maker-framework

Multi-agent reasoning orchestration decomposes complex tasks into atomic subtasks using MAD, runs parallel agents, validates outputs via red-flagging, and aggregates results through first-to-ahead-by-k voting to deliver deterministic, verified outcomes.

Verifying multi-step LLM reasoning chains is best handled by applying a red-flagging system to detect anomalies and using first-to-ahead-by-k voting to reach consensus among parallel agents, ensuring deterministic and validated results.

DAG-based task orchestration structures multi-agent workflows by decomposing complex tasks into atomic subtasks for parallel execution, managing dependencies, and applying a configurable criticality model to handle reliability-critical pipelines.

Yes, the framework applies to reliability-critical tasks across domains such as code review, financial analysis, and data extraction, transforming probabilistic LLM outputs into verifiable results using MAD decomposition and voting.

You build end-to-end pipelines by using maker_build_dag for DAG orchestration, running parallel agents, applying maker_red_flag for output validation, and aggregating consensus with maker_vote to return a verified result.