seacloud-sandbox 0.3.1

SeaCloudAI sandbox SDK for control-plane, build-plane, and runtime CMD APIs.

Sandbox Python SDK

Python SDK for Sandbox control-plane, build-plane, and nano-executor CMD APIs.

Product Highlights

SeaCloudAI Sandbox gives you a cloud runtime for code execution, agent workflows, lightweight services, and custom template builds.

• Start fast with official templates: use base for files, commands, git, and PTY; use code-interpreter for multi-language code execution; use agent templates such as claude or codex when those environments are published.
• Manage the full sandbox lifecycle: create, connect, pause, resume, refresh timeout, inspect logs, and delete sandboxes through one SDK.
• Run real workloads inside the sandbox: write files, execute commands, start background services, open PTY sessions, and expose HTTP services with sandbox.get_host(port).
• Move from local code to reusable templates: upload files to a running sandbox for quick iteration, then bake local code into a custom template with Template.copy(...).
• Use one workflow across languages: Node, Python, and Go SDKs expose the same core sandbox, runtime, and template-building concepts.
• Keep an E2B-style public workflow: lifecycle, files, commands, PTY, code interpreter, and template helpers follow familiar E2B-style patterns while using SeaCloudAI gateway and runtime configuration.

Install

pip install seacloud-sandbox

If you previously installed 0.1.2, upgrade to 0.1.3 or later. 0.1.2 shipped without the sandbox.build package in the published artifact.

Entrypoints

Preferred public API:

• preferred sandbox entrypoint: Sandbox.create(...)
• additional sandbox lifecycle helpers: Sandbox.connect(...), Sandbox.list(...), Sandbox.get_info(...), Sandbox.get_full_info(...), Sandbox.pause(...), Sandbox.kill(...), Sandbox.set_timeout(...)
• sandbox runtime modules from the returned object: sandbox.commands, sandbox.files, sandbox.git, sandbox.pty
• preferred template entrypoint: Template.build(...), Template.build_in_background(...), Template.list(...), Template.get(...), Template.delete(...)
• low-level transport modules: sandbox.control, sandbox.build, sandbox.cmd

High-level Sandbox / Template helpers read gateway config from SEACLOUD_BASE_URL / SEACLOUD_API_KEY. Low-level sandbox.control, sandbox.build, and sandbox.cmd clients can still be initialized explicitly when needed. Runtime access is derived from sandbox create/detail/connect responses; callers should not hardcode runtime endpoints or tokens.

E2B Alignment

• Supported alignment target: sandbox lifecycle, files, commands, git, PTY, and the high-level template DSL are designed to follow the same public workflow as e2b-docs/sdk.
• Code interpreter alignment: sandbox.run_code(...) is available for python, javascript, typescript, bash, r, and java. Python results support display(...), last-expression capture, tables, Matplotlib PNG/chart payloads, a persistent default execution context, and stateful create_code_context/list_code_contexts/restart_code_context/remove_code_context helpers. Non-Python contexts use the same API surface but currently behave as stateless execution profiles.
• Known unsupported area: snapshot APIs are not exposed because the underlying platform does not support them yet.
• Known partial area: only Python contexts are stateful. Non-Python contexts still execute in isolated one-shot processes.
• Runtime normalization note: the SDK smooths a few runtime-specific quirks so the high-level behavior stays E2B-like, such as missing-process kill() results, PTY reconnect output framing, and a one-time retry for transient reconnect-stream open failures.

Environment

Use environment variables for gateway configuration in all examples and quick starts:

• SEACLOUD_BASE_URL: preferred gateway entrypoint
• SEACLOUD_API_KEY: preferred API key

Set them once in your shell:

export SEACLOUD_BASE_URL="https://sandbox-gateway.cloud.seaart.ai"
export SEACLOUD_API_KEY="..."

Default production gateway:

https://sandbox-gateway.cloud.seaart.ai

High-level Sandbox.create(...) requires an explicit template reference. Pass a concrete template ID such as tpl-... or a stable official template type such as base, code-interpreter, claude, or codex when your environment publishes those official templates.

From Zero To One

This section is the recommended path for first-time users. It starts from environment setup, then creates sandboxes from official templates, runs commands, exposes a frontend through envdUrl, and finally builds a reusable custom template from local code.

1. Configure Environment

export SEACLOUD_BASE_URL="https://sandbox-gateway.cloud.seaart.ai"
export SEACLOUD_API_KEY="..."

