stream-chain

Multi-agent workflow orchestration chains outputs between steps, allowing each stage to consume the previous result. Stream-Chain enables this by passing data sequentially through custom chains or predefined pipelines to build scalable data workflows.

Building automated data transformation pipelines requires chaining sequential task execution across agents. Stream-Chain applies streaming-style orchestration to route data flow between steps, maintaining context and coordinating tasks for cohesive processing results.

Predefined pipelines are supported for common automation tasks to accelerate delivery. Stream-Chain provides two modes, allowing you to run battle-tested workflows or define flexible custom step-by-step prompts where each stage consumes the previous result.

Maintaining context across sequential steps in a data processing pipeline requires optional memory and pipeline customization definitions. Stream-Chain supports memory coordination to preserve state between chained steps for cohesive multi-agent task results.

Timeouts and verbose options are configurable features for pipeline orchestration. Stream-Chain requires no external dependencies and provides clear syntax to set these parameters, ensuring reliable execution and detailed logging for multi-step workflows.

Streaming chains for sequential task execution rely on each step's output becoming the next step's input, meaning a failure in one stage can halt the downstream data flow. Stream-Chain addresses this with configurable timeouts and memory to manage coordination limits.