feat: Agent 运行时、对话 API、作业助手与引擎修复及前端执行超时
- agent_runtime 模块与 agent_chat API，前端 AgentChat 视图与路由对接 - workflow_engine: code 节点命名空间与 json 引用修复 - llm_service: 工具调用 extra_body（如 DeepSeek） - create_homework_manager_agent / _3 脚本与测试脚本扩展 - frontend: WORKFLOW_EXECUTION_HTTP_TIMEOUT_MS、AgentChatPreview/MainLayout 等 - 文档：架构说明与自主 Agent 改造完成情况 Made-with: Cursor
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renjianbo
@@ -983,6 +983,64 @@ class WorkflowEngine:
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return ctx.get("assistant_display_name")
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return None
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def _format_prior_conversation_for_llm(
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self, input_data: Dict[str, Any], original_prompt_template: str
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) -> Optional[str]:
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"""
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Agent 多轮对话：执行请求若携带 conversation_history，而提示词未使用
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{{memory.conversation_history}} 等占位符，则在此处拼进最终 prompt，避免模型「失忆」。
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"""
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t = original_prompt_template or ""
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if "memory.conversation_history" in t or re.search(
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r"\{\{[^}]*conversation_history[^}]*\}\}", t
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):
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return None
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hist: Any = None
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if isinstance(input_data, dict):
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hist = input_data.get("conversation_history")
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if hist is None and isinstance(input_data.get("memory"), dict):
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hist = input_data["memory"].get("conversation_history")
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if hist is None and isinstance(input_data.get("right"), dict):
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r = input_data["right"]
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hist = r.get("conversation_history")
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if hist is None and isinstance(r.get("memory"), dict):
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hist = r["memory"].get("conversation_history")
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if not hist or not isinstance(hist, list):
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return None
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lines: List[str] = []
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max_turns = 24
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for msg in hist[-max_turns:]:
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if not isinstance(msg, dict):
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continue
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role = msg.get("role", "")
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content = msg.get("content", "")
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if content is None:
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continue
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if not isinstance(content, str):
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content = str(content)
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content = content.strip()
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if not content:
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continue
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if role == "user":
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lines.append(f"用户：{content}")
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elif role in ("assistant", "agent"):
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lines.append(f"助手：{content}")
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else:
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lines.append(f"{role}：{content}")
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if not lines:
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return None
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body = "\n".join(lines)
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max_chars = 12000
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if len(body) > max_chars:
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body = body[-max_chars:] + "\n…（更早的对话已截断）"
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return f"【本轮之前的对话】\n{body}"
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def _resolve_vector_db_query_embedding(
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self, input_data: Any, query_vector_config: Any
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) -> Optional[List[Any]]:
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@@ -1605,6 +1663,8 @@ class WorkflowEngine:
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logger.info(f"[rjb] 使用JSON或字符串转换: user_query={user_query}")
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logger.info(f"[rjb] 最终提取的user_query: {user_query}")
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history_block = self._format_prior_conversation_for_llm(input_data, prompt)
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# 如果prompt中没有占位符，或者仍有未填充的变量，将用户输入附加到prompt
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is_generic_instruction = False # 初始化变量
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@@ -1633,25 +1693,43 @@ class WorkflowEngine:
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if is_generic_instruction:
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# 如果是通用指令，直接使用用户输入作为prompt
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formatted_prompt = str(user_query)
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if history_block:
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formatted_prompt = f"{history_block}\n\n{str(user_query)}"
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else:
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formatted_prompt = str(user_query)
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logger.info(f"[rjb] 检测到通用指令，直接使用用户输入作为prompt: {user_query[:50] if user_query else 'None'}")
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else:
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# 否则，将用户输入附加到prompt
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formatted_prompt = f"{formatted_prompt}\n\n{user_query}"
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if history_block:
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formatted_prompt = f"{formatted_prompt}\n\n{history_block}\n\n{user_query}"
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else:
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formatted_prompt = f"{formatted_prompt}\n\n{user_query}"
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logger.info(f"[rjb] 非通用指令，将用户输入附加到prompt")
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else:
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# 如果没有提取到用户查询，附加整个input_data
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formatted_prompt = f"{formatted_prompt}\n\n{json_module.dumps(input_data, ensure_ascii=False)}"
