This guide examines seven sandbox platforms that can support AI-agent code execution workflows, including several with documented LangChain or LangGraph-adjacent integrations, starting with Modal, a serverless compute platform that combines secure sandboxed execution with GPU acceleration and a code-first developer experience with SDKs in Python, TypeScript, and Go.
Key Takeaways
- Secure isolation is non-negotiable for agent code execution: LangGraph agents generate and run code autonomously, making sandboxed execution critical. Modal compute jobs are containerized and virtualized using gVisor, while E2B employs Firecracker microVMs for hardware-level separation
- GPU access differentiates sandbox platforms: Modal supports a broad range of NVIDIA GPUs for accelerated workloads, including T4, L4, A10, L40S, A100 40GB/80GB, RTX PRO 6000, H100, H200, and B200, enabling LangGraph agents to handle ML inference and compute-intensive analysis alongside code execution
- Scale capacity varies dramatically: Modal product pages state support for scaling to 50,000+ concurrent sandbox sessions for production use cases, while E2B's public Pro plan supports up to 1,100 concurrent sandboxes with purchased extra concurrency, and Enterprise concurrency is custom
- Cold start performance impacts agent responsiveness: Modal is engineered for fast cold starts and faster feedback loops through its optimized container stack and filesystem, helping containers come online quickly along with snapshotting primitives such as filesystem, directory, and memory snapshots that further reduce startup latency. Other providers such as Daytona and E2B also support sandbox cold start optimization
- Code-first SDKs accelerate LangGraph integration: Modal's decorator-based SDKs in Python, TypeScript, and Go eliminate YAML configuration, enabling faster iteration when building LangGraph agent workflows
1. Modal
Modal delivers serverless compute for secure code execution at scale, the core sandbox workload for LangGraph agents, with on-demand GPU access layered on top for workloads requiring acceleration. The platform takes your code, containerizes it, and executes it in the cloud with automatic scaling, all defined through code-first SDKs available in Python, TypeScript, and Go.
Core Capabilities
- gVisor-based isolation: Modal compute jobs are containerized and virtualized using gVisor, and Sandboxes provide secure containers for executing untrusted user or agent code
- Production-scale capacity: Modal product pages state support for scaling to 50,000+ concurrent sandbox sessions for production use cases; actual concurrency depends on plan and account limits, with Enterprise offering custom higher limits
- Fast cold starts: Engineered for fast cold starts and faster feedback loops, Modal's optimized container stack and filesystem help containers come online quickly without letting large images slow startup down. The platform also provides snapshotting primitives, including filesystem, directory, and memory snapshots, that can further reduce startup latency. For Functions, Memory Snapshots can reduce initialization-heavy cold starts
- Code-first SDKs: Define compute, storage, and networking via code in Python, TypeScript, or Go; no YAML or configuration files required. Sandboxes support executing code in any language the workload requires
- On-demand GPU access: Agents can call upon GPUs when workloads require acceleration, with NVIDIA options including T4, L4, A10, L40S, A100 40GB/80GB, RTX PRO 6000, H100, H200, B200, and B200+ (which may run compatible workloads on B200 or B300)
LangGraph Integration
Modal provides a native LangGraph + Sandboxes example demonstrating sandbox patterns for coding agents. The platform's code-first approach, with Python as one of three supported SDK languages, aligns naturally with LangGraph's Python-based agent framework, enabling teams to define sandbox environments and agent logic in the same codebase.
Security and Compliance
Modal has completed a SOC 2 Type II audit and supports HIPAA-compliant workloads on Enterprise plans via a BAA. The platform uses gVisor for compute isolation, TLS 1.3 for public APIs, and encrypts user data in transit and at rest.
Production-Proven Results
Modal powers production workloads for AI companies running agent infrastructure:
Best For: Teams building LangGraph agents that need secure code execution at scale, with on-demand GPU access when workloads call for ML inference, model processing, or compute-intensive analysis, especially those seeking production-grade infrastructure with proven enterprise scale.
2. E2B
E2B specializes in secure sandboxes for AI agents, focusing on ephemeral code execution with Firecracker microVM isolation. The platform reports that 88% of Fortune 100 companies have signed up for E2B, with notable users including Perplexity, Hugging Face, Manus, Groq, and Lindy.
