new wf_server: local static workflow server; docs

This commit is contained in:
lda
2026-06-03 06:29:56 +07:00 Verified
parent e1d71d7d37
commit f13f099949
10 changed files with 808 additions and 155 deletions
+4
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@@ -2,6 +2,7 @@ from __future__ import annotations
from .listing import matches_query, paged_list_payload
from .artifacts import WorkflowArtifactApi
from .local_sources import builtin_sources, get_qualified_spec, qualify_spec
from .models import RawWorkflowPlan, TraceRange
from .capabilities import WorkflowCapabilityApi
from .constants import (
@@ -43,8 +44,11 @@ from .durable_context import durable_workflow_api, require_workflow_stores
__all__ = [
"DEFAULT_CALL_STEP_ID",
"builtin_sources",
"get_qualified_spec",
"matches_query",
"paged_list_payload",
"qualify_spec",
"DEFAULT_ERROR_OUTCOME",
"DEFAULT_ERROR_STEP_ID",
"DEFAULT_OK_OUTCOME",
+186
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@@ -0,0 +1,186 @@
from __future__ import annotations
from collections.abc import Mapping
from typing import TYPE_CHECKING, Any
from wf_authoring import NodeSpec, coalesce, concat, constant, default_if_none
from wf_authoring import extract_field, filter_items, filter_items_present, first_item
from wf_authoring import first_item_maybe, first_item_or_none, is_empty, last_item
from wf_authoring import last_item_or_none, length, node, pick_key, pick_path
from wf_authoring import project_fields, rename_fields, runtime_error, truthy
from wf_authoring import extract_text_content
from wf_core.runtime.ops.merges import DEFAULT_REDUCER_DEFINITIONS
from wf_platform import (
CapabilityBuckets,
CapabilitySource,
SourcePermissions,
SourceVisibility,
)
if TYPE_CHECKING:
from wf_core import ReducerSpec
BUILTIN_SOURCE_ID = "wf.std"
"""Internal source id for workflow standard-library node specs."""
BUILTIN_CONNECTION_ID = BUILTIN_SOURCE_ID
"""Compatibility alias for older MCP broker code."""
MCP_SOURCE_ID = "wf.mcp"
"""Reserved source id for future workflow-safe MCP utility node specs."""
RECIPE_SOURCE_ID = "wf.recipes"
"""Internal source id for first-party composed workflow recipes."""
AUTHORING_STD_SPECS: tuple[NodeSpec[Any, Any], ...] = (
coalesce,
default_if_none,
constant,
pick_key,
pick_path,
project_fields,
rename_fields,
truthy,
runtime_error,
first_item,
first_item_or_none,
first_item_maybe,
last_item,
last_item_or_none,
length,
is_empty,
filter_items,
filter_items_present,
extract_field,
concat,
)
"""Existing authoring ops exposed through the workflow stdlib."""
RECIPE_SPECS: tuple[NodeSpec[Any, Any], ...] = (extract_text_content,)
"""Composed first-party recipes exposed as workflow-facing capabilities."""
def qualify_node_name(source_id: str, local_name: str) -> str:
"""Return one source-qualified node name without assuming MCP connections."""
if not source_id:
raise ValueError("source_id must not be empty")
if not local_name:
raise ValueError("local node name must not be empty")
return f"{source_id}.{local_name}"
def qualify_spec(source_id: str, spec: NodeSpec[Any, Any]) -> NodeSpec[Any, Any]:
"""Return a copy of a spec with its node name scoped to a source."""
return NodeSpec(
name=qualify_node_name(source_id, spec.name),
input_model=spec.input_model,
output_model=spec.output_model,
outcomes=spec.outcomes,
fn=spec.fn,
description=spec.description,
is_async=spec.is_async,
accepts_context=spec.accepts_context,
input_schema_contract=spec.input_schema_contract,
output_schema_contract=spec.output_schema_contract,
)
def get_qualified_spec(
sources: Mapping[str, CapabilitySource],
qualified_name: str,
) -> NodeSpec[Any, Any]:
"""Resolve a namespaced node spec from enabled planner-visible sources."""
