# Episodic Memory in Agentic Frameworks: Suggesting Next Tasks

arXiv: https://arxiv.org/abs/2511.17775 · HTML: https://mlsanity.com/paper/2511.17775/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Agent memory systems, one of 16 directions in LLM agents and tool use.
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 21 Nov 2025.

## Placed in

- LLM agents and tool use / [Agent memory systems](https://mlsanity.com/agents/agent-memory-systems/)

## Builds on (most cited first, 4 of 4)

- [Cognitive Architectures for Language Agents](https://mlsanity.com/paper/2309.02427/) (2023)
- [Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast](https://mlsanity.com/paper/2402.08567/) (2024)
- [A Review of Large Language Models and Autonomous Agents in Chemistry](https://mlsanity.com/paper/2407.01603/) (2024)
- [Augmenting Modelers with Semantic Autocompletion of Processes](https://mlsanity.com/paper/2105.11385/) (2021)

## Built on by (most cited first, 1 of 1)

- [VIGIL: Towards Edge-Extended Agentic AI for Enterprise IT Support](https://mlsanity.com/paper/2603.16110/) (2026)

## Abstract (arXiv)

Agentic frameworks powered by Large Language Models (LLMs) can be useful tools in scientific workflows by enabling human-AI co-creation. A key challenge is recommending the next steps during workflow creation without relying solely on LLMs, which risk hallucination and require fine-tuning with scarce proprietary data. We propose an episodic memory architecture that stores and retrieves past workflows to guide agents in suggesting plausible next tasks. By matching current workflows with historical sequences, agents can recommend steps based on prior patterns.