Core Capabilities
- Firecracker microVMs: Hardware-level isolation with a dedicated kernel per session, providing strong security boundaries for untrusted AI-generated code
- Cold start support: Supports sandbox initialization for agent interactions
- Open-source option: Self-hosting available for organizations with data sovereignty requirements
- Multi-language SDKs: Support for Python and TypeScript/JavaScript integration patterns
- Template system: Reproducible sandbox environments with versioning for consistent agent execution
LangGraph Integration
E2B has documented LangChain provider support and can be integrated into agent workflows. The platform's code interpreter functionality is well-suited for agents that need to execute generated Python code.
Use Case Focus
E2B excels at ephemeral code execution, spinning up isolated environments for agents to run generated code, then tearing them down. The platform's public Pro plan supports 100 concurrent sandboxes by default, with purchasable extra concurrency up to 1,100; Enterprise concurrency is custom. Pro plans support up to 24-hour session lengths.
Best For: Teams building LangGraph agents focused on code execution and testing where maximum security isolation is the priority, particularly those needing Firecracker microVM-level separation for highly sensitive workloads.
3. Daytona
Daytona provides AI-first sandbox infrastructure with cold start support. The platform currently positions itself as infrastructure for AI-generated code and AI-agent sandboxes, and offers GPU support alongside configurable runtime persistence.
Core Capabilities
- Cold start support: Supports sandbox creation with cold start optimization for agent workflows
- Configurable session length: Daytona supports indefinite-running sandboxes when auto-stop is disabled; by default, sandboxes auto-stop after 15 minutes of inactivity
- GPU support: Available for ML workloads alongside persistent storage
- Docker/OCI compatibility: Standard container image and snapshot support with dedicated sandbox environments
- Persistent workspaces: Maintain state across sessions without recreation overhead
LangGraph Integration
Daytona provides a DaytonaSandbox backend that integrates directly with LangChain's official sandbox documentation, making it a straightforward choice for LangGraph agent deployments.
Architecture Approach
Daytona focuses on persistent workspaces that maintain state across sessions. This approach benefits LangGraph agents that need to preserve context, cached dependencies, or intermediate results without recreation overhead.
Best For: Teams building LangGraph agents that require cold start support and configurable session duration, particularly for long-running agent workflows that benefit from persistent state.
4. Northflank
Northflank is an enterprise-focused platform offering sandbox infrastructure with flexible isolation options and bring-your-own-cloud deployment. The platform says it has been in production since 2019 and has run millions of microVMs monthly since 2021, handling over 2 million monthly workloads.
Core Capabilities
- Multiple isolation options: Choice of Kata Containers, Firecracker, or gVisor based on security requirements
- BYOC deployment: Deploy on AWS, GCP, Azure, or bare-metal infrastructure for compliance and data residency requirements
- GPU support: Available across L4 through H200 for ML workloads
- Unlimited session length: No restrictions on sandbox runtime duration
- Full observability: Comprehensive metrics and monitoring for production deployments
LangGraph Integration
Northflank can be used as general sandbox infrastructure for Python agent workloads through its standard OCI container support, though no official LangGraph/DeepAgents sandbox backend documentation for Northflank was found in current LangChain DeepAgents sandbox docs.
Enterprise Focus
Northflank is positioned for regulated industries and organizations requiring data residency controls. The BYOC model allows teams to run sandbox infrastructure within their own cloud accounts while leveraging Northflank's orchestration layer.
Best For: Enterprise teams building LangGraph agents with strict compliance requirements, particularly those needing to deploy sandbox infrastructure within their own cloud accounts for data sovereignty.
5. LangSmith Sandboxes
LangSmith Sandboxes provide native integration with the LangChain ecosystem, offering code execution environments purpose-built for LangGraph agent observability and debugging.
Core Capabilities
- Ecosystem-native integration: Direct integration with LangGraph and DeepAgents, plus LangSmith observability
- Secure isolation: LangSmith Sandboxes provide secure, locked-down code-execution environments; public documentation does not disclose the underlying isolation technology
- Pooling and autoscaling: Support for sandbox pooling and autoscaling to reduce wait times; public documentation does not state a cold-start SLA or benchmark
- LangSmith observability: Native integration with LangSmith's agent tracing and debugging capabilities
- Long-running sessions: Support for sessions lasting minutes or hours; exact public duration limits were not stated in available sources
LangGraph Integration
As part of the LangChain ecosystem, LangSmith Sandboxes offer native integration with LangChain/DeepAgents and LangSmith observability. The deepagents documentation provides comprehensive guidance on using sandboxes within LangGraph agent workflows.
Use Case Focus
LangSmith Sandboxes are primarily designed for teams already invested in the LangChain ecosystem who want unified observability across agent execution and sandbox code execution.
