mnesis

Name: mnesis
Author: Lucenor

Lucenor February 19, 2026

16 copies 0 downloads

A Python library that solves context window degradation in long-running LLM agents by moving memory management out of the model layer and into a deterministic engine.

<img src="https://raw.githubusercontent.com/Lucenor/mnesis/main/docs/images/logo_icon.png" alt="mnesis logo" width="120"> <img src="https://raw.githubusercontent.com/Lucenor/mnesis/main/docs/images/logo_wordmark.png" alt="mnesis" width="320"> Lossless Context Management for long-horizon LLM agents <a href="https://pypi.org/project/mnesis/"><img src="https://img.shields.io/pypi/v/mnesis?color=5c6bc0&labelColor=1a1a2e" alt="PyPI"></a> <a href="https://pypi.org/project/mnesis/"><img src="https://img.shields.io/pypi/pyversions/mnesis?color=5c6bc0&labelColor=1a1a2e" alt="Python"></a> <a href="LICENSE"><img src="https://img.shields.io/badge/license-Apache%202.0-5c6bc0?labelColor=1a1a2e" alt="License"></a> <a href="https://github.com/Lucenor/mnesis/actions/workflows/ci.yml"><img src="https://img.shields.io/github/actions/workflow/status/Lucenor/mnesis/ci.yml?color=5c6bc0&labelColor=1a1a2e" alt="CI"></a> <a href="https://codecov.io/github/Lucenor/mnesis"><img src="https://img.shields.io/codecov/c/github/Lucenor/mnesis?color=5c6bc0&labelColor=1a1a2e" alt="Coverage"></a> <a href="https://mnesis.lucenor.tech"><img src="https://img.shields.io/badge/docs-mnesis.lucenor.tech-5c6bc0?labelColor=1a1a2e" alt="Docs"></a> <a href="https://github.com/Lucenor/mnesis/attestations"><img src="https://img.shields.io/badge/provenance-attested-5c6bc0?labelColor=1a1a2e&logo=githubactions&logoColor=white" alt="Attestation"></a> <a href="https://scorecard.dev/viewer/?uri=github.com/Lucenor/mnesis"><img src="https://api.scorecard.dev/projects/github.com/Lucenor/mnesis/badge" alt="OpenSSF Scorecard"></a>

LLMs suffer from context rot: accuracy degrades 30–40% before hitting nominal token limits — not because the model runs out of space, but because reasoning quality collapses as the window fills with stale content.

The standard fix — telling the model to "summarize itself" — is unreliable. The model may sile

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