Pre-release

This release is a pre-release and may not be stable for production use.

pydantic-monty-client

Python client for the Monty sandbox.

Most users want pydantic-monty instead, which pulls in this package plus pydantic-monty-runtime and is documented in full on its PyPI page.

Install this package directly to use the websocket client alone, or if you're installing the monty binary another way.

uv add pydantic-monty-client
# or
pip install pydantic-monty-client

Usage with a remote monty server and websockets

You can use this library alone to connect to a remote monty server via websockets.

from pydantic_monty import AsyncMontyWebsocket


async def main() -> None:
    url = '...'
    async with AsyncMontyWebsocket(url) as pool:
        async with pool.checkout() as session:
            output = await session.feed_run('1 + 1')
            print('output ->', output)


if __name__ == '__main__':
    import asyncio

    asyncio.run(main())

Usage with a local monty worker

Host objects and classes cross the boundary through the ClassInstance / ClassType wrappers; see the pydantic-monty README.

This requires the pydantic-monty-runtime package, which is generally installed as part of the pydantic-monty meta-package.

from pydantic_monty import Monty

with Monty() as pool:
    with pool.checkout(limits={'max_suspensions': 100}) as session:
        print(session.feed_run('1 + 2'))
        #> 3

max_suspensions limits host-serviced suspensions per checkout (default 1000; it cannot be disabled). Exceeding it aborts the feed with an uncatchable RuntimeError; the session remains usable, but its suspension count remains spent.

or in async code:

from pydantic_monty import AsyncMonty


async def main() -> None:
    async with AsyncMonty() as pool:
        async with pool.checkout() as session:
            output = await session.feed_run('1 + 1')
            print('output from local worker ->', output)
            #> output from local worker -> 2


if __name__ == '__main__':
    import asyncio

    asyncio.run(main())

Tracing snapshot handlers

All sync and async snapshot types provide snapshot.trace_context() for manual handlers. It returns a standard OpenTelemetry Context, not a context manager, and requires opentelemetry-api to be installed. Use OTel's attach / detach to activate it, including across await in the same task:

from opentelemetry import context

from pydantic_monty import FunctionSnapshot, Monty, MontyComplete

with Monty() as pool:
    with pool.checkout() as session:
        snapshot = session.feed_start('greet(name)', inputs={'name': 'Ada'})
        assert isinstance(snapshot, FunctionSnapshot)
        token = context.attach(snapshot.trace_context())
        try:
            greeting = f'hello {snapshot.args[0]}'
        finally:
            context.detach(token)
        result = snapshot.resume({'return_value': greeting})
        assert isinstance(result, MontyComplete)
        print(result.output)
        #> hello Ada

The returned context preserves baggage and other entries captured at feed_start / load_snapshot, with the suspension's span when Monty tracing is enabled. Without Monty tracing it returns the captured context unchanged. Context is not serialized: restoring captures the restoring caller's context instead. The method does not activate the context or resume execution. It raises ImportError without opentelemetry-api, or RuntimeError after resume. Previously returned contexts remain usable but do not keep the suspension span open. resume_auto() already activates the suspension span around callbacks. See the snapshot documentation.

Restoring snapshots

session.load_session() and session.load_snapshot() require unmodified snapshots from a trusted, compatible Monty producer. The caller must establish provenance and integrity before loading; Monty does not authenticate snapshots. Invalid snapshots have no correctness or availability guarantees. Successful loading does not establish validity. See the snapshot security documentation.

Working directory

Pass cwd='/data' to session.feed_run() or session.feed_start() to set the sandbox's virtual working directory. The async session methods accept the same option. The path must be absolute and uses POSIX / separators on every host. On the first feed, omitting cwd selects the first mount's virtual path, or / if no mount is supplied. The directory then persists across feeds, including successful os.chdir(path=...) calls, until another feed sets cwd. os.getcwd() and Path.cwd() report it, and relative open(), os, and pathlib requests resolve against it. Setting cwd does not grant filesystem access; provide mount= or os= to handle filesystem operations.

OSAccess(max_urandom_bytes=...) sets the largest os.urandom() request the default handler serves, 1 MiB by default. Larger requests raise MemoryError before allocating. Unseeded random generators request host entropy only under os_policy={'random_start': 'call_host'}. Otherwise they use worker OS entropy or the configured seed.

By default, date.today(), datetime.now() and the time module's clocks read the worker's clock. time.process_time() reports 0.0 unless os_policy={'process_time': 'elapsed'} opts into the session's execution time. The pool handles time.sleep() and asyncio.sleep(), capped per call by sleep_system_max. Setting datetime or sleep to 'call_host' in checkout(os_policy=...) routes those calls to os=. Every time module clock then reaches AbstractOS.time(caller) as the one OS function time.time, with caller naming the function that asked ('time.monotonic', 'time.localtime', ...). A subclass that overrides def time(self) without the caller parameter raises TypeError on any clock read. OSAccess answers from the host process and caps each wait at max_sleep.

A random.Random instance or the random.Random class returned from the sandbox converts to its repr string. Return the generated values or rng.getstate() instead.

See the pydantic-monty README for more details.

Project links

Data verified by PyPI on Sep 21, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Stable release available (0.0.23)

Owner

PyPI data
Data sourced directly from PyPI's database.

GitHub Statistics

Data verified by PyPI on Sep 21, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License (MIT)

Requires

Python >=3.10

Provides Extra

opentelemetry

Classifiers

Report project as malware

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Total release size: 289.1 MB

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