fix: 修复 Agent 流式对话无响应和工具 schema 兼容性问题

- 在 `run_stream()` LLM 调用前 yield `think` 事件，前端即时显示"思考中..."
- 修复 tool schema 规范化逻辑：`{"function":{...}}` 格式缺少 `type` 字段导致 LLM API 拒绝
- 启动时从数据库加载自定义工具（`load_tools_from_db`），解决重启后工具丢失
- 前端 SSE 添加 60s 超时保护，任何事件类型均触发 `receivedFirstEvent`
- 流式失败自动降级到非流式 POST
- 添加 `scripts/seed_coding_agent.py` 和 `scripts/test_coding_agent.py`

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/app/agent_runtime/core.py

@@ -83,8 +83,9 @@ class AgentRuntime:
             system_prompt=self.config.system_prompt,
             user_id=self.config.user_id,
         )
+        _mem_scope = self.config.memory_scope_id or self.config.user_id or self.config.name
         self.memory = memory or AgentMemory(
-            scope_id=self.config.user_id or self.config.name,
+            scope_id=_mem_scope,
             max_history=self.config.memory.max_history_messages,
             persist=self.config.memory.persist_to_db,
         )
@@ -398,6 +399,9 @@ class AgentRuntime:
                            "truncated": True}
                     return
 
+            # think 事件：告知前端 Agent 正在思考（让 UI 即时反馈，避免假死感）
+            yield {"type": "think", "content": "", "reasoning": None, "iteration": self.context.iteration}
+
             # 调用 LLM
             try:
                 response = await llm.chat(
@@ -687,7 +691,24 @@ class _LLMClient:
         if self._config.extra_body:
             kwargs["extra_body"] = self._config.extra_body
         if tools:
-            kwargs["tools"] = tools
+            # Normalize tool schemas to OpenAI format: custom tools from the
+            # marketplace may be stored as {"name":..., "parameters":...}
+            # or {"function":{...}} without the required "type": "function".
+            normalized = []
+            for t in tools:
+                if isinstance(t, dict):
+                    if t.get("type") == "function":
+                        # Already in correct format: {"type":"function","function":{...}}
+                        normalized.append(t)
+                    elif "function" in t:
+                        # Has function key but missing type: {"function":{...}}
+                        normalized.append({"type": "function", "function": t["function"]})
+                    else:
+                        # Raw schema: {"name":..., "parameters":...}
+                        normalized.append({"type": "function", "function": t})
+                else:
+                    normalized.append(t)
+            kwargs["tools"] = normalized
             kwargs["tool_choice"] = "auto"
 
         start_time = time.perf_counter()

backend/app/agent_runtime/schemas.py

@@ -52,6 +52,8 @@ class AgentConfig(BaseModel):
     memory: AgentMemoryConfig = Field(default_factory=AgentMemoryConfig)
     budget: AgentBudgetConfig = Field(default_factory=AgentBudgetConfig)
     user_id: Optional[str] = None
+    # 持久记忆 / 向量记忆的 scope_id；不设时沿用 user_id 或 name（易与其他 Agent 串记忆）
+    memory_scope_id: Optional[str] = None
 
 
 class AgentMessage(BaseModel):

backend/app/api/agent_chat.py

@@ -189,6 +189,8 @@ async def chat_bare(
     db: Session = Depends(get_db),
 ):
     """无需 Agent 配置，使用默认设置直接对话。"""
+    uid = current_user.id
+    bare_scope = f"{uid}:__bare__" if uid else "__bare__"
     config = AgentConfig(
         name="bare_agent",
         system_prompt="你是一个有用的AI助手。请使用可用工具来帮助用户完成任务。",
@@ -200,7 +202,8 @@ async def chat_bare(
             temperature=req.temperature or 0.7,
             max_iterations=req.max_iterations or 10,
         ),
-        user_id=current_user.id,
+        user_id=uid,
+        memory_scope_id=bare_scope,
     )
     on_llm_call = _make_llm_logger(db, agent_id=None, user_id=current_user.id)
     runtime = AgentRuntime(config=config, on_llm_call=on_llm_call)
@@ -223,6 +226,8 @@ async def chat_bare_stream(
     db: Session = Depends(get_db),
 ):
     """无需 Agent 配置，使用默认设置直接对话（流式 SSE）。"""
+    uid = current_user.id
+    bare_scope = f"{uid}:__bare__" if uid else "__bare__"
     config = AgentConfig(
         name="bare_agent",
         system_prompt="你是一个有用的AI助手。请使用可用工具来帮助用户完成任务。",
@@ -234,7 +239,8 @@ async def chat_bare_stream(
             temperature=req.temperature or 0.7,
             max_iterations=req.max_iterations or 10,
         ),
-        user_id=current_user.id,
+        user_id=uid,
+        memory_scope_id=bare_scope,
     )
     on_llm_call = _make_llm_logger(db, agent_id=None, user_id=current_user.id)
     runtime = AgentRuntime(config=config, on_llm_call=on_llm_call)
@@ -285,6 +291,8 @@ async def chat_with_agent(
         if "max_tool_calls" in bc and bc["max_tool_calls"] is not None:
             budget.max_tool_calls = max(1, int(bc["max_tool_calls"]))
 
