AIWatch.

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!