LLM Context Management Overview

• LLM Context Management is the practice of tracking, storing, and manipulating LLM context (e.g., sessions and dialogue history) to support scalable, long-horizon reasoning.
• It employs distributed architectures, token-based representation, and compression techniques to enhance memory efficiency and reduce computational overhead.
• Advanced strategies like context folding, versioned memory, and multi-agent protocols enable robust, reproducible workflows in LLM-powered applications.

LLM context management encompasses the methodologies, protocols, and system architectures employed to track, store, manipulate, and serve context information—such as user sessions, dialogue history, tool states, and external knowledge—for LLM-powered applications. The effectiveness of context management is essential for supporting long-horizon reasoning, distributed and multi-agent deployments, low-latency inference, memory efficiency, and robust, reproducible workflows. Contemporary research develops context management strategies tailored for various scenarios: from stateless LLM serving at the edge, to dynamic context folding for RL agents, versioned memory for developmental autonomy, system-level memory management in multi-agent or mobile settings, and post-hoc attribution. This article surveys central techniques, designs, metrics, and open challenges in LLM context management, with a focus on methods established in recent arXiv literature.

1. Architectural Principles and Distributed Protocols

LLM context management systems are frequently structured with explicit architectural separation between the client interface, context manager, inference engine, and context storage/replication backend. DisCEdge is exemplary for geo-distributed deployments: clients send edge nodes their {user ID, session ID, turn counter, new prompt}, which a local Context Manager (CM) verifies for staleness, tokenizes (only for the new turn), prepends with the tokenized session history (context), and forwards to the inference engine. The context—strictly represented as a token sequence—is updated only with the newly generated tokens, and then stored in a distributed key-value store. Updates are asynchronously replicated across edge nodes, with token-based context avoiding O(n) repeated tokenization and reducing both computational and network overheads compared to raw-text strategies (Malekabbasi et al., 27 Nov 2025).

Consistency protocols in such distributed systems rely on client-driven validation—using turn counters to guarantee that CMs operate on at least the required session state. Strong consistency (waiting until the requested turn is present) and available (using possibly stale context) options are supported. Synchronization metrics are analytically modeled by

$$L_{\text{sync}} \approx RTT + \frac{|\Delta|}{\text{BW}}, \quad B_{\text{sync}} = |\Delta| \cdot N_{\text{peers}},$$

where $\Delta$ is the newly tokenized prompt, and $N_{\text{peers}}$ is the degree of replication.

2. Tokenization, State Representation, and Compression

Tokenization is central to nearly all modern LLM context management. Instead of storing raw text, contexts are represented as finite token sequences $C = \{t_1, t_2, ..., t_n\}, t_i \in \mathbb{N}$. This enables efficient, incremental updates: only new turns are tokenized, prior context is cached and transmitted as token arrays. Systems such as DisCEdge and LeoAM exploit this token granularity to reduce memory bandwidth (up to 90% client request size reduction compared to client-side storage) (Malekabbasi et al., 27 Nov 2025, Sun et al., 25 Jun 2025).

Compression and memory hierarchy strategies further enhance context scalability:

• LeoAM on commodity GPUs applies adaptive chunking, dividing the KV-cache into variable-sized chunks driven by the skew in attention weights, and maintaining a three-tier GPU–CPU–Disk pipeline. Chunks are represented by lightweight "abstracts" (e.g., min/max vectors of key embeddings), enabling efficient bound evaluation and selective loading. Dynamic quantization (e.g., INT4) is applied per chunk, achieving up to 3.46×–5.47× speedups with <1% quality loss (Sun et al., 25 Jun 2025).
• LLMs for on-device LLMaaS uses fine-grained KV chunking (default 16 tokens), tolerance-aware per-chunk compression (assigning bits based on attention-derived information density), and a Least Compression-Tolerable and Recently-Used (LCTRU) queue for eviction (Yin et al., 18 Mar 2024).

3. Multi-Agent and Workflow Context Management

Multi-agent systems and tool-using agents require structured context sharing and message-passing protocols:

• Tele-LLM-Hub introduces the Telecom Model Context Protocol (TeleMCP): structured, type-validated ContextObject envelopes and aggregation operators for KPIVector (contextual KPI summaries). Context objects propagate along edges of agent workflow graphs, with schema enforcement, provenance tracking, and workflow composition tools (Shah et al., 12 Nov 2025).
• SagaLLM decomposes planning and validation into sub-agents: checkpointing, explicit state restoration, and constraint validation enable robust context preservation, transactional rollback (viewed as a Saga with atomicity/consistency guarantees), and high retention in complex multi-stage planning (Chang et al., 15 Mar 2025).

Consistency analysis for shared (single context) vs. separate (per-agent) designs is formalized by the Response Consistency Index (RCI), quantifying the probabilities that context holds correct statements uncorrupted by noise, as a function of memory window size, noise rate, and inter-agent dependencies. Explicit mathematical forms are provided for both designs, enabling principled trade-off analysis (Helmi, 9 Apr 2025).

