Sandbox Agents

A sandbox gives an agent an isolated, Unix-like execution environment with a filesystem, shell, installed packages, mounted data, exposed ports, snapshots, and controlled access to external systems.

Agent workflows get brittle when the model needs that kind of workspace but only receives prompt context. Large document sets, generated artifacts, commands, previews, and resumable work all need an environment the agent can inspect and change.

Use sandboxes when the agent needs to manipulate files, run commands, mount a data room, produce artifacts, expose a service, or continue stateful work later.

The key split is the boundary between the harness and compute. The harness is the control plane around the model: it owns the agent loop, model calls, tool routing, handoffs, approvals, tracing, recovery, and run state. Compute is the sandbox execution plane where model-directed work reads and writes files, runs commands, installs dependencies, uses mounted storage, exposes ports, and snapshots state.

Keeping those boundaries separate lets your application keep sensitive control plane work in trusted infrastructure while the sandbox stays focused on provider-specific execution. The sandbox can run code against files with narrow credentials and mounts; the harness can keep auth, billing, audit logs, human review, and recovery state outside any one container.

When to use a sandbox

Use a sandbox when the agent’s answer depends on work done in a sandbox workspace, not just reasoning over prompt context.

Common pain points include:

• The task needs a directory of documents, not a single prompt.
• The agent should write files that your application can inspect later.
• The agent needs commands, packages, or scripts to complete the work.
• The workflow produces artifacts such as Markdown, CSV, JSONL, screenshots, or generated websites.
• A service, notebook, or report preview needs to run on an exposed port.
• Work pauses for human review and then resumes in the same workspace.

If your workflow only needs a short model response and no persistent workspace, call the Responses API directly or use the basic Agents SDK runtime without a sandbox.

If shell access is only one occasional tool, start with the hosted shell tool in Using tools. Use sandbox agents when workspace isolation, sandbox provider choice, or resumable filesystem state is part of the product design.

What sandboxes add

SandboxAgent is still an Agent. It keeps the usual agent surface, including instructions, prompt, tools, handoffs, MCP servers, model settings, structured output, guardrails, and hooks. What changes is the execution boundary: the runner prepares the agent against a live sandbox session that owns files, commands, ports, and provider-specific isolation.

Sandbox-specific defaults belong on SandboxAgent. Per-run sandbox-session choices belong in the run’s sandbox configuration.

Sandbox agents also don’t change what a turn means. A turn is still a model step, not a single shell command or sandbox action. Some work may stay inside the sandbox execution layer. The agent runtime consumes another turn only when it needs another model response after sandbox work has happened.

Create the workspace

Manifest describes the desired starting contents and layout for a fresh sandbox workspace. Use it for the files, repos, input artifacts, helper files, mounts, output directories, and environment setup the agent should see.

Treat the manifest as a fresh-session contract, not the full source of truth for every live sandbox. The effective workspace for a run can instead come from a reused live sandbox session, serialized sandbox session state, or a snapshot chosen at run time.

Manifest entry paths are workspace-relative. They can’t be absolute paths or escape the workspace with .., which keeps the workspace contract portable across local, Docker, and hosted clients.

Good manifest design means:

• Put repos, input artifacts, and output directories in the manifest.
• Put longer task specs and repo-local instructions in workspace files such as repo/task.md or AGENTS.md.
• Use relative workspace paths in instructions, for example repo/task.md or output/report.md.
• Keep mounted storage scoped to the inputs the agent should read or write.
• Treat mount entries as ephemeral workspace entries: snapshot and persistence flows skip mounted remote storage instead of copying it into saved workspace contents.

Mount files and storage

Useful data often already lives somewhere else. Instead of pasting large documents into context, mount them into the sandbox and let the agent work with files.

Examples:

• Mount a due-diligence data room and ask the agent to produce a cited summary.
• Mount a support export and ask the agent to cluster issues into a report.
• Mount generated artifacts so another system can review them.

