mirror of
https://github.com/langbot-app/LangBot.git
synced 2026-06-27 07:54:19 +00:00
feat(agent-runner): add plugin runner host integration
This commit is contained in:
huanghuoguoguo
@@ -0,0 +1,490 @@
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"""Agent run context builder for provisioning AgentRunContext envelopes."""
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from __future__ import annotations
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import uuid
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import time
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import typing
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from ...core import app
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from .descriptor import AgentRunnerDescriptor
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from .persistent_state_store import get_persistent_state_store
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from .host_models import AgentEventEnvelope, AgentBinding
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DEFAULT_RUNNER_TIMEOUT_SECONDS = 300
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# Internal models for the agent runner context protocol.
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class AgentTrigger(typing.TypedDict):
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"""Agent trigger information."""
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type: str
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source: str
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timestamp: int | None
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class ConversationContext(typing.TypedDict):
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"""Conversation context."""
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conversation_id: str | None
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thread_id: str | None
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launcher_type: str | None
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launcher_id: str | None
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sender_id: str | None
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bot_id: str | None
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workspace_id: str | None
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session_id: str | None
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class AgentInput(typing.TypedDict):
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"""Agent input."""
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text: str | None
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contents: list[dict[str, typing.Any]]
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attachments: list[dict[str, typing.Any]]
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class AgentRunState(typing.TypedDict):
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"""Agent run state with 4 scopes."""
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conversation: dict[str, typing.Any]
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actor: dict[str, typing.Any]
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subject: dict[str, typing.Any]
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runner: dict[str, typing.Any]
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# Resource payload models matching langbot-plugin-sdk/resources.py.
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class ModelResource(typing.TypedDict):
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"""Model resource payload."""
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model_id: str
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model_type: str | None
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provider: str | None
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operations: list[str]
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class ToolResource(typing.TypedDict):
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"""Tool resource payload."""
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tool_name: str
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tool_type: str | None
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description: str | None
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operations: list[str]
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class KnowledgeBaseResource(typing.TypedDict):
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"""Knowledge base resource payload."""
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kb_id: str
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kb_name: str | None
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kb_type: str | None
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operations: list[str]
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class SkillResource(typing.TypedDict):
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"""Skill resource payload."""
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skill_name: str
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display_name: str | None
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description: str | None
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class StorageResource(typing.TypedDict):
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"""Storage resource payload."""
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plugin_storage: bool
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workspace_storage: bool
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class AgentResources(typing.TypedDict):
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"""Agent resources payload."""
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models: list[ModelResource]
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tools: list[ToolResource]
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knowledge_bases: list[KnowledgeBaseResource]
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skills: list[SkillResource]
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storage: StorageResource
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platform_capabilities: dict[str, typing.Any]
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class AgentRuntimeContext(typing.TypedDict):
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"""Agent runtime context."""
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langbot_version: str | None
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trace_id: str | None
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deadline_at: float | None
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metadata: dict[str, typing.Any]
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class AgentRunContextPayload(typing.TypedDict):
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"""AgentRunContext payload passed to an agent runner.
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Protocol v1 structure - matches SDK AgentRunContext.
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Note: The 'config' field contains the current Agent/runner config
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from ai.runner_config[runner_id] while the current Query entry remains
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a temporary configuration container. It is not plugin instance config.
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"""
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run_id: str
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trigger: AgentTrigger
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conversation: ConversationContext | None
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event: dict[str, typing.Any] # REQUIRED for Protocol v1
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actor: dict[str, typing.Any] | None
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subject: dict[str, typing.Any] | None
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input: AgentInput
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delivery: dict[str, typing.Any] # REQUIRED for Protocol v1
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resources: AgentResources
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context: dict[str, typing.Any] # ContextAccess - REQUIRED for Protocol v1
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state: AgentRunState
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runtime: AgentRuntimeContext
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config: dict[str, typing.Any] # Agent/runner config from ai.runner_config[runner_id]
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adapter: dict[str, typing.Any] | None # Entry adapter context
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metadata: dict[str, typing.Any] # Additional metadata
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class AgentRunContextBuilder:
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"""Builder for provisioning AgentRunContext.
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Responsibilities:
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- Generate new run_id (UUID, not query id)
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- Set trigger type based on event source
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- Build conversation context from event
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- Build input from event
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- Build state snapshot from PersistentStateStore
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- Build runtime context with host info, trace_id, deadline
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- Set config from current Agent/runner configuration.
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Query adaptation belongs to QueryEntryAdapter, not this builder.
