Files
openai--openai-agents-python/examples/sandbox/docker/mounts/mount_smoke.py
T
Steve Coffey 2d665c9a67 Sandbox Agents (#2889)
### Sandbox Agents

This release adds **Sandbox Agents**, a beta SDK surface for running
agents with a persistent, isolated workspace. Sandbox agents keep the
normal `Agent` and `Runner` flow, but add workspace manifests,
sandbox-native capabilities, sandbox clients, snapshots, and resume
support so agents can work over real files, run commands, edit
repositories, generate artifacts, and continue work across runs.

Key pieces:

- `SandboxAgent`: an `Agent` with sandbox defaults such as
`default_manifest`, sandbox instructions, capabilities, and `run_as`.
- `Manifest`: a fresh-workspace contract for files, directories, local
files, local directories, Git repos, environment, users, groups, and
mounts.
- `SandboxRunConfig`: per-run sandbox wiring for client creation, live
session injection, serialized session resume, manifest overrides,
snapshots, and materialization concurrency limits.
- Built-in capabilities for shell access, filesystem editing and image
inspection, skills, memory, and compaction.
- Workspace snapshots and serialized sandbox session state for
reconnecting to existing work or seeding a fresh sandbox from saved
contents.

### Sandbox clients and hosted providers

Sandbox agents now support local, containerized, and hosted execution
backends:

- `UnixLocalSandboxClient` for fast local development.
- `DockerSandboxClient` for container isolation and image parity.
- Hosted sandbox clients for Blaxel, Cloudflare, Daytona, E2B, Modal,
Runloop, and Vercel through optional extras.

The release also adds provider-specific examples and mount strategies
for common storage backends, including S3, Cloudflare R2, Google Cloud
Storage, Azure Blob Storage, and S3 Files where supported by the
selected backend.

### Sandbox memory

Adds a sandbox memory capability that lets future sandbox-agent runs
learn from prior runs. Memory stores extracted lessons in the sandbox
workspace, injects a concise summary into later runs, and uses
progressive disclosure so agents can search deeper rollout summaries
only when useful.

Memory supports:

- Read-only or generate-only modes.
- Live updates when the agent discovers stale memory.
- Multi-turn grouping through `conversation_id`, SDK `Session`,
`RunConfig.group_id`, or generated run IDs.
- Separate memory layouts for isolating memory across agents or
workflows.
- S3-backed examples for persisted memory across runs.

### Workspace mounts, snapshots, and resume

This release adds a full workspace entry and mount model for sandbox
sessions:

- Local files and directories.
- Synthetic files and directories.
- Git repository entries.
- Remote storage mounts for S3, R2, GCS, Azure Blob Storage, and S3
Files.
- Provider-specific mount strategies across Docker, Modal, Cloudflare,
Blaxel, Daytona, E2B, and Runloop.
- Portable snapshots with path normalization, symlink preservation,
mount-safe snapshotting, and remote snapshot support.
- Resume paths through runner-managed `RunState`, explicit
`SandboxSessionState`, or saved snapshots.

### Examples and tutorials

Adds a large `examples/sandbox/` suite covering:

- Local Unix and Docker sandbox runners.
- Docker mount smoke tests for S3, GCS, Azure Blob Storage, and S3
Files.
- Sandbox coding tasks with skills.
- Sandbox agents as tools and handoff patterns.
- Memory examples, including multi-agent/multi-turn memory and S3-backed
memory.
- Tax-prep and healthcare-support workflows.
- Dataroom QA and metric extraction tutorials.
- Repository code review tutorial.
- Vision website clone tutorial.
- Provider examples for Blaxel, Cloudflare, Daytona, E2B, Modal,
Runloop, Temporal, and Vercel.

### Runtime, tracing, and model plumbing

The release includes the runtime plumbing needed to make sandbox agents
work naturally inside the existing SDK:

- Runner-managed sandbox preparation, capability binding, session
lifecycle, state serialization, and resume behavior.
- Sandbox-aware `RunState` serialization.
- Unified sandbox tracing with SDK spans.
- Token usage on tracing spans.
- Runner-managed prompt cache key defaults.
- OpenAI agent registration and harness ID configuration.
- Safer redaction of sensitive MCP tool outputs when sensitive tracing
is disabled.
- Additional OpenAI client/model utilities and Chat Completions
coverage.


## Documentation & Other Changes

- docs: add Asqav to external tracing processors list.
- docs: update translated document pages.

Co-authored-by: Abdulrahman Alfozan <alfozan@openai.com>
Co-authored-by: Aditya Singh <60082699+adityasingh2400@users.noreply.github.com>
Co-authored-by: Andi Liu <andi@openai.com>
Co-authored-by: Aron <263346377+aron-cf@users.noreply.github.com>
Co-authored-by: ashwinnathan-openai <ashwinnathan@openai.com>
Co-authored-by: Codex <noreply@openai.com>
Co-authored-by: cploujoux <cploujoux@blaxel.ai>
Co-authored-by: elainegan-openai <168589666+elainegan-openai@users.noreply.github.com>
Co-authored-by: Elias Freider <freider@users.noreply.github.com>
Co-authored-by: Erik Dunteman <erik@erikds-macbook-air.local>
Co-authored-by: Jason Liu <jasonliu@openai.com>
Co-authored-by: Jason Steving <32336750+jasonsteving99@users.noreply.github.com>
Co-authored-by: Kazuhiro Sera <seratch@openai.com>
Co-authored-by: Lovre Pešut <lovre.pesut@gmail.com>
Co-authored-by: Lucas Wang <lucas_wang@lucas-futures.com>
Co-authored-by: Matt Brockman <matt.brockman@e2b.dev>
Co-authored-by: Mish Ushakov <mishushakov@users.noreply.github.com>
Co-authored-by: Naresh <ghostwriternr@gmail.com>
Co-authored-by: nicholasclark-openai <nicholasclark@openai.com>
Co-authored-by: qiyaoq-oai <qiyaoq@openai.com>
Co-authored-by: Scott Trinh <scott@scotttrinh.com>
Co-authored-by: tode-rl <tony@runloop.ai>
Co-authored-by: Wendy Jiao <wendyjiao@openai.com>
2026-04-15 10:00:40 -07:00

