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Agent memory framework

Agent Memory Framework: A Useful Operating Model

An agent memory framework is not just a vector database. It is the operating model that decides what gets captured, how it is compressed, when it is retrieved, who can approve it, and how mistakes are removed.

Five layers

The simplest durable model has five layers: capture, normalize, store, retrieve, and govern. Each layer should be observable and reversible.

  • Capture observations from agents and tools.
  • Normalize into facts, decisions, tasks, preferences, and warnings.
  • Store with source provenance and retention rules.
  • Retrieve by relevance, freshness, and scope.
  • Govern through review, export, deletion, and audit.

Evaluation

Memory should be measured. A team can track whether memory reduces repeated prompts, prevents repeated mistakes, improves task completion, and avoids exposing irrelevant private information.

  • Context hit rate for repeated repo work.
  • False recall rate during code tasks.
  • Deletion and retention SLA.
  • Checkout, ingestion, and retrieval success rate.

Practical playbook

  1. Define memory types before choosing infrastructure.
  2. Write promotion rules for team-shared memory.
  3. Keep source provenance visible in the UI.
  4. Review retrieval quality every week during rollout.

Related guides

Agentmemory githubClaude Code agent memoryAgent memory surveyMem0