Run the examples from packages/python:

pip install -e .
python examples/zero_to_one.py

2. Create A Base Sandbox

Use base for normal sandbox lifecycle, files, commands, git, and PTY. It is the right starting point for command execution and filesystem work.

from sandbox import Sandbox

sandbox = Sandbox.create("base", timeout=1800, waitReady=True)

try:
    print("sandbox", sandbox.sandbox_id, sandbox.sandbox_domain)

    sandbox.files.write("/root/workspace/hello.txt", "hello\n")
    print(sandbox.files.read("/root/workspace/hello.txt"))

    result = sandbox.commands.run(
        "sh",
        args=["-lc", "pwd && uname -a && ls -la /root/workspace"],
    )
    print(result["exit_code"], result["stdout"])
finally:
    sandbox.delete()

3. Pick Official Templates By Workload

Workload	Template	Use it for
Basic shell, files, git, PTY, and lightweight services	base	General sandbox lifecycle and filesystem/command workflows.
Multi-language code execution	code-interpreter	sandbox.run_code(...) for Python, JavaScript, TypeScript, Bash, R, and Java. Python contexts are stateful.
Agent CLI workflows	claude / codex	Environments where those official agent templates are published with the CLIs preinstalled.
Reproducible production workloads	tpl-...	A concrete custom or official template ID pinned from config.

code_sandbox = Sandbox.create("code-interpreter", waitReady=True)

try:
    execution = code_sandbox.run_code("x = 41\nx + 1")
    print(execution.text)
finally:
    code_sandbox.delete()

4. Manage Lifecycle

Lifecycle timeout values are seconds. Runtime command timeout_ms values are milliseconds.

sandbox = Sandbox.create("base", timeout=1800, waitReady=True)

info = sandbox.get_info()
print(info["sandbox_id"], info["state"])

sandbox.set_timeout(3600)

paused = sandbox.pause()
print("paused", paused)

sandbox.connect(timeout=1800)
print("running", sandbox.is_running())

sandbox.delete()

5. Deploy A Frontend And Open It Through envdUrl

Use a template that has Python or Node available. code-interpreter is a convenient default for this static frontend example because it can run python3 -m http.server.

app = Sandbox.create("code-interpreter", timeout=1800, waitReady=True)

try:
    app.files.make_dir("/root/workspace/frontend")
    app.files.write(
        "/root/workspace/frontend/index.html",
        "<h1>Hello from sandbox</h1>",
    )

    app.commands.run(
        "python3",
        args=["-m", "http.server", "3000", "--bind", "0.0.0.0"],
        cwd="/root/workspace/frontend",
        background=True,
    )

    print("open", app.get_host(3000))
finally:
    app.delete()

get_host(3000) derives a public proxy URL from the sandbox envdUrl. Keep envdAccessToken / traffic_access_token private; they are sandbox-scoped secrets.

Service access notes:

• Bind HTTP services to 0.0.0.0, not 127.0.0.1, so the runtime proxy can reach them.
• Use sandbox.get_host(port) instead of constructing proxy URLs manually.
• If the URL does not open, check that the process is still running, the port matches, and the selected template exposes runtime access fields.

6. Upload Local Code Files

There are two common upload paths:

• Runtime upload to an existing sandbox: use sandbox.files.write(...) / write_files(...) when you want to place generated files into a running sandbox.
• Template build upload: use Template.copy(...) when you want local files or directories baked into a reusable template image.

Upload a local file into a running sandbox:

from pathlib import Path

data = Path("./my-frontend/index.html").read_bytes()
sandbox.files.write("/root/workspace/frontend/index.html", data)

Upload one local file into a template build:

Template() \
    .from_template("base") \
    .copy("./package.json", "/workspace/app/package.json", force_upload=True)

Upload a local directory recursively:

Template() \
    .from_template("base") \
    .copy(
        "./my-frontend",
        "/workspace/frontend",
        force_upload=True,
        mode=0o755,
        resolve_symlinks=True,
    )

The first argument is a local path on your machine. The second argument is the destination path inside the template filesystem. force_upload=True is useful during development when the local files change frequently and you want the SDK to re-upload them instead of reusing a cached content hash.