for source in sources.values():
if not source.enabled or not source.visibility.planner:
continue
spec = source.capabilities.node_specs.get(qualified_name)
if spec is not None:
return spec
raise KeyError(f"unknown qualified node {qualified_name!r}")
def _qualified_specs(
source_id: str,
specs: tuple[NodeSpec[Any, Any], ...],
) -> dict[str, NodeSpec[Any, Any]]:
"""Return specs with authoring names rewritten under one source id."""
local_specs = [
node(spec, name=spec.name.removeprefix("authoring.")) for spec in specs
]
qualified_specs = [qualify_spec(source_id, spec) for spec in local_specs]
return {spec.name: spec for spec in qualified_specs}
def builtin_specs() -> dict[str, NodeSpec[Any, Any]]:
"""Return primitive built-in NodeSpecs available to raw workflow plans."""
return _qualified_specs(BUILTIN_SOURCE_ID, AUTHORING_STD_SPECS)
def recipe_specs() -> dict[str, NodeSpec[Any, Any]]:
"""Return composed first-party recipe specs."""
return _qualified_specs(RECIPE_SOURCE_ID, RECIPE_SPECS)
def builtin_reducers() -> dict[str, ReducerSpec]:
"""Return built-in reducers owned by the workflow standard library."""
return {
definition.spec.name: definition.spec
for definition in DEFAULT_REDUCER_DEFINITIONS.values()
}
def builtin_reducer_definitions():
"""Return executable built-in reducers for trusted runtime dependency wiring."""
return dict(DEFAULT_REDUCER_DEFINITIONS)
def builtin_sources() -> dict[str, CapabilitySource]:
"""Return all local workflow-facing capability sources."""
return {
BUILTIN_SOURCE_ID: CapabilitySource(
id=BUILTIN_SOURCE_ID,
kind="system",
capabilities=CapabilityBuckets(
node_specs=builtin_specs(),
reducers=builtin_reducers(),
reducer_definitions=builtin_reducer_definitions(),
),
visibility=SourceVisibility(
planner=True,
mcp_client=True,
admin_dashboard=True,
),
permissions=SourcePermissions(safe_for_workflow=True),
description="Workflow standard-library nodes.",
),
RECIPE_SOURCE_ID: CapabilitySource(
id=RECIPE_SOURCE_ID,
kind="system",
capabilities=CapabilityBuckets(node_specs=recipe_specs()),
visibility=SourceVisibility(
planner=True,
mcp_client=True,
admin_dashboard=True,
),
permissions=SourcePermissions(safe_for_workflow=True),
description="First-party workflow recipes composed from standard nodes.",
),
}
__all__ = [
"AUTHORING_STD_SPECS",
"BUILTIN_CONNECTION_ID",
"BUILTIN_SOURCE_ID",
"MCP_SOURCE_ID",
"RECIPE_SOURCE_ID",
"RECIPE_SPECS",
"builtin_reducer_definitions",
"builtin_reducers",
"builtin_sources",
"builtin_specs",
"get_qualified_spec",
"qualify_node_name",
"qualify_spec",
"recipe_specs",
]
+37 -129
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@@ -1,133 +1,41 @@
"""Compatibility exports for workflow local sources.
Canonical local workflow source helpers live in `wf_api.local_sources` so
non-MCP process hosts can construct `wf.std` without importing broker internals.
"""
from __future__ import annotations
from typing import TYPE_CHECKING, Any
from wf_authoring import NodeSpec, coalesce, concat, constant, default_if_none
from wf_authoring import extract_field, filter_items, filter_items_present, first_item
from wf_authoring import first_item_maybe, first_item_or_none, is_empty, last_item
from wf_authoring import last_item_or_none, length, node, pick_key, pick_path
from wf_authoring import project_fields, rename_fields, runtime_error, truthy
from wf_authoring import extract_text_content
from wf_core.runtime.ops.merges import DEFAULT_REDUCER_DEFINITIONS
from wf_platform import (
CapabilityBuckets,
CapabilitySource,
SourcePermissions,
SourceVisibility,
from wf_api.local_sources import (
AUTHORING_STD_SPECS,
BUILTIN_CONNECTION_ID,
BUILTIN_SOURCE_ID,
MCP_SOURCE_ID,
RECIPE_SOURCE_ID,
RECIPE_SPECS,
builtin_reducer_definitions,
builtin_reducers,
builtin_sources,
builtin_specs,
get_qualified_spec,
qualify_node_name,
qualify_spec,
recipe_specs,
)
from wf_mcp.broker.service.specs import qualify_spec
if TYPE_CHECKING:
from wf_core import ReducerSpec
BUILTIN_CONNECTION_ID = "wf.std"
"""Internal source id for workflow standard-library node specs."""
