此 LangSmith SDK 提供以编程方式创建和与沙箱交互的接口。

安装

# uv
uv add "langsmith[sandbox]"

# pip
pip install "langsmith[sandbox]"

npm install langsmith
# or
yarn add langsmith

此 [sandbox] Python 安装的额外依赖 websockets，可实现实时流式传输和 timeout=0。如果没有它， run() 会自动回退到 HTTP。对于 TypeScript，请安装可选的 ws 包以支持 WebSocket 流式传输：

npm install ws

创建并运行沙箱

当您想从可重用的自定义文件系统镜像启动时，请传递快照 ID 或名称；请参阅 快照 以了解该流程。

from langsmith.sandbox import SandboxClient

# Client uses LANGSMITH_ENDPOINT and LANGSMITH_API_KEY from environment
client = SandboxClient()

# Create a sandbox with the default runtime and run code
with client.sandbox() as sb:
    result = sb.run("python -c 'print(2 + 2)'")
    print(result.stdout)  # "4\n"
    print(result.success)  # True

// Client uses LANGSMITH_ENDPOINT and LANGSMITH_API_KEY from environment
const client = new SandboxClient();

// Create a sandbox with the default runtime and run code
const sandbox = await client.createSandbox();
const result = await sandbox.run("node -e 'console.log(2 + 2)'");
console.log(result.stdout); // "4\n"

// Don't forget to clean up
await sandbox.delete();

运行命令

每个 run() 调用返回一个 ExecutionResult ，包含 stdout, stderr, exit_code和 success.

with client.sandbox() as sb:
    result = sb.run("echo 'Hello, World!'")

    print(result.stdout)     # "Hello, World!\n"
    print(result.stderr)     # ""
    print(result.exit_code)  # 0
    print(result.success)    # True

    # Commands that fail return non-zero exit codes
    result = sb.run("exit 1")
    print(result.success)    # False
    print(result.exit_code)  # 1

const sandbox = await client.createSandbox();
try {
  const result = await sandbox.run("echo 'Hello, World!'");

  console.log(result.stdout);     // "Hello, World!\n"
  console.log(result.stderr);     // ""
  console.log(result.exit_code);  // 0

  // Pass environment variables and working directory
  const envResult = await sandbox.run("echo $MY_VAR", {
    env: { MY_VAR: "test-value" },
    cwd: "/tmp",
  });
} finally {
  await sandbox.delete();
}

流式输出

对于长时间运行的命令，请使用回调或 CommandHandle.

使用回调进行流式处理

with client.sandbox() as sb:
    result = sb.run(
        "make build",
        timeout=600,
        on_stdout=lambda s: print(s, end=""),
        on_stderr=lambda s: print(s, end="", file=sys.stderr),
    )
    print(f"\nBuild {'succeeded' if result.success else 'failed'}")

const result = await sandbox.run("make build", {
  timeout: 600,
  onStdout: (data) => process.stdout.write(data),
  onStderr: (data) => process.stderr.write(data),
});
console.log(`Exit code: ${result.exit_code}`);

使用 CommandHandle 进行流式处理

设置 wait=False 以获取 CommandHandle ，以便完全控制输出流。

with client.sandbox() as sb:
    handle = sb.run("make build", timeout=600, wait=False)

    print(f"Command ID: {handle.command_id}")

    for chunk in handle:
        prefix = "OUT" if chunk.stream == "stdout" else "ERR"
        print(f"[{prefix}] {chunk.data}", end="")

    result = handle.result
    print(f"\nExit code: {result.exit_code}")

const handle = await sandbox.run("python train.py", {
  wait: false,
  timeout: 600,
});

console.log(`Command ID: ${handle.commandId}`);
console.log(`PID: ${handle.pid}`);

for await (const chunk of handle) {
  if (chunk.stream === "stdout") {
    process.stdout.write(chunk.data);
  } else {
    process.stderr.write(chunk.data);
  }
}

const result = await handle.result;
console.log(`Exit code: ${result.exit_code}`);

