ShadowMerge exploits relation-channel conflicts to poison graph-based agent memory, achieving 93.8% average attack success rate on Mem0 and real-world datasets while bypassing existing defenses.
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MemGPT: Towards LLMs as Operating Systems
Canonical reference. 77% of citing Pith papers cite this work as background.
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Pith papers citing it
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abstract
Large language models (LLMs) have revolutionized AI, but are constrained by limited context windows, hindering their utility in tasks like extended conversations and document analysis. To enable using context beyond limited context windows, we propose virtual context management, a technique drawing inspiration from hierarchical memory systems in traditional operating systems that provide the appearance of large memory resources through data movement between fast and slow memory. Using this technique, we introduce MemGPT (Memory-GPT), a system that intelligently manages different memory tiers in order to effectively provide extended context within the LLM's limited context window, and utilizes interrupts to manage control flow between itself and the user. We evaluate our OS-inspired design in two domains where the limited context windows of modern LLMs severely handicaps their performance: document analysis, where MemGPT is able to analyze large documents that far exceed the underlying LLM's context window, and multi-session chat, where MemGPT can create conversational agents that remember, reflect, and evolve dynamically through long-term interactions with their users. We release MemGPT code and data for our experiments at https://memgpt.ai.
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- abstract Large language models (LLMs) have revolutionized AI, but are constrained by limited context windows, hindering their utility in tasks like extended conversations and document analysis. To enable using context beyond limited context windows, we propose virtual context management, a technique drawing inspiration from hierarchical memory systems in traditional operating systems that provide the appearance of large memory resources through data movement between fast and slow memory. Using this technique, we introduce MemGPT (Memory-GPT), a system that intelligently manages different memory tiers i
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representative citing papers
MemEvoBench is presented as the first standardized benchmark for long-horizon memory safety in LLM agents, covering adversarial memory injection, noisy tool outputs, and biased feedback across QA and workflow tasks.
A language-model-driven agentic AI system autonomously executes multi-stage physics experiments at a production synchrotron light source, reducing preparation time by two orders of magnitude while upholding safety constraints.
The authors create the first large-scale dataset and taxonomy of failure modes in multi-agent LLM systems to explain their limited performance gains.
Momento benchmark reveals current agents fail at multi-session tasks mainly by misestimating user state and treating old session history as current context instead of stale data needing re-validation.
LongDS benchmark shows state-of-the-art agents achieve only 48.45% accuracy on long-horizon data analysis tasks, with performance dropping 47 points from early to late turns and state-maintenance errors causing most failures.
MemPoison enables stealthy memory poisoning in LLM agents via dialogue by using semantic relational bridges, entity masquerading, and joint embedding optimization to bypass selective extraction and rewriting, achieving up to 0.95 attack success rate.
A Behavioral Specification interpretive layer improves representational accuracy for AI personalization by compressing user data into patterns, outperforming raw corpora and commercial memory systems on held-out behavioral predictions across 14 autobiographical corpora while reducing context cost.
VitaBench 2.0 introduces a benchmark for long-term personalized and proactive agent behavior, with results indicating substantial gaps in current frontier LLMs.
MemFail introduces diagnostic datasets that isolate failure modes in LLM memory systems by testing summarization, storage, and retrieval operations separately.
AGORA is an inference-free step-level compressor for LLM agent prompts that retains at least 75% of uncompressed performance in most tested settings where token-level methods collapse due to action-grammar destruction.
Introduces QGP and PushBench to evaluate LLM agent persistence on quantitative goals, showing specialized controllers outperform baselines on verifier-checked artifact collection tasks.
GraphFlow uses a unified wGraph to dynamically instantiate workflows and manage KV caches for LLM agents, reporting 4.95 pp average gains and 4x memory reduction on five benchmarks.
MemConflict provides a benchmark for testing LLM long-term memory systems under dynamic, static, and conditional conflicts involving temporal validity, factual correctness, and contextual applicability.
