stream-chain

Multi-agent workflows can be orchestrated by streaming each step's output into the next, ensuring sequential data flow and memory-friendly coordination. This approach enforces deterministic execution and supports both run and pipeline modes for complex automation tasks.

Chaining prompts sequentially is achieved by defining custom chains where subsequent steps consume prior outputs. This maintains context propagation across steps, enabling iterative refinement and better traceability throughout the automation pipeline.

Predefined pipelines can be used to leverage battle-tested workflows for common tasks, accelerating setup. They enforce deterministic execution and coordinate context propagation with optional script and reference resources for multi-agent coordination.

Context propagation is supported across multiple agent steps to maintain state and enable iterative refinement. The orchestration engine coordinates context with optional script and reference resources, ensuring memory-friendly coordination during sequential data flow.

Different agent types can be coordinated by orchestrating multi-agent workflows where each agent's output streams into the next step. This enforces deterministic execution and supports both run and pipeline modes for collaborative automation.

Streaming orchestration for automation pipelines should be avoided when tasks require non-sequential or parallel execution rather than deterministic step-by-step data flow. It is specifically designed for sequential context propagation and memory-friendly coordination.