7. Build Your Own Template From Local Code

This uploads a local directory into the build context with copy(...), builds a new template, and sets a startup command for future sandboxes created from that template.

from sandbox import Template, wait_for_port

built = Template.build(
    Template()
    .from_template("nfs")
    .copy("./my-frontend", "/app", force_upload=True)
    .run_cmd("cd /app && npm install && npm run build")
    .set_start_cmd(
        "mkdir -p /agent-workspace && if [ -z \"$(ls -A /agent-workspace 2>/dev/null)\" ]; then cp -a /app/. /agent-workspace/; fi && cd /agent-workspace && npm run start",
        wait_for_port(3000),
    ),
    "my-frontend:v1",
    base_template_id="tpl-nfs-0e70a5ababc44412",
    workdir="/agent-workspace",
    volume_mounts=[
        {
            "name": "workspace",
            "path": "/agent-workspace",
            "storageType": "nfs",
            "nfsHostPath": "/mnt/prod-sandbox-nfs-filesystem01",
        },
    ],
    wait=True,
    poll_interval=2.0,
    request_timeout_ms=180_000,
)

print(built["template_id"], built["build_id"])

workdir sets the default shell/file root. The actual persistent mount is declared by volume_mounts; for NFS you must provide storageType: "nfs" and the environment-specific nfsHostPath.

Create a sandbox from the new template:

sandbox = Sandbox.create(built["template_id"], waitReady=True)
print(sandbox.get_host(3000))

8. Recommended Production Flow

1. Prototype with an official template such as base or code-interpreter.
2. Upload local files to a running sandbox for fast iteration.
3. Move stable setup into Template.copy(...), run_cmd(...), set_start_cmd(...), and set_ready_cmd(...).
4. Build and pin the resulting tpl-... value in application config.
5. Keep sandbox cleanup in finally blocks or a lifecycle manager, and set explicit lifecycle timeout values for each workload.

Troubleshooting

• 401 / 403: verify SEACLOUD_API_KEY and that the process sees the environment variable.
• Requests go to the wrong gateway: check SEACLOUD_BASE_URL; include the https:// scheme.
• Runtime APIs return 404: use a template that starts nano-executor and returns envdUrl / envdAccessToken.
• waitReady or builds time out: increase lifecycle timeout and SDK HTTP request_timeout_ms for long starts or image builds.
• Frontend URL is unreachable: bind to 0.0.0.0, confirm the port passed to get_host(...), and inspect whether the background process exited.
• Build with local files fails: make sure Template.copy(...) points to an existing local path and use force_upload=True while iterating.

Production Readiness

• Initialize environment variables once per process and reuse bound sandbox/template objects.
• Treat every quick start as creating billable or quota-bound resources unless it explicitly cleans them up.
• Prefer explicit template references from configuration over hardcoded example values.
• In SeaCloudAI environments, prefer official template types such as base, code-interpreter, claude, or codex when you want a stable platform-managed entrypoint.
• Template semantics matter: base is the minimal runtime template for lifecycle, files, commands, git, and PTY. It does not imply a multi-language execution environment. Use code-interpreter for sandbox.run_code(...), and use agent-specific templates such as claude or codex when you need those CLIs preinstalled.
• Use longer SDK HTTP timeouts for waitReady flows and image builds.
• Derive runtime access from sandbox responses instead of storing runtime endpoints or tokens in config.

Compatibility

• Python: use a supported CPython version for the published package and pin the SDK version in production deployments.
• API model: this SDK targets the unified SeaCloudAI sandbox gateway and keeps public template APIs limited to user-facing fields.
• Stability: operator/admin routes may exist on the gateway, but they are not part of the public SDK workflow described in this README.
• Retry model: treat create/delete/build operations as remote control-plane actions; add idempotency and retry policy in your application layer according to your workload.
• Timeout semantics: sandbox lifecycle uses E2B-style timeout seconds. Commands, PTY, git, and code execution helpers use timeout_ms milliseconds. request_timeout_ms is only the SDK HTTP request timeout in milliseconds.

