Agentic Memory System

An Agentic Memory System is a persistent multi-tier agent memory subsystem that stores and retrieves interaction history and knowledge elements.

• AKA: Agent Memory, Conversation Memory, Long-Term Agent Store.
• Context:
  • It can typically include Short-Term Buffers for recent conversation turns and long-term databases for persistent knowledge.
  • It can typically store Tool Outputs alongside user preferences and world knowledge for future retrieval.
  • It can typically implement External Storage Integrations through vector databases and knowledge graphs.
  • ...
  • It can often integrate Summarization Algorithms to prevent context window overflow.
  • It can often provide Memory Recall Functions to planning modules and context managers.
  • ...
  • It can range from being a Simple Agentic Memory System to being a Complex Agentic Memory System, depending on its storage architecture.
  • It can range from being a Volatile Agentic Memory System to being a Persistent Agentic Memory System, depending on its retention policy.
  • ...
  • It can support Semantic Searches across stored memorys.
  • It can enable Memory Consolidations for long-term retention.
  • It can facilitate Cross-Session Continuitys for user experience.
  • ...
• Example(s):
  • Vector-Based Agentic Memory Systems, such as:
    • Embedding Memory Store maintaining conversation snippets as vector embeddings.
    • Semantic Memory Database organizing memorys by conceptual similarity.
  • Structured Agentic Memory Systems, such as:
    • Key-Value Memory Store tracking task completions in multi-agent workflows.
    • Graph Memory System representing memory relationships as knowledge graphs.
  • Hybrid Agentic Memory Systems, such as:
    • AutoGen Message Memory persisting agent communications for replay capability.
    • Hierarchical Memory System combining working memory with long-term storage.
  • ...
• Counter-Example(s):
  • Stateless Chat APIs, which maintain no history between calls.
  • Reactive Agents, which base decisions solely on current input.
  • Session-Bound Systems, which deliberately discard memory after session end.
• See: Context Management Layer, LLM-based Agent Memory Module, Conversation History Summarization, Vector Database.

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