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tail = json_module.dumps(input_data, ensure_ascii=False)
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if history_block:
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formatted_prompt = f"{formatted_prompt}\n\n{history_block}\n\n{tail}"
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else:
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formatted_prompt = f"{formatted_prompt}\n\n{tail}"
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elif has_unfilled_variables or re.search(r'\{\{[^}]+\}\}', formatted_prompt):
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# 如果有占位符但未填充，先尝试清理所有未填充的模板变量
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# 使用正则表达式替换所有 {{...}} 格式的未填充变量
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formatted_prompt = re.sub(r'\{\{[^}]+\}\}', '', formatted_prompt)
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# 如果有占位符但未填充，附加用户需求说明
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if user_query:
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formatted_prompt = f"{formatted_prompt}\n\n用户需求：{user_query}\n\n请根据用户需求来完成任务。"
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user_tail = f"用户需求：{user_query}\n\n请根据用户需求来完成任务。"
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if history_block:
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formatted_prompt = f"{formatted_prompt}\n\n{history_block}\n\n{user_tail}"
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else:
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formatted_prompt = f"{formatted_prompt}\n\n{user_tail}"
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else:
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# 如果没有用户查询，附加整个input_data
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formatted_prompt = f"{formatted_prompt}\n\n输入数据：{json_module.dumps(input_data, ensure_ascii=False)}\n\n请根据输入数据来完成任务。"
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data_tail = f"输入数据：{json_module.dumps(input_data, ensure_ascii=False)}\n\n请根据输入数据来完成任务。"
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if history_block:
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formatted_prompt = f"{formatted_prompt}\n\n{history_block}\n\n{data_tail}"
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else:
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formatted_prompt = f"{formatted_prompt}\n\n{data_tail}"
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logger.info(f"[rjb] LLM节点prompt格式化: node_id={node_id}, original_prompt='{prompt[:50] if len(prompt) > 50 else prompt}', has_any_placeholder={has_any_placeholder}, user_query={user_query}, is_generic_instruction={is_generic_instruction}, final_prompt前200字符='{formatted_prompt[:200] if len(formatted_prompt) > 200 else formatted_prompt}'")
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prompt = formatted_prompt
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@@ -1720,6 +1798,9 @@ class WorkflowEngine:
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llm_extra_kw["api_key"] = api_key
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if base_url is not None:
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llm_extra_kw["base_url"] = base_url
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_xb = node_data.get("extra_body")
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if isinstance(_xb, dict) and _xb:
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llm_extra_kw["extra_body"] = _xb
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# 记录实际发送给LLM的prompt
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logger.info(f"[rjb] 准备调用LLM: node_id={node_id}, provider={provider}, model={model}, prompt前200字符='{prompt[:200] if len(prompt) > 200 else prompt}'")
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@@ -1821,7 +1902,45 @@ class WorkflowEngine:
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'status': 'failed',
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'error': f'LLM调用失败: {str(e)}'
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}
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elif node_type == 'agent':
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# Agent 节点：自主 ReAct 循环，支持多步工具调用
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if self.logger:
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self.logger.info(
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"Agent 节点开始执行",
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data={"node_id": node_id, "input": input_data},
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)
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try:
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from app.agent_runtime.workflow_integration import run_agent_node
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_agent_on_tool = None
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if hasattr(self, '_on_tool_executed_budget'):
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_agent_on_tool = self._on_tool_executed_budget
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result = await run_agent_node(
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node_data=node.get("data", {}),
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input_data=input_data,
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execution_logger=self.logger,
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user_id=self.trusted_model_config_user_id,
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on_tool_executed=_agent_on_tool,
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)
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if self.logger:
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duration = int((time.time() - start_time) * 1000)
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self.logger.log_node_complete(
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node_id, node_type, result.get("output"), duration,
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)
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return result
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except Exception as e:
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if self.logger:
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duration = int((time.time() - start_time) * 1000)
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self.logger.log_node_error(node_id, node_type, e, duration)
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logger.error(f"Agent 节点执行失败: {e}", exc_info=True)
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return {
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"output": None,
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"status": "failed",
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"error": f"Agent 执行失败: {e}",
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}
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elif node_type == 'condition':
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# 条件节点：判断分支（output 必须透传上游 dict，否则 sourceHandle true/false 下游只收到布尔值，丢失 reply/memory）
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condition = node.get('data', {}).get('condition', '')
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@@ -1892,6 +2011,9 @@ class WorkflowEngine:
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expanded_input.update(_bp)
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for _k in ('true', 'false', '_condition_result', '_condition_error'):