Quick Start

Control Plane

from sandbox import Sandbox

sandbox = Sandbox.create(
    "base",
    timeout=1800,
    waitReady=True,
)
try:
    print(sandbox.sandbox_id, sandbox.sandbox_domain)
finally:
    sandbox.delete()

Bound Sandbox Workflow

from sandbox import Sandbox

listed = Sandbox.list()

for sandbox in listed:
    print(sandbox["sandboxID"], sandbox.get("state") or sandbox.get("status"))

Template Build

from sandbox import Template, wait_for_file

built = Template.build(
    Template()
    .from_image("docker.io/library/alpine:3.20")
    .run_cmd("echo hello-from-python >/tmp/hello.txt")
    .set_ready_cmd(wait_for_file("/tmp/hello.txt")),
    "demo:v1",
)

print(built["template_id"], built["build_id"])

High-level template helpers currently include:

• lifecycle and status: Template.build, Template.build_in_background, Template.exists, Template.get_build_status, Template.list, Template.get, Template.delete
• serialization: Template.to_json, Template.to_dockerfile
• base images and registries: from_dockerfile, from_base_image, from_node_image, from_python_image, from_bun_image, from_ubuntu_image, from_debian_image, from_aws_registry, from_gcp_registry
• build-step helpers: copy, copy_items, skip_cache, apt_install, git_clone, make_dir, make_symlink, npm_install, pip_install, bun_install, remove, rename
• execution and config helpers: run_cmd, set_envs, set_workdir, set_user, set_start_cmd, set_ready_cmd, files_hash
• supported local copy options: force_upload, mode, resolve_symlinks, user
• supported command and path options: run_cmd(..., user=...), git_clone(..., user=...), make_dir(..., user=...), make_symlink(..., user=...), remove(..., user=...), rename(..., user=...)
• intentionally not exposed yet: MCP server helpers and devcontainer helpers

Runtime Modules

from sandbox import Sandbox

sandbox = Sandbox.create(
    "base",
    waitReady=True,
)

try:
    sandbox.files.write("/root/workspace/hello.txt", b"hello from python")
    print(sandbox.files.read("/root/workspace/hello.txt"))
    print(sandbox.get_host(3000))
finally:
    sandbox.delete()

Code Interpreter

Use a template that actually includes the code-interpreter environment here. In SeaCloudAI environments, prefer an official code-interpreter template or a concrete tpl-code-interpreter-... template ID. Do not use base for this example.

from sandbox import Sandbox

sandbox = Sandbox.create(
    "code-interpreter",
    waitReady=True,
)

try:
    execution = sandbox.run_code(
        """
import pandas as pd

df = pd.DataFrame([{"name": "Ada", "score": 99}])
display(df)
99
        """,
        on_stdout=lambda chunk: print("stdout:", chunk.line),
        on_stderr=lambda chunk: print("stderr:", chunk.line),
        on_result=lambda result: print("result:", result),
    )

    print(execution.text)
finally:
    sandbox.delete()

For Python, repeated sandbox.run_code(...) calls reuse the sandbox's default code context. You can create additional Python contexts with create_code_context(...) when you need isolated state. For other languages, create_code_context(...) returns a reusable execution profile that supplies default language, cwd, and timeout_ms values, but each run still executes in a fresh one-shot process.

Bound sandbox helpers currently include:

• lifecycle: reload, connect, resume, get_info, get_full_info, logs, pause, kill, delete, refresh, set_timeout, is_running pause() returns True when a running sandbox is newly paused and False when it was already paused. get_info() / get_full_info() return normalized sandbox info with sandbox_id, template_id, sandbox_domain, traffic_access_token, started_at, end_at, and state. Lifecycle helpers accept request_timeout_ms for SDK HTTP request timeout overrides.
• runtime conveniences: get_metrics, get_host, download_url, upload_url, proxy
• code interpreter: run_code, create_code_context, list_code_contexts, restart_code_context, remove_code_context
• commands module: run, exec, list, connect, kill, send_stdin run() / exec() accept timeout_ms, stdin, on_stdout, on_stderr, and user; callbacks and open-stdin mode use the runtime streaming protocol. connect(pid, on_stdout=..., on_stderr=...) attaches output callbacks to an existing process stream. Command handles expose both send_stdin(...) and the E2B-style send_input(...) alias.
• filesystem module: exists, get_info, list, make_dir, read, write, write_files, remove, rename, watch_dir get_info() / list() / rename() return normalized entries with type: "file" | "dir" | "symlink" and modified_time. write() / write_files() return E2B-style write info with name, path, and type. read(..., format="text"|"bytes"|"stream") returns text, bytes, or the raw response stream. File methods accept user; make_dir() returns False when the path already exists. watch_dir(path, on_event, ...) returns a stop handle instead of the raw stream. It also supports user, timeout_ms, and on_exit.
• git module: clone, pull, checkout, status
• pty module: create, connect, kill, send_stdin, send_input, resize pty.connect(pid, on_stdout=..., on_stderr=...) attaches output callbacks when reconnecting to a PTY.