发送 stdin 和终止命令

with client.sandbox() as sb:
    handle = sb.run(
        "python -c 'name = input(\"Name: \"); print(f\"Hello {name}\")'",
        timeout=30,
        wait=False,
    )

    for chunk in handle:
        if "Name:" in chunk.data:
            handle.send_input("World\n")
        print(chunk.data, end="")

    result = handle.result

const handle = await sandbox.run("python -i", { wait: false });

// Send input to stdin
handle.sendInput("print(2 + 2)\n");
handle.sendInput("exit()\n");

for await (const chunk of handle) {
  process.stdout.write(chunk.data);
}

终止正在运行的命令：

with client.sandbox() as sb:
    handle = sb.run("python server.py", timeout=0, wait=False)

    for chunk in handle:
        print(chunk.data, end="")
        if "Ready" in chunk.data:
            break

    handle.kill()

const handle = await sandbox.run("sleep 300", { wait: false });
handle.kill();

const result = await handle.result;
console.log(result.exit_code); // non-zero

重新连接到正在运行的命令

如果客户端断开连接，请使用命令 ID 重新连接：

with client.sandbox() as sb:
    handle = sb.run("make build", timeout=600, wait=False)
    command_id = handle.command_id

    # Later, possibly in a different process
    handle = sb.reconnect(command_id)
    for chunk in handle:
        print(chunk.data, end="")
    result = handle.result

const handle = await sandbox.run("long-task", { wait: false });
const commandId = handle.commandId;

// Later, or from a different client
const newHandle = await sandbox.reconnect(commandId);
for await (const chunk of newHandle) {
  process.stdout.write(chunk.data);
}

文件操作

在沙箱中读取和写入文件：

with client.sandbox() as sb:
    # Write a file
    sb.write("/app/script.py", "print('Hello from file!')")

    # Run the script
    result = sb.run("python /app/script.py")
    print(result.stdout)  # "Hello from file!\n"

    # Read a file (returns bytes)
    content = sb.read("/app/script.py")
    print(content.decode())  # "print('Hello from file!')"

    # Write binary files
    sb.write("/app/data.bin", b"\x00\x01\x02\x03")

const sandbox = await client.createSandbox();
try {
  // Write a file (string content)
  await sandbox.write("/app/script.py", "print('Hello from file!')");

  // Run the script
  const result = await sandbox.run("python /app/script.py");
  console.log(result.stdout);  // "Hello from file!\n"

  // Read a file (returns Uint8Array)
  const content = await sandbox.read("/app/script.py");
  console.log(new TextDecoder().decode(content));

  // Write binary files
  await sandbox.write("/app/data.bin", new Uint8Array([0x00, 0x01, 0x02, 0x03]));
} finally {
  await sandbox.delete();
}

沙箱生命周期和保留期

沙箱由基于 **空闲 活动** 和 **stopped** state.

两个值都必须是 60 的倍数（分钟分辨率）。完整生命周期如下：

running ──(idle for idle_ttl_seconds)──▶ stopped ──(delete_after_stop_seconds)──▶ deleted

您也可以调用 stop_sandbox / stopSandbox 显式调用——这也 填充 stopped_at 并启动删除计时器。

# Default retention (server defaults: 10-min idle stop, 14-day delete)
with client.sandbox() as sb:
    sb.run("echo hello")

# Aggressive: stop after 5 min idle, delete 1 hour after stop
sb = client.create_sandbox(
    idle_ttl_seconds=300,
    delete_after_stop_seconds=3600,
)

# Long-running: never auto-stop, delete 7 days after manual stop
sb = client.create_sandbox(
    idle_ttl_seconds=0,
    delete_after_stop_seconds=604800,
)

# Update retention on an existing sandbox
sb = client.update_sandbox(
    sb.name,
    idle_ttl_seconds=1800,
    delete_after_stop_seconds=2592000,  # 30 days
)

// Default retention (server defaults applied)
const sandbox = await client.createSandbox();

// Aggressive: stop after 5 min idle, delete 1 hour after stop
const sb = await client.createSandbox({
  idleTtlSeconds: 300,
  deleteAfterStopSeconds: 3600,
});

// Long-running: never auto-stop, delete 7 days after manual stop
const longRunning = await client.createSandbox({
  idleTtlSeconds: 0,
  deleteAfterStopSeconds: 604800,
});

