francwa
23a9dd7990
The Workflow STM component stored an active workflow as
{type, target, stage, started_at}. Now that start_workflow takes a
workflow_name and a params dict, those keys match what they actually
hold:
type -> name (the YAML workflow name, e.g. media.organize_media)
target -> params (the dict passed to start_workflow)
ShortTermMemory.start_workflow parameters renamed accordingly. All
consumers (prompt builder workflow scope + STM context, start/end
workflow tools) updated.
87 lines
2.5 KiB
Python
87 lines
2.5 KiB
Python
"""Workflow scoping tools — start_workflow / end_workflow meta-tools.
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These tools let the agent enter and leave a workflow scope. While a
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workflow is active, the PromptBuilder narrows the visible tool catalog
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to the noyau + the workflow's declared tools, so the LLM doesn't have
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to reason over the full set.
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"""
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import logging
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from typing import Any
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from alfred.infrastructure.persistence import get_memory
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from ..workflows import WorkflowLoader
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logger = logging.getLogger(__name__)
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_loader_cache: list[WorkflowLoader] = []
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def _get_loader() -> WorkflowLoader:
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"""Lazily build the module-level WorkflowLoader."""
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if not _loader_cache:
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_loader_cache.append(WorkflowLoader())
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return _loader_cache[0]
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def start_workflow(workflow_name: str, params: dict) -> dict[str, Any]:
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"""See specs/start_workflow.yaml for full description."""
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loader = _get_loader()
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workflow = loader.get(workflow_name)
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if workflow is None:
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return {
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"status": "error",
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"error": "unknown_workflow",
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"message": f"Workflow '{workflow_name}' not found",
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"available_workflows": loader.names(),
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}
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memory = get_memory()
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current = memory.stm.workflow.current
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if current is not None:
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return {
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"status": "error",
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"error": "workflow_already_active",
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"message": (
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f"Workflow '{current.get('name')}' is already active. "
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"Call end_workflow before starting a new one."
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),
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"active_workflow": current.get("name"),
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}
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memory.stm.start_workflow(workflow_name, params or {})
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memory.save()
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logger.info(f"start_workflow: '{workflow_name}' with params={params}")
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return {
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"status": "ok",
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"workflow": workflow_name,
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"description": workflow.get("description", ""),
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"steps": workflow.get("steps", []),
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"tools": workflow.get("tools", []),
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}
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def end_workflow(reason: str) -> dict[str, Any]:
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"""See specs/end_workflow.yaml for full description."""
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memory = get_memory()
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current = memory.stm.workflow.current
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if current is None:
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return {
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"status": "error",
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"error": "no_active_workflow",
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"message": "No workflow is currently active.",
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}
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workflow_name = current.get("name")
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memory.stm.end_workflow()
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memory.save()
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logger.info(f"end_workflow: '{workflow_name}' reason={reason!r}")
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return {
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"status": "ok",
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"workflow": workflow_name,
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"reason": reason,
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}
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