Building an AI Memory System and Forgetting About It
The author built an AI memory system for their assistant Claude, which has been running since February. The system has evolved over time, becoming more robust and integrated into the author's workflow.
Why it matters
This article provides insights into the evolution and scaling of an AI memory system, which can be valuable for developers working on similar systems.
Key Points
- 1The memory system became invisible as it became reliable infrastructure
- 2The system has scaled to 124 distilled files, 121 working memory files, 57 documentation caches, and 28 lines of core context
- 3Key changes include real-time indexing, unified search, a documentation cache, and knowledge graph embeddings
Details
The author built an AI memory system for their assistant Claude, which has been running since February. The system went through an iterative process of design, extension, and debugging. Over time, the system became more reliable and integrated into the author's workflow, to the point where they stopped actively monitoring it. The system has scaled significantly, with 124 distilled files, 121 working memory files, 57 documentation caches, and 28 lines of core context. Key changes since the original design include real-time indexing, unified search, a documentation cache, and the use of knowledge graph embeddings. The documentation cache, in particular, has proven to be a valuable addition, allowing the agent to access vendor documentation quickly and reliably without needing to fetch it from the web.
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