Memento: Fine-tuning LLM Agents without Fine-tuning LLMs

Dev.to / 4/30/2026

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Key Points

Memento is presented as a way to fine-tune LLM agents without performing traditional fine-tuning of the underlying LLM model.
The approach focuses on adapting agent behavior through agent-level techniques rather than retraining or updating the base model weights.
It targets practical workflows where developers want more controllable, task-specific agent performance with less cost and complexity than full model fine-tuning.
The article emphasizes enabling better agent outcomes by modifying how the agent acts (e.g., prompting/instructions or agent configuration) instead of fine-tuning the LLM itself.

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