Memtomem FAQs

Name: Memtomem
Author: memtomem

Question 1

What is Memtomem and how does it help AI agents?

Accepted Answer

Memtomem provides AI agents with a searchable, long-term memory infrastructure. It indexes your markdown notes, documents, and code, allowing agents to retrieve relevant information and prevent 'forgetting' between sessions, enhancing their capabilities.

Question 2

What kind of files can Memtomem index?

Accepted Answer

Memtomem supports a wide range of file types, including Markdown, Python, JavaScript/TypeScript, JSON, YAML, and TOML. It uses semantic chunking to intelligently process content from these different formats.

Question 3

How does Memtomem ensure accurate search results?

Accepted Answer

It employs a powerful hybrid search mechanism that combines BM25 keyword matching, dense vector (semantic) search, and Reciprocal Rank Fusion (RRF) to deliver highly accurate and relevant search results.

Question 4

Is indexing with Memtomem efficient?

Accepted Answer

Yes, Memtomem features incremental indexing using chunk-level SHA-256 diffs. This means only changed sections of your files are re-embedded, ensuring efficient updates even for large and frequently modified knowledge bases.

Question 5

What are some key maintenance features of Memtomem?

Accepted Answer

Memtomem includes built-in memory maintenance features such as near-duplicate detection to avoid redundancy, time-based decay and TTL expiration for managing relevance, and auto-tagging to help organize your indexed content.

Memtomem

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