stream-chain

Multi-agent workflows are orchestrated by streaming outputs sequentially between steps, propagating full context for reliable collaboration. This approach supports custom chains and predefined pipelines for coding, data processing, and automation tasks.

Yes, you can chain multiple agents together for sequential automation tasks by streaming outputs between steps. It supports custom chains and predefined pipelines while propagating full context across the entire workflow.

Context propagation in multi-step pipelines works by streaming full outputs between steps, preserving all intermediate data for auditing and debugging. This ensures every agent in the workflow chain receives the complete prior context.

Yes, multi-agent workflow orchestration supports configurable timeouts to prevent indefinite hanging. It also enforces persistent memory and pipeable configurations to manage complex automation and data processing pipelines reliably.

The best way to handle swarm-like coordination is using custom chains with persistent memory and streaming outputs. This orchestrates multiple agents by propagating full context and enforcing configurable timeouts for reliable collaboration.

You need persistent memory in streaming pipelines to preserve outputs between steps for auditing and debugging. This ensures full context propagation across the workflow, allowing reliable sequential processing and multi-agent collaboration.