# Memento 发布了一个基于记忆的持续学习框架，让LLM智能体通过存储和检索过往经验来学习，无需调整模型权重

- 来源：Meng Shao
- 发布时间：2026-07-29 22:01
- AIWatch 分数：62
- AIWatch 标记：当日精选
- AIWatch 链接：https://aiwatch.icu/events/evt_01kyq6vk3tyra03xzapcxd448d
- 原文链接：https://x.com/shao__meng/status/2082474799008534592

## 精选理由

常规快讯，保留列表

## AI 摘要

Memento 发布了一个基于记忆的持续学习框架，让LLM智能体通过存储和检索过往经验来学习，无需调整模型权重。

## 正文

Memento is a memory based continual learning framework for LLM agents that lets them learn from experience over time without touching model weights.

It maintains a Case Bank of past trajectories including tasks, step sequences, tool usage, and outcomes.

When a new task comes in, the agent plans and acts by pulling from similar past cases instead of starting from zero.

Memento follows a planner and executor setup:

The Planner (an LLM) breaks the task into subtasks, retrieves relevant cases, and chooses a plan.
The Executor runs those subtasks using tools like search, code execution, or document processing through the Model Context Protocol (MCP), then logs the results back into memory.

Key Features:

• Memory based continual learning that improves agents through stored experience
• Planner and executor architecture with case based reasoning for task decomposition
• Unified tool ecosystem for search, code execution, document processing, media analysis and more
• Learning without weight updates by retrieving and reusing relevant past cases
• Strong results on long horizon and out of distribution tasks in reported benchmarks

It is 100% open source.

Link to the GitHub repo in the comments!