// Update retention on an existing sandbox
await client.updateSandbox(sb.name, {
  idleTtlSeconds: 1800,
  deleteAfterStopSeconds: 2592000, // 30 days
});

命令生命周期和 TTL

沙箱守护进程通过两个超时机制管理命令会话生命周期：

• 会话 TTL（已完成的命令）：命令完成后，其会话会在内存中保留一个 TTL 期间。在此期间您可以重新连接以检索输出。TTL 到期后，会话将被清理。
• 空闲超时（运行中的命令）：没有连接客户端的运行命令会在空闲超时后被终止（默认值：5 分钟）。每次客户端连接时，空闲计时器都会重置。设置为 -1 表示无空闲超时。

组合生命周期选项

with client.sandbox() as sb:
    # Long-running task: 30-min idle timeout, 1-hour session TTL
    handle = sb.run(
        "python train.py",
        timeout=0,              # No command timeout
        idle_timeout=1800,      # Kill after 30min with no clients
        ttl_seconds=3600,       # Keep session for 1 hour after exit
        wait=False,
    )

    # Fire-and-forget: no idle timeout, infinite TTL
    handle = sb.run(
        "python background_job.py",
        timeout=0,
        idle_timeout=-1,        # Never kill due to idle
        ttl_seconds=-1,         # Keep session forever
        wait=False,
    )

const sandbox = await client.createSandbox();
try {
  // Long-running task: 30-min idle timeout, 1-hour session TTL
  const handle = await sandbox.run("python train.py", {
    timeout: 0,              // No command timeout
    idleTimeout: 1800,       // Kill after 30min with no clients
    ttlSeconds: 3600,        // Keep session for 1 hour after exit
    wait: false,
  });

  // Fire-and-forget: no idle timeout, infinite TTL
  const bg = await sandbox.run("python background_job.py", {
    timeout: 0,
    idleTimeout: -1,         // Never kill due to idle
    ttlSeconds: -1,          // Keep session forever
    wait: false,
  });
} finally {
  await sandbox.delete();
}

设置 kill_on_disconnect=True （Python）或 killOnDisconnect: true （TypeScript）以便在最后一个客户端断开时立即终止命令，而不是等待空闲超时。

服务 URL（Python）

通过经过身份验证的 URL 访问在沙箱中运行的 HTTP 服务。您可以在浏览器中打开它、从代码中调用它，或与队友共享它。

with client.sandbox() as sb:
    sb.run("python -m http.server 8000", timeout=0, wait=False)

    svc = sb.service(port=8000)

    # Open in a browser
    print(svc.browser_url)

    # Or make requests with built-in helpers (auth is injected automatically)
    resp = svc.get("/api/data")
    resp = svc.post("/api/data", json={"key": "value"})

有关更多详细信息（包括用例、REST API 访问和完整的 FastAPI 示例），请参阅 服务 URL.

TCP 隧道（Python）

像访问本地服务一样访问在沙箱中运行的任何 TCP 服务。隧道会打开一个本地 TCP 端口，并通过 WebSocket 将连接转发到沙箱内的目标端口。

# Snapshot built from the official postgres:16 image
sb = client.create_sandbox(snapshot_id=postgres_snapshot_id)
pg_handle = sb.run(
    "POSTGRES_HOST_AUTH_METHOD=trust docker-entrypoint.sh postgres",
    timeout=0,
    wait=False,
)


try:
    with sb.tunnel(remote_port=5432, local_port=25432) as t:
        conn = psycopg2.connect(
            host="127.0.0.1",
            port=t.local_port,
            user="postgres",
        )
        cursor = conn.cursor()
        cursor.execute("SELECT version()")
        print(cursor.fetchone())
        conn.close()
finally:
    pg_handle.kill()
    client.delete_sandbox(sb.name)

隧道适用于任何 TCP 服务（Redis、HTTP 服务器等），您可以同时打开多个隧道：

with sb.tunnel(remote_port=5432, local_port=25432) as t1, \
     sb.tunnel(remote_port=6379, local_port=26379) as t2:
    # Use both Postgres and Redis simultaneously
    pass

