memory systems

AI agent memory systems provide persistent information storage across interactions, enabling agents to maintain context, learn from experiences, and build knowledge over time. Unlike stateless LLM calls, memory-equipped agents remember user preferences, past conversations, completed tasks, and accumulated facts. Memory implementation typically uses vector databases (Pinecone, Weaviate, Chroma) storing text chunks with embeddings for semantic retrieval. When processing new inputs, the agent queries relevant memories using embedding similarity, injecting retrieved context into the prompt. Memory types mirror cognitive science: sensory/buffer memory for immediate input, working memory for current task context, episodic memory for specific event records, and semantic memory for general knowledge. Memory management includes consolidation (transferring important information to long-term storage), forgetting (removing outdated or irrelevant entries), and summarization (compressing detailed records). Practical considerations include memory scope (per-user vs. shared), update triggers (every interaction vs. periodic consolidation), and retrieval strategies (similarity threshold, recency weighting, importance scoring). Frameworks like LangChain, LlamaIndex, and AutoGPT provide memory abstractions. Effective memory transforms agents from stateless responders to persistent assistants that improve over time.