A local, auditable multi-model workflow engine that lets you define YAML graphs for orchestrating LLM agents across vendors, with MCP tools for validation, dry-runs, execution, and human approval, all fully observable in a local web interface.
Enables orchestration of MCP tool calls through declarative YAML-defined directed graphs with data transformation, conditional routing, and observable execution flows.
Enables deterministic, step-by-step execution of YAML-defined skills and workflows for AI agents, with progressive disclosure of active-step instructions and tools. It manages workflow state, conditional DAG transitions, loops, and persistent sessions via start_workflow, next_step, and end_workflow.
Lightweight AI agent orchestrator with built-in Architect AI, enabling users to automate tasks by describing them via chat or Claude Code + MCP, with multi-team isolation.