aiagent/backend/app/agent_runtime/workflow_integration.py

"""
Agent Runtime ⇄ WorkflowEngine 桥接。

让 workflow_engine.execute_node() 通过寥寥几行调用 Agent Runtime。
"""
from __future__ import annotations

import logging
from typing import Any, Dict, Optional

from app.agent_runtime.core import AgentRuntime
from app.agent_runtime.schemas import (
    AgentConfig,
    AgentLLMConfig,
    AgentToolConfig,
    AgentBudgetConfig,
)

logger = logging.getLogger(__name__)


async def run_agent_node(
    node_data: Dict[str, Any],
    input_data: Dict[str, Any],
    execution_logger: Optional[Any] = None,
    user_id: Optional[str] = None,
    on_tool_executed: Optional[Any] = None,
    on_llm_invocation: Optional[Any] = None,
    budget_limits: Optional[Dict[str, int]] = None,
) -> Dict[str, Any]:
    """
    在工作流中执行 Agent 节点。

    node_data 支持的字段：
        system_prompt  — Agent 人格/指令（支持 {{variable}} 模板）
        tools          — 可选工具白名单，默认全部
        exclude_tools  — 可选工具黑名单
        model          — 模型名称
        provider       — 提供商（openai/deepseek）
        temperature    — 温度
        max_iterations — ReAct 最大步数
        memory         — 是否启用长期记忆

    input_data 中的 "query" 或 "input" 字段作为用户输入。
    """
    # 1. 解析配置
    query = (
        input_data.get("query")
        or input_data.get("input")
        or input_data.get("text", "")
    )
    if not isinstance(query, str):
        query = str(query) if query else ""

    if not query:
        return {"output": "错误：Agent 节点未收到用户输入", "status": "error"}

    # 2. 解析 system_prompt（支持模板变量）
    raw_prompt = node_data.get("system_prompt", "你是一个有用的AI助手。")
    try:
        formatted_prompt = raw_prompt.format(**input_data)
    except (KeyError, ValueError):
        formatted_prompt = raw_prompt

    # 3. 构建 Agent 配置
    llm_config = AgentLLMConfig(
        provider=node_data.get("provider", "openai"),
        model=node_data.get("model", "gpt-4o-mini"),
        temperature=float(node_data.get("temperature", 0.7)),
        max_iterations=int(node_data.get("max_iterations", 10)),
    )
    # 允许节点内联 api_key/base_url
    if node_data.get("api_key"):
        llm_config.api_key = node_data["api_key"]
    if node_data.get("base_url"):
        llm_config.base_url = node_data["base_url"]

    # 3a. 构建预算配置（接收工作流级预算限制）
    budget = AgentBudgetConfig()
    if budget_limits:
        if "max_llm_invocations" in budget_limits:
            budget.max_llm_invocations = max(1, int(budget_limits["max_llm_invocations"]))
        if "max_tool_calls" in budget_limits:
            budget.max_tool_calls = max(1, int(budget_limits["max_tool_calls"]))

    agent_config = AgentConfig(
        name=node_data.get("label", "agent_node"),
        system_prompt=formatted_prompt,
        llm=llm_config,
        tools=AgentToolConfig(
            include_tools=node_data.get("tools", []),
            exclude_tools=node_data.get("exclude_tools", []),
        ),
        memory={
            "enabled": node_data.get("memory", True),
            "persist_to_db": node_data.get("memory", True),
        },
        budget=budget,
        user_id=user_id,
        self_review_enabled=node_data.get("self_review_enabled", False),
    )

    # 4. 执行 Agent
    runtime = AgentRuntime(
        config=agent_config,
        execution_logger=execution_logger,
        on_tool_executed=on_tool_executed,
    )
    # 注入 LLM 预算回调（使 Agent 内部 LLM 调用计入工作流预算）
    if on_llm_invocation:
        runtime.on_llm_invocation = on_llm_invocation

    result = await runtime.run(query)

    # 5. 返回结果（兼容工作流引擎的输出格式）
    if result.success:
        return {
            "output": result.content,
            "status": "success",
            "agent_meta": {
                "iterations": result.iterations_used,
                "tool_calls": result.tool_calls_made,
                "truncated": result.truncated,
            },
        }
    else:
        return {
            "output": result.content,
            "status": "error",
            "error": result.error,
        }


async def run_orchestrator_node(
    node_data: Dict[str, Any],
    input_data: Dict[str, Any],
    execution_logger: Optional[Any] = None,
    user_id: Optional[str] = None,
    on_tool_executed: Optional[Any] = None,
    on_llm_invocation: Optional[Any] = None,
) -> Dict[str, Any]:
    """
    在工作流中执行多 Agent 编排节点。

    node_data 支持的字段：
        mode              — "route" | "sequential" | "debate" | "pipeline"
        agents            — Agent ID 列表（必填，至少 2 个）
        routing_prompt    — route 模式的路由指令（可选）
        aggregation_prompt— debate 模式的汇总指令（可选）
        model             — 覆盖各 Agent 的模型（可选）
        provider          — 覆盖各 Agent 的提供商（可选）
        temperature       — 覆盖各 Agent 的温度（可选）
        max_iterations    — 覆盖各 Agent 的最大步数（可选）

    input_data 中的 "query" 或 "input" 字段作为用户输入。
    """
    from sqlalchemy.orm import Session
    from app.core.database import SessionLocal
    from app.models.agent import Agent
    from app.agent_runtime.orchestrator import (
        AgentOrchestrator,
        OrchestratorAgentConfig,
    )

