The `plan execute` CLI command only performed phase transitions
(Strategize → Execute) without ever invoking the `PlanExecutor` to
drive the strategize or execute actors. `PlanExecutor.__init__`
unconditionally created `StrategizeStubActor()` and
`ExecuteStubActor()` which parse text locally and return empty
changesets — no real LLM call was made.
Added `_get_plan_executor()` helper that resolves `ProviderRegistry`
from the DI container and constructs `LLMStrategizeActor` /
`LLMExecuteActor` for real LLM invocations via LangChain. Updated
`execute_plan` CLI command to detect plan phase/state and
automatically run the appropriate actor:
- Strategize/queued → run strategize actor → transition to Execute
- Strategize/complete → phase transition only (backward compat)
- Execute/queued → run execute actor → mark complete
New `llm_actors.py` module provides `LLMStrategizeActor` (task
decomposition into numbered steps) and `LLMExecuteActor` (code
generation with FILE: blocks). Both resolve `provider/model` actor
names (e.g. `openai/gpt-4`) to live LangChain LLM instances.
`PlanExecutor.__init__` now accepts optional `strategize_actor` and
`execute_actor` parameters, falling back to stubs when None.
Existing mock-based BDD tests remain backward-compatible via duck-
typing fallback (MagicMock.phase is not a PlanPhase, so the legacy
`service.execute_plan()` path is taken).
Includes Behave BDD scenarios testing custom actor injection into
PlanExecutor.
ISSUES CLOSED: #960
freemo