stream-chain

Multi-agent workflow coordination is automated by streaming outputs between sequential steps, enabling complex end-to-end data transformations and pipeline orchestration across projects. You define custom chains or predefined pipelines to execute the sequence.

Streaming outputs between pipeline steps is handled by passing the complete output of the previous step as context to the next. This ensures each agent receives full sequential context for continuous data transformation.

Yes, custom prompt sequences can be executed using the run command, which provides full control over the chain. It supports streaming context between steps and maintains memory persistence across sessions.

Yes, predefined pipelines are supported for common tasks like code analysis and data transformation. You can reuse these workflows using the pipeline command instead of building custom chains from scratch.

While streaming pipelines support configurable timeouts and memory persistence, complex project orchestration requires careful step sequencing. Users must manage timeout configurations and ensure each step's output format matches the next step's input expectations.