests) and a package/versioning layout complex enough that installs rely on documented make targets. Overall this is a strong, well-architected application SDK — suitable both for direct use and as a reference for multi-provider integration frameworks.