Mímir: A Python Memory System Built on 21 Neuroscience Mechanisms

Mímir is a Python memory system for AI agents built on 21 mechanisms from published cognitive science research, developed as an alternative to traditional RAG approaches that treat memory like a database.

Key Neuroscience Mechanisms

• Flashbulb memory (Brown & Kulik 1977) – high-arousal events get permanent stability floors
• Reconsolidation (Nader et al 2000) – recalled memories drift 5% toward current mood
• Retrieval-Induced Forgetting (Anderson 1994) – retrieving one memory actively suppresses similar competitors
• Zeigarnik Effect – unresolved failures stay extra vivid, agents keep retrying what didn't work
• Völva's Vision – during sleep_reset(), random memory pairs are sampled and synthesised into insight memories the agent wakes up with
• Yggdrasil – a persistent memory graph with 6 edge types connecting episodic, procedural, and social memory into a unified knowledge structure

Technical Implementation

Retrieval uses a hybrid BM25 + semantic + date index with 5-signal re-ranking (keyword, semantic, vividness, mood congruence, recency). This approach finally got MSC competitive with raw TF-IDF after keyword-only systems were beating purely semantic ones.

Benchmark Results

Tested on 6 standard memory benchmarks (Mem2ActBench, MemoryBench, LoCoMo, LongMemEval, MSC, MTEB):

• Beats VividnessMem on Mem2ActBench by 13% Tool Accuracy
• 96% R@10 on LongMemEval
• 100% on 3 of 6 LongMemEval categories (knowledge-update, single-session-preference, single-session-user)
• MSC essentially tied with TF-IDF baseline (was losing by 11% before the hybrid bridge)

Installation and Architecture

Install via pip install vividmimir. The system orchestrates two separately published packages – VividnessMem (neurochemistry engine) and VividEmbed (389-d emotion-aware embeddings) – but works standalone with graceful fallbacks if you don't want the full stack.

The repository and full benchmark results are available at github.com/Kronic90/Mimir.