Provider integrations expose their own mount helpers, credential handling, and persistence behavior. Keep the application contract the same: mount only the inputs the agent should use, tell the agent where to read and write, and check generated artifacts before using them.

Handle secrets and credentials

Treat sandbox credentials as runtime configuration, not prompt content. The agent may need access to credentials for package managers, storage mounts, or provider APIs, but those credentials shouldn’t appear in user prompts, agent instructions, task files, committed manifests, or generated artifacts.

Use these rules:

• Prefer provider-native secret systems for hosted sandbox providers.
• Keep cloud storage credentials scoped to the mount or provider option that needs them.
• Use Manifest.environment for values the sandbox process needs at startup, and mark sensitive or generated entries as ephemeral when you want to rebuild them instead of persisting them.
• Avoid saving secrets, generated mount config, local tokens, or files that shouldn’t survive the run.
• Review artifacts before moving them out of the sandbox, especially when the agent can read private documents or mounted storage.

The SDK supports manifest environment values and provider-specific mount credentials. General secret-store integration is provider-specific, so keep this page focused on the contract: your runtime or sandbox provider should inject credentials instead of teaching them to the model as instructions.

Give the agent capabilities

Capabilities attach sandbox-native behavior to a SandboxAgent. They can shape the workspace before a run starts, append sandbox-specific instructions, expose tools that bind to the live sandbox session, and adjust model behavior or input handling for that agent.

By default, a SandboxAgent includes filesystem, shell, and compaction capabilities. If you pass a capabilities list, it replaces the default list, so include any default capabilities the agent still needs.

Prefer built-in capabilities when they fit. Write a custom capability only when you need a sandbox-specific tool or instruction surface that the built-ins don’t cover.

Load skills

Some tasks need repeatable instructions, scripts, references, or assets before the agent starts. Use the Skills capability so the agent can discover that working context during the run.

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
import {
  Capabilities,
  SandboxAgent,
  gitRepo,
  skills,
} from "@openai/agents/sandbox";

const agent = new SandboxAgent({
  name: "Tax prep assistant",
  instructions: "Use the mounted skill before preparing the return.",
  capabilities: [
    ...Capabilities.default(),
    skills({
      from: gitRepo({
        repo: "owner/tax-prep-skills",
        ref: "main",
      }),
    }),
  ],
});

1
2
3
4
5
6
7
8
9
10
11
from agents.sandbox import SandboxAgent
from agents.sandbox.capabilities import Capabilities, Skills
from agents.sandbox.entries import GitRepo

agent = SandboxAgent(
    name="Tax prep assistant",
    instructions="Use the mounted skill before preparing the return.",
    capabilities=Capabilities.default() + [
        Skills(from_=GitRepo(repo="owner/tax-prep-skills", ref="main")),
    ],
)

Choose the skill source based on how you want it materialized:

• Use a lazy local directory source for larger local skill directories when you want the model to discover the index first and load only what it needs.
• Use a local directory source for a small local bundle to stage up front.
• Use a Git repo source when the skills bundle has its own release cadence or many sandboxes use it.

Expose previews and ports

Sometimes the artifact isn’t a file; it’s a running process. Use an exposed port when the agent creates a local app, notebook, report server, browser preview, or other service that you need to inspect outside the sandbox.

Port setup is provider-specific, but the product contract is the same: the agent starts the service inside the sandbox, the sandbox client exposes the port, and your application shares or inspects the resulting preview URL.

Run a sandbox agent

The shortest useful sandbox loop is:

1. Build a Manifest that describes the workspace.
2. Create a SandboxAgent with the capabilities the model needs.
3. Choose a sandbox client for the environment where work should run.
4. Run the agent with the per-run sandbox configuration.
5. Inspect, copy, resume, or snapshot the artifacts that matter to your application.

Start with Unix-local for local development on macOS or Linux. It gives you the smallest local loop because the runner can create a temporary workspace from the agent’s default manifest and clean it up after the run.