SocialMemBench provides 1,031 QA pairs from 43 synthetic social networks to show that existing AI memory frameworks perform poorly in multi-party group settings compared to full-context baselines.
EXG is an experience graph framework for self-evolving LLM agents that supports online real-time growth and offline reuse to enhance solution quality and efficiency on code generation and reasoning benchmarks.
S-Bus reconstructs read sets from HTTP traffic for multi-agent LLM state coordination, delivering Observable-Read Isolation with formal proofs and empirical safety matching traditional databases.
RecMem reduces memory construction token costs by up to 87% in long-running LLM agents by consolidating memory only upon sustained recurrence of semantically similar interactions, while exceeding the accuracy of three prior systems.
SMMBench is a benchmark evaluating multimodal agents on cross-source reasoning, conflict resolution, preference reasoning, and action prediction, showing current systems struggle with evidence distributed across heterogeneous sources.
MemDocAgent generates consistent hierarchical repository-level code documentation by combining dependency-aware traversal with memory-guided agent interactions that accumulate work traces.
EvolveMem enables autonomous self-evolution of LLM memory retrieval configurations via LLM diagnosis and safeguards, delivering 25.7% gains over strong baselines on LoCoMo and 18.9% on MemBench with positive cross-benchmark transfer.
LongMemEval-V2 is a new benchmark where AgentRunbook-C reaches 72.5% accuracy on long-term agent memory tasks, beating RAG baselines at 48.5% and basic coding agents at 69.3%.
All tested LLM memory systems fail at dependency reasoning in multi-entity evolving scenarios, with only an expensive file-based setup showing partial recovery.
PRISM is a tiered benchmark with 300 human-verified tasks across five photorealistic apartments that diagnoses embodied agent failures in basic ability, reasoning ability, and long-horizon ability using an agent-agnostic API.
citing papers explorer
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Why Do Multi-Agent LLM Systems Fail?
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VitaBench 2.0: Evaluating Personalized and Proactive Agents in Long-Term User Interactions
VitaBench 2.0 introduces a benchmark for long-term personalized and proactive agent behavior, with results indicating substantial gaps in current frontier LLMs.
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MemFail: Stress-Testing Failure Modes of LLM Memory Systems
MemFail introduces diagnostic datasets that isolate failure modes in LLM memory systems by testing summarization, storage, and retrieval operations separately.
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AGORA: Adapter-Grounded Observation-Action Retention for Inference-Free Prompt Compression in LLM Agents
AGORA is an inference-free step-level compressor for LLM agent prompts that retains at least 75% of uncompressed performance in most tested settings where token-level methods collapse due to action-grammar destruction.
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EXG: Self-Evolving Agents with Experience Graphs
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Remember the Decision, Not the Description: A Rate-Distortion Framework for Agent Memory
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TimeClaw: A Time-Series AI Agent with Exploratory Execution Learning
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When Stored Evidence Stops Being Usable: Scale-Conditioned Evaluation of Agent Memory
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MEMOREPAIR: Barrier-First Cascade Repair in Agentic Memory
MemoRepair formalizes the cascade update problem in agentic memory and solves it via a min-cut reduction that eliminates invalidated memory exposure to 0% while recovering 91-94% of valid successors at 57-76% of baseline repair cost.
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Perturbation Dose Responses in Recursive LLM Loops: Raw Switching, Stochastic Floors, and Persistent Escape under Append, Replace, and Dialog Updates
In 30-step recursive LLM loops, append-mode persistent escape from source basins reaches 50% near 400 tokens under full history but plateaus below 50% under tail-clip memory policy, while replace-mode switching largely reflects state reset.
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MEMAUDIT: An Exact Package-Oracle Evaluation Protocol for Budgeted Long-Term LLM Memory Writing
MEMAUDIT is a new exact optimization protocol for evaluating budgeted LLM memory writing that uses package-oracle fixes and MILP solvers to separate representation quality, validity preservation, and selection effects.
