Changelog¶

All notable changes to RADICAL AsyncFlow will be documented in this file.

The format is based on Keep a Changelog.

[Unreleased]¶

[0.3.0] - 2026-02-24¶

Added¶

• prompt_task decorator — new first-class task type alongside function_task and executable_task. The decorated async function returns a prompt string that is forwarded to an AI execution backend (e.g. DragonVllmInferenceBackend from RHAPSODY). NoopExecutionBackend returns "Dummy Prompt Output" for testing; LocalExecutionBackend raises NotImplementedError with a clear message directing users to register an AI backend.
• Multi-backend registry — WorkflowEngine.create(backend=...) now accepts either a single backend or a list of pre-initialized named backends (each must expose a .name attribute). The first backend in the list is the default. Built-in backends (LocalExecutionBackend, NoopExecutionBackend) gain a name parameter defaulting to "default".
• Per-task backend routing — task decorators (function_task, executable_task, prompt_task) accept an optional backend="<name>" parameter to route a specific task to a named backend. Tasks without backend= are sent to the default backend.
• Parallel backend submission — when a batch contains tasks routed to multiple backends, submit() dispatches all backend calls concurrently via asyncio.gather. shutdown() similarly drains all registered backends in parallel.
• Example 04-concurrent_backends.py — demonstrates a compute + AI dual-backend workflow running document processing pipelines concurrently.
• Tests — unit tests (tests/unit/test_prompt_task.py) and integration tests (tests/integration/test_multi_backend.py) covering prompt_task registration, validation, multi-backend routing, parallel submission, and graceful shutdown.

Fixed¶

• async_wrapper exception propagation — errors raised during task registration (e.g. prompt_task returning None, or executable_task returning a non-string) are now propagated to the task's asyncio.Future instead of being silently swallowed by the detached asyncio.create_task. Previously, such errors would cause await task(...) to hang indefinitely.

Changed¶

• Documentation — all HPC backend references across docs, examples, and the Jupyter tutorial now point to the official RHAPSODY documentation instead of the GitHub repository, and include a direct link to the AsyncFlow integration guide.
• Examples — HPC example scripts (04-dask_execution_backend.py, 05-radical_execution_backend.py, 06-dragon_execution_backend.py) updated with module-level docstrings referencing RHAPSODY docs and the AsyncFlow integration guide.
• Proof-of-concept examples — all basic/getting-started examples (basic.md, async_workflows.md, composite_workflow.md, examples 01–03) consistently use LocalExecutionBackend; RHAPSODY backends are only shown in dedicated HPC sections.
• Install command — RHAPSODY install command corrected to pip install rhapsody-py across all docs and examples.

[0.2.0] - 2026-02-12¶

Added¶

• Local execution backend — LocalExecutionBackend ships built-in, supporting both ThreadPoolExecutor and ProcessPoolExecutor with cloudpickle serialization.
• No-op backend — NoopExecutionBackend for testing and dry_run workflows without computational overhead.
• HPC execution via RHAPSODY — HPC backends (RadicalExecutionBackend, DaskExecutionBackend, DragonExecutionBackendV3) are now provided by the separate RHAPSODY package and plug into AsyncFlow via simple import.
• Composite workflows — @flow.block decorator for grouping tasks into logical units with support for nested blocks and inter-block dependencies.
• Task dependency resolution — automatic DAG-based scheduling with implicit and explicit data dependencies between function_task and executable_task types.
• Pluggable backend architecture — any backend conforming to the execution interface can be passed to WorkflowEngine.create(), enabling custom and third-party backends.
• Pre-commit pipeline — ruff, docformatter, typos, actionlint, detect-secrets, and standard pre-commit hooks.
• CI/CD — GitHub Actions workflows for unit tests, integration tests, pre-commit checks, and documentation deployment across Python 3.9-3.13.

Changed¶

• Backend decoupling — HPC backends (Dask, RadicalPilot, Concurrent, Dragon) moved from radical.asyncflow to the standalone rhapsody package. AsyncFlow now only ships LocalExecutionBackend and NoopExecutionBackend.
• Imports for HPC backends — HPC backends are now imported from rhapsody.backends instead of radical.asyncflow.
• Documentation — all docs and examples updated to reflect the new architecture, with clear separation between built-in local backends and RHAPSODY HPC backends.

[0.1.0] - 2025-12-07¶

Initial public release of RADICAL AsyncFlow.

Added¶

• Async-first workflow engine with DAG-based task scheduling.
• Support for async and sync function execution within workflows.
• Execution backend abstraction with pluggable backend interface.
• RADICAL-based HPC execution backend integration.
• Agentic workflow support.
• Event-driven shutdown mechanism for backend lifecycle management.
• Task cancellation signaling and cooperative cancellation support.
• Enhanced async context manager support for workflow execution.
• Unit and integration testing framework.
• Initial project documentation.
• Migration to pyproject.toml with tox-based test environments.

Changed¶

• Renamed project from flow to asyncflow.
• Removed legacy synchronous execution paradigm in favor of fully async architecture.
• Refactored execution loop for thread-based backend.
• Updated RADICAL execution backend callback mechanism to support service-style operation.
• Improved error propagation for failed tasks.
• Refactored internal utilities to remove dependency on radical.utils.
• Eliminated redundant data dependency linking logic.

Fixed¶

• Corrected handling of task failure without explicit wait().
• Fixed handle_task_failure logic for consistent error reporting.
• Resolved process-based function execution launch issues.
• Ensured pre_exec directives are properly applied.
• Addressed multiple execution edge cases in RADICAL backend.