Recommended Usage

For most integrations, prefer the env-first high-level flow:

• set SEACLOUD_BASE_URL and SEACLOUD_API_KEY
• create sandboxes with Sandbox.create(...)
• continue through sandbox.commands, sandbox.files, sandbox.git, and sandbox.pty
• build templates with Template.build(...) and Template.build_in_background(...)
• only drop to sandbox.control, sandbox.build, or sandbox.cmd when you need transport-level request control

Low-level methods remain available when you need tighter request control:

• continue from the returned sandbox object with reload(), connect(), resume(), get_info(), get_full_info(), get_metrics(), get_host(), logs(), pause(), refresh(), set_timeout(), kill(), delete(), and is_running()
• use Template.build(...), Template.build_in_background(...), Template.exists(...), Template.get_build_status(...), Template.list(...), Template.get(...), and Template.delete(...) for the preferred template workflow
• use ControlService, BuildService, and runtime service helpers from the submodules only for raw control/build/cmd workflows
• use template_build() when you want a small fluent helper that expands into the public build request payload

Low-level submodules remain available when you need direct request/response models or tighter transport control.

API Surface

Control Plane APIs

• high-level lifecycle: create, connect, list
• follow-up control actions from the returned object: reload(), connect(), resume(), get_info(), get_full_info(), get_metrics(), get_host(), logs(), pause(), refresh(), set_timeout(), kill(), delete(), is_running()
• low-level control module: ControlService from sandbox.control
• low-level service methods: metrics, shutdown, create_sandbox, list_sandboxes, get_sandbox, get_sandbox_metrics, list_sandbox_metrics, delete_sandbox, get_sandbox_logs, pause_sandbox, connect_sandbox, set_sandbox_timeout, refresh_sandbox, send_heartbeat

Monitoring And Metrics

The SDK exposes two different metrics surfaces:

• Control-plane sandbox metrics use Atlas through the gateway. Prefer these for dashboards and fleet monitoring because they can include Grafana/Kata enriched fields such as load average, CPU breakdown, memory pressure, disk I/O, network throughput, and task counts.
• Runtime metrics call the sandbox nano-executor /metrics endpoint through envdUrl. Use these when you are already connected to one runtime and only need the raw in-sandbox snapshot. The runtime payload currently focuses on CPU, memory, and disk fields; network and disk-rate fields are available from the control-plane metrics surface.

Control-plane metrics:

import os

from sandbox.control import ControlService, SandboxMetricsParams

client = ControlService(
    base_url=os.environ["SEACLOUD_BASE_URL"],
    api_key=os.environ["SEACLOUD_API_KEY"],
)

single = client.get_sandbox_metrics("sandbox-abc")
print(single["cpuUsedPct"], single.get("load1"), single.get("memoryUsagePercent"))
print(single.get("networkSentBytesPerSecond"), single.get("diskWriteBytesPerSecond"))

batch = client.list_sandbox_metrics(
    SandboxMetricsParams(sandbox_ids=["sandbox-abc", "sandbox-def"], limit=2)
)
print([item["sandboxID"] for item in batch["items"]])

Control-plane snapshot fields include:

• identity and status: sandboxID, collectedAt, error
• CPU: cpuCount, cpuUsedPct, load1, load5, load15, cpuUserRate, cpuSystemRate, cpuIOWaitRate, cpuStealRate
• memory: memTotal, memUsed, memTotalMiB, memUsedMiB, memCache, memoryAvailableBytes, memoryUsagePercent, swap fields
• disk: diskUsed, diskTotal, diskReadOpsPerSecond, diskWriteOpsPerSecond, diskReadBytesPerSecond, diskWriteBytesPerSecond
• network: netRxBytes, netTxBytes, networkRecvBytesPerSecond, networkSentBytesPerSecond, packet/error/drop rates
• tasks and raw runtime snapshot: taskCurrent, taskMax, raw

Runtime metrics:

runtime_metrics = sandbox.get_metrics()
print(runtime_metrics["cpu_used_pct"])
print(runtime_metrics["mem_used_mib"], runtime_metrics["mem_total_mib"])
print(runtime_metrics["disk_used"], runtime_metrics["disk_total"])

Use client.metrics() or client.build.metrics() only when you need the Prometheus text output for the gateway services themselves. Those service metrics are not per-sandbox runtime metrics.

