MARC v1: An Open-Source Multi-Agent Framework for Clinical AI Reasoning and Coordination

2026-08-13

AI Summary

This work addresses the lack of interpretability, traceability, and modularity in prompt-based approaches using monolithic large language models for clinical AI reasoning. To overcome these limitations, the authors propose a deterministic multi-agent collaborative architecture in which role-specific agents handle distinct tasks—namely information extraction, reasoning, answer generation, and evaluation—enabling explicit context propagation and intermediate result tracking. The framework incorporates a YAML-based configuration mechanism and a Decomposer module that automatically generates task-specific prompts, facilitating zero-code customization and deployment. The resulting system is open-source, model-agnostic, and fully traceable across all stages, eliminating the need for manual prompt engineering, supporting stage-level error attribution, and significantly enhancing usability and transparency for non-technical clinical users.

Abstract

We present Multi-Agent Reasoning and Coordination (MARC), an open-source framework that replaces monolithic LLM prompting with deterministic multi-agent orchestration for clinical reasoning. MARC coordinates role-specialized agents for extraction, reasoning, answer generation, and evaluation, with explicit context passing and traceable intermediate outputs, enabling stage-wise failure attribution. We additionally introduce a Decomposer module that generates task-specific agent prompts from a plain-language description, eliminating manual prompt engineering. The framework supports both API-based and local CPU-compatible deployments and is entirely configurable via YAML, without code modifications. MARC is designed to be model-agnostic, interpretable, and accessible to clinical domain experts without programming expertise. The full framework is available at https://github.com/Penn-RAIL/MARC-v1.

Problem

• clinical AI reasoning
• multi-agent coordination
• interpretable AI
• modular reasoning
• prompt engineering

Innovation

• multi-agent framework
• clinical AI reasoning
• deterministic orchestration
• prompt decomposition
• model-agnostic