By 2026, the memory architecture of AI Agents is undergoing a paradigm shift from “vector retrieval” to “graph reasoning.” Memory frameworks have validated an industry consensus: pure vector databases fail to capture the evolutionary relationships between entities and multi-hop causal chains, while dumping complex data wholesale into large language models is both expensive and unsustainable—this is precisely the critical bottleneck preventing Agents from evolving from “having memory” to “being capable of reasoning.” Meanwhile, over 95% of enterprise-grade Agent deployments are moving toward private cloud or on-premise environments. The business intelligence and customer data distilled within Agent memory makes data sovereignty a non-negotiable red line, urgently requiring a memory infrastructure that can be autonomously controlled. This presentation proposes a new perspective: streaming graph computing engines are inherently the operating system kernel for Agent memory. Apache GeaFlow (Incubating)’s incremental computation capabilities correspond to the real-time extraction and merging of memories—where each new conversation triggers only local subgraph updates; dynamic graph state management corresponds to temporal memory graphs—where vertices store semantic embeddings, edges store relationships and temporal metadata, and properties store key textual descriptions; distributed snapshot mechanisms correspond to memory consistency guarantees in enterprise-scale multi-Agent collaboration scenarios. Through the MCP (Model Context Protocol) standard, GeaFlow serves as a plug-and-play memory backend for Agent frameworks. In technical practice, we will demonstrate how to build an enterprise knowledge assistant with “long-term memory” based on GeaFlow, showcasing the dynamic construction and reasoning processes of memory graphs, and outline the evolution roadmap for GraphMemory—encompassing streaming graph neural network inference, proactive memory synthesis, and natural forgetting mechanisms based on graph algorithms.

Speakers:

Litao Lin: Apache GeaFlow (Incubating) Committer

Apache GeaFlow (Incubating) Committer. As a core member of the project, he participated in the architecture design and development of the GeaFlow graph computing engine from scratch, with a particular focus on the design and implementation of the graph computing DSL and the evolution of data intelligence technologies. He is deeply involved in open source community activities and currently works at Ant Group.