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expanded_input.pop(_k, None)
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# 展开 left：双入边 transform 的上游一路常挂在 sourceHandle=left（另一路为 LLM/code 的 right）
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if isinstance(expanded_input.get('left'), dict):
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expanded_input.update(expanded_input['left'])
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# 展开 right：merge / json-parse 后 reply、user_profile 常在 right 或嵌套 JSON 字符串中
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if isinstance(expanded_input.get('right'), dict):
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expanded_input.update(expanded_input['right'])
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@@ -2365,7 +2487,6 @@ class WorkflowEngine:
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try:
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import os
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import json
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import base64
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from pathlib import Path
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@@ -2806,7 +2927,6 @@ class WorkflowEngine:
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if queue_type == 'rabbitmq':
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# RabbitMQ实现
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import aio_pika
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import json
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# 获取RabbitMQ配置
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host = replace_variables(node_data.get('host', 'localhost'), input_data)
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@@ -2885,7 +3005,6 @@ class WorkflowEngine:
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elif queue_type == 'kafka':
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# Kafka实现
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from kafka import KafkaProducer
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import json
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# 获取Kafka配置
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bootstrap_servers = replace_variables(node_data.get('bootstrap_servers', 'localhost:9092'), input_data)
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@@ -3530,6 +3649,31 @@ class WorkflowEngine:
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if not isinstance(base_up, dict):
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base_up = {}
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memory['user_profile'] = {**base_up, **upd}
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hb_upd = input_data.get('homework_board_update')
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if isinstance(hb_upd, str) and hb_upd.strip().startswith('{'):
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try:
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hb_upd = json_module.loads(hb_upd)
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except Exception:
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hb_upd = {}
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if not isinstance(hb_upd, dict):
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hb_upd = {}
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if hb_upd:
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ctx = memory.get('context')
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if not isinstance(ctx, dict):
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ctx = {}
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base_hb = ctx.get('homework_board')
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if not isinstance(base_hb, dict):
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base_hb = {}
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merged_hb = {**base_hb, **hb_upd}
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new_items = hb_upd.get('items')
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old_items = base_hb.get('items')
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if isinstance(new_items, list) and len(new_items) > 0:
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merged_hb['items'] = new_items
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elif isinstance(old_items, list):
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merged_hb['items'] = old_items
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ctx['homework_board'] = merged_hb
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memory['context'] = ctx
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# 确保memory中有必要的字段
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if 'conversation_history' not in memory:
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@@ -4937,10 +5081,27 @@ class WorkflowEngine:
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try:
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if language.lower() == 'python':
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# 受限执行环境（禁止无 __builtins__，否则 isinstance 等不可用）
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local_vars = {'input_data': input_data, 'result': None}
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_code_globs = {'__builtins__': _CODE_NODE_SAFE_BUILTINS, 'hashlib': hashlib, 're': re}
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exec(code, _code_globs, local_vars)
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result = local_vars.get('result', local_vars.get('output', input_data))
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# 注入 loads/dumps；使用「globals == locals」同一命名空间 exec，
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# 避免嵌套函数 LOAD_GLOBAL 找不到仅在 locals 里的 loads，以及 json 作用域异常。
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_code_globs = {
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'__builtins__': _CODE_NODE_SAFE_BUILTINS,
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'hashlib': hashlib,
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're': re,
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'json': json,
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}
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shared_ns: Dict[str, Any] = dict(_code_globs)
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shared_ns.update(
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{
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'input_data': input_data,
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'result': None,
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'loads': json.loads,
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'dumps': json.dumps,
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}
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)
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exec(code, shared_ns, shared_ns)
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result = shared_ns.get(
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'result', shared_ns.get('output', input_data)
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)
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elif language.lower() == 'javascript':
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# JS 执行需要外部运行时，这里仅占位
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result = {
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