> ## Documentation Index
> Fetch the complete documentation index at: https://mneno.lollopanta.it/llms.txt
> Use this file to discover all available pages before exploring further.

# Introduction

> Mneno is an anti-context-rot memory runtime for AI agents and AI applications.

Mneno is a Python SDK for developers building copilots, support agents, internal AI tools, and long-running AI applications.

It is not just a memory store. Mneno is an **anti-context-rot memory runtime**: it keeps context useful, compact, explainable, and verifiable as an application runs over time.

## Why Mneno?

Long-running AI systems accumulate stale facts, duplicated notes, noisy transcripts, and vague summaries. That context eventually makes retrieval worse, not better.

Mneno is built around **scoring**, **lifecycle management**, **compaction**, and **explainable decisions** so you can see what memory was retrieved, excluded, promoted, merged, discarded, or superseded and why.

## Key Features

* **Memory Scoring**: Rank memories by recency, relevance, importance, and freshness signals.
* **Explainable Retrieval**: Search returns ranked results with score components and reasons.
* **Auto-Compaction**: Preserve key claims when context gets too large or noisy.
* **Context Building**: Build deterministic context packages for prompt injection within a token budget.
* **Persistent Storage**: Support for in-memory, JSON file, and SQLite storage.
* **Provider Agnostic**: Protocol-based integration for embeddings, LLMs, and rerankers.
* **Lifecycle and Hierarchy**: Preserve audit history across active, superseded, archived, and conflicted memories organized into deterministic layers.
* **Sessions and Timelines**: Group memories into ordered sessions, reconstruct timelines, and recover continuity across related work.
* **Structured Extraction**: Extract memories deterministically or through an optional validated `LLMProvider`.
* **Local Observability**: Record in-memory traces for retrieval, context building, compaction, conflicts, hierarchy, extraction, and sessions.
* **Evaluation Hooks**: Measure retrieval, context, compaction, token efficiency, latency, and explainability for future Mneno Bench adapters.

## Getting Started

<CardGroup cols={2}>
  <Card title="Quickstart" icon="rocket" href="/quickstart">
    Get Mneno up and running in minutes.
  </Card>

  <Card title="Core Concepts" icon="brain" href="/concepts/scoring">
    Learn how Mneno handles memory and context.
  </Card>

  <Card title="Observability" icon="wave-pulse" href="/concepts/observability">
    Debug memory decisions with local traces.
  </Card>

  <Card title="Evaluation" icon="chart-line" href="/advanced/evaluation">
    Produce benchmark-friendly metrics and exports.
  </Card>
</CardGroup>