Documentation

The OpenAI Agents SDK is a framework for building agentic workflows. E2B provides a native integration that lets you run SandboxAgent instances inside isolated E2B sandboxes — giving your agents full filesystem, terminal, and network access in a secure environment. To use E2B as the sandbox backend:

Create a sandbox session with E2BSandboxClient.
Build a SandboxAgent with your instructions and model.
Run the agent and pass the sandbox session through RunConfig.

Install the dependencies

Install the OpenAI Agents SDK with the E2B extra to pull in the sandbox integration.

pip install openai-agents[e2b]

You will also need API keys for OpenAI and E2B.

export OPENAI_API_KEY="..."
export E2B_API_KEY="..."

Basic example

Create an E2BSandboxClient, start a session, and run a SandboxAgent inside it. The agent gets full access to the sandbox environment — it can run commands, read and write files, and inspect the workspace.

Create a session

Initialize the E2BSandboxClient and create a sandbox session. The pause_on_exit option keeps the sandbox available after the script finishes so you can inspect its state.

from agents.extensions.sandbox import (
    E2BSandboxClient,
    E2BSandboxClientOptions,
    E2BSandboxType,
)

client = E2BSandboxClient()
session = await client.create(
    options=E2BSandboxClientOptions(
        sandbox_type=E2BSandboxType.E2B,
        timeout=900,
        pause_on_exit=True,
    )
)

Build and run the agent

Define a SandboxAgent with a name, model, and instructions, then run it against the sandbox session using Runner.run. The result contains the agent’s final output.

from agents import ModelSettings, Runner
from agents.run import RunConfig
from agents.sandbox import SandboxAgent, SandboxRunConfig

agent = SandboxAgent(
    name="Workspace Inspector",
    model="gpt-5.4",
    instructions=(
        "Inspect the workspace, explain what files exist, and summarize the project."
    ),
    model_settings=ModelSettings(tool_choice="required"),
)

result = await Runner.run(
    agent,
    "Look around the workspace and summarize what you find.",
    run_config=RunConfig(sandbox=SandboxRunConfig(session=session)),
)
print(result.final_output)

Shut down

Always shut down the session when you’re done to release sandbox resources.

await session.shutdown()

Full example

The complete script that ties the steps above together.

import asyncio

from agents import ModelSettings, Runner
from agents.run import RunConfig
from agents.sandbox import SandboxAgent, SandboxRunConfig
from agents.extensions.sandbox import (
    E2BSandboxClient,
    E2BSandboxClientOptions,
    E2BSandboxType,
)


async def main() -> None:
    client = E2BSandboxClient()
    session = await client.create(
        options=E2BSandboxClientOptions(
            sandbox_type=E2BSandboxType.E2B,
            timeout=900,
            pause_on_exit=True,
        )
    )

    try:
        agent = SandboxAgent(
            name="Workspace Inspector",
            model="gpt-5.4",
            instructions=(
                "Inspect the workspace, explain what files exist, and summarize the project."
            ),
            model_settings=ModelSettings(tool_choice="required"),
        )

        result = await Runner.run(
            agent,
            "Look around the workspace and summarize what you find.",
            run_config=RunConfig(sandbox=SandboxRunConfig(session=session)),
        )
        print(result.final_output)
    finally:
        await session.shutdown()


asyncio.run(main())

Build an app with multiple versions

A common pattern is to start from the same starter app and create multiple versions in separate sandboxes — useful when comparing a first pass with a polished revision, or generating live preview URLs for each version. Based on the homepage_vite_basic_updated.ipynb notebook from the Agents SDK repo.

Define a manifest

A Manifest describes the starter files your agent will work with. Each entry is a File with its content encoded as bytes. This lets you seed multiple sandboxes from the same baseline — useful when comparing different versions of an app.

from agents.sandbox import Manifest
from agents.sandbox.entries import File

def build_manifest() -> Manifest:
    return Manifest(
        entries={
            "package.json": File(content=PACKAGE_JSON.encode()),
            "index.html": File(content=INDEX_HTML.encode()),
            "vite.config.js": File(content=VITE_CONFIG_JS.encode()),
            "src/main.tsx": File(content=MAIN_TSX.encode()),
            "src/App.tsx": File(content=APP_TSX.encode()),
        }
    )

Create a sandbox session

Create a sandbox session with the manifest, exposed ports for live previews, and internet access so the agent can install npm packages.

