cascade-orchestrator

The Cascade Orchestrator is a robust framework designed to manage sophisticated, multi-stage AI workflows. By decoupling micro-skills from the orchestration logic, it enables developers to compose complex automated processes that handle sequential pipelines, parallel fan-out execution, and conditional branching. It is specifically engineered for production-grade environments where reliability is paramount, integrating advanced patterns such as Codex-driven auto-fix loops, multi-model routing (Gemini, Codex, Claude), and swarm coordination via the ruv-swarm MCP. This orchestrator acts as the central brain for multi-agent systems, ensuring that context persists across stages and that failures are handled via intelligent fallbacks or automated sandbox recovery.

• Advanced Workflow Patterns: Supports sequential, parallel, conditional, and iterative (codex-sandbox) stages to handle intricate task dependencies.

• Multi-Model Routing: Dynamically routes specific tasks to the optimal AI model based on context size, latency needs, or specialized reasoning capabilities.

• Codex Sandbox Iteration: Automatically spawns isolated sandbox environments to execute tests and perform auto-fixes on failed agent outputs.

• Swarm Coordination: Orchestrates distributed execution using ruv-swarm MCP for high-throughput parallel processing tasks.

• Memory Persistence: Maintains shared state and workflow context across multiple stages, reducing redundant computation and improving decision coherence.

• Audit-Pipeline Integration: Implements observability patterns for tracking agent performance, error rates, and iteration cycles.

• Typical Inputs/Outputs: Accepts declarative YAML-based cascade definitions as input and produces aggregated task outputs, status logs, and error telemetry as output.

• Practical Usage: Best suited for software engineers and systems architects building automated code quality pipelines, data transformation workflows, and adaptive multi-agent systems.

• Constraints: Requires proper API configuration for integrated models and infrastructure support for the ruv-swarm MCP protocol to enable full parallelization.

• Optimization: When building complex cascades, ensure that memory persistence is used to store intermediate results, which significantly reduces token usage and improves system responsiveness across long-running pipelines.