dsh-tensorlake-sandbox

@tensorlakeai/dsh-sandbox moves DeepSeek Harness file, subprocess, Bash, terminal, and LSP operations into one short-lived Tensorlake microVM. It is an installable dsh bundle and does not require changes to the Harness installation.

Prerequisites

• Node.js ^22.19.0 or >=24.0.0
• @deepseek-ai/dsh 0.1.0-rc.6 or a later compatible release
• A Tensorlake project with TENSORLAKE_API_KEY set in the host environment
• DEEPSEEK_API_KEY set in the host environment for the default DeepSeek model provider

Keep credentials in environment variables or a secret manager; do not commit them to the profile or repository.

Install

Install dsh and add this bundle to the profile you run:

npm install --global @deepseek-ai/dsh
dsh plugin --profile headless add @tensorlakeai/dsh-sandbox
TENSORLAKE_API_KEY=... DEEPSEEK_API_KEY=... dsh --profile headless "build and test this repo"

During development, install a local checkout from its directory:

npm install
npm run build
dsh plugin --profile headless add .

Use dsh --profile headless --dump-config to verify that the @tensorlakeai/dsh-sandbox layer disables the host subprocess and fs-sandbox providers, inserts the Tensorlake runtime, subprocess, and filesystem rows, and keeps bash-sandbox mounted in danger-full-access mode. In that mode Harness's sandbox-aware Bash executor delegates directly to the Tensorlake subprocess provider while still satisfying the permission-preset capability contract.

Smoke test

Run one headless task that exercises both the subprocess and filesystem providers:

dsh --profile headless \
  "Use Bash to run pwd and id. Create smoke-test.txt containing hello, read it back, and report the results."

A successful run reports /home/tl-user/workspace from pwd, the tl-user identity from id, and reads hello back from the file. The model-facing working directory is the same remote Linux path, so the response should not mention or fall back from a host-machine path.

Configuration

The bundle starts an ephemeral sandbox on profile boot and terminates it when dsh exits. The runtime module accepts these Cordis config fields:

Each run prints the sandbox ID at both lifecycle boundaries. The IDs should match:

Tensorlake sandbox created: <sandbox-id>
Tensorlake sandbox terminated: <sandbox-id>

Field	Default	Meaning
apiKey	TENSORLAKE_API_KEY	Tensorlake API credential used only by the host SDK
cwd	/home/tl-user/workspace	Absolute Linux working directory shared by file and process providers
timeoutSecs	600	Sandbox inactivity timeout
cpus	Tensorlake default	Virtual CPU allocation
memoryMb	Tensorlake default	Memory allocation in MiB
diskMb	Tensorlake default	Root disk allocation in MiB

The shipped bundle derives both the runtime cwd and policy workspace from DSH_TENSORLAKE_CWD. Prefer that single setting when changing the workspace so the Bash policy and remote providers cannot drift:

DSH_TENSORLAKE_CWD=/workspace/project dsh --profile headless "build and test this repo"

To configure the rows directly in the profile's cordis.patch.yml, override both together. A patch replaces the complete config, so restate every non-default field you need:

- id: sandbox-policy
  config:
    mode: danger-full-access
    workspaceRoot: /workspace/project

- id: tensorlake-runtime
  config:
    cwd: /workspace/project
    timeoutSecs: 1800
    cpus: 2
    memoryMb: 4096

apiKey is optional and should normally remain omitted. The package never copies TENSORLAKE_API_KEY, DEEPSEEK_API_KEY, other credential-shaped environment variables, or DSH_* variables into sandbox processes. A caller may still pass an explicit environment entry through a Harness tool or service request.

Runtime requirements

The Tensorlake image must provide bash, Node.js, and GNU base64, cat, chmod, env, find, grep, ln, mkdir, mktemp, mv, ps, realpath, rm, stat, and tee. The default managed Ubuntu image provides these tools. The runtime verifies that a configured cwd is writable and uses the managed image's passwordless sudo to create and hand off a protected path when necessary.

The package targets @deepseek-ai/dsh 0.1.0-rc.6 or later compatible release. The dsh installation supplies its optional Cordis, filesystem, subprocess, and Schemastery peers through the profile module fallback. The package uses only public ctx.fs and ctx.subprocess service definitions; no DeepSeek Harness source registration, generated catalogs, or in-repository configuration is required.

Known limitations

• tensorlake@0.5.103, the current SDK release, pins undici@8.3.0 and nanoid@3.3.11; npm audit --omit=dev reports high-severity advisories for those transitive versions. No audit-clean current Tensorlake SDK release is available, so review the upstream advisories before production use and update the SDK pin when Tensorlake publishes one.

Develop

npm install
npm run check
npm pack

The three Loader entry points are @tensorlakeai/dsh-sandbox/runtime, @tensorlakeai/dsh-sandbox/filesystem, and @tensorlakeai/dsh-sandbox/subprocess. Each module default-exports its service class; do not add function-plugin named exports to those modules because the Cordis Loader treats mixed export forms as a function-plugin namespace.