from agents.extensions.sandbox import (
    E2BSandboxClient,
    E2BSandboxClientOptions,
    E2BSandboxType,
)

session = await E2BSandboxClient().create(
    manifest=build_manifest(),
    options=E2BSandboxClientOptions(
        sandbox_type=E2BSandboxType.E2B,
        timeout=1800,
        exposed_ports=(4173,),
        allow_internet_access=True,
        pause_on_exit=True,
    ),
)
await session.start()

Run the agent

Build a SandboxAgent with capabilities like ApplyPatch and Shell, then run it against the sandbox session.

from agents import ModelSettings, Runner
from agents.run import RunConfig
from agents.sandbox import SandboxAgent, SandboxRunConfig
from agents.sandbox.capabilities import ApplyPatch, Shell

agent = SandboxAgent(
    name="E2B Vite Builder",
    model="gpt-5.4-mini",
    instructions="Update the Vite app in the sandbox workspace and return a concise summary.",
    default_manifest=build_manifest(),
    capabilities=[ApplyPatch(), Shell()],
    model_settings=ModelSettings(tool_choice="required"),
)

result = await Runner.run(
    agent,
    "Make the basic version now.",
    run_config=RunConfig(sandbox=SandboxRunConfig(session=session)),
)

Start a preview server

After the agent finishes, install dependencies, start the Vite dev server, and resolve the exposed port to get a live preview URL.

await session.exec(
    "sh", "-lc",
    (
        "npm install >/tmp/e2b-demo-npm-install.log 2>&1 && "
        "nohup npm run dev -- --host 0.0.0.0 --port 4173 "
        ">/tmp/e2b-demo-vite.log 2>&1 &"
    ),
    shell=False,
    timeout=120,
)
preview_url = (await session.resolve_exposed_port(4173)).url_for("http")

Full example

The complete run_version() helper ties all the steps above together. Call it once per version to get isolated sandboxes with their own preview URLs. Based on the homepage_vite_basic_updated.ipynb notebook from the Agents SDK repo.

from agents import ModelSettings, Runner
from agents.run import RunConfig
from agents.sandbox import Manifest, SandboxAgent, SandboxRunConfig
from agents.sandbox.capabilities import ApplyPatch, Shell
from agents.sandbox.entries import File
from agents.extensions.sandbox import (
    E2BSandboxClient,
    E2BSandboxClientOptions,
    E2BSandboxType,
)


def build_manifest() -> Manifest:
    return Manifest(
        entries={
            "package.json": File(content=PACKAGE_JSON.encode()),
            "index.html": File(content=INDEX_HTML.encode()),
            "vite.config.js": File(content=VITE_CONFIG_JS.encode()),
            "src/main.tsx": File(content=MAIN_TSX.encode()),
            "src/App.tsx": File(content=APP_TSX.encode()),
        }
    )


async def run_version(version_name: str, version_prompt: str) -> dict[str, str]:
    session = await E2BSandboxClient().create(
        manifest=build_manifest(),
        options=E2BSandboxClientOptions(
            sandbox_type=E2BSandboxType.E2B,
            timeout=1800,
            exposed_ports=(4173,),
            allow_internet_access=True,
            pause_on_exit=True,
        ),
    )
    await session.start()

    agent = SandboxAgent(
        name=f"E2B Vite {version_name.title()} Builder",
        model="gpt-5.4-mini",
        instructions=(
            "Update the Vite app in the sandbox workspace and return a concise summary."
        ),
        developer_instructions=(
            f"Version goal: {version_prompt}\n"
            "Start from the tiny Vite starter. You may create src/styles.css if you want."
        ),
        default_manifest=build_manifest(),
        capabilities=[ApplyPatch(), Shell()],
        model_settings=ModelSettings(tool_choice="required"),
    )

    result = await Runner.run(
        agent,
        f"Make the {version_name} version now.",
        run_config=RunConfig(sandbox=SandboxRunConfig(session=session)),
    )

    await session.exec(
        "sh", "-lc",
        (
            "npm install >/tmp/e2b-demo-npm-install.log 2>&1 && "
            "nohup npm run dev -- --host 0.0.0.0 --port 4173 "
            ">/tmp/e2b-demo-vite.log 2>&1 &"
        ),
        shell=False,
        timeout=120,
    )
    preview_url = (await session.resolve_exposed_port(4173)).url_for("http")

    return {
        "summary": str(result.final_output),
        "preview_url": preview_url,
    }

MCP-powered research agents

You can create sandboxes with MCP servers enabled, then connect the Agents SDK to the sandbox’s MCP gateway. This gives you an agent that can discover sources with search-oriented MCP servers, verify pages in a browser, and keep all of that execution inside the sandbox. The Agents SDK repo includes a concrete example in deep_research_mcp.py.

