MARC v1: An Open-Source Multi-Agent Framework for Clinical AI Reasoning and Coordination
MARC v1 is an open-source multi-agent framework for clinical reasoning that replaces monolithic LLM prompting with deterministic multi-agent orchestration. It coordinates role-specialized agents for extraction, reasoning, answer generation, and evaluation, with explicit context passing and traceable intermediate outputs. The framework includes a Decomposer module that generates task-specific agent prompts from plain-language descriptions. It supports API-based and local CPU-compatible deployments, is configurable via YAML without code modifications, and is model-agnostic. The framework is available at https://github.com/Penn-RAIL/MARC-v1.
Researchers from Penn-RAIL released MARC v1, an open-source framework for clinical AI reasoning that uses deterministic multi-agent orchestration instead of monolithic LLM prompting. The framework coordinates specialized agents for extraction, reasoning, answer generation, and evaluation, with explicit context passing and traceable intermediate outputs for stage-wise failure attribution. A Decomposer module generates task-specific agent prompts from plain-language descriptions, eliminating manual prompt engineering. MARC supports API-based and local CPU-compatible deployments, is configurable via YAML without code changes, and is model-agnostic. The code is available on GitHub.
MARC v1 introduces deterministic multi-agent orchestration for clinical reasoning, replacing monolithic LLM prompting. The framework's role-specialized agents (extraction, reasoning, answer generation, evaluation) with explicit context passing and traceable intermediate outputs enable stage-wise failure attribution. The Decomposer module automatically generates task-specific agent prompts from plain-language descriptions, reducing manual prompt engineering. The system is model-agnostic and supports both API-based and local CPU-compatible deployments, with YAML-based configuration requiring no code modifications.
The release of MARC v1 signals a shift toward modular, interpretable multi-agent systems in clinical AI, addressing the need for traceability and domain-expert accessibility. By enabling local CPU-compatible deployment and YAML configuration, the framework lowers barriers for clinical institutions without extensive programming resources. The open-source nature may accelerate adoption and adaptation in healthcare settings, potentially influencing how clinical AI tools are built and validated.
MARC v1 offers potential business value for healthcare organizations seeking interpretable, customizable clinical AI reasoning tools without heavy engineering investment. Its open-source, model-agnostic, and CPU-compatible design reduces infrastructure costs and vendor lock-in. The framework's traceability and stage-wise failure attribution may support regulatory compliance and clinical validation, making it attractive for enterprises needing auditable AI systems.
Observable next signals include adoption of MARC v1 in clinical research or pilot deployments, community contributions or forks on GitHub, and comparative evaluations against monolithic LLM approaches in clinical reasoning tasks. Further development may extend the framework to other domains or integrate with electronic health record systems. The Decomposer module's ability to generate prompts from plain language could inspire similar automation in other multi-agent frameworks.