    # 1. 解析输入
    query = (
        input_data.get("query")
        or input_data.get("input")
        or input_data.get("text", "")
    )
    if not isinstance(query, str):
        query = str(query) if query else ""

    if not query:
        return {"output": "错误：Orchestrator 节点未收到用户输入", "status": "error"}

    # 2. 解析编排模式
    mode = node_data.get("mode", "debate").lower()
    if mode not in ("route", "sequential", "debate", "pipeline"):
        return {"output": f"错误：不支持的编排模式 '{mode}'，可选: route, sequential, debate, pipeline", "status": "error"}

    # 3. 解析 Agent 列表
    agent_ids = node_data.get("agents", [])
    if not agent_ids or not isinstance(agent_ids, list) or len(agent_ids) < 1:
        return {"output": "错误：Orchestrator 节点需要至少 1 个 Agent", "status": "error"}

    # 4. 从 DB 加载 Agent 配置
    db: Optional[Session] = None
    try:
        db = SessionLocal()

        # 覆盖配置（可选）
        override_model = node_data.get("model")
        override_provider = node_data.get("provider")
        override_temperature = node_data.get("temperature")
        override_max_iterations = node_data.get("max_iterations")

        agent_configs: list = []
        for agent_id in agent_ids:
            agent = db.query(Agent).filter(Agent.id == agent_id).first()
            if not agent:
                logger.warning("Orchestrator: Agent %s 不存在，跳过", agent_id)
                continue

            # 从 workflow_config 提取 Agent 的 LLM 配置
            wc = agent.workflow_config or {}
            nodes = wc.get("nodes", [])
            system_prompt = agent.description or ""
            model = override_model or "deepseek-v4-flash"
            provider = override_provider or "deepseek"
            temperature = float(override_temperature) if override_temperature else 0.7
            max_iterations = int(override_max_iterations) if override_max_iterations else 10
            tools_whitelist: list = []

            for n in nodes:
                if n.get("type") not in ("agent", "llm", "template"):
                    continue
                cfg = n.get("data", {}) if isinstance(n, dict) else getattr(n, "data", {})
                system_prompt = cfg.get("system_prompt", "") or system_prompt
                if not override_model:
                    model = cfg.get("model", model)
                if not override_provider:
                    provider = cfg.get("provider", provider)
                if not override_temperature:
                    temperature = float(cfg.get("temperature", temperature))
                if not override_max_iterations:
                    max_iterations = int(cfg.get("max_iterations", max_iterations))
                tools_whitelist = cfg.get("tools", tools_whitelist)
                break

            agent_configs.append(OrchestratorAgentConfig(
                id=agent.id,
                name=agent.name or "Agent",
                system_prompt=system_prompt,
                model=model,
                provider=provider,
                temperature=temperature,
                max_iterations=max_iterations,
                tools=tools_whitelist,
                description=agent.description or "",
            ))

        if not agent_configs:
            return {"output": "错误：没有找到可用的 Agent", "status": "error"}

        # 5. 创建 Orchestrator 并执行
        orchestrator = AgentOrchestrator(
            default_llm_config=AgentLLMConfig(
                model=override_model or "deepseek-v4-flash",
                temperature=0.3,
            ),
        )
        result = await orchestrator.run(
            mode=mode,
            question=query,
            agents=agent_configs,
            on_llm_call=on_llm_invocation,
        )

        # 6. 返回结构化结果
        return {
            "output": result.final_answer,
            "status": "success",
            "orchestrator_meta": {
                "mode": result.mode,
                "agent_count": len(agent_configs),
                "steps": [
                    {
                        "agent_id": s.agent_id,
                        "agent_name": s.agent_name,
                        "input": s.input[:200] if s.input else "",
                        "output": s.output[:500] if s.output else "",
                        "iterations_used": s.iterations_used,
                        "tool_calls_made": s.tool_calls_made,
                        "error": s.error,
                    }
                    for s in result.steps
                ],
            },
        }

    except Exception as e:
        logger.error("Orchestrator 节点执行失败: %s", e, exc_info=True)
        return {
            "output": None,
            "status": "failed",
            "error": f"Orchestrator 执行失败: {e}",
        }
    finally:
        if db:
            db.close()