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Four-Axis Decision Alignment for Long-Horizon Enterprise AI Agents
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EMBER: Autonomous Cognitive Behaviour from Learned Spiking Neural Network Dynamics in a Hybrid LLM Architecture
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When to Forget: A Memory Governance Primitive
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ClawVM: Harness-Managed Virtual Memory for Stateful Tool-Using LLM Agents
ClawVM introduces a harness-managed virtual memory system for LLM agents that ensures deterministic residency and durability of state under token budgets by using typed pages and validated writeback.
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Springdrift: An Auditable Persistent Runtime for LLM Agents with Case-Based Memory, Normative Safety, and Ambient Self-Perception
Springdrift provides an auditable persistent runtime for long-lived LLM agents with case-based memory, normative safety gating, and ambient self-perception, shown in a 23-day single-instance deployment where the agent self-diagnosed bugs and maintained cross-channel context.
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SuperLocalMemory V3.3: The Living Brain -- Biologically-Inspired Forgetting, Cognitive Quantization, and Multi-Channel Retrieval for Zero-LLM Agent Memory Systems
SuperLocalMemory V3.3 implements a cognitive memory taxonomy with mathematical forgetting and multi-channel retrieval, reaching 70.4% on LoCoMo in zero-LLM mode.
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WMF-AM: Probing LLM Working Memory via Depth-Parameterized Cumulative State Tracking
WMF-AM is a depth-parameterized benchmark that measures LLMs' cumulative state tracking ability without scratchpads, validated on 28 models across arithmetic and non-arithmetic tasks with ablations confirming the construct.
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E-mem: Multi-agent based Episodic Context Reconstruction for LLM Agent Memory
E-mem uses a heterogeneous multi-agent setup for episodic context reconstruction in LLM agents, reaching over 54% F1 on LoCoMo while cutting token cost by over 70% compared to prior methods like GAM.
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$How^{2}$: How to learn from procedural How-to questions
$How^{2}$ is a memory agent framework enabling agents to ask, store, and reuse answers to how-to questions at varying abstraction levels for better lifelong planning in environments like Plancraft.
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SkillRevise: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision
SkillRevise iteratively refines initial LLM-generated agent skills using execution traces to diagnose defects and apply repairs, raising success rates from 36.05% to 61.63% on SkillsBench across three benchmarks and five LLMs.
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Selective QA over Conflicting Multi-Source Personal Memory: A Diagnostic Testbed and Method Comparison
Introduces a benchmark with 34,560 instances for selective QA over conflicting multi-source personal memory and compares fusion methods against LLMs.
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GRASP: Gated Regression-Aware Skill Proposer for Self-Improving LLM Agents
GRASP adds a regression-aware acceptance gate to skill proposal for LLM agents, producing large gains on clinical benchmarks while preventing silent regressions on prior behavior.
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MemCog: From Memory-as-Tool to Memory-as-Cognition in Conversational Agents
MemCog introduces a Memory-as-Cognition paradigm with Navigable Memory Store, Cross-Dimensional Navigation Interface, and Proactive Reasoning Protocol, claiming SOTA results on LoCoMo, LongMemEval, and a new ProactiveMemBench.
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Ratchet: A Minimal Hygiene Recipe for Self-Evolving LLM Agents
Ratchet provides a minimal hygiene recipe for self-managing skill libraries in frozen LLM agents, delivering +0.328 rolling-mean pass@1 gain on MBPP+ hard-100 and +0.22 peak lift on SWE-bench Verified.
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The Log is the Agent: Event-Sourced Reactive Graphs for Auditable, Forkable Agentic Systems
ActiveGraph inverts traditional agent frameworks by treating the append-only event log as the primary source of truth, from which the reactive graph is projected, yielding deterministic replay, forking, and lineage tracking.
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Solvita: Enhancing Large Language Models for Competitive Programming via Agentic Evolution
Solvita is an agentic evolution system using Planner, Solver, Oracle, and Hacker agents with trainable graph knowledge networks updated by reinforcement learning on pass/fail and vulnerability signals to achieve SOTA code generation performance.
