Introduction to Renku
Architecture Overview
Section titled “Architecture Overview”┌─────────────────────────────────────────────────────────────────────┐│ Blueprint YAML ││ • Defines workflow: inputs, producers, connections │└─────────────────────────────────────────────────────────────────────┘ │ ▼┌─────────────────────────────────────────────────────────────────────┐│ Planner ││ • Loads blueprint tree (blueprints + producers) ││ • Resolves dimensions and expands loops ││ • Builds execution graph with canonical IDs ││ • Creates layered execution plan │└─────────────────────────────────────────────────────────────────────┘ │ ▼┌─────────────────────────────────────────────────────────────────────┐│ Runner ││ • Executes jobs layer by layer ││ • Resolves inputs from upstream artifacts ││ • Builds fan-in descriptors from connections ││ • Invokes AI providers via producer implementations ││ • Stores artifacts and logs events │└─────────────────────────────────────────────────────────────────────┘ │ ▼┌─────────────────────────────────────────────────────────────────────┐│ Providers ││ • OpenAI (text generation, structured output) ││ • Replicate (video, audio, image models) ││ • fal.ai (video, audio, image models) ││ • Renku (timeline composition, video export) │└─────────────────────────────────────────────────────────────────────┘Typical Video Generation Workflow
Section titled “Typical Video Generation Workflow”- Script Generation: An LLM generates a narration script divided into segments
- Prompt Generation: The LLM creates visual prompts for each segment
- Media Generation: AI models generate video clips, images, or audio per segment
- Timeline Composition: Renku assembles segments into a playable timeline
- Video Export: Remotion renders the final video file
Key Concepts
Section titled “Key Concepts”Understanding these concepts will help you work effectively with Renku:
Blueprint
Section titled “Blueprint”A YAML file that defines your complete video generation workflow. Blueprints specify:
- What inputs the user provides (topic, duration, style, etc.)
- What artifacts are produced (script, images, audio, video)
- Which producers to use and how to connect them
- How to iterate over segments (loops)
Blueprints are the “recipe” for your video.
Producer
Section titled “Producer”A reusable module that invokes AI models. Producers:
- Accept inputs (prompts, configuration)
- Call one or more AI provider APIs
- Produce artifacts (text, images, audio, video)
Examples: ScriptProducer, VideoProducer, AudioProducer, TimelineComposer
Artifact
Section titled “Artifact”Any output produced by a producer:
- Text (scripts, prompts)
- Images (PNG, JPEG)
- Audio (MP3, WAV)
- Video (MP4)
- JSON (timelines, metadata)
Artifacts can be scalar (single value) or arrays (one per segment).
A dimension that enables parallel execution. If you have 5 narration segments, a loop with countInput: NumOfSegments creates 5 instances of any producer assigned to that loop.
Connection
Section titled “Connection”Data flow edges that wire outputs to inputs. Connections define how artifacts flow from one producer to another:
- Blueprint input → Producer input
- Producer artifact → Another producer’s input
- Producer artifact → Blueprint artifact (final output)
An execution layer contains jobs that can run in parallel. The planner uses topological sorting to determine which jobs depend on others and groups independent jobs into the same layer.
Workspace
Section titled “Workspace”The directory structure where Renku stores:
- Blueprint catalog
- Build outputs (artifacts, manifests, logs)
- Configuration
What’s Next?
Section titled “What’s Next?”Ready to try Renku? Start with the Install Page, then continue to the CLI Quick Start to generate your first video.