stream-chain

Multi-agent streaming workflows are coordinated by passing outputs from one step to the next. This approach reduces latency and manual handoffs in automated pipelines by maintaining context across steps for real-time agent coordination.

You can build custom automation pipelines by defining a sequence of prompts where each step uses the previous output as context. This creates a continuous chain that streams data transformations across multiple agents.

Yes, you can use predefined pipelines for common data transformation tasks. These battle-tested pipelines accelerate results by providing ready-made workflows for sequential task orchestration across multiple agents.

Sequential task orchestration is the process of streaming outputs from one agent to the next in a defined order. You need it for complex, multi-step data workflows where manual handoffs create latency bottlenecks.

Multi-agent automation pipelines support configurable timeouts, verbosity levels, and debugging modes. These settings help manage both custom chains and predefined pipelines during sequential task orchestration.

The best way to reduce latency in multi-agent data workflows is streaming outputs directly from one step to the next. This approach eliminates manual handoffs and maintains context across automated pipelines.