GraphPlanner: Graph Memory-Augmented Agentic Routing for Multi-Agent LLMs

Tao Feng, Haozhen Zhang, Zijie Lei, Peixuan Han, Jiaxuan You

Keywords: Multi-agent LLMs, Memory utilization, Heterogeneous agents, Graph

TL;DR: GraphPlanner is a graph memory-augmented framework that enables multi-agent LLM routing by modeling cooperation and memory with reinforcement learning, achieving scalable, efficient, and generalizable routing.

Abstract:

LLM routing has achieved promising results in integrating the strengths of di-
verse models while balancing efficiency and performance. However, to support
more realistic and challenging applications, routing must extend into agentic LLM
settings—where task planning, multi-round cooperation among heterogeneous
agents, and memory utilization are indispensable. To address this gap, we pro-
pose GraphPlanner, a heterogeneous graph memory-augmented agentic router
for multi-agent LLMs that generates routing workflows for each query and sup-
ports both inductive and transductive inference. GraphPlanner formulates
workflow generation as a Markov Decision Process (MDP), where at each step
it selects both the LLM backbone and the agent role (Planner, Executor, Sum-
marizer). By leveraging a heterogeneous graph, denoted as GARNet, to capture
interaction memories among queries, agents, and responses, GraphPlanner
integrates historical memory and workflow memory into richer state represen-
tations. The entire pipeline is optimized with reinforcement learning, jointly
improving task-specific performance and computational efficiency. We evalu-
ate GraphPlanner across 14 diverse LLM tasks and demonstrate that: (1)
GraphPlanner outperforms strong single- and multi-round routers, improv-
ing accuracy by up to 9.3% while reducing GPU cost from 186.26 GiB to
1.04 GiB; (2) GraphPlanner generalizes robustly to unseen tasks and LLMs,
exhibiting strong zero-shot capabilities; and (3) GraphPlanner effectively
leverages historical memories, supporting both inductive and transductive infer-
ence for more adaptive routing. Our code for GraphPlanner is released at
https://github.com/ulab-uiuc/GraphPlanner.

Primary Area: learning on graphs and other geometries & topologies

Submission Number: 20197