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
import { run } from "@openai/agents";
import {
  Manifest,
  SandboxAgent,
  file,
  shell,
} from "@openai/agents/sandbox";
import { UnixLocalSandboxClient } from "@openai/agents/sandbox/local";

const manifest = new Manifest({
  entries: {
    "account_brief.md": file({
      content:
        "# Northwind Health\n\n" +
        "- Segment: Mid-market healthcare analytics provider.\n" +
        "- Renewal date: 2026-04-15.\n",
    }),
    "implementation_risks.md": file({
      content:
        "# Delivery risks\n\n" +
        "- Security questionnaire is not complete.\n" +
        "- Procurement requires final legal language by April 1.\n",
    }),
  },
});

const agent = new SandboxAgent({
  name: "Renewal Packet Analyst",
  model: "gpt-5.5",
  instructions:
    "Review the workspace before answering. Keep the response concise, " +
    "business-focused, and cite the file names that support each conclusion.",
  defaultManifest: manifest,
  capabilities: [shell()],
});

const result = await run(
  agent,
  "Summarize the renewal blockers and recommend the next two actions.",
  {
    sandbox: {
      client: new UnixLocalSandboxClient(),
    },
  },
);

console.log(result.finalOutput);

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
import asyncio

from agents import Runner
from agents.run import RunConfig
from agents.sandbox import Manifest, SandboxAgent, SandboxRunConfig
from agents.sandbox.capabilities import Shell
from agents.sandbox.entries import File
from agents.sandbox.sandboxes.unix_local import UnixLocalSandboxClient

manifest = Manifest(
    entries={
        "account_brief.md": File(
            content=(
                b"# Northwind Health\n\n"
                b"- Segment: Mid-market healthcare analytics provider.\n"
                b"- Renewal date: 2026-04-15.\n"
            )
        ),
        "implementation_risks.md": File(
            content=(
                b"# Delivery risks\n\n"
                b"- Security questionnaire is not complete.\n"
                b"- Procurement requires final legal language by April 1.\n"
            )
        ),
    }
)

agent = SandboxAgent(
    name="Renewal Packet Analyst",
    model="gpt-5.5",
    instructions=(
        "Review the workspace before answering. Keep the response concise, "
        "business-focused, and cite the file names that support each conclusion."
    ),
    default_manifest=manifest,
    capabilities=[Shell()],
)


async def main():
    result = await Runner.run(
        agent,
        "Summarize the renewal blockers and recommend the next two actions.",
        run_config=RunConfig(
            sandbox=SandboxRunConfig(client=UnixLocalSandboxClient()),
            workflow_name="Unix-local sandbox review",
        ),
    )
    print(result.final_output)


asyncio.run(main())

For complete local examples, see the TypeScript sandbox agent quickstart and Python unix_local_runner.py.

Switch providers

The provider is part of the run configuration, not the agent definition. Keep the SandboxAgent, manifest, and capabilities stable, then swap the sandbox client and provider options for the environment you want.

This example uses Docker for local container isolation. Hosted providers follow the same pattern with their own client classes and options.

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
import { run } from "@openai/agents";
import { SandboxAgent } from "@openai/agents/sandbox";
import { DockerSandboxClient } from "@openai/agents/sandbox/local";

const agent = new SandboxAgent({
  name: "Workspace reviewer",
  model: "gpt-5.5",
  instructions: "Inspect the sandbox workspace before answering.",
});

const result = await run(agent, "Inspect the workspace.", {
  sandbox: {
    client: new DockerSandboxClient({
      image: "node:22-bookworm-slim",
    }),
  },
});

console.log(result.finalOutput);

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
from docker import from_env as docker_from_env

from agents import Runner
from agents.run import RunConfig
from agents.sandbox import SandboxRunConfig
from agents.sandbox.config import DEFAULT_PYTHON_SANDBOX_IMAGE
from agents.sandbox.sandboxes.docker import DockerSandboxClient, DockerSandboxClientOptions

docker_run_config = RunConfig(
    sandbox=SandboxRunConfig(
        client=DockerSandboxClient(docker_from_env()),
        options=DockerSandboxClientOptions(image=DEFAULT_PYTHON_SANDBOX_IMAGE),
    ),
    workflow_name="Docker sandbox review",
)

result = await Runner.run(
    agent,
    "Summarize the renewal blockers and recommend the next two actions.",
    run_config=docker_run_config,
)

For runnable examples, see the TypeScript sandbox clients guide and basic example, plus Python basic.py for provider selection, docker_runner.py for Docker, and main.py for a data-room flow in the SDK repository.