Configure MCP servers

Pass MCP server configurations when creating the sandbox session. This example enables Browserbase for browser automation and Exa for search.

session = await client.create(
    options=E2BSandboxClientOptions(
        sandbox_type=E2BSandboxType.E2B,
        timeout=900,
        pause_on_exit=True,
        mcp={
            "browserbase": {
                "apiKey": os.environ["BROWSERBASE_API_KEY"],
                "geminiApiKey": os.environ["GEMINI_API_KEY"],
                "projectId": os.environ["BROWSERBASE_PROJECT_ID"],
            },
            "exa": {
                "apiKey": os.environ["EXA_API_KEY"],
            },
        },
    ),
)

Connect to the MCP gateway

Use MCPServerStreamableHttp to connect to the sandbox’s MCP gateway. This gives the agent access to all the MCP servers you configured above.

from agents.mcp import MCPServerStreamableHttp

async with MCPServerStreamableHttp(
    name="E2B MCP Gateway",
    params={
        "url": mcp_url,
        "headers": {"Authorization": f"Bearer {mcp_token}"},
        "timeout": 30,
        "sse_read_timeout": 300,
    },
) as server:
    ...

Run the agent

Pass the MCP server to the SandboxAgent via mcp_servers. The agent can now discover and call tools from all configured MCP servers.

from agents import ModelSettings, Runner
from agents.run import RunConfig
from agents.sandbox import SandboxAgent, SandboxRunConfig

agent = SandboxAgent(
    name="Deep Research Assistant",
    model="gpt-5.4",
    instructions=(
        "Use Exa to discover strong sources and Browserbase to verify important claims."
    ),
    mcp_servers=[server],
    model_settings=ModelSettings(tool_choice="required"),
)

result = await Runner.run(
    agent,
    "Research browser automation infrastructure for AI agents.",
    run_config=RunConfig(sandbox=SandboxRunConfig(session=session)),
)

Full example

The complete script combining session creation, MCP gateway connection, and agent execution.

from agents import ModelSettings, Runner
from agents.mcp import MCPServerStreamableHttp
from agents.run import RunConfig
from agents.sandbox import SandboxAgent, SandboxRunConfig

session = await client.create(
    options=E2BSandboxClientOptions(
        sandbox_type=E2BSandboxType.E2B,
        timeout=900,
        pause_on_exit=True,
        mcp={
            "browserbase": {
                "apiKey": os.environ["BROWSERBASE_API_KEY"],
                "geminiApiKey": os.environ["GEMINI_API_KEY"],
                "projectId": os.environ["BROWSERBASE_PROJECT_ID"],
            },
            "exa": {
                "apiKey": os.environ["EXA_API_KEY"],
            },
        },
    ),
)

async with MCPServerStreamableHttp(
    name="E2B MCP Gateway",
    params={
        "url": mcp_url,
        "headers": {"Authorization": f"Bearer {mcp_token}"},
        "timeout": 30,
        "sse_read_timeout": 300,
    },
) as server:
    agent = SandboxAgent(
        name="Deep Research Assistant",
        model="gpt-5.4",
        instructions=(
            "Use Exa to discover strong sources and Browserbase to verify important claims."
        ),
        mcp_servers=[server],
        model_settings=ModelSettings(tool_choice="required"),
    )

    result = await Runner.run(
        agent,
        "Research browser automation infrastructure for AI agents.",
        run_config=RunConfig(sandbox=SandboxRunConfig(session=session)),
    )

Parallel sandbox workers

Use one coordinator agent to launch multiple specialized review or analysis lanes across separate sandboxes. Each lane runs in its own isolated environment, and the coordinator synthesizes the results into a single summary. The Agents SDK repo includes a concrete example in fullstack_code_review_parallel.py. That example uses separate E2B-backed lanes for frontend review, backend review, and git tree review.

