social-app/backend/src/core/agentscope/runtime/runner.py

from __future__ import annotations

from dataclasses import dataclass
from datetime import datetime, timezone
from typing import TYPE_CHECKING, Any
from uuid import UUID

from ag_ui.core.types import RunAgentInput
from agentscope.formatter import OpenAIChatFormatter
from agentscope.memory import InMemoryMemory
from agentscope.message import Msg
from agentscope.model import OpenAIChatModel
from core.agentscope.prompts.system_prompt import build_system_prompt
from core.agentscope.runtime.json_react_agent import JsonReActAgent
from core.agentscope.runtime.model_tracking import TrackingChatModel
from core.agentscope.runtime.stage_emitter import PipelineStageEmitter
from core.agentscope.runtime.tool_selection_registry import TOOL_SELECTION_REGISTRY
from core.agentscope.tools.toolkit import build_stage_toolkit
from core.agentscope.utils import patch_agentscope_json_repair_compat
from core.config.settings import config
from core.db.session import AsyncSessionLocal
from models.llm import Llm
from models.llm_factory import LlmFactory
from models.system_agents import SystemAgents
from schemas.agent.runtime_models import (
    AgentOutput,
)
from schemas.agent.forwarded_props import (
    ClientTimeContext,
    parse_forwarded_props_client_time,
)
from schemas.agent.system_agent import AgentType, SystemAgentLLMConfig
from schemas.user import UserContext
from services.litellm.service import LiteLLMService
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession

if TYPE_CHECKING:
    from core.agentscope.runtime.orchestrator import PipelineLike


@dataclass(frozen=True)
class SystemAgentRuntimeConfig:
    agent_type: AgentType
    model_code: str
    api_base_url: str
    api_key: str
    llm_config: SystemAgentLLMConfig


@dataclass(frozen=True)
class StageExecutionResult:
    message: Msg
    payload: dict[str, Any]
    response_metadata: dict[str, Any]


class AgentScopeRunner:
    def __init__(self, *, litellm_service: LiteLLMService | None = None) -> None:
        patch_agentscope_json_repair_compat()
        self._litellm_service: LiteLLMService = litellm_service or LiteLLMService()

    async def execute(
        self,
        *,
        user_context: UserContext,
        context_messages: list[Msg],
        pipeline: PipelineLike,
        run_input: RunAgentInput,
        system_agent_mode: str,
    ) -> dict[str, Any]:
        owner_id = UUID(user_context.id)
        runtime_client_time = self._resolve_runtime_client_time(run_input=run_input)
        stage_agent_type = self._resolve_stage_agent_type(system_agent_mode)

        async with AsyncSessionLocal() as session:
            stage_config = await self._load_stage_config(
                session=session,
                agent_type=stage_agent_type,
            )
            stage_toolkit = self._build_stage_toolkit(
                session=session,
                owner_id=owner_id,
                stage_config=stage_config,
            )
            worker_output = await self._execute_worker_step(
                pipeline=pipeline,
                run_input=run_input,
                user_context=user_context,
                context_messages=context_messages,
                toolkit=stage_toolkit,
                stage_config=stage_config,
                runtime_client_time=runtime_client_time,
            )

            return {
                "worker": worker_output.model_dump(mode="json", exclude_none=True),
            }

    def _build_stage_toolkit(
        self,
        *,
        session: AsyncSession,
        owner_id: UUID,
        stage_config: SystemAgentRuntimeConfig,
    ) -> Any:
        enabled_tool_names = TOOL_SELECTION_REGISTRY.resolve(stage_config=stage_config)
        return build_stage_toolkit(
            agent_type=stage_config.agent_type,
            session=session,
            owner_id=owner_id,
            enabled_tool_names=enabled_tool_names,
        )

    @staticmethod
    def _resolve_stage_agent_type(system_agent_mode: str) -> AgentType:
        mode = system_agent_mode.strip().lower() if system_agent_mode else "worker"
        if mode == AgentType.MEMORY.value:
            return AgentType.MEMORY
        return AgentType.WORKER

    async def _load_stage_config(
        self,
        *,
        session: AsyncSession,
        agent_type: AgentType,
    ) -> SystemAgentRuntimeConfig:
        return await self._load_system_agent_config(
            session=session,
            agent_type=agent_type,
        )

    async def _execute_worker_step(
        self,
        *,
        pipeline: PipelineLike,
        run_input: RunAgentInput,
        user_context: UserContext,
        context_messages: list[Msg],
        toolkit: Any,
        stage_config: SystemAgentRuntimeConfig,
        runtime_client_time: ClientTimeContext | None,
    ) -> AgentOutput:
        step_name = stage_config.agent_type.value
        worker_output_model = AgentOutput
        await self._emit_step_event(
            pipeline=pipeline,
            run_input=run_input,
            step_name=step_name,
            event_type="STEP_STARTED",
        )
        worker_result = await self._run_worker_stage(
            user_context=user_context,
            context_messages=context_messages,
            toolkit=toolkit,
            run_input=run_input,
            stage_config=stage_config,
            worker_output_model=worker_output_model,
            pipeline=pipeline,
            runtime_client_time=runtime_client_time,
        )
        worker_output = worker_output_model.model_validate(worker_result.payload)
        await self._emit_step_event(
            pipeline=pipeline,
            run_input=run_input,
            step_name=step_name,
            event_type="STEP_FINISHED",
        )
        return worker_output

