""" 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()