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Eduard van Valkenburg 57c901a245 Python: Fix hyperlight WasmSandbox cross-thread Drop and harden hosted-agent sample (#5603)
* update hyperlight to beta and move samples, add hosted agent sample

* Python: Fix hyperlight WasmSandbox cross-thread Drop and harden sample

Root cause: when a worker-side closure raised, the exception's __traceback__
retained frame locals that included the partially constructed PyO3 sandbox.
Future.result() re-raised that exception on the caller thread, and when the
caller's exception was eventually GC'd the frame locals were released
off-thread, dec_ref'ing the unsendable sandbox from the wrong thread and
tripping the PyO3 panic
'_native_wasm::WasmSandbox is unsendable, but is being dropped on another thread'.

Fix:
* Add _SandboxWorker._run_on_worker which catches every exception on the
  worker, drops __traceback__ there, deletes the original exception, and
  re-raises a fresh instance on the caller thread. initialize and execute
  route through it; dispose keeps its bare-submit semantics.
* Add an opt-in diagnostic module _drop_diagnostic (no-op unless
  HYPERLIGHT_TRACE_DROPS=1) that installs a sys.unraisablehook and dumps
  owner-thread + per-thread stacks on any future cross-thread unsendable
  Drop. Useful for triaging similar PyO3 regressions.
* Tests: cross-thread invocation, traceback-leak isolation, _SandboxEntry
  attribute-shape check, and a stale-reference stress test driven through
  asyncio.to_thread.

Sample (samples/04-hosting/foundry-hosted-agents/responses/06_hyperlight_codeact):
* Dockerfile installs agent-framework-* from in-tree source with python/ as
  build context so unreleased fixes can be validated end-to-end.
* call_server.py pins the Responses API version.
* main.py enables include_detailed_errors=True so future tool failures
  surface the actual exception text instead of a bare 'Error: Function
  failed.' string.
* README.md documents the in-tree-package build and the Hyperlight
  hypervisor requirement (/dev/kvm on Linux, MSHV on Windows). Hosted
  environments without hypervisor passthrough surface 'No Hypervisor was
  found for Sandbox'; this is a hosting constraint, not a hyperlight bug.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Python: remove _drop_diagnostic from hyperlight package

The diagnostic module was useful while bisecting the cross-thread Drop bug,
but it is no longer needed now that _SandboxWorker._run_on_worker prevents
the panic at the source.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Python: address PR review feedback on hyperlight

- Use lazy agent_framework.hyperlight import in sample main.py.
- Env-driven endpoint (FOUNDRY_AGENT_ENDPOINT) in call_server.py; remove personal URLs.
- Align agent.yaml model deployment with manifest (gpt-4.1-mini).
- Tighten Dockerfile requirements guard; drop dangling deploy.ps1 reference.
- Preserve exception args when sanitizing tracebacks in _run_on_worker.
- Add public _SandboxWorker.is_alive(); update test to avoid private attr.
- Add namespace coverage tests for agent_framework.hyperlight lazy loader.
- Add prominent note: Foundry hosted-agent runtime does not yet support
  Hyperlight (no hypervisor exposed); container works locally with /dev/kvm.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Python: bump hyperlight-sandbox dependencies to 0.4.x

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Python: renumber hyperlight codeact sample to 08

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Coerce worker exception args to strings for cross-thread safety

Stringify exc.args on the worker thread before propagating, so any
PyO3 unsendable object captured in args (e.g. via a caller-supplied
callback or underlying SDK) cannot be Dropped on the calling thread.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* moved sample

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-05-05 10:06:16 +00:00

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FOUNDRY_PROJECT_ENDPOINT="..."
AZURE_AI_MODEL_DEPLOYMENT_NAME="..."