This　paper　focuses　on　the　prescribed　performance　adaptive　containment　control　problem　for　a　class　of　nonlinear　nonstrict-feedback　multiagent　systems　(MASs)　with　unknown　disturbances　and　full-state　constraints.　First,　the　radial　basis　function　neural　networks　(RBF　NNs)　technology　is　employed　to　approximate　the　unknown　nonlinear　functions　in　the　system,　and　the　problem　of　“explosion　of　complexity”　caused　by　repeated　derivation　of　virtual　controls　is　solved　by　using　the　dynamic　surface　control　(DSC)　technology.　Then,　the　nonlinear　disturbance　observers　are　designed　to　estimate　the　external　disturbance,　and　the　barrier　Lyapunov　functions　(BLFs)　and　the　prescribed　performance　function　(PPF)　are　combined　to　achieve　the　control　objective　of　prescribed　performance　without　violating　the　full-state　constraints.　The　theoretical　result　shows　that　all　signals　in　the　closed-loop　system　are　semiglobally　uniformly　ultimately　bounded　(SGUUB),　and　the　local　neighborhood　containment　errors　can　converge　to　the　specified　boundary.　Finally,　two　simulation　examples　show　the　effectiveness　of　the　proposed　method.　Note　to　Practitioners—The　containment　control　problem　is　a　hot　topic　in　the　field　of　control,　which　plays　an　important　role　in　practical　engineering.　Especially　for　this　problem　of　nonlinear　MASs,　the　mathematical　models　are　difficult　to　be　obtained　accurately.　This　paper　investigates　the　prescribed　performance　adaptive　containment　control　problem　for　the　nonlinear　nonstrict-feedback　MASs,　whose　model　can　be　extended　to　more　complex　engineering　applications,　such　as　unmanned　aerial　vehicle　formations　and　intelligent　traffic　management.　It　is　worth　noting　that　external　disturbances　and　state　constraint　problems　often　exist　in　practical　applications.　Therefore,　the　disturance　observers　are　designed　to　compensate　for　the　system　disturbances,　which　can　eliminate　the　impacts　of　disturbances　on　the　systems.　By　introducing　BLFs,　it　is　ensured　that　all　states　of　the　system　are　constrained　within　the　specified　regions.　To　sum　up,　the　paper　proposes　a　prescribed　performance　adaptive　containment　control　strategy,　which　contributes　to　the　development　of　containment　control　for　MASs　in　practical　applications.　IEEE