Best For: Teams deeply integrated with the LangChain/LangSmith ecosystem who prioritize unified observability and debugging for LangGraph agents over raw performance or scale.
6. Fly.io Sprites
Fly.io Sprites launched in January 2026 as persistent VM sandboxes designed for AI agent workloads. The platform focuses on maintaining state across sessions with checkpoint and restore capabilities.
Core Capabilities
- Persistent VMs: Sandbox environments that maintain state across sessions without teardown
- Checkpoint/restore: Ability to save and resume sandbox state for long-running agent workflows
- Edge deployment: Leverage Fly.io's global edge network for reduced latency
- Standard VM access: Full Linux environment with root access for agent workloads
Architecture Approach
Fly.io Sprites emphasizes persistent state over ephemeral execution. The checkpoint/restore capability allows LangGraph agents to pause and resume complex workflows without losing context or installed dependencies.
Maturity Consideration
As a product launched in early 2026, Fly.io Sprites is newer to the AI agent sandbox market compared to established providers like Modal and E2B. No official LangGraph/DeepAgents sandbox backend documentation for Fly.io Sprites was found in current LangChain docs.
Best For: Teams building LangGraph agents that need persistent VM environments with checkpoint/restore capabilities, particularly those already using Fly.io's infrastructure.
7. Blaxel
Blaxel is a sandbox platform built specifically for AI agents, focusing on persistent "agent computers" that stay on standby and resume when needed.
Core Capabilities
- Persistent sandboxes: Sandboxes that remain on automatic standby rather than being torn down after each task
- REST API and MCP server: File system and process access exposed through programmatic interfaces
- Template support: Reusable sandbox templates for standardized environments including code generation agents and Git PR review agents
- Volumes for persistence: Storage that survives sandbox destruction and recreation
Architecture Approach
Blaxel emphasizes persistent state over ephemeral execution. Their documentation recommends treating sandboxes as persistent computers that retain shell history, installed dependencies, and context over time.
Use Case Focus
Blaxel targets teams building agents that need continuity across workflows instead of clean-room execution on every task. The platform supports resume from standby and sandbox creation capabilities for agent workloads.
Best For: Teams building LangGraph agents that benefit from persistent sandbox environments with resume capabilities and continuity across multiple agent sessions.
Why Modal Stands Out for LangGraph Sandboxes
Purpose-Built for AI Agent Workloads
Modal's architecture is specifically engineered for agentic and machine learning workloads. The platform's AI-native container runtime and optimized filesystem are built for the unique demands of secure code execution, GPU-accelerated computation, and dynamic scaling that LangGraph agents require.
Unmatched Scale for Production Agents
Some self-serve sandbox plans publish concurrency limits in the hundreds or low thousands, while other providers publish higher workload-scale claims or custom enterprise limits. Modal's product pages state support for scaling to 50,000+ concurrent sandbox sessions for production use cases, with actual concurrency depending on plan and account limits. In production, Lovable's case study reports more than 1 million sandboxes over 48 hours and up to 20,000 concurrent sandboxes at peak, describing Modal as "the only infrastructure provider that enabled us to reliably run tens of thousands of app creation sessions in an instant."
GPU Access When Agents Need It
LangGraph agents increasingly need to call upon ML models for code analysis, generation, and understanding. Modal supports a broad range of NVIDIA GPUs for accelerated workloads, including T4, L4, A10, L40S, A100 40GB/80GB, RTX PRO 6000, H100, H200, B200, and B200+, enabling agents to match compute resources to the task at hand without managing separate infrastructure.
Code-First Developer Experience
Modal's code-first SDKs in Python, TypeScript, and Go eliminate infrastructure configuration overhead. Teams define sandbox environments, compute requirements, and scaling behavior directly in code using decorators. Sandboxes support executing code in any language the workload requires. This approach aligns naturally with LangGraph's Python-based agent framework, enabling unified codebases where agent logic and infrastructure live together.
Enterprise Security and Compliance
With a completed SOC 2 Type II audit, HIPAA support via BAA on Enterprise plans, and comprehensive security practices including gVisor-based compute isolation and TLS 1.3, Modal meets the compliance requirements that enterprise LangGraph deployments demand.
For teams building LangGraph agents that require secure code execution, production-grade reliability, and on-demand GPU access, Modal's combination of AI-native infrastructure, sandboxed execution at scale, and proven enterprise deployment makes it the clear choice.
Explore the Modal documentation to get started building LangGraph agents on secure, scalable infrastructure.
View the Docs