Episodic Memory in Agentic Frameworks: Suggesting Next Tasks
On the map: LLM agents and tool use → Agent memory systems
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.
Key facts. Cited by 1 paper 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.
Builds on
4 of 9 references.
References · 4
- 2023Cognitive Architectures for Language Agentscited by 429
- 2024Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fastcited by 153
- 2024A Review of Large Language Models and Autonomous Agents in Chemistrycited by 99
- 2021Augmenting Modelers with Semantic Autocompletion of Processescited by 1
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Citing papers per year: 2026: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2025Position: Episodic Memory is the Missing Piece for Long-Term LLM Agents
- 2025LEGOMem: Modular Procedural Memory for Multi-agent LLM Systems for Workflow Automation
- 2025Intrinsic Memory Agents: Heterogeneous Multi-Agent LLM Systems through Structured Contextual Memory
- 2026Episodic-Semantic Memory Architecture for Long-Horizon Scientific Agents
- 2026AutoSci: A Memory-Centric Agentic System for the Full Scientific Research Lifecycle
- 2026EvoMemBench: Benchmarking Agent Memory from a Self-Evolving Perspective
- 2026AMA-Bench: Evaluating Long-Horizon Memory for Agentic Applications
- 2026Just-in-Time Memory: Learning to Curate Task-Adaptive Memory for LLM Agents