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"""
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ap: app.Application
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def __init__(self, ap: app.Application):
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self.ap = ap
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@staticmethod
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def _positive_int(value: typing.Any) -> int | None:
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if isinstance(value, bool):
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return None
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if isinstance(value, int) and value > 0:
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return value
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if isinstance(value, str) and value.isdigit():
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parsed_value = int(value)
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if parsed_value > 0:
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return parsed_value
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return None
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@staticmethod
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def _is_llm_model_resource(model_resource: ModelResource) -> bool:
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operations = model_resource.get('operations')
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if isinstance(operations, list) and operations:
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return bool({'invoke', 'stream'} & {str(operation) for operation in operations})
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return model_resource.get('model_type') != 'rerank'
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async def _build_model_context_window_tokens(self, resources: AgentResources) -> int | None:
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model_mgr = getattr(self.ap, 'model_mgr', None)
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if model_mgr is None:
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return None
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for model_resource in resources.get('models', []):
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if not self._is_llm_model_resource(model_resource):
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continue
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model_uuid = model_resource.get('model_id')
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if not isinstance(model_uuid, str) or not model_uuid:
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continue
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try:
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model = await model_mgr.get_model_by_uuid(model_uuid)
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except Exception as exc:
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logger = getattr(self.ap, 'logger', None)
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if logger is not None:
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logger.debug(f'Failed to resolve model context window for {model_uuid}: {exc}')
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continue
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model_entity = getattr(model, 'model_entity', None)
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context_length = self._positive_int(getattr(model_entity, 'context_length', None))
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return context_length
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return None
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async def build_context_from_event(
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self,
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event: AgentEventEnvelope,
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binding: AgentBinding,
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descriptor: AgentRunnerDescriptor,
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resources: AgentResources,
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) -> AgentRunContextPayload:
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"""Build AgentRunContext from event-first envelope.
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This is the main entry point for Protocol v1.
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Does NOT inline full history by default.
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Args:
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event: Event envelope
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binding: Agent binding
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descriptor: Runner descriptor
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resources: Built resources
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Returns:
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AgentRunContextPayload for the runner
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"""
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# Generate new run_id
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run_id = str(uuid.uuid4())
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# Build trigger from event
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trigger: AgentTrigger = {
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'type': event.event_type,
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'source': event.source,
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'timestamp': event.event_time or int(time.time()),
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}
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# Build conversation context from event
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conversation: ConversationContext | None = None
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if event.conversation_id:
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conversation = {
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'session_id': None,
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'conversation_id': event.conversation_id,
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'thread_id': event.thread_id,
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'launcher_type': None, # Will be filled from actor/subject if needed
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'launcher_id': None,
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'sender_id': event.actor.actor_id if event.actor else None,
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'bot_id': event.bot_id,
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'workspace_id': event.workspace_id,
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}
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# Build event context (Protocol v1 event-first)
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event_context = {
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'event_id': event.event_id,
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'event_type': event.event_type,
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'event_time': event.event_time,
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'source': event.source,
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'source_event_type': event.source_event_type,
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'raw_ref': event.raw_ref.model_dump(mode='json') if event.raw_ref else None,
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'data': event.data,
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}
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# Build actor context
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actor_context = None
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if event.actor:
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actor_context = {
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'actor_type': event.actor.actor_type,
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'actor_id': event.actor.actor_id,
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'actor_name': event.actor.actor_name,
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}
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# Build subject context
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subject_context = None
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if event.subject:
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subject_context = {
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'subject_type': event.subject.subject_type,
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'subject_id': event.subject.subject_id,
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'data': event.subject.data,
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}
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# Build input from event
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input: AgentInput = {
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'text': event.input.text,
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'contents': [c.model_dump(mode='json') if hasattr(c, 'model_dump') else c for c in event.input.contents],
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'attachments': [
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a.model_dump(mode='json') if hasattr(a, 'model_dump') else a for a in event.input.attachments
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],
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}
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# Build context access (no history inlined by default for Protocol v1)
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# Populate with actual values from stores
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context_access = await self._build_context_access(event, descriptor, binding)
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# Build state snapshot from persistent state store (event-first Protocol v1)
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persistent_state_store = get_persistent_state_store(self.ap.persistence_mgr.get_db_engine())
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state: AgentRunState = await persistent_state_store.build_snapshot_from_event(event, binding, descriptor)
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model_context_window_tokens = await self._build_model_context_window_tokens(resources)
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# Build runtime context
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runtime: AgentRuntimeContext = {
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'langbot_version': self.ap.ver_mgr.get_current_version(),
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'trace_id': run_id,
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'deadline_at': self._build_deadline_from_binding(binding),
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'metadata': {
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'bot_id': event.bot_id,
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'workspace_id': event.workspace_id,
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'streaming_supported': event.delivery.supports_streaming,
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'model_context_window_tokens': model_context_window_tokens,
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},
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}
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# Build delivery context
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delivery_context = {
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'surface': event.delivery.surface,
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'reply_target': event.delivery.reply_target,
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'supports_streaming': event.delivery.supports_streaming,
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'supports_edit': event.delivery.supports_edit,
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'supports_reaction': event.delivery.supports_reaction,
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'max_message_size': event.delivery.max_message_size,
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'platform_capabilities': event.delivery.platform_capabilities,
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}
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# Build adapter context (empty for event-first)
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adapter_context = {
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'extra': {},
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}
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# Build full context - Protocol v1 structure
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context: AgentRunContextPayload = {
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'run_id': run_id,
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'trigger': trigger,
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'conversation': conversation,
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'event': event_context, # REQUIRED
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'actor': actor_context,
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'subject': subject_context,
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'input': input,
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'delivery': delivery_context, # REQUIRED
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'resources': resources,
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'context': context_access, # ContextAccess - REQUIRED
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'state': state,
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'runtime': runtime,
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'config': binding.runner_config,
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'adapter': adapter_context,
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'metadata': {}, # Additional metadata
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}
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return context
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def _build_deadline_from_binding(self, binding: AgentBinding) -> float | None:
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"""Build deadline timestamp from binding timeout config.
