用知识图谱提升大模型多智能体系统中工具与智能体的精准匹配。
Agent-as-a-Graph: Knowledge Graph-Based Tool and Agent Retrieval for LLM Multi-Agent Systems
- 将工具和智能体构建成知识图谱，通过节点与边关系表达能力
- 在LiveMCPBenchmark上召回率和排序指标提升14.9%与14.6%
- 适合需要精细工具检索的复杂多智能体应用场景
大型语言模型多智能体系统可实现海量并行子智能体的可扩展编排与检索，每个智能体配备数百至数千个模型上下文协议（MCP）服务器和工具。然而，现有方法通常仅基于单一智能体描述进行查询匹配，忽略了智能体内部工具的细粒度能力，导致智能体选择不优。本文提出Agent-as-a-Graph检索方法，将工具及其所属智能体作为知识图谱中的节点与边，实现检索增强生成。检索过程包括：首先通过向量搜索获取相关智能体与工具节点；其次使用特定类型加权倒数排名融合（wRRF）对工具和智能体进行重排序；最后在知识图谱中遍历父智能体，确定最终候选集。在LiveMCPBenchmark上的实验表明，该方法相较现有最优检索器，在Recall@5和nDCG@5上分别提升14.9%和14.6%，且在wRRF优化中取得2.4%的提升。
原文摘要 · Abstract (English)
Recent advances in Large Language Model Multi-Agent Systems enable scalable orchestration and retrieval of specialized, parallelized subagents, each equipped with hundreds or thousands of Model Context Protocol (MCP) servers and tools. However, existing agent, MCP, and retrieval methods typically match queries against a single agent description, obscuring fine-grained tool capabilities of each agent, resulting in suboptimal agent selection. We introduce Agent-as-a-Graph retrieval, a knowledge graph retrieval augmented generation approach that represents both tools and their parent agents as nodes and edges in a knowledge graph. During retrieval, i) relevant agents and tool nodes are first retrieved through vector search, ii) we apply a type-specific weighted reciprocal rank fusion (wRRF) for reranking tools and agents, and iii) parent agents are traversed in the knowledge graph for the final set of agents. We evaluate Agent-as-a-Graph on the LiveMCPBenchmark, achieving 14.9% and 14.6% improvements in Recall@5 and nDCG@5 over prior state-of-the-art retrievers, and 2.4% improvements in wRRF optimizations.
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