mirror of
https://github.com/langbot-app/LangBot.git
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feat(agent-runner): integrate AgentRunner Protocol v1 with plugin system
Phase 0 integration complete - verified minimal loop with local-agent stub runner. Changes: - Add AgentRunOrchestrator for plugin-based agent execution - Add AgentResultNormalizer for Protocol v1 result conversion - Add AgentRunnerDescriptor for runner ID parsing (plugin:author/name/runner) - Update chat handler to use new orchestrator instead of direct runner lookup - Add plugin handler methods for list_agent_runners and run_agent - Add connector methods for AgentRunner protocol forwarding - Update pipeline API to include runner options in metadata - Add integration docs and implementation plan Integration verified: - Runner: plugin:langbot/local-agent/default - Input: "你好" - Output: [stub] Echo: 你好 - Date: 2026-05-10 10:09 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
huanghuoguoguo
254
src/langbot/pkg/agent/runner/context_builder.py
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254
src/langbot/pkg/agent/runner/context_builder.py
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"""Agent run context builder for converting Query to SDK v1 AgentRunContext."""
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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 langbot_plugin.api.entities.builtin.pipeline import query as pipeline_query
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from ...core import app
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from .descriptor import AgentRunnerDescriptor
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from .config_migration import ConfigMigration
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# Internal models for SDK v1 context protocol matching SDK v1 resources.py
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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 # 'pipeline' or 'event_router'
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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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session_id: str | None
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conversation_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_uuid: str | None
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pipeline_uuid: 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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message_chain: dict[str, typing.Any] | None
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attachments: list[dict[str, typing.Any]]
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# SDK v1 Protocol resource models - matching langbot-plugin-sdk/resources.py
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class ModelResource(typing.TypedDict):
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"""Model resource per SDK v1."""
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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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class ToolResource(typing.TypedDict):
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"""Tool resource per SDK v1."""
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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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class KnowledgeBaseResource(typing.TypedDict):
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"""Knowledge base resource per SDK v1."""
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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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class FileResource(typing.TypedDict):
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"""File resource per SDK v1."""
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file_id: str
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file_name: str | None
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mime_type: str | None
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source: str | None
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class StorageResource(typing.TypedDict):
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"""Storage resource per SDK v1."""
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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 per SDK v1."""
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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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files: list[FileResource]
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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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sdk_protocol_version: str
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query_id: int | None
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trace_id: str | None
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deadline_at: int | None
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metadata: dict[str, typing.Any]
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class AgentRunContextV1(typing.TypedDict):
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"""SDK v1 AgentRunContext per PROTOCOL_V1.md."""
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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] | None # Reserved for EBA
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actor: dict[str, typing.Any] | None # Reserved for EBA
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subject: dict[str, typing.Any] | None # Reserved for EBA
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messages: list[dict[str, typing.Any]]
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input: AgentInput
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resources: AgentResources
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runtime: AgentRuntimeContext
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config: dict[str, typing.Any]
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class AgentRunContextBuilder:
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"""Builder for converting Query to SDK v1 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 to 'message.received' for pipeline
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- Build conversation context from session
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- Convert messages to SDK format
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- Build input from user_message and message_chain
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- Set resources from AgentResourceBuilder result
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- Build runtime context with host info, trace_id, deadline
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- Set config from runner instance configuration
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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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async def build_context(
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self,
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query: pipeline_query.Query,
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descriptor: AgentRunnerDescriptor,
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resources: AgentResources,
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) -> AgentRunContextV1:
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"""Build AgentRunContext from Query.
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Args:
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query: Pipeline query
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descriptor: Runner descriptor
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resources: Built resources from AgentResourceBuilder
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Returns:
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AgentRunContextV1 dict matching PROTOCOL_V1.md
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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
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trigger: AgentTrigger = {
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'type': 'message.received',
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'source': 'pipeline',
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'timestamp': int(time.time()),
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}
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# Build conversation context
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conversation: ConversationContext | None = None
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if query.session:
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conversation = {
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'session_id': f'{query.session.launcher_type.value}_{query.session.launcher_id}',
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'conversation_id': getattr(query.session.using_conversation, 'uuid', None),
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'launcher_type': query.session.launcher_type.value,
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'launcher_id': query.session.launcher_id,
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'sender_id': str(query.sender_id),
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'bot_uuid': query.bot_uuid,
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'pipeline_uuid': query.pipeline_uuid,
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}
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# Build input
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input: AgentInput = self._build_input(query)
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# Build messages
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messages = self._build_messages(query)
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# Get runner config
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runner_config = ConfigMigration.resolve_runner_config(
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query.pipeline_config,
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descriptor.id,
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)
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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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'sdk_protocol_version': descriptor.protocol_version,
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'query_id': query.query_id,
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'trace_id': run_id, # Use run_id as trace_id for now
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'deadline_at': None, # TODO: set from runner config timeout
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'metadata': {
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'bot_name': query.variables.get('_monitoring_bot_name', 'Unknown'),
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'pipeline_name': query.variables.get('_monitoring_pipeline_name', 'Unknown'),
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},
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}
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# Build full context
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context: AgentRunContextV1 = {
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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': None, # Reserved for EBA
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'actor': None, # Reserved for EBA
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'subject': None, # Reserved for EBA
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'messages': messages,
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'input': input,
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'resources': resources,
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'runtime': runtime,
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'config': runner_config,
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}
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return context
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def _build_input(self, query: pipeline_query.Query) -> AgentInput:
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"""Build AgentInput from query."""
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text = None
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contents: list[dict[str, typing.Any]] = []
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if query.user_message:
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# Extract text if content is single text element
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if isinstance(query.user_message.content, list):
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for elem in query.user_message.content:
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contents.append(elem.model_dump(mode='json'))
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if elem.type == 'text':
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text = getattr(elem, 'text', None)
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else:
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# Single string content
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text = str(query.user_message.content)
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contents.append({'type': 'text', 'text': text})
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# Include message_chain for platform-specific format
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message_chain_dict = None
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if query.message_chain:
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message_chain_dict = query.message_chain.model_dump(mode='json')
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return {
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'text': text,
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'contents': contents,
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'message_chain': message_chain_dict,
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'attachments': [], # TODO: extract attachments from message_chain
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}
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def _build_messages(self, query: pipeline_query.Query) -> list[dict[str, typing.Any]]:
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"""Build messages list from query."""
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messages: list[dict[str, typing.Any]] = []
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if query.messages:
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for msg in query.messages:
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messages.append(msg.model_dump(mode='json'))
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return messages
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