Sumanth

@Sumanth_077

Fine-tune LLM agents without fine-tuning LLMs! 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: 1. The Planner (an LLM) breaks the task into subtasks, retrieves relevant cases, and chooses a plan. 2. 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!