stream-chain

To build a multi-agent workflow for stepwise code analysis, you orchestrate custom agent chains that stream outputs sequentially, passing refined context from one step to the next for progressive transformation.

Streaming context in a multi-agent pipeline is the mechanism of passing the output of one agent directly as the input to the next, maintaining end-to-end context across steps for coherent and auditable results.

The best way to coordinate complex data transformations across multiple agents is using a predefined pipeline that orchestrates execution flow with optional timeout and debug controls, reducing manual handoffs.

You do not need predefined pipelines to automate multi-agent collaboration, as you can define custom chains with bespoke sequential prompts, but predefined pipelines offer reusable workflows for common tasks like refactoring.

You can define custom chains with bespoke sequential prompts for multi-agent orchestration, requiring a sequence of at least two steps with clear input prompts for each stage to manage execution flow.

A limitation of streaming outputs between agent steps is the requirement of a minimum of two steps, and without optional timeout controls, long-running execution flows in complex pipelines may hang or delay final outputs.