异步支持（Python）

Python SDK 提供了一个完整的异步客户端：

from langsmith.sandbox import AsyncSandboxClient

async def main():
    async with AsyncSandboxClient() as client:
        async with await client.sandbox() as sb:
            result = await sb.run("python -c 'print(1 + 1)'")
            print(result.stdout)  # "2\n"

            await sb.write("/app/test.txt", "async content")
            content = await sb.read("/app/test.txt")
            print(content.decode())

            # Async streaming
            handle = await sb.run("make build", timeout=600, wait=False)
            async for chunk in handle:
                print(chunk.data, end="")
            result = await handle.result

            # Async service URLs
            svc = await sb.service(port=8000)
            resp = await svc.get("/api/data")
            url = await svc.get_service_url()
            token = await svc.get_token()

跟踪沙箱活动

通过 env 上的 run() 参数传递 LangSmith 跟踪环境变量，以发送在沙箱中运行的代码的跟踪信息。在进程退出前调用 flush() 以确保所有跟踪信息都已发送。

from langsmith.sandbox import SandboxClient

client = SandboxClient()

tracing_env = {
    "LANGSMITH_API_KEY": "lsv2_pt_...",
    "LANGSMITH_ENDPOINT": "https://api.smith.langchain.com",
    "LANGSMITH_TRACING": "true",
    "LANGSMITH_PROJECT": "my-sandbox-traces",
}

with client.sandbox() as sandbox:
    sandbox.run("pip install langsmith", timeout=120, env=tracing_env)
    result = sandbox.run("python3 my_agent.py", env=tracing_env)
    print(result.stdout)

const client = new SandboxClient();

const tracingEnv = {
  LANGSMITH_API_KEY: "lsv2_pt_...",
  LANGSMITH_ENDPOINT: "https://api.smith.langchain.com",
  LANGSMITH_TRACING: "true",
  LANGSMITH_PROJECT: "my-sandbox-traces",
};

const sandbox = await client.createSandbox();
try {
  await sandbox.run("pip install langsmith", { timeout: 120, env: tracingEnv });
  const result = await sandbox.run("python3 my_agent.py", { env: tracingEnv });
  console.log(result.stdout);
} finally {
  await sandbox.delete();
}

在沙箱内，任何带有 LangSmith 检测的代码（@traceable、LangChain、LangGraph）会自动从注入的环境变量中获取跟踪配置。

错误处理

两个 SDK 都为特定错误处理提供了类型化异常：

from langsmith.sandbox import (
    SandboxClientError,       # Base exception
    ResourceCreationError,    # Provisioning failed
    ResourceNotFoundError,    # Resource doesn't exist
    ResourceTimeoutError,     # Operation timed out
    SandboxNotReadyError,     # Sandbox not ready yet
    SandboxConnectionError,   # Network/WebSocket error
    CommandTimeoutError,      # Command exceeded timeout
    QuotaExceededError,       # Quota limit reached
)

try:
    with client.sandbox() as sb:
        result = sb.run("sleep 999", timeout=10)
except CommandTimeoutError as e:
    print(f"Command timed out: {e}")
except ResourceNotFoundError as e:
    print(f"{e.resource_type} not found: {e}")
except SandboxClientError as e:
    print(f"Error: {e}")

  LangSmithSandboxError,
  LangSmithResourceNotFoundError,
  LangSmithResourceTimeoutError,
  LangSmithSandboxConnectionError,
  LangSmithCommandTimeoutError,
  LangSmithQuotaExceededError,
} from "langsmith/sandbox";

try {
  const sandbox = await client.createSandbox("not-a-real-snapshot");
  await sandbox.delete();
} catch (e) {
  if (e instanceof LangSmithResourceNotFoundError) {
    console.log(`${e.resourceType} not found: ${e.message}`);
  } else if (e instanceof LangSmithResourceTimeoutError) {
    console.log(`Timeout waiting for ${e.resourceType}: ${e.message}`);
  } else if (e instanceof LangSmithSandboxError) {
    console.log(`Error: ${e.message}`);
  }
}