Unverified50% confidenceFactExact time
The mainstream approaches to dealing with the statelessness of large models fall into three categories: directly concatenating conversation history into the context window, encoding memories as embedding vectors in a vector database for retrieval, and having the model autonomously decide which information is worth writing to persistent storage and reading it back on demand.
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9/18/2026
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VerifiedVector memory databases convert historical conversation content into high-dimensional semantic vectors, and during new conversations dynamically retrieve relevant memory fragments via similarity search and inject them into the context window.75% similarUnverifiedMainstream solutions for addressing the limited context window of large language models include vector database retrieval-augmented generation (RAG), summary compression, and external memory storage.71% similarUnverifiedIn LLM scenarios, the blackboard system concept is reproduced in the form of shared memory or vector databases, using vector databases such as Pinecone and Weaviate to store intermediate knowledge fragments for semantic retrieval.71% similarUnverified向量数据库是支撑Agent长期记忆的关键基础设施，文本通过嵌入模型转换为高维向量，语义相近的文本在高维空间中距离更近70% similarUnverifiedPersistent memory typically relies on vector databases for semantic retrieval, converting information into high-dimensional dense vectors via embedding models and storing them in systems such as Qdrant, Pinecone, Weaviate, and Chroma.69% similar
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