Crab: A Semantics-Aware Checkpoint/Restore Runtime for Agent Sandboxes
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
Autonomous agents act through sandboxed containers and microVMs whose state spans filesystems, processes, and runtime artifacts. Checkpoint and restore (C/R) of this state is needed for fault tolerance, spot execution, RL rollout branching, and safe rollback-yet existing approaches fall into two extremes: application-level recovery preserves chat history but misses OS-side effects, while full per-turn checkpointing is correct but too expensive under dense co-location. The root cause is an agent-OS semantic gap: agent frameworks see tool calls but not their OS effects; the OS sees state changes but lacks turn-level context to judge recovery relevance. This gap hides massive sparsity: over 75% of agent turns produce no recovery-relevant state, so most checkpoints are unnecessary. Crab (Checkpoint-and-Restore for Agent SandBoxes) is a transparent host-side runtime that bridges this gap without modifying agents or C/R backends. An eBPF-based inspector classifies each turn's OS-visible effects to decide checkpoint granularity; a coordinator aligns checkpoints with turn boundaries and overlaps C/R with LLM wait time; and a host-scoped engine schedules checkpoint traffic across co-located sandboxes. On shell-intensive and code-repair workloads, Crab raises recovery correctness from 8% (chat-only) to 100%, cuts checkpoint traffic by up to 87%, and stays within 1.9% of fault-free execution time.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 13 of its 49 references that are in the map. First posted on arXiv 30 Apr 2026.
Builds on
13 of 49 references.
Closest ancestors · 3
Same direction, newest first
- 2026AgentCgroup: Understanding and Controlling OS Resources of AI Agents10 Feb
- 2025Speculative Actions: A Lossless Framework for Faster Agentic Systems5 Oct
- 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May
Other references · 10
- 2026Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfacescited by 515
- 2023SpotServe: Serving Generative Large Language Models on Preemptible Instancescited by 83
- 2025Tree Search for LLM Agent Reinforcement Learningcited by 73
- 2025Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystemcited by 28
- 2025RollArt: Disaggregated Multi-Task Agentic RL Training at Scalecited by 11
- 2025AnchorTP: Resilient LLM Inference with State-Preserving Elastic Tensor Parallelismcited by 3
- 2025FailSafe: High-performance Resilient Servingcited by 3
- 2026Making MoE-based LLM Inference Resilient with Tarragoncited by 2
- 2021VELOC: VEry Low Overhead Checkpointing in the Age of Exascalecited by 1
- 2025FailLite: Failure-Resilient Model Serving for Resource-Constrained Edge Environmentscited by 1
Built on by
Citing papers per year: 2026: 11.
11 papers.
- 2026DeltaBox: Scaling Stateful AI Agents with Millisecond-Level Sandbox Checkpoint/Rollbackcited by 10
- 2026FAVA: Formal Authorization for Verified Agents with Evidence-Backed Permission Graphscited by 2
- 2026Resume Means Resume: A Machine-Checked Conformance Contract for Checkpoint, Interrupt, and Resume Semantics in Workflow Persistence Layerscited by 2
- 2026Business Arena: Benchmarking LLM Agents in a Realistic Marketplacecited by 2
- 2026Safe to Resume? Breaking Execution Continuity of Agent Execution via Rollbackcited by 2
- 2026APPA: Recoverable Information-Flow Control for Real-World LLM Agentscited by 1
- 2026Towards an Agent Operating System - Lessons from Classical and Cloud OScited by 0
- 2026Aborted but Not Forgotten: KV-Cache Retention Breaks Rollback Consistency in Language Agentscited by 0
- 2026REVISE: Validity-Guided Recovery for Online Revisions in Agent Workflowscited by 0
- 2026Recoverability as a System Primitive for Long-Horizon AI Agentscited by 0
- 2026CUA-Sandbox: Efficient Environments for Computer-Use Agent Reinforcement Learningcited by 0
- 2026CUA-Sandbox: Efficient Environments for Computer-Use Agent Reinforcement Learning26 Sep 2026
- 2026Recoverability as a System Primitive for Long-Horizon AI Agents12 Sep 2026
- 2026REVISE: Validity-Guided Recovery for Online Revisions in Agent Workflows1 Sep 2026
- 2026Safe to Resume? Breaking Execution Continuity of Agent Execution via Rollback29 Aug 2026
- 2026Aborted but Not Forgotten: KV-Cache Retention Breaks Rollback Consistency in Language Agents16 Aug 2026
- 2026Business Arena: Benchmarking LLM Agents in a Realistic Marketplace9 Aug 2026
- 2026Resume Means Resume: A Machine-Checked Conformance Contract for Checkpoint, Interrupt, and Resume Semantics in Workflow Persistence Layers4 Aug 2026
- 2026FAVA: Formal Authorization for Verified Agents with Evidence-Backed Permission Graphs29 Jul 2026
- 2026Towards an Agent Operating System - Lessons from Classical and Cloud OS27 Jul 2026
- 2026APPA: Recoverable Information-Flow Control for Real-World LLM Agents27 Jul 2026
- 2026DeltaBox: Scaling Stateful AI Agents with Millisecond-Level Sandbox Checkpoint/Rollback21 May 2026
Similar, not cited
Close in wording, no citation either way
- 2026DART: Semantic Recoverability for Structured Tool Agents
- 2026ACRFence: Preventing Semantic Rollback Attacks in Agent Checkpoint-Restore
- 2026ToolMisuseBench: An Offline Deterministic Benchmark for Tool Misuse and Recovery in Agentic Systems
- 2026Planarian: Managing Agent State with Statepoints
- 2026AgentRewind: Recoverable Execution for Long-Horizon LLM Agents
- 2026EvoUndo: Recoverability-Constrained Self-Evolution for LLM Agent Harnesses
- 2026The Hidden Footprint: Making Storage a First-Class Metric for LLM Agent Evaluation
- 2026From Faulty Memories to Corrected Actions: Dependency-Guided Rollback Repair for Memory-Augmented Agents