搞多智能体配置管理头疼的可以看这篇，ACM 把 LangGraph、CrewAI 这些框架统一成一套治理模型，还能做依赖分析和审计。
arXiv 论文提出 Agentic Configuration Management (ACM)，一个框架无关的治理与配置参考模型，用于异构智能体系统。ACM 结合类型化且独立版本化的配置项、不可变修订与基线、配置与运行时分离、生命周期与保障语义、依赖感知影响传播及运行时溯源。作者提供 Python 参考实现，适配 LangGraph、CrewAI 和 OpenAI Agents SDK，并用 27 个治理场景和 9 个量化影响传播案例评估。结果显示三个框架在投影后产生治理等价的 ACM 表示，影响语义在有限格上单调传播，保证收敛与唯一最小不动点。
Agentic Configuration Management (ACM): A Reference Configuration Model for Governed Agentic Systems
Agentic systems are increasingly composed of heterogeneous agents, prompts, tools, models, skills, composite subsystems, policies, and execution workflows whose configurations evolve across frameworks and runtime environments. Existing LLMOps and AgentOps platforms support orchestration and observability but do not provide a common configuration-governance model for representing and governing these systems as coherent, versioned configurations. This paper introduces Agentic Configuration Management (ACM), a framework-independent governance and configuration reference model for heterogeneous agentic systems. ACM combines typed and independently versioned Agentic Configuration Items, immutable revisions and baselines, explicit configuration-runtime separation, lifecycle and assurance semantics, dependency-aware impact propagation, and runtime provenance. Heterogeneous native configurations are normalized through semantic projection into a canonical Configuration Graph on which common governance semantics operate. We provide a Python reference implementation with adapters for LangGraph, CrewAI, and the OpenAI Agents SDK. The evaluation combines 27 governance scenarios with nine quantitative impact-propagation cases. For the evaluated configurations, the three frameworks yield governance-equivalent ACM representations and reproducible governance outcomes after projection. The impact semantics are formalized as monotone propagation over a finite lattice, establishing convergence, termination, and uniqueness of the least fixed point above the initial impact valuation. These results provide evidence that common governance semantics can support reproducibility, auditability, dependency analysis, and interoperability across heterogeneous agentic execution abstractions within the evaluated scope.