In　the　realm　of　personalized　recommender　systems,　the　challenge　of　adapting　to　evolving　user　preferences　and　the　continuous　influx　of　new　users　and　items　is　paramount.　Conventional　models,　typically　reliant　on　a　static　training-test　approach,　struggle　to　keep　pace　with　these　dynamic　demands.　Streaming　recommendation,　particularly　through　continual　graph　learning,　has　emerged　as　a　novel　solution,　attracting　significant　attention　in　academia　and　industry.　However,　existing　methods　in　this　area　either　rely　on　historical　data　replay,　which　is　increasingly　impractical　due　to　stringent　data　privacy　regulations;　or　are　inability　to　effectively　address　the　over-stability　issue;　or　depend　on　model-isolation　and　expansion　strategies,　which　necessitate　extensive　model　expansion　and　are　hampered　by　timeconsuming　updates　due　to　large　parameter　sets.　To　tackle　these　difficulties,　we　present　GPT4Rec,　a　Graph　Prompt　Tuning　method　for　streaming　Recommendation.　Given　the　evolving　user-item　interaction　graph,　GPT4Rec　first　disentangles　the　graph　patterns　into　multiple　views.　After　isolating　specific　interaction　patterns　and　relationships　in　different　views,　GPT4Rec　utilizes　lightweight　graph　prompts　to　efficiently　guide　the　model　across　varying　interaction　patterns　within　the　user-item　graph.　Firstly,　node-level　prompts　are　employed　to　instruct　the　model　to　adapt　to　changes　in　the　attributes　or　properties　of　individual　nodes　within　the　graph.　Secondly,　structure-level　prompts　guide　the　model　in　adapting　to　broader　patterns　of　connectivity　and　relationships　within　the　graph.　Finally,　view-level　prompts　are　innovatively　designed　to　facilitate　the　aggregation　of　information　from　multiple　disentangled　views.　These　prompt　designs　allow　GPT4Rec　to　synthesize　a　comprehensive　understanding　of　the　graph,　ensuring　that　all　vital　aspects　of　the　user-item　interactions　are　considered　and　effectively　integrated.　Experiments　on　four　diverse　real-world　datasets　demonstrate　the　effectiveness　and　efficiency　of　our　proposal.