Advanced patterns

Once the basic loop works, sandboxes become useful for workflows where the agent needs a sandbox workspace instead of more prompt context. These examples are workflow patterns, not separate APIs: the same harness can route, pause, resume, and trace the workflow while each sandbox keeps execution close to the files, tools, and ports it needs.

Resume or seed future work

Useful agent work often outlives one request. A user reviews an artifact, a step needs approval, or the next step depends on a later event.

Keep three state concepts separate:

In practice, the runner resolves the sandbox session in this order:

1. If you pass a live sandbox session, the runner reuses that session directly.
2. Otherwise, if the run is resuming from RunState, the runner resumes from the stored sandbox session state.
3. Otherwise, if you pass explicit serialized sandbox state, the runner resumes from that state.
4. Otherwise, the runner creates a fresh sandbox session. For that fresh session, it uses the per-run manifest when provided, or the agent’s default manifest if not.

The sandbox resume example serializes the stopped session state, resumes it through the same client, and then passes the resumed session back into the next run:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
import { run } from "@openai/agents";
import { Manifest, SandboxAgent } from "@openai/agents/sandbox";
import { UnixLocalSandboxClient } from "@openai/agents/sandbox/local";

const manifest = new Manifest();
const client = new UnixLocalSandboxClient({
  snapshot: { type: "local", baseDir: "/tmp/my-sandbox-snapshots" },
});
const agent = new SandboxAgent({
  name: "Workspace builder",
  model: "gpt-5.5",
  instructions: "Inspect the sandbox workspace before answering.",
});

const session = await client.create({ manifest });
let conversation: any[] = [];
let frozenSessionState;

try {
  const firstResult = await run(agent, "Build the first version of the app.", {
    maxTurns: 20,
    sandbox: { session },
  });

  conversation = firstResult.history;
  frozenSessionState = await client.serializeSessionState?.(session.state);
} finally {
  await session.close?.();
}

if (!frozenSessionState || !client.deserializeSessionState || !client.resume) {
  throw new Error("Sandbox client does not support session resume.");
}

const resumedSession = await client.resume(
  await client.deserializeSessionState(frozenSessionState),
);

try {
  conversation.push({
    role: "user",
    content: "Continue from the existing workspace and add tests.",
  });

  await run(agent, conversation, {
    maxTurns: 20,
    sandbox: { session: resumedSession },
  });
} finally {
  await resumedSession.close?.();
}

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
async with session:
    first_result = await Runner.run(
        agent,
        "Build the first version of the app.",
        max_turns=20,
        run_config=RunConfig(
            sandbox=SandboxRunConfig(session=session),
            workflow_name="Sandbox resume example",
        ),
    )

conversation = first_result.to_input_list()
frozen_session_state = client.deserialize_session_state(
    client.serialize_session_state(session.state)
)

conversation.append(
    {
        "role": "user",
        "content": "Continue from the existing workspace and add tests.",
    }
)

resumed_session = await client.resume(frozen_session_state)
try:
    async with resumed_session:
        second_result = await Runner.run(
            agent,
            conversation,
            max_turns=20,
            run_config=RunConfig(
                sandbox=SandboxRunConfig(session=resumed_session),
                workflow_name="Sandbox resume example",
            ),
        )
finally:
    await client.delete(resumed_session)

Fresh-session inputs such as manifest and snapshot only apply when the runner creates a new sandbox session. If you inject a live session, capability processing can add compatible non-mount entries, but it can’t change root, environment, users, or groups; remove existing entries; replace entry types; or add or change mount entries on the already-running sandbox.

