LLM Memory

MarkMem is a git-native, plain-markdown memory layer for chatbots that stores every fact as an inspectable Markdown file in a git repository, backed by a rebuildable SQLite cache. It combines bi-temporal facts in YAML, a Presidio PII write gate, and git as the compliance surface, so you can cat, grep, and git-diff your chatbot’s memory instead of trusting an opaque vector database. What is MarkMem and why do chatbots forget? Every chatbot that carries context across sessions faces the same problem: the model’s context window is finite, and once a conversation scrolls past it, the details are gone. Traditional memory layers solve this by embedding facts into a vector database and retrieving the most similar chunks at query time. That works, but it turns your chatbot’s memory into a black box — you cannot read it, audit it, or prove what it contains. ...

Memoket Kite is an open-source memory layer for AI agents that replaces the standard embeddings-plus-vector-database stack with a single portable, topic-indexed file of structured facts. It scores 93.51% on the LoCoMo long-conversation benchmark and 85.60% on LongMemEval-S while reading only about 1.5k tokens of context — the top overall score on both benchmarks with no vector stack at all. What Is Memoket Kite? A Memory Layer for AI Agents Memoket Kite (KITE) is a Python library, released under the Apache License 2.0, that gives AI agents persistent, source-backed memory. Its tagline is “Follow the thread, not the nearest match.” Instead of storing conversations as opaque embedding vectors and retrieving by similarity, KITE turns what an agent hears into typed, dated, topic-indexed facts that you can open and read in a plain file. ...