stream-chain

Multi-agent workflow orchestration sequences AI agents so the output of one becomes the input for the next, enabling complex data transformations. This approach handles sequential task execution using streaming JSON to pass data between chained prompts.

To automate codebase refactoring, define a custom prompt chain that sequentially analyzes the codebase, identifies improvement areas, generates a refactoring plan, and implements changes. The pipeline feeds each step's output directly into the next.

Yes, you can use predefined pipelines for common development tasks like software testing and optimization. These battle-tested workflows automate sequential execution without needing to define custom prompt chains from scratch for standard processes.

No external dependencies or components are required to run multi-agent chaining. The system operates independently using streaming JSON for prompt chains, though it can integrate with swarm coordination and memory management for advanced operations.

The best way to manage sequential AI tasks without losing context is using a chained pipeline with memory management integration. This ensures each agent receives the previous step's output as input, maintaining data integrity throughout the transformation workflow.