MCP_SOURCE_ID = "wf.mcp"
"""Reserved source id for future workflow-safe MCP utility node specs."""
RECIPE_SOURCE_ID = "wf.recipes"
"""Internal source id for first-party composed workflow recipes."""
AUTHORING_STD_SPECS: tuple[NodeSpec[Any, Any], ...] = (
coalesce,
default_if_none,
constant,
pick_key,
pick_path,
project_fields,
rename_fields,
truthy,
runtime_error,
first_item,
first_item_or_none,
first_item_maybe,
last_item,
last_item_or_none,
length,
is_empty,
filter_items,
filter_items_present,
extract_field,
concat,
)
"""Existing authoring ops that are also exposed through the workflow stdlib."""
RECIPE_SPECS: tuple[NodeSpec[Any, Any], ...] = (extract_text_content,)
"""Composed first-party recipes exposed as capabilities."""
def _qualified_specs(
source_id: str,
specs: tuple[NodeSpec[Any, Any], ...],
) -> dict[str, NodeSpec[Any, Any]]:
"""Return specs with authoring names rewritten under one source id."""
local_specs = [
node(spec, name=spec.name.removeprefix("authoring.")) for spec in specs
]
qualified_specs = [qualify_spec(source_id, spec) for spec in local_specs]
return {spec.name: spec for spec in qualified_specs}
def builtin_specs() -> dict[str, NodeSpec[Any, Any]]:
"""Return primitive built-in NodeSpecs available to raw broker workflow plans."""
return _qualified_specs(BUILTIN_CONNECTION_ID, AUTHORING_STD_SPECS)
def recipe_specs() -> dict[str, NodeSpec[Any, Any]]:
"""Return composed first-party recipe specs.
Recipes are wrapper-node subgraphs today. They are useful workflow-facing
capabilities, but parent runs do not yet see their child graph frames.
"""
return _qualified_specs(RECIPE_SOURCE_ID, RECIPE_SPECS)
def builtin_reducers() -> dict[str, ReducerSpec]:
"""Return built-in reducers owned by the workflow standard library."""
return {
definition.spec.name: definition.spec
for definition in DEFAULT_REDUCER_DEFINITIONS.values()
}
def builtin_reducer_definitions():
"""Return executable built-in reducers for trusted runtime dependency wiring."""
return dict(DEFAULT_REDUCER_DEFINITIONS)
def builtin_sources() -> dict[str, CapabilitySource]:
"""Return all broker-local capability sources."""
return {
BUILTIN_CONNECTION_ID: CapabilitySource(
id=BUILTIN_CONNECTION_ID,
kind="system",
capabilities=CapabilityBuckets(
node_specs=builtin_specs(),
reducers=builtin_reducers(),
reducer_definitions=builtin_reducer_definitions(),
),
visibility=SourceVisibility(
planner=True,
mcp_client=True,
admin_dashboard=True,
),
permissions=SourcePermissions(safe_for_workflow=True),
description="Workflow standard-library nodes.",
),
RECIPE_SOURCE_ID: CapabilitySource(
id=RECIPE_SOURCE_ID,
kind="system",
capabilities=CapabilityBuckets(node_specs=recipe_specs()),
visibility=SourceVisibility(
planner=True,
mcp_client=True,
admin_dashboard=True,
),
permissions=SourcePermissions(safe_for_workflow=True),
description="First-party workflow recipes composed from standard nodes.",
),
}
__all__ = [
"AUTHORING_STD_SPECS",
"BUILTIN_CONNECTION_ID",
"BUILTIN_SOURCE_ID",
"MCP_SOURCE_ID",
"RECIPE_SOURCE_ID",
"RECIPE_SPECS",
"builtin_reducer_definitions",
"builtin_reducers",
"builtin_sources",
"builtin_specs",
"get_qualified_spec",
"qualify_node_name",
"qualify_spec",
"recipe_specs",
]
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@@ -0,0 +1,19 @@
from __future__ import annotations
from .context import (
InMemoryWorkflowEventRecorder,
LocalWorkflowRuntimeRunner,
StaticWorkflowSpecProvider,
WorkflowServer,
WorkflowServerConfig,
build_local_static_workflow_server,
)
__all__ = [
"InMemoryWorkflowEventRecorder",
"LocalWorkflowRuntimeRunner",
"StaticWorkflowSpecProvider",
"WorkflowServer",