    async def _load_system_agent_config(
        self,
        *,
        session: AsyncSession,
        agent_type: AgentType,
    ) -> SystemAgentRuntimeConfig:
        stmt = (
            select(SystemAgents, Llm, LlmFactory)
            .join(Llm, SystemAgents.llm_id == Llm.id)
            .join(LlmFactory, Llm.factory_id == LlmFactory.id)
            .where(SystemAgents.agent_type == agent_type.value)
        )
        row = (await session.execute(stmt)).one_or_none()
        if row is None:
            raise RuntimeError(f"system agent config not found: {agent_type.value}")
        system_agent, llm, factory = row
        status = str(system_agent.status).strip().lower()
        if status != "active":
            raise RuntimeError(f"system agent is not active: {agent_type.value}")
        return SystemAgentRuntimeConfig(
            agent_type=agent_type,
            model_code=llm.model_code,
            api_base_url=factory.request_url,
            api_key=self._resolve_provider_api_key(factory_name=factory.name),
            llm_config=SystemAgentLLMConfig.model_validate(system_agent.config or {}),
        )

    @staticmethod
    def _resolve_provider_api_key(*, factory_name: str) -> str:
        normalized_factory_name = factory_name.strip().upper()
        if normalized_factory_name == "VOLCENGINE":
            normalized_factory_name = "ARK"

        provider_keys = {
            str(key).strip().upper(): str(value).strip()
            for key, value in config.llm.provider_keys.items()
            if str(value).strip()
        }
        api_key = provider_keys.get(normalized_factory_name, "")
        if not api_key:
            raise RuntimeError(f"provider api key missing for factory: {factory_name}")
        return api_key

    async def _run_worker_stage(
        self,
        *,
        user_context: UserContext,
        context_messages: list[Msg],
        toolkit: Any,
        run_input: RunAgentInput,
        stage_config: SystemAgentRuntimeConfig,
        worker_output_model: type[AgentOutput],
        pipeline: PipelineLike,
        runtime_client_time: ClientTimeContext | None,
    ) -> StageExecutionResult:
        worker_input = list(context_messages)
        tracking_model = self._build_model(stage_config=stage_config)
        emitter = PipelineStageEmitter(
            pipeline=pipeline,
            session_id=run_input.thread_id,
            run_id=run_input.run_id,
            stage=stage_config.agent_type.value,
            emit_text_events=True,
            emit_tool_events=True,
        )
        agent = self._build_agent(
            agent_name=stage_config.agent_type.value,
            system_prompt=build_system_prompt(
                agent_type=stage_config.agent_type,
                llm_config=stage_config.llm_config,
                user_context=user_context,
                now_utc=datetime.now(timezone.utc),
                runtime_client_time=runtime_client_time,
                tools=None,
            ),
            toolkit=toolkit,
            model=tracking_model,
            emitter=emitter,
        )
        response_msg = await agent.reply_json(
            worker_input, output_model=worker_output_model
        )
        worker_payload = worker_output_model.model_validate(response_msg.metadata or {})
        response_metadata = self._litellm_service.build_usage_metadata(
            model=stage_config.model_code,
            usage_summary=tracking_model.usage_summary(),
        )
        await emitter.emit_final_text_end(
            worker_output=worker_payload.model_dump(mode="json", exclude_none=True),
            response_metadata=response_metadata,
        )
        return StageExecutionResult(
            message=response_msg,
            payload=worker_payload.model_dump(mode="json", exclude_none=True),
            response_metadata=response_metadata,
        )

    def _build_model(
        self, *, stage_config: SystemAgentRuntimeConfig
    ) -> TrackingChatModel:
        generate_kwargs: dict[str, Any] = {
            "temperature": stage_config.llm_config.temperature,
            "max_tokens": stage_config.llm_config.max_tokens,
            "timeout": stage_config.llm_config.timeout_seconds,
        }
        generate_kwargs["extra_body"] = {"enable_thinking": False}

        model = OpenAIChatModel(
            model_name=stage_config.model_code,
            api_key=stage_config.api_key,
            stream=False,
            client_kwargs={"base_url": stage_config.api_base_url},
            generate_kwargs=generate_kwargs,
        )
        return TrackingChatModel(model)

    def _build_agent(
        self,
        *,
        agent_name: str,
        system_prompt: str,
        toolkit: Any,
        model: TrackingChatModel,
        emitter: PipelineStageEmitter | None = None,
    ) -> JsonReActAgent:
        return JsonReActAgent(
            name=agent_name,
            sys_prompt=system_prompt,
            model=model,
            formatter=OpenAIChatFormatter(),
            toolkit=toolkit,
            memory=InMemoryMemory(),
            emitter=emitter,
        )

    async def _emit_step_event(
        self,
        *,
        pipeline: PipelineLike,
        run_input: RunAgentInput,
        step_name: str,
        event_type: str,
    ) -> None:
        await pipeline.emit(
            session_id=run_input.thread_id,
            event={
                "type": event_type,
                "threadId": run_input.thread_id,
                "runId": run_input.run_id,
                "stepName": step_name,
            },
        )

    def _resolve_runtime_client_time(
        self, *, run_input: RunAgentInput
    ) -> ClientTimeContext | None:
        return parse_forwarded_props_client_time(
            getattr(run_input, "forwarded_props", None)
        )


AgentScopeReActRunner = AgentScopeRunner