This split lets the harness resume the agent loop while the sandbox provider restores or recreates the workspace. Current sample code for these paths lives in the TypeScript resume session state example and Python main.py and sandbox_agent_with_remote_snapshot.py.

Persist memory across runs

Sandbox memory lets future sandbox-agent runs learn from prior runs. It’s separate from SDK-managed conversational Session memory: sessions preserve message history, while sandbox memory distills useful lessons from prior workspace runs into files the agent can read later.

Use memory when the agent should carry forward user preferences, corrections, project-specific lessons, or task summaries without replaying every previous turn. Resume and snapshots preserve workspace state; memory preserves reusable guidance about work that happened in the workspace.

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
import {
  Manifest,
  SandboxAgent,
  filesystem,
  memory,
  shell,
} from "@openai/agents/sandbox";

const manifest = new Manifest();

const agent = new SandboxAgent({
  name: "Memory-enabled reviewer",
  instructions:
    "Inspect the workspace and retain useful lessons for follow-up runs.",
  defaultManifest: manifest,
  capabilities: [memory(), filesystem(), shell()],
});

1
2
3
4
5
6
7
8
from agents.sandbox.capabilities import Filesystem, Memory, Shell

agent = SandboxAgent(
    name="Memory-enabled reviewer",
    instructions="Inspect the workspace and retain useful lessons for follow-up runs.",
    default_manifest=manifest,
    capabilities=[Memory(), Filesystem(), Shell()],
)

Memory enables both reads and generation by default. Memory reads require shell access so the agent can search and open memory files. By default, live memory updates also require filesystem access, so the agent can repair stale memory or update memory when the user asks.

Memory reads use progressive disclosure. The SDK injects memory_summary.md at the start of a run, the agent searches MEMORY.md when prior work looks relevant, and it opens rollout summaries only when it needs more detail.

By default, memory artifacts live in the sandbox workspace:

workspace/
  sessions/
    <rollout-id>.jsonl
  memories/
    memory_summary.md
    MEMORY.md
    raw_memories.md
    phase_two_selection.json
    raw_memories/
      <rollout-id>.md
    rollout_summaries/
      <rollout-id>_<slug>.md
    skills/

The runtime appends run segments during the sandbox session. When the session closes, memory generation first extracts conversation summaries and raw memories, then consolidates those raw memories into MEMORY.md and memory_summary.md. To reuse memory in a later run, preserve the configured memory directories by keeping the same live sandbox session, resuming from session state, starting from a snapshot, or mounting persistent storage such as S3.

For multi-turn sandbox chats, use a stable SDK session together with the same live sandbox session. Memory groups runs by the explicit conversation ID, then the SDK session ID, then the run group ID, and finally a generated per-run ID. The sandbox session ID identifies the live workspace; it’s not the memory conversation ID.

For runnable examples, see the TypeScript memory guide, plus Python memory.py for a local snapshot flow, memory_s3.py for S3-backed memory storage, and memory_multi_agent_multiturn.py for separate memory layouts across agents.

Compose sandbox agents

Sandbox agents compose with the rest of the SDK.

Use a handoff when a non-sandbox intake agent should delegate only the workspace-heavy part of a workflow to a sandbox agent. The top-level run continues, but the sandbox agent becomes the active agent for the next turn.

Use agents as tools when an outer orchestrator should call one or more sandbox agents as nested tools. Each sandbox tool-agent can have its own sandbox run configuration, sandbox client, manifest, and provider options.

For examples, see handoffs.py and sandbox_agents_as_tools.py.

Sandbox providers

Start with Unix-local for fast local iteration or Docker when you want local container isolation. Move to a hosted provider when the task needs managed execution, provider-specific isolation, scaling, previews, storage mounts, snapshots, or credentials that should live outside your application server.

Use provider docs for provider-specific setup, credentials, isolation, storage, previews, and persistence behavior.