This article explains the four types of AI agent memory systems and how to implement them for production use.
•Working memory (context window) acts as the LLM's RAM; models range from GPT-4o (128K tokens) to Gemini 2.5 Pro (1M tokens), but quality degrades with very long contexts
•Short-term memory manages conversation history within a session using sliding window techniques to keep recent messages plus system prompts
•Long-term memory persists across sessions via file-based storage, vector databases like ChromaDB for semantic search, or relational databases for structured data
•Episodic memory records full task sequences with actions, outcomes, and lessons learned, enabling agents to prioritize past successful strategies
•Production agents combine these types using hierarchical memory (promote/demote across layers) or RAG pipelines that retrieve only relevant memories into the context budget
This summary was automatically generated by AI based on the original article and may not be fully accurate.
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Databricks
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