Unverified50% confidenceConceptExact time
Large models have limited context windows, and each call is stateless, so for an Agent to achieve long-term memory it usually needs to convert conversations or documents into vector embeddings and store them in a vector database, then retrieve relevant content via similarity search and concatenate it into the prompt.
1
Sources
50%
Confidence
Long-term
Relevance
10/31/2026
First Seen
Sources
Java Engineers Transitioning to Agent Development: A Complete Four-Week Learning Roadmap
bilibiliAI项目开发10/31/2026
Related Entities
Related Claims
VerifiedEnterprise-grade Agent systems typically introduce external memory modules (such as vector databases) to store long-term memory, in order to overcome context window limitations.79% similarUnverifiedIn large model usage, the context window is a limited resource, and token consumption accumulates with conversation turns, file reads, and code injection.77% similarUnverifiedThe model has no memory between messages; each time a message is sent, the application resends the entire conversation (system instructions, conversation history, and the new message) to the model, and this package is called the context window, which has a size limit.72% similarUnverified在Cursor使用场景中，发送给模型的上下文包括编辑器自动附加的代码上下文、文件结构信息和项目索引数据，隐含Token消耗往往是用户可见输入的数倍甚至数十倍72% similarUnverified解决上下文压缩问题的方案包括分层记忆架构、外部记忆数据库以及专为长任务设计的Agent框架71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1119667API
curl https://kongchang.com/api/v1/knowledge/claims/1119667MCP
get_claim(id=1119667)