4. Long-Horizon and Proactive Context Management

New agentic context-management techniques for long-horizon (hundreds of turns, deep pipelines) applications involve learned, structured, or versioned approaches:

• Context-Folding (FoldGRPO) allows an agent to branch into a sub-trajectory and fold it back, replacing intermediate steps with a concise summary. The folding operation is learned end-to-end via RL with process rewards targeting context size, subtask alignment, and failure minimization. FoldGRPO matches or surpasses summarization-based and ReAct baselines with a 10× smaller active context window (Sun et al., 13 Oct 2025).
• AgentFold operationalizes context as a multi-scale "workspace" combining granular and deep folding directives, all learned with supervised fine-tuning. Results show substantially reduced context growth (sublinear <7k tokens after 100 turns vs. >91k for ReAct) while preserving 98–99% survival probability of key details (Ye et al., 28 Oct 2025).
• Git-Context-Controller (GCC) formalizes agent memory as a versioned hierarchy akin to a Git repository: memory is manipulated by COMMIT, BRANCH, MERGE, and CONTEXT operations, structuring milestones, experiments, and recovery. Agents using GCC demonstrate SOTA results in software engineering and self-replication tasks, with explicit branch-driven memory prototyping and hand-off across sessions (Wu, 30 Jul 2025).

In contrast, simple observation masking—omitting all but the M most recent observations—can deliver solve-rate and cost performance equal or superior to complex LLM summarization for code-centric long-horizon agents, due to the dominance of observation tokens and preservation of chain-of-thought information (Lindenbauer et al., 29 Aug 2025).

5. Memory Management, System Services, and Throughput

LLMaaS, opportunistic HPC, and agent OS designs demand high-throughput system-level context management:

• Platform-level managers (AIOS) create, snapshot, and restore isolated LLMContext objects, with preemptive scheduling and LRU-K eviction, supporting both text- and logits-based state. APIs exposed in AIOS enable concurrent serving of up to 2,000 agent contexts, with <5% overhead per context switch and BLEU/BERTScore preservation (Mei et al., 25 Mar 2024).
• In HPC workloads, "pervasive context management" decouples expensive cold model loading from fast inference by retaining model weights, tokenizers, and runtime state in GPU memory, amortizing startup costs across many inference tasks. Scheduling policies allocate workloads to "warmed" GPUs for drastically improved throughput and minimal latency under preemption (Phung et al., 15 Oct 2025, Phung et al., 16 Sep 2025).

Short-term tool memory management, as in MemTool, addresses LLMs' limited context windows in tool-rich, multi-turn conversations. By enabling agentic, workflow, or hybrid removal/addition policies (e.g., agent-decided or LLM-pruned), one achieves ≥90% tool-removal efficiency and stable task completion, with mode selection matched to model reasoning capabilities and context constraints (Lumer et al., 29 Jul 2025).

6. Context Attribution and Traceback

TracLLM addresses the post-hoc attribution problem: assigning responsibility for generated outputs in long-context LLMs to specific context passages. TracLLM unifies and improves perturbation-based methods (e.g., Shapley, LOO, LIME) through informed search over text groups, scoring, pruning, and ensemble denoising. This enables O(log n) scaling in the number of attribution calls, supports passage-level and multi-evidence attribution, and achieves >90% precision/recall for attack identification under feasible runtime (Wang et al., 4 Jun 2025). Applications include debugging, forensic analysis, and knowledge source highlighting.

7. Open Challenges and Recommendations

Research highlights continuing challenges in:

• Efficient, hierarchical context sharing and cache eviction policies for multi-tenant, distributed, and mobile settings (Malekabbasi et al., 27 Nov 2025, Yin et al., 18 Mar 2024).
• Proactive, learned context manipulation at multiple scales, especially in the presence of subtask structure, nonmonotonic workflows, or emergent behavior (Ye et al., 28 Oct 2025, Sun et al., 13 Oct 2025).
• Balancing context completeness vs. noise/size—masking is efficient but may lose rare long-range signals, while summarization introduces cost and possible information loss (Lindenbauer et al., 29 Aug 2025).
• Security, privacy, and resource isolation in shared and multi-agent resource pools (Phung et al., 15 Oct 2025, Mei et al., 25 Mar 2024).
• Generalization of context management methods to multi-modal and cross-domain tasks, such as spoken-language processing with complete or compressed spoken histories (Ghazal et al., 10 Oct 2025).

Best practices include:

• Storing context as token arrays, not raw text, for incremental update and replication efficiency (Malekabbasi et al., 27 Nov 2025).
• Integrating strong turn-counters and replication protocols to guard against context staleness or inconsistency.
• Employing modular agents with minimal working contexts and explicit checkpointing for stateful or multi-agent workflows (Chang et al., 15 Mar 2025).
• Using persistent, versioned, or multi-scale context stores for developmental autonomy and reproducibility (Wu, 30 Jul 2025, Ye et al., 28 Oct 2025).
• Selecting context management mode (masking, folding, summarization) to match domain, memory, and reasoning demands (Lindenbauer et al., 29 Aug 2025, Sun et al., 13 Oct 2025).
• Instrumenting attribution to enable monitoring, debugging, and trusted outputs in RAG and agent systems (Wang et al., 4 Jun 2025).

LLM context management is thus a cross-cutting systems and algorithmic discipline, incorporating techniques from database replication, distributed systems, reinforcement learning, program synthesis, and operating systems. Advances in this area underpin progress toward scalable, adaptive, multi-agent, and long-horizon LLM applications.