154 lines
4.6 KiB
Python

from __future__ import annotations
import os
import uuid
from collections.abc import Sequence
from dataclasses import dataclass
from pathlib import Path
import docker # type: ignore[import-untyped]
from agents import ModelSettings, Runner
from agents.run import RunConfig
from agents.sandbox import Manifest, SandboxAgent, SandboxRunConfig
from agents.sandbox.entries import Mount
from agents.sandbox.errors import MountCommandError
from agents.sandbox.sandboxes.docker import (
DockerSandboxClient,
DockerSandboxClientOptions,
)
from agents.sandbox.session.sandbox_session import SandboxSession
from examples.sandbox.misc.workspace_shell import WorkspaceShellCapability
IMAGE = "agents-sandbox-docker-mount-example:latest"
DOCKERFILE = Path(__file__).resolve().parent.parent / "Dockerfile.mount"
@dataclass(frozen=True)
class MountSmokeCase:
"""One mount target to verify inside a shared Docker sandbox session."""
name: str
mount_dir: str
mount: Mount
def require_env(name: str) -> str:
"""Return a required environment variable or stop with a clear message."""
value = os.getenv(name)
if not value:
raise SystemExit(f"Missing required environment variable: {name}")
return value
def ensure_mount_image() -> None:
"""Build the Docker image with the in-container mount CLIs if it is missing."""
docker_client = docker.from_env()
try:
docker_client.images.get(IMAGE)
return
except docker.errors.ImageNotFound:
pass
print(f"building {IMAGE} from {DOCKERFILE.name}...")
docker_client.images.build(
path=str(DOCKERFILE.parent),
dockerfile=DOCKERFILE.name,
tag=IMAGE,
rm=True,
)
def build_agent(name: str, manifest: Manifest) -> SandboxAgent:
"""Create the minimal shell-only agent used by these mount smoke tests."""
return SandboxAgent(
name=name,
model=os.getenv("OPENAI_MODEL", "gpt-5.4"),
instructions=(
"Use the shell tool only. Write the requested exact content to the requested exact "
"path, read the file back with cat, and then reply with only `done`."
),
default_manifest=manifest,
capabilities=[WorkspaceShellCapability()],
model_settings=ModelSettings(tool_choice="required"),
)
async def _check_case(
sandbox: SandboxSession,
agent: SandboxAgent,
provider: str,
mount_case: MountSmokeCase,
) -> None:
key = f"docker-{provider}-mount-example-{mount_case.mount_dir}-{uuid.uuid4().hex}.txt"
path = Path("/workspace") / mount_case.mount_dir / key
expected = f"hello from {mount_case.name} {uuid.uuid4().hex}"
result = await Runner.run(
agent,
(
f"Write exactly this content to {path} with `printf %s`, not `echo`: {expected}\n"
f"Then read {path} back with cat."
),
run_config=RunConfig(
sandbox=SandboxRunConfig(session=sandbox),
workflow_name=f"Docker {provider} mount smoke test ({mount_case.name})",
),
)
print(result.final_output)
read_back = await sandbox.read(path)
actual = read_back.read()
if not isinstance(actual, bytes):
raise TypeError(f"Expected bytes from session.read(), got {type(actual)!r}")
actual_text = actual.decode("utf-8")
if actual_text == f"{expected}\n":
actual_text = expected
assert actual_text == expected, f"read back {actual!r}, expected {expected!r}"
print(f"{mount_case.name}: ok")
async def run_mount_smoke_test(
*,
provider: str,
agent_name: str,
mount_cases: Sequence[MountSmokeCase],
) -> None:
"""Start one Docker sandbox session and verify read/write on every mount target."""
ensure_mount_image()
manifest = Manifest(
entries={mount_case.mount_dir: mount_case.mount for mount_case in mount_cases},
)
agent = build_agent(agent_name, manifest)
client = DockerSandboxClient(docker.from_env())
try:
sandbox = await client.create(
manifest=manifest,
options=DockerSandboxClientOptions(image=IMAGE),
)
except docker.errors.NotFound as exc:
if 'plugin "rclone" not found' in str(exc):
raise SystemExit("rclone Docker volume plugin not found") from exc
raise
try:
await sandbox.start()
except MountCommandError as exc:
print(f"mount command: {exc.context.get('command')}")
print(f"mount stderr: {exc.context.get('stderr')}")
raise
try:
for mount_case in mount_cases:
await _check_case(sandbox, agent, provider, mount_case)
finally:
await client.delete(sandbox)