Define the agents

Create specialized SandboxAgent instances for each review lane, plus a regular Agent as the coordinator that will synthesize the results.

from agents import Agent
from agents.sandbox import SandboxAgent

frontend_agent = SandboxAgent(
    name="Frontend Reviewer",
    model="gpt-5.4",
    instructions="Review the rendered frontend and identify UX and code risks.",
)

backend_agent = SandboxAgent(
    name="Backend Reviewer",
    model="gpt-5.4",
    instructions="Review the API implementation and identify validation and auth risks.",
)

coordinator = Agent(
    name="Review Coordinator",
    model="gpt-5.4",
    instructions=(
        "Run the available review lanes, compare their evidence, and produce a single summary."
    ),
)

Run the lanes

Run each review agent against its own sandbox session. The lanes are independent and can run concurrently.

from agents import Runner
from agents.run import RunConfig
from agents.sandbox import SandboxRunConfig

frontend_result = await Runner.run(
    frontend_agent,
    "Review the frontend workspace.",
    run_config=RunConfig(sandbox=SandboxRunConfig(session=frontend_session)),
)

backend_result = await Runner.run(
    backend_agent,
    "Review the backend workspace.",
    run_config=RunConfig(sandbox=SandboxRunConfig(session=backend_session)),
)

Synthesize the results

Feed the findings from each lane into the coordinator agent to produce a single summary.

final_result = await Runner.run(
    coordinator,
    f"Frontend findings: {frontend_result.final_output}\n\n"
    f"Backend findings: {backend_result.final_output}",
)

Full example

The complete script defining agents, running parallel review lanes, and synthesizing results.

from agents import Agent, Runner
from agents.run import RunConfig
from agents.sandbox import SandboxAgent, SandboxRunConfig

frontend_agent = SandboxAgent(
    name="Frontend Reviewer",
    model="gpt-5.4",
    instructions="Review the rendered frontend and identify UX and code risks.",
)

backend_agent = SandboxAgent(
    name="Backend Reviewer",
    model="gpt-5.4",
    instructions="Review the API implementation and identify validation and auth risks.",
)

coordinator = Agent(
    name="Review Coordinator",
    model="gpt-5.4",
    instructions=(
        "Run the available review lanes, compare their evidence, and produce a single summary."
    ),
)

frontend_result = await Runner.run(
    frontend_agent,
    "Review the frontend workspace.",
    run_config=RunConfig(sandbox=SandboxRunConfig(session=frontend_session)),
)

backend_result = await Runner.run(
    backend_agent,
    "Review the backend workspace.",
    run_config=RunConfig(sandbox=SandboxRunConfig(session=backend_session)),
)

final_result = await Runner.run(
    coordinator,
    f"Frontend findings: {frontend_result.final_output}\n\n"
    f"Backend findings: {backend_result.final_output}",
)

Reference examples

The OpenAI Agents SDK repository includes several complete examples demonstrating E2B sandbox integration.

Basic and updated app

Build and iterate on a Vite app across isolated sandboxes

MCP deep research

Research agent with Browserbase and Exa via MCP

Parallel code review

Multi-lane code review with a coordinator agent

E2B agents setup

Getting started notebook for E2B sandbox integration

Getting started

Use cases

Agents in sandbox

Code interpreting

Sandbox

Templates

Filesystem

Volumes

Commands

MCP gateway

CLI

Deployment

Migration

Troubleshooting

​Install the dependencies

​Basic example

​Create a session

​Build and run the agent

​Shut down

​Full example

​Build an app with multiple versions

​Define a manifest

​Create a sandbox session

​Run the agent

​Start a preview server

​Full example

​MCP-powered research agents

​Configure MCP servers

​Connect to the MCP gateway

​Run the agent

​Full example

​Parallel sandbox workers

​Define the agents

​Run the lanes

​Synthesize the results

​Full example

​Reference examples

Basic and updated app

MCP deep research

Parallel code review

E2B agents setup

Install the dependencies

Basic example

Create a session

Build and run the agent

Shut down

Full example

Build an app with multiple versions

Define a manifest

Create a sandbox session

Run the agent

Start a preview server

Full example

MCP-powered research agents

Configure MCP servers

Connect to the MCP gateway

Run the agent

Full example

Parallel sandbox workers

Define the agents

Run the lanes

Synthesize the results

Full example

Reference examples