2770 Docker D9e87c50

Docker Sandbox

DockerSandbox provides isolated code execution for your pydantic-ai agents. Run untrusted code safely in Docker containers.

!!! warning "Requires Docker" bash pip install pydantic-ai-backend[docker] Ensure Docker is installed and the daemon is running.

Basic Usage with pydantic-ai

from dataclasses import dataclass
from pydantic_ai import Agent
from pydantic_ai_backends import DockerSandbox, create_console_toolset

@dataclass
class Deps:
    backend: DockerSandbox

# Create sandbox with pre-configured runtime
sandbox = DockerSandbox(runtime="python-datascience")

try:
    # Add console tools to your agent
    toolset = create_console_toolset()
    agent = Agent("openai:gpt-4o", deps_type=Deps)
    agent = agent.with_toolset(toolset)

    # Agent can safely execute arbitrary code in Docker
    result = agent.run_sync(
        "Load the iris dataset with sklearn, analyze it with pandas, "
        "and create a visualization with matplotlib",
        deps=Deps(backend=sandbox),
    )
    print(result.output)
finally:
    sandbox.stop()  # Clean up container

Runtime Configurations

Pre-configured environments with packages pre-installed:

from pydantic_ai_backends import DockerSandbox, RuntimeConfig

# Use built-in runtime
sandbox = DockerSandbox(runtime="python-datascience")

# Or define custom runtime for your use case
runtime = RuntimeConfig(
    name="ml-env",
    base_image="python:3.12-slim",
    packages=["torch", "transformers", "pandas"],
)
sandbox = DockerSandbox(runtime=runtime)

Built-in Runtimes

SessionManager for Multi-User

For web apps where each user needs isolated execution:

from dataclasses import dataclass
from pydantic_ai import Agent
from pydantic_ai_backends import SessionManager, DockerSandbox, create_console_toolset

@dataclass
class UserDeps:
    backend: DockerSandbox
    user_id: str

# Create session manager
manager = SessionManager(
    default_runtime="python-datascience",
    workspace_root="/app/workspaces",  # Persistent storage per user
)

async def handle_user_request(user_id: str, message: str):
    # Get or create sandbox for this user
    sandbox = await manager.get_or_create(user_id)

    # Create agent with user's isolated sandbox
    toolset = create_console_toolset()
    agent = Agent("openai:gpt-4o", deps_type=UserDeps)
    agent = agent.with_toolset(toolset)

    result = await agent.run(
        message,
        deps=UserDeps(backend=sandbox, user_id=user_id),
    )
    return result.output

# Each user's code runs in isolated container
# User A cannot see User B's files

Architecture

                    ┌─────────────────┐
                    │ SessionManager  │
                    └────────┬────────┘
                             │
        ┌────────────────────┼────────────────────┐
        │                    │                    │
┌───────▼───────┐   ┌───────▼───────┐   ┌───────▼───────┐
│ DockerSandbox │   │ DockerSandbox │   │ DockerSandbox │
│   (User A)    │   │   (User B)    │   │   (User C)    │
│  pydantic-ai  │   │  pydantic-ai  │   │  pydantic-ai  │
│    Agent      │   │    Agent      │   │    Agent      │
└───────────────┘   └───────────────┘   └───────────────┘

Persistent Storage

By default, files are lost when container stops. Use volumes for persistence:

sandbox = DockerSandbox(
    runtime="python-datascience",
    volumes={"/host/data": "/workspace/data"},  # Mount host directory
)

With SessionManager, each user gets their own persistent directory:

manager = SessionManager(
    workspace_root="/app/workspaces",  # Creates /app/workspaces/{user_id}/
)

Security

• Each user gets a separate Docker container
• Users cannot access each other's files
• Containers can have resource limits (CPU, memory)
• Network isolation available via Docker networking
• No host filesystem access (unless explicitly mounted)

Next Steps

• Multi-User Example - Web app with SessionManager
• Docker Sandbox Example - Full example
• API Reference - Complete API