stream-chain

Sequential multi-agent workflow orchestration passes each step's output directly into the next agent for full context retention. This prevents context loss and duplicated work across chained codebase analysis, refactoring, and test generation tasks.

Yes, you can build custom prompt chains for codebase analysis and refactoring. The system executes configurable sequential workflows, allowing custom prompts to flow into one another while preserving cross-step context.

Predefined optimized pipelines exist for common development tasks including codebase analysis, test generation, performance optimization, and data transformation. These ready-to-use workflows eliminate manual coordination between agents.

Yes, multi-agent sequential processing supports configurable timeouts. You can define execution limits for custom prompt chains and predefined pipelines while maintaining cross-step context retention throughout the workflow.

Use a custom sequential workflow to analyze authentication systems for vulnerabilities, categorize issues by severity, propose prioritized fixes, and generate security test cases. Each agent's output flows directly into the next step automatically.

Manual coordination of multiple AI agents causes context loss, duplicated work, and disjointed outputs. Sequential workflow orchestration solves this by passing each step's output directly into the next agent for full context preservation.