"WorkflowServerConfig",
"build_local_static_workflow_server",
]
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@@ -0,0 +1,273 @@
from __future__ import annotations
from collections.abc import Mapping
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any
from wf_api import WorkflowApi, durable_workflow_api
from wf_api.local_sources import builtin_sources, get_qualified_spec
from wf_api.models import RawWorkflowPlan, TraceRange
from wf_api.operation_context import (
WorkflowEventRecorder,
WorkflowOperationContext,
WorkflowRuntimeRunner,
WorkflowSpecProvider,
)
from wf_api.runtime_dependencies import resolve_runtime_dependencies
from wf_api.saved_subgraphs import (
SavedSubgraphTree,
prepare_saved_subgraphs,
resolve_saved_subgraph_tree,
)
from wf_api.stores import WorkflowStores, file_workflow_stores
from wf_artifacts import WorkflowArtifact, WorkflowDeployment
from wf_authoring import NodeSpec
from wf_core import (
NodeUse,
RunState,
Workflow,
execute_workflow_result_async,
resume_workflow_result_async,
)
from wf_platform import CapabilitySource
@dataclass(frozen=True, slots=True)
class WorkflowServerConfig:
"""Configuration for the first local/static workflow server slice."""
store_root: Path
@dataclass(slots=True)
class InMemoryWorkflowEventRecorder(WorkflowEventRecorder):
"""Small process-local event sink for server composition tests."""
events: list[dict[str, Any]] = field(default_factory=list)
def record_event(self, event: object) -> None:
self.events.append({"kind": "adapter_event", "event": event})
def record_workflow_event(
self,
event_type: str,
*,
capability_id: str,
payload: dict[str, Any],
) -> None:
self.events.append(
{
"kind": event_type,
"capability_id": capability_id,
"payload": payload,
}
)
@dataclass(frozen=True, slots=True)
class StaticWorkflowSpecProvider(WorkflowSpecProvider):
"""Source provider for local/static server capabilities."""
sources: Mapping[str, CapabilitySource]
@property
def capability_sources(self) -> dict[str, CapabilitySource]:
return dict(self.sources)
def get_qualified_spec(self, qualified_name: str) -> NodeSpec[Any, Any]:
return get_qualified_spec(self.sources, qualified_name)
@dataclass(slots=True)
class LocalWorkflowRuntimeRunner(WorkflowRuntimeRunner):
"""Run workflow plans against local/static source catalogs."""
specs: StaticWorkflowSpecProvider
artifact_store: Any
def compile_plan(
self,
plan: RawWorkflowPlan,
node_name_bindings: dict[str, str] | None = None,
) -> Workflow:
node_defs: dict[str, Any] = {}
bindings = node_name_bindings or {}
for step in plan.nodes:
if not isinstance(step, NodeUse):
continue
qualified_name = bindings.get(step.node, step.node)
spec = self.specs.get_qualified_spec(qualified_name)
node_defs[qualified_name] = spec.to_node_def()
nodes = []
for node in plan.nodes:
node_payload = node.model_dump(by_alias=True)
if isinstance(node, NodeUse):
node_payload["node"] = bindings.get(node.node, node.node)
nodes.append(node_payload)
return Workflow.model_validate(
{
"name": plan.name,
"input_schema": plan.input_schema,
"state_schema": plan.state_schema,
"output_schema": plan.output_schema,
"output": [binding.model_dump(mode="json") for binding in plan.output],
"outcomes": plan.outcomes,
"start": plan.start,
"node_defs": [node.model_dump() for node in node_defs.values()],
"nodes": nodes,
"edges": [edge.model_dump(by_alias=True) for edge in plan.edges],
}
)
def prepare_workflow_runtime(
self,
plan: RawWorkflowPlan,
*,
deployment: WorkflowDeployment | None,
artifact: WorkflowArtifact | None,
saved_subgraph_tree: SavedSubgraphTree | None = None,