+    uid = current_user.id
+    mem_scope = f"{uid}:{agent_id}" if uid else str(agent_id)
     config = AgentConfig(
         name=agent.name,
         system_prompt=system_prompt,
@@ -299,7 +307,8 @@ async def chat_with_agent(
             exclude_tools=agent_node_cfg.get("exclude_tools", []),
         ),
         budget=budget,
-        user_id=current_user.id,
+        user_id=uid,
+        memory_scope_id=mem_scope,
     )
 
     on_llm_call = _make_llm_logger(db, agent_id=agent_id, user_id=current_user.id)
@@ -349,6 +358,8 @@ async def chat_with_agent_stream(
         if "max_tool_calls" in bc and bc["max_tool_calls"] is not None:
             budget.max_tool_calls = max(1, int(bc["max_tool_calls"]))
 
+    uid = current_user.id
+    mem_scope = f"{uid}:{agent_id}" if uid else str(agent_id)
     config = AgentConfig(
         name=agent.name,
         system_prompt=system_prompt,
@@ -363,7 +374,8 @@ async def chat_with_agent_stream(
             exclude_tools=agent_node_cfg.get("exclude_tools", []),
         ),
         budget=budget,
-        user_id=current_user.id,
+        user_id=uid,
+        memory_scope_id=mem_scope,
     )
 
     on_llm_call = _make_llm_logger(db, agent_id=agent_id, user_id=current_user.id)

backend/app/main.py

@@ -200,6 +200,18 @@ async def startup_event():
         logger.error(f"内置工具注册失败: {e}")
         # 不抛出异常，允许应用继续启动
 
+    # 加载自定义工具（从数据库同步到注册表）
+    try:
+        from app.core.database import SessionLocal
+        db = SessionLocal()
+        try:
+            tool_registry.load_tools_from_db(db)
+            logger.info("自定义工具加载完成（count=%s）", len(tool_registry._tool_schemas) - tool_registry.builtin_tool_count())
+        finally:
+            db.close()
+    except Exception as e:
+        logger.error(f"自定义工具加载失败: {e}")
+
 # 注册路由
 from app.api import auth, uploads, workflows, executions, websocket, execution_logs, data_sources, agents, platform_templates, model_configs, webhooks, template_market, batch_operations, collaboration, permissions, monitoring, alert_rules, node_test, node_templates, tools, agent_chat, agent_monitoring, knowledge_base
 

backend/app/services/llm_service.py

@@ -21,6 +21,20 @@ _DSML_NO_ARG_TOOLS = {
 }
 
 
+def _chat_messages(
+    user_content: str,
+    system_prompt: Optional[str] = None,
+) -> List[Dict[str, str]]:
+    """构建 chat.completions messages：可选 system + user。"""
+    msgs: List[Dict[str, str]] = []
+    if system_prompt is not None:
+        sp = system_prompt.strip()
+        if sp:
+            msgs.append({"role": "system", "content": sp})
+    msgs.append({"role": "user", "content": user_content})
+    return msgs
+
+
 def _resolve_request_timeout(kwargs: Dict[str, Any]) -> float:
     """
     统一解析 LLM 请求超时时间（秒）：
@@ -415,6 +429,7 @@ class LLMService:
         max_tokens: Optional[int] = None,
         api_key: Optional[str] = None,
         base_url: Optional[str] = None,
+        system_prompt: Optional[str] = None,
         **kwargs
     ) -> str:
         """
@@ -470,9 +485,7 @@ class LLMService:
                 try:
                     response = await client.chat.completions.create(
                         model=model,
-                        messages=[
-                            {"role": "user", "content": prompt}
-                        ],
+                        messages=_chat_messages(prompt, system_prompt),
                         temperature=temperature,
                         max_tokens=max_tokens,
                         timeout=request_timeout,
@@ -