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Boucle de mémoire — auto-amélioration d'agent

La boucle de mémoire : avant de planifier, le planificateur interroge la mémoire pour des leçons similaires, puis y réécrit la nouvelle leçon une fois la tâche close.

Code FlowZap complet

Copiez et collez le code FlowZap suivant dans un projet de votre compte FlowZap pour voir ce diagramme.

user { # User
  n1: circle label:"Start task"
  n2: rectangle label:"Submit complex task"
  n4: rectangle label:"Receive acknowledgement"
  n26: rectangle label:"Receive improved result"
  n1.handle(right) -> n2.handle(left)
  n2.handle(bottom) -> planner.n3.handle(top) [label="Task"]
}

planner { # Planner
  n3: rectangle label:"Acknowledge task"
  n5: rectangle label:"Ask for similar lessons"
  n8: rectangle label:"Receive prior lessons"
  n9: rectangle label:"Build plan"
  n10: rectangle label:"Dispatch execution"
  n13: rectangle label:"Receive execution output"
  n14: rectangle label:"Request evaluation"
  n17: rectangle label:"Receive evaluation"
  n18: diamond label:"Good enough?"
  n19: rectangle label:"Store new lesson"
  n22: rectangle label:"Confirm lesson saved"
  n23: rectangle label:"Send retry plan"
  n25: rectangle label:"Send improved result"
  n3.handle(top) -> user.n4.handle(bottom) [label="Ack"]
  n3.handle(right) -> n5.handle(left)
  n5.handle(bottom) -> memory.n6.handle(top) [label="Retrieve lessons"]
  n8.handle(right) -> n9.handle(left)
  n9.handle(right) -> n10.handle(left)
  n10.handle(bottom) -> executor.n11.handle(top) [label="Execute plan"]
  n13.handle(right) -> n14.handle(left)
  n14.handle(bottom) -> evaluator.n15.handle(top) [label="Evaluate output"]
  n17.handle(right) -> n18.handle(left)
  n18.handle(right) -> n19.handle(left) [label="Yes"]
  n18.handle(bottom) -> n23.handle(top) [label="No"]
  n19.handle(bottom) -> memory.n20.handle(top) [label="Save lesson"]
  n22.handle(right) -> n25.handle(left)
  n23.handle(bottom) -> executor.n24.handle(top) [label="Retry with lessons"]
  n25.handle(top) -> user.n26.handle(bottom) [label="Improved result"]
}

memory { # Memory Store
  n6: rectangle label:"Search memory"
  n7: rectangle label:"Return best lessons"
  n20: rectangle label:"Persist new lesson"
  n21: rectangle label:"Confirm persistence"
  n6.handle(right) -> n7.handle(left)
  n7.handle(top) -> planner.n8.handle(bottom) [label="Lessons"]
  n20.handle(right) -> n21.handle(left)
  n21.handle(top) -> planner.n22.handle(bottom) [label="Saved"]
}

executor { # Executor
  n11: rectangle label:"Run first attempt"
  n12: rectangle label:"Return output"
  n24: rectangle label:"Run improved attempt"
  n27: rectangle label:"Return improved output"
  n11.handle(right) -> n12.handle(left)
  n12.handle(top) -> planner.n13.handle(bottom) [label="Output"]
  n24.handle(right) -> n27.handle(left)
  n27.handle(top) -> evaluator.n28.handle(bottom) [label="Improved output"]
}

evaluator { # Evaluator
  n15: rectangle label:"Judge first output"
  n16: rectangle label:"Return verdict"
  n28: rectangle label:"Judge improved output"
  n29: rectangle label:"Return final verdict"
  n15.handle(right) -> n16.handle(left)
  n16.handle(top) -> planner.n17.handle(bottom) [label="Verdict"]
  n28.handle(right) -> n29.handle(left)
  n29.handle(top) -> planner.n19.handle(bottom) [label="Pass"]
}

Installer le paquet MCPnpm install -g flowzap-mcpDocumentation MCP

Modèles associés

Boucle d'évaluation du goût agentique

Exécutant historique, évaluateur LLM-as-judge et consommateur de nouvelle génération : l'évaluateur note la trajectoire complète, rejette le travail structurellement bâclé et ne transmet que les charges validées.

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