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Args:
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binding: Agent binding with runner_config
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Returns:
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Deadline timestamp or None
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"""
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timeout = binding.runner_config.get('timeout', DEFAULT_RUNNER_TIMEOUT_SECONDS)
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if timeout is None:
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return None
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try:
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timeout_seconds = float(timeout)
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except (TypeError, ValueError):
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return None
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if timeout_seconds <= 0:
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return None
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return time.time() + timeout_seconds
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async def _build_context_access(
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self,
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event: AgentEventEnvelope,
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descriptor: AgentRunnerDescriptor,
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binding: AgentBinding | None = None,
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) -> dict[str, typing.Any]:
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"""Build ContextAccess with actual values from stores.
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Args:
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event: Event envelope
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descriptor: Runner descriptor
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binding: Agent binding (required for state_policy in event-first mode)
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Returns:
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ContextAccess dict
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"""
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conversation_id = event.conversation_id
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permissions = descriptor.permissions
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history_perms = set(permissions.history)
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event_perms = set(permissions.events)
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storage_perms = set(permissions.storage)
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history_page_enabled = 'page' in history_perms and conversation_id is not None
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history_search_enabled = 'search' in history_perms and conversation_id is not None
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event_get_enabled = 'get' in event_perms
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event_page_enabled = 'page' in event_perms and conversation_id is not None
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steering_pull_enabled = (
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bool(getattr(descriptor.capabilities, 'steering', False)) and conversation_id is not None
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)
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run_get_enabled = True
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run_list_enabled = conversation_id is not None
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run_events_page_enabled = True
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run_cancel_enabled = True
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run_append_result_enabled = False
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run_finalize_enabled = False
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run_claim_enabled = False
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run_renew_claim_enabled = False
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run_release_claim_enabled = False
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runtime_register_enabled = False
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runtime_heartbeat_enabled = False
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runtime_list_enabled = False
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# Determine state API availability based on binding state_policy.
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state_enabled = False
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storage_enabled = False
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if binding is not None:
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state_policy = binding.state_policy
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if state_policy.enable_state and state_policy.state_scopes:
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state_enabled = True
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resource_policy = binding.resource_policy
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storage_enabled = ('plugin' in storage_perms and resource_policy.allow_plugin_storage) or (
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'workspace' in storage_perms and resource_policy.allow_workspace_storage
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)
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# Get latest cursor and has_history_before if conversation exists
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latest_cursor = None
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has_history_before = False
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if conversation_id:
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try:
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from .transcript_store import TranscriptStore
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store = TranscriptStore(self.ap.persistence_mgr.get_db_engine())
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latest_cursor = await store.get_latest_cursor(conversation_id)
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if latest_cursor:
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has_history_before = True
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except Exception as e:
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self.ap.logger.warning(f'Failed to get transcript cursor: {e}')
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return {
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'conversation_id': conversation_id,
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'thread_id': event.thread_id,
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'latest_cursor': latest_cursor,
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'event_seq': None, # Will be populated when EventLog is written
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'transcript_seq': int(latest_cursor) if latest_cursor else None,
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'has_history_before': has_history_before,
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'inline_policy': {
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'mode': 'current_event',
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'delivered_count': 0,
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'source_total_count': None,
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'messages_complete': False,
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'reason': 'current_event_only',
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},
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'available_apis': {
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'prompt_get': False,
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'history_page': history_page_enabled,
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'history_search': history_search_enabled,
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'event_get': event_get_enabled,
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'event_page': event_page_enabled,
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'state': state_enabled,
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'storage': storage_enabled,
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'steering_pull': steering_pull_enabled,
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'run_get': run_get_enabled,
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'run_list': run_list_enabled,
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'run_events_page': run_events_page_enabled,
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'run_cancel': run_cancel_enabled,
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'run_append_result': run_append_result_enabled,
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'run_finalize': run_finalize_enabled,
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'run_claim': run_claim_enabled,
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'run_renew_claim': run_renew_claim_enabled,
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'run_release_claim': run_release_claim_enabled,
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'runtime_register': runtime_register_enabled,
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'runtime_heartbeat': runtime_heartbeat_enabled,
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'runtime_list': runtime_list_enabled,
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},
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}
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