stream-chain

You orchestrate multi-agent workflows by chaining outputs so each step builds on the previous one, turning scattered prompts into structured, end-to-end streaming pipelines. Context is preserved across all steps for consistent results.

Chaining AI agent outputs in a streaming pipeline is best handled by enforcing sequential step execution with configurable timeouts and verbosity. This ensures outputs flow through a predefined sequence from data extraction to transformation to validation.

You can use both predefined pipelines and custom runs for multi-agent orchestration. Battle-tested pipelines handle standard automation tasks, while bespoke chains can be crafted for unique, multi-step data processing workflows.

Yes, stream-chain supports context propagation across multiple agent runs. It preserves memory and context across sequential steps and runs, ensuring consistent results throughout the entire pipeline execution.

You configure timeouts and memory usage for multi-step agent pipelines by adjusting the configurable settings provided during chain execution. This allows control over verbosity and resource allocation across sequential workflow steps.

You need a streaming pipeline for multi-agent automation when a task requires sequential data transformations, such as extracting, transforming, and validating data in a single chain, rather than relying on isolated single prompts.