Operator APIs

The low-level control service also includes operator-oriented methods such as get_pool_status, start_rolling_update, get_rolling_update_status, and cancel_rolling_update.

These routes are intended for platform operators, not normal application workloads. Keep them out of business-facing integrations unless you are explicitly building operational tooling.

Template Facade

Preferred template path:

• Template() for build DSL
• Template.build(...) and Template.build_in_background(...) for create + build + optional polling
• Template.list(...), Template.get(...), Template.delete(...), Template.exists(...), Template.get_build_status(...) for lifecycle and status
• Template.to_json(...), Template.to_dockerfile(...) for export helpers

Template builder conveniences include:

• base images and registries: from_dockerfile, from_base_image, from_node_image, from_python_image, from_bun_image, from_ubuntu_image, from_debian_image, from_aws_registry, from_gcp_registry
• file and command helpers: copy, copy_items, skip_cache, apt_install, git_clone, make_dir, make_symlink, npm_install, pip_install, bun_install, remove, rename, run_cmd
• execution and config helpers: set_envs, set_workdir, set_user, set_start_cmd, set_ready_cmd, files_hash
• supported local copy options: force_upload, mode, resolve_symlinks, user
• supported command and path options: run_cmd(..., user=...), git_clone(..., user=...), make_dir(..., user=...), make_symlink(..., user=...), remove(..., user=...), rename(..., user=...)
• intentionally not exposed yet: MCP server helpers and devcontainer helpers

Build Plane Namespace

Low-level BuildService from sandbox.build exposes:

• system: metrics
• templates: create_template, list_templates, get_template_by_alias, resolve_template_ref, get_template, update_template, delete_template
• builds: create_build, get_build_file, rollback_template, list_builds, get_build, get_build_status, get_build_logs
• tags: assign_template_tags, delete_template_tags, list_template_tags

The public template contract is split into three layers: E2B create fields (name, tags, cpuCount, memoryMB), Atlas extension fields under extensions (baseTemplateID, visibility, envs, volumeMounts, workdir), E2B update field public, and build-only fields on create_build (fromImage, fromTemplate, steps, startCmd, readyCmd, registry credentials, steps[].filesHash). Each mount declares its own storage through volumeMounts[i].storageType plus the matching storage fields such as nfsHostPath, storageClass/storageSizeGB, persistentVolumeClaim, or objectBucket. workdir sets the sandbox default working directory and file API root; it does not create a mount by itself. Runtime behavior defaults from the image source: templates inheriting SeaCloud base/runtime templates keep the managed runtime, while direct external images run as plain business containers. startCmd and readyCmd only provide startup and readiness commands on top of that default. Public create calls reject unsupported top-level write fields such as alias and public; public update calls only accept public.

create_template rejects visibility="official" on public routes, including extensions.visibility == "official".

create_build now follows the E2B wire contract directly: COPY contexts are passed through steps[].filesHash, and the SDK returns the raw 202 {} trigger response without adding helper fields.

get_template_by_alias is a pure alias lookup endpoint. It should only be used with an actual published alias value.

resolve_template_ref is the SeaCloud stable-ref lookup endpoint. It resolves a template by templateID, official template type, or visible alias.

Resource Safety

• The quick starts are written for disposable resources and should be adapted before copy-pasting into production jobs.
• Prefer explicit cleanup with sandbox.delete() and Template.delete(...) when running probes, smoke tests, or CI.
• For long-lived workloads, move cleanup and timeout policy into your own lifecycle manager instead of relying on sample code defaults.

Runtime Namespace

Bound sandbox runtime modules and low-level CMD services expose:

• system: metrics, envs, configure, ports
• proxy and file transfer: proxy, download, files_content, upload_bytes, upload_json, upload_multipart, write_batch, compose_files, read_file, write_file
• filesystem RPC: list_dir, stat, make_dir, remove, move, edit
• watchers: watch_dir, create_watcher, get_watcher_events, remove_watcher
• process RPC: start, connect, list_processes, send_input, send_signal, close_stdin, update, stream_input, get_result, run

Useful CMD helpers:

• sandbox.cmd.CmdRequestOptions: username, signature, signature expiration, range, request_timeout_ms, extra headers
• ProcessStream and FilesystemWatchStream: Connect-RPC stream readers
• ConnectFrame: low-level frame parser