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Hypergraph Enterprise Agentic Reasoner over Heterogeneous Business Systems
HEAR uses a stratified hypergraph ontology to orchestrate evidence-driven multi-hop reasoning over heterogeneous business systems, reaching 94.7% accuracy on supply-chain root-cause tasks with open-weight models.
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PREPING: Building Agent Memory without Tasks
Preping builds agent memory via proposer-guided synthetic practice and selective validation, matching offline/online methods at 2-3x lower deployment cost.
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EvoMAS: Learning Execution-Time Workflows for Multi-Agent Systems
EvoMAS trains a workflow adapter with policy gradients to dynamically instantiate stage-specific multi-agent workflows from a fixed agent pool, using explicit task-state construction and terminal success signals, and outperforms static baselines on GAIA, HLE, and DeepResearcher.
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Memanto: Typed Semantic Memory with Information-Theoretic Retrieval for Long-Horizon Agents
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Stateless Decision Memory for Enterprise AI Agents
Deterministic Projection Memory (DPM) delivers stateless, deterministic decision memory for enterprise AI agents that matches or exceeds summarization-based approaches at tight memory budgets while improving speed, determinism, and auditability.
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SOCIA-EVO: Automated Simulator Construction via Dual-Anchored Bi-Level Optimization
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When Agents Go Quiet: Output Generation Capacity and Format-Cost Separation for LLM Document Synthesis
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Drawing on Memory: Dual-Trace Encoding Improves Cross-Session Recall in LLM Agents
Dual-trace encoding improves LLM agent cross-session recall from 53.5% to 73.7% accuracy by storing facts alongside concrete scene reconstructions, with largest gains in temporal reasoning and multi-session aggregation.
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GAM: Hierarchical Graph-based Agentic Memory for LLM Agents
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ACF: A Collaborative Framework for Agent Covert Communication under Cognitive Asymmetry
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ATANT: An Evaluation Framework for AI Continuity
ATANT defines AI continuity via seven properties and offers a 10-checkpoint, LLM-free test using 250 stories to check if systems retrieve correct facts without cross-contamination.
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HYVE: Hybrid Views for LLM Context Engineering over Machine Data
HYVE cuts LLM token usage on machine data by 50-90% using database-style hybrid views and a request-scoped datastore while maintaining or improving quality on tasks like anomaly detection and chart generation.
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Real-Time Procedural Learning From Experience for AI Agents
PRAXIS enables AI agents to acquire procedural knowledge in real time by indexing and retrieving state-action-result experiences, leading to better accuracy, reliability, and efficiency on web browsing benchmarks.
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The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
Survey that defines agentic RL for LLMs via POMDPs, introduces a taxonomy of planning/tool-use/memory/reasoning capabilities and domains, and compiles open environments from over 500 papers.
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ActiveMem: Distributed Active Memory for Long-Horizon LLM Reasoning
ActiveMem proposes a heterogeneous distributed memory framework for LLM agents that separates planning from active memory management, reporting SOTA accuracy with lower overhead on BrowseComp-Plus and GAIA.
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Meta-Cognitive Memory Policy Optimization for Long-Horizon LLM Agents
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VikingMem: A Memory Base Management System for Stateful LLM-based Applications
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MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation
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AlphaMemo: Structured Search-Process Memory for Self-Evolving Alpha Mining Agents
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Causal Intervention-Based Memory Selection for Long-Horizon LLM Agents
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Episodic-Semantic Memory Architecture for Long-Horizon Scientific Agents
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NeuSymMS: A Hybrid Neuro-Symbolic Memory System for Persistent, Self-Curating LLM Agents
NeuSymMS is a hybrid neuro-symbolic memory system that extracts facts via LLMs and manages them with explicit CLIPS rules for scoping, deduplication, and dual-horizon persistence in LLM agents.