) -> tuple[Workflow, dict[str, Any], dict[str, Any], dict[str, Any]]:
plan_node_names = [
node.node for node in plan.nodes if isinstance(node, NodeUse)
]
runtime_artifact = artifact or WorkflowArtifact(
id=plan.name,
version=1,
title=plan.name,
input_schema=plan.input_schema,
output_schema=plan.output_schema,
outcomes=("completed",),
plan=plan.model_dump(mode="json", by_alias=True),
)
dependencies = resolve_runtime_dependencies(
artifact=runtime_artifact,
deployment=deployment,
sources=self.specs.capability_sources,
plan_node_names=plan_node_names,
)
prepared_subgraphs = {}
if saved_subgraph_tree is not None:
prepared_subgraphs = prepare_saved_subgraphs(
tree=saved_subgraph_tree,
deployment=deployment,
sources=self.specs.capability_sources,
compile_plan=self.compile_plan,
)
elif artifact is not None and self.artifact_store is not None:
tree = resolve_saved_subgraph_tree(
root_artifact=artifact,
artifact_store=self.artifact_store,
)
prepared_subgraphs = prepare_saved_subgraphs(
tree=tree,
deployment=deployment,
sources=self.specs.capability_sources,
compile_plan=self.compile_plan,
)
workflow = self.compile_plan(plan, dependencies.node_name_bindings)
return (
workflow,
dependencies.node_registry,
dependencies.reducers,
prepared_subgraphs,
)
async def run_workflow_from_plan(
self,
plan: RawWorkflowPlan,
workflow_input: dict[str, Any],
deployment: WorkflowDeployment | None = None,
artifact: WorkflowArtifact | None = None,
saved_subgraph_tree: SavedSubgraphTree | None = None,
) -> RunState:
workflow, registry, reducers, prepared_subgraphs = (
self.prepare_workflow_runtime(
plan,
deployment=deployment,
artifact=artifact,
saved_subgraph_tree=saved_subgraph_tree,
)
)
return await execute_workflow_result_async(
workflow,
workflow_input,
registry,
reducers=reducers,
subgraphs=prepared_subgraphs,
)
async def resume_workflow_from_plan(
self,
plan: RawWorkflowPlan,
run: RunState,
*,
resume_payload: dict[str, Any],
resume_outcome: str,
deployment: WorkflowDeployment | None = None,
artifact: WorkflowArtifact | None = None,
saved_subgraph_tree: SavedSubgraphTree | None = None,
) -> RunState:
workflow, registry, reducers, prepared_subgraphs = (
self.prepare_workflow_runtime(
plan,
deployment=deployment,
artifact=artifact,
saved_subgraph_tree=saved_subgraph_tree,
)
)
return await resume_workflow_result_async(
workflow,
run,
registry,
resume_payload=resume_payload,
resume_outcome=resume_outcome,
reducers=reducers,
subgraphs=prepared_subgraphs,
)
@dataclass(frozen=True, slots=True)
class WorkflowServer:
"""First-slice long-lived server composition without transport concerns."""
config: WorkflowServerConfig
stores: WorkflowStores
context: WorkflowOperationContext
api: WorkflowApi
events: InMemoryWorkflowEventRecorder
@staticmethod
def trace_range(*, start: int, limit: int) -> TraceRange:
return TraceRange(start=start, limit=limit)
def build_local_static_workflow_server(root: str | Path) -> WorkflowServer:
"""Build a durable local/static workflow server composition."""
config = WorkflowServerConfig(store_root=Path(root))
stores = file_workflow_stores(config.store_root)
events = InMemoryWorkflowEventRecorder()
specs = StaticWorkflowSpecProvider(builtin_sources())
runtime = LocalWorkflowRuntimeRunner(
specs=specs,
artifact_store=stores.artifact_store,
)
context = WorkflowOperationContext(
artifact_store=stores.artifact_store,
draft_workspace_store=stores.draft_workspace_store,
run_store=stores.run_store,
events=events,
specs=specs,
runtime=runtime,
live_sources=None,
)
api = durable_workflow_api(context)
return WorkflowServer(
config=config,
stores=stores,
context=context,
api=api,
events=events,
)