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🛠 This page is for engineering teams self-hosting their own Lightdash instance. On Lightdash Cloud, sandboxes are fully managed for you — there’s nothing to configure.

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What sandboxes are for

Some Lightdash features use an AI agent (Claude Code) that writes and runs code on your behalf. Today that’s:
  • AI writeback — the agent edits your dbt project (e.g. adds a metric or dimension), runs lightdash compile to validate it, and opens a pull request.
  • Data app generation — the agent generates and builds a small web app from a prompt.
Running model-generated code directly on the Lightdash server would be unsafe: the code is untrusted, can run arbitrary commands, and needs its own toolchain (git, dbt, the Lightdash CLI, Node). Lightdash instead runs each agent inside a sandbox — an isolated, disposable environment with a constrained network. The agent does its work there, Lightdash collects the result (a PR, a built app), and the sandbox is torn down. Sandboxes are also what make these features fast and multi-turn: a sandbox can be suspended between turns and resumed later, so a conversation with the agent keeps its state without holding a container open the whole time.

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Sandbox providers

The sandbox backend is pluggable. Lightdash talks to a provider-neutral interface, so the same feature code runs on whichever backend your deployment is configured for. You select the provider with the SANDBOX_PROVIDER environment variable. Both E2B and AWS Lambda MicroVMs are supported production backends. E2B is the managed default; Lambda MicroVMs is for teams who want sandboxes to run inside their own AWS account. More providers (Kubernetes, ECS) are planned.
The local Docker provider is for development only. It launches plain Docker containers via the Docker socket, which is root-equivalent on the host and provides no real isolation between the sandbox and your machine. It refuses to start when NODE_ENV=production. Do not use it for a production deployment.

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E2B (production default)

E2B runs each sandbox as a Firecracker microVM in E2B’s cloud. It’s the default — if you don’t set SANDBOX_PROVIDER, Lightdash uses E2B. To use it you need an E2B account and API key, and the agent needs an Anthropic API key:
The sandbox images are E2B templates. Lightdash uses separate templates for data apps and for AI writeback so they can be pinned or rolled back independently. These default to the published Lightdash templates and rarely need to be set:

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AWS Lambda MicroVMs (self-hosted production)

AWS Lambda MicroVMs run each sandbox as a Firecracker microVM inside your own AWS account, so untrusted agent code and your repository contents never leave your infrastructure. The microVMs have no public IP — your backend reaches each one through an AWS-managed endpoint that requires a short-lived per-microVM token — and you control their outbound network access (see Networking and IAM). This is the recommended sandbox provider for customers deploying Lightdash on AWS — it keeps the sandbox boundary inside your existing AWS account and avoids sending agent workloads or repository contents to a third-party service.

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Prerequisites

Provision these with your own IaC, in the same AWS account and region your Lightdash backend already runs in:
  • Two MicroVM images — one for data app generation and one for AI writeback (they bundle different toolchains). Build them from the Dockerfiles in the Lightdash repo (sandboxes/data-apps/, sandboxes/ai-writeback/, and the exec agent in sandboxes/microvm-agent/), push them to ECR, and register each as a Lambda MicroVM image on the AWS-managed al2023 base — ARM_64, 4 GB memory, with the agent’s /ready hook on port 8080. Each registration returns an image ARN for the config below.
  • Control-plane permissions on your backend’s existing IAM role — add RunMicrovm, GetMicrovm, SuspendMicrovm, ResumeMicrovm, TerminateMicrovm, and CreateMicrovmAuthToken.
Registering an image uses AWS’s create-microvm-image, which needs a build role (trusting lambda.amazonaws.com) and an S3 location to stage the build context. These are build-time only — not the S3 bucket Lightdash already uses for results and snapshots, and the backend never touches them at runtime.

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Configure the provider

The backend uses its ambient AWS credentials (instance role / IRSA / standard SDK credential chain) to call the Lambda MicroVMs control plane, so no access keys are configured here.

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Networking and IAM

Configure the network connectors before going to production. The AWS-managed defaults give the microVM open inbound and outbound access, which means untrusted agent code can reach the public internet from inside your AWS account. We currently recommend pointing the egress connector at a VPC connector that has no outbound access by default, and only opening up the destinations the agent actually needs (your dbt repository host, the Anthropic / Bedrock API, your ECR registry).
Override these to tighten the network boundary or to give the microVM an IAM role:

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Local Docker provider (development)

For local development you can run sandboxes as plain Docker containers on your own machine — no E2B account required. This is the recommended way to work on or try the AI features locally. It uses the same images E2B builds, but as plain local Docker images. Two separate images are used (different toolchains), mirroring the two E2B templates:

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Prerequisites

  • Docker running locally, with the daemon reachable from the Lightdash backend.
  • S3-compatible object storage configured (locally this is MinIO). Suspended-sandbox snapshots are tarred to object storage so a conversation survives the container being destroyed — see external object storage.
  • An Anthropic API key (ANTHROPIC_API_KEY) for the agent.

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Setup

  1. Build the local sandbox images (each builds from sandboxes/<feature>/):
    These are large (the writeback image bundles dbt, the Lightdash CLI and Claude Code) and only need rebuilding when the sandbox toolchain changes.
  2. Point Lightdash at the Docker provider:
  3. Restart the backend and the scheduler so both pick up the new environment. Data app generation runs in the scheduler worker, so a stale SANDBOX_PROVIDER there will keep it on E2B. (With PM2, a plain restart reuses the cached env — delete and re-start the processes, or restart with --update-env, to actually reload the env file.)

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Snapshot lifecycle

Every turn suspends its own sandbox, so in steady state nothing sits idle. For the Lambda MicroVMs provider, two timers configure the AWS-side idle policy as a backstop — when to auto-suspend a sandbox left running and when to auto-terminate a suspended one:
These are read only by the Lambda MicroVMs provider. E2B manages idle sandboxes itself, and the Docker dev provider has no idle handling.

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Environment variable reference