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Lesson

OOM and memory management

Many agents work in demos but fail in production because they accumulate tool results, API responses, and long conversations within the same context.

  • Identify why OOM occurs in agents and long-running workflows.
  • Separate context, persistent state, logs, and heavy results.
  • Apply limits, queues, and alerts before the system dies without explanation.

Common causes

  • Storing complete tool responses in the agent state.
  • Putting entire documents into context instead of chunks and references.
  • Retries that duplicate messages, results, and traces.
  • n8n workflows running in a single process without separate workers.
  • Huge buffers instead of streaming or batch processing.
Terminal
politica_memoria:
  max_tool_result_chars: 6000
  guardar_en_contexto:
    - resumen
    - ids
    - citas
  guardar_fuera:
    - html completo
    - pdf extraido
    - respuestas api largas
  si_supera_limite:
    - persistir_blob
    - resumir
    - enlazar_por_id
    - continuar
24/7 offline agents on real hardwareRetries, idempotency, and exactly-once
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