stream-chain

Chaining AI steps coordinates sequential tasks by automatically streaming the output from one step as input to the next. stream-chain handles this by preserving context across steps and managing per-step controls like timeout and debug flags, so each stage receives the complete previous output for seamless data flow.

Yes, stream-chain includes battle-tested predefined pipelines for common development workflows: code analysis, refactoring, testing, and optimization. You can also define custom chains with full control over the sequence and prompts for maximum flexibility.

Run a custom chain that sequences multiple development tasks: analyze your codebase, identify improvements, create a refactoring plan, apply changes, and generate unit tests. stream-chain coordinates this by streaming outputs between steps while maintaining rich context throughout.

Multi-agent data workflows benefit from explicit sequencing and context propagation across steps. stream-chain provides streaming output between sequential steps, per-step controls for timeouts and debugging, and input-to-output mapping that eliminates manual coordination errors.

No, stream-chain automatically propagates context from each step to the next. Each step receives the complete output of the previous step, so you don't need to manually pass data or worry about losing information across your pipeline.