pydantic-ai-daytona

Daytona sandbox capability for Pydantic AI agents. DaytonaSandbox gives an agent an isolated cloud sandbox for running commands and working with files — a place to execute untrusted or model-generated code without touching the application host.

Installation

pip install pydantic-ai-daytona

Configuration

Get your API key from the Daytona Dashboard and set it in the environment (or a .env file):

export DAYTONA_API_KEY="your-daytona-api-key"

You can also pass api_key (plus optional api_url / target) directly to DaytonaSandbox.

Quickstart

from pydantic_ai import Agent
from pydantic_ai_daytona import DaytonaSandbox

agent = Agent(
    'anthropic:claude-sonnet-4-6',
    capabilities=[DaytonaSandbox()],
)

result = agent.run_sync('Write a Python script that prints the first 10 primes and run it.')
print(result.output)

By default every agent run gets a fresh sandbox, created from the Daytona default snapshot and deleted when the run ends.

The capability contributes four tools:

Command output labels stdout and stderr separately, reports non-zero exit codes, and is truncated tail-first per stream, so errors and exit status remain visible. When a file read is cut short by the safety caps (or by limit), the result ends with the next offset to pass to continue reading — the model pages through large files the way grep -n numbers them.

Sandbox lifecycle

Owned (default) — each run creates a sandbox and deletes it when the run ends:

DaytonaSandbox(snapshot='my-snapshot')          # or image='python:3.12-slim'

For fully untrusted code, block outbound network access at creation. Independently, ephemeral=True hardens cleanup: Daytona deletes the sandbox the moment it stops, even if your process crashed before its own cleanup ran.

DaytonaSandbox(network_block_all=True, ephemeral=True, labels={'app': 'support-agent'})

Attached — reuse a sandbox you manage elsewhere; it is started if stopped and never deleted:

DaytonaSandbox(sandbox_id='my-sandbox-id')

Injected session — share one sandbox across several runs while controlling its lifetime yourself:

from pydantic_ai_daytona import DaytonaSandbox, DaytonaSandboxSession

async def main():
    async with DaytonaSandboxSession(snapshot='my-snapshot') as session:
        agent = Agent('anthropic:claude-sonnet-4-6', capabilities=[DaytonaSandbox(session=session)])
        await agent.run('Create /workspace/app.py with a hello-world FastAPI app.')
        await agent.run('Now add a /health endpoint to the app you created.')

Configuration reference

Development

pip install -e ".[dev]"
pytest                          # unit tests (offline)
pytest tests/integration_tests  # live tests (needs DAYTONA_API_KEY)
ruff check .

License

Apache-2.0