GraphPlanner: Graph Memory-Augmented Agentic Routing for Multi-Agent LLMs
LLM routing has achieved promising results in integrating the strengths of diverse 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 propose GraphPlanner, a heterogeneous graph memory-augmented agentic router for multi-agent LLMs that generates routing workflows for each query and supports both inductive and transductive inference. GraphPlanner formulates workflow generation as a Markov Decision Process (MDP), where at each step it selects b...
| Authors | Tao Feng, Haozhen Zhang, Zijie Lei et al. |
|---|---|
| Primary category | cs.CL |
| Published | 26 April 2026 |
| Last updated at source | 26 April 2026 |
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