Module Layout

• sandbox: root high-level Sandbox / Template facade
• sandbox.control: control-plane models and low-level APIs
• sandbox.build: build-plane models and low-level APIs
• sandbox.cmd: runtime models and low-level APIs
• sandbox.core: shared transport and error primitives

Notes

• High-level helpers always read gateway auth and endpoint from SEACLOUD_BASE_URL / SEACLOUD_API_KEY. Only low-level transport clients should be initialized with explicit base_url / api_key.
• Runtime access should be derived from bound sandbox objects or low-level sandbox instances.
• Low-level create/detail responses include envdUrl and envdAccessToken when nano-executor access is enabled.
• Runtime file/process APIs require a template image that starts nano-executor and returns runtime access fields; if runtime APIs return 404, verify the selected template supports CMD runtime routes.
• Runtime requests can override the SDK HTTP timeout per call through CmdRequestOptions(request_timeout_ms=...).
• The bound sandbox exposes traffic_access_token as an E2B-style alias of the runtime access token returned by the gateway.
• waitReady=True can take longer than the default lifecycle wait in production; pass a larger timeout on sandbox create/connect flows when you need a longer ready/pause wait budget.
• HTTP errors are classified into typed exceptions such as NotFoundError, RateLimitError, and ServerError. Transport timeouts raise RequestTimeoutError.
• High-level kill() helpers send SIGNAL_SIGKILL and return False when the runtime reports a missing process through either 404 or ESRCH.
• PTY handles normalize reconnect output into pty even when the runtime emits the bytes through stdout / stderr.
• Runtime reconnect streams such as connect() and watch_dir() retry once on transient TLS EOF / remote-close failures before surfacing the transport error.
• Sandbox lifecycle timeout is validated to 0..86400 seconds; refresh duration to 0..3600 seconds.
• Build validation accepts E2B-style COPY / ENV / RUN / WORKDIR / USER steps, force, and structured fromImageRegistry credentials (registry / aws / gcp).
• Some gateways do not expose /admin/*; integration tests skip those cases on 404.
• Some filesystem layouts reject watcher APIs entirely; the integration suite skips watcher coverage when the runtime reports that limitation.

Security

• Do not commit SEACLOUD_API_KEY, envdAccessToken, or sandbox access tokens.
• Treat runtime tokens as sandbox-scoped secrets. Prefer bound sandbox objects or low-level sandbox instances so response-scoped runtime access is not copied into configuration.
• Do not log raw API keys or runtime tokens. SDK exceptions may include response bodies, so avoid logging full error payloads in shared systems.

Production Smoke

Use production smoke tests only with explicitly provided credentials and disposable sandboxes:

SANDBOX_RUN_INTEGRATION=1 \
SANDBOX_TEST_BASE_URL="${SEACLOUD_BASE_URL}" \
SANDBOX_TEST_API_KEY="${SEACLOUD_API_KEY}" \
SANDBOX_TEST_TEMPLATE_ID=tpl-base-dc11799b9f9f4f9e \
PYTHONPATH=src python -m unittest discover -s tests -p 'test_*.py' -v

tpl-base-dc11799b9f9f4f9e is a known-good SeaCloudAI runtime template for validating CMD routes such as list_dir, read_file, write_file, and run. You can also run the same disposable smoke flow from GitHub Actions with .github/workflows/integration-smoke.yml after setting the SANDBOX_TEST_API_KEY repository secret.

Integration Tests

SANDBOX_RUN_INTEGRATION=1 \
SANDBOX_TEST_BASE_URL="${SEACLOUD_BASE_URL}" \
SANDBOX_TEST_API_KEY="${SEACLOUD_API_KEY}" \
SANDBOX_TEST_TEMPLATE_ID=... \
PYTHONPATH=src python -m unittest discover -s tests -p 'test_*.py' -v

Use a runtime-enabled template for CMD integration coverage. For SeaCloudAI production smoke tests, tpl-base-dc11799b9f9f4f9e is a known-good runtime template. The same smoke suite is available as a manual GitHub Actions dispatch in .github/workflows/integration-smoke.yml.

Release

• See CHANGELOG.md for release notes.
• See RELEASE_CHECKLIST.md before tagging or publishing a new version.
• GitHub Actions can publish to PyPI through Trusted Publishing with .github/workflows/publish.yml.