stream-chain

Multi-agent AI workflows are orchestrated by streaming outputs between sequential steps, piping data from one agent to the next. This preserves context and ensures reliable data flow for complex automation pipelines.

Automating data transformation pipelines involves defining custom chains and predefined pipelines for development tasks. The system executes these sequentially, passing transformed data between dependent steps automatically.

Memory integration and custom scripts are supported in multi-step streaming workflows. You can configure prompts, add optional components like references and assets, and enable cross-agent collaboration for scalable automation.

To set up a three-step chain, run the stream-chain process with three configurable prompts. Observe the sequential outputs as the system pipes the result from one agent directly into the next step.

Run and pipeline modes are supported for engineering tasks. These modes allow you to execute complex automation workflows using configurable prompts and custom pipeline definitions tailored to your specific requirements.

Use predefined pipelines for common development tasks to quickly execute standard automation workflows. Create custom chains when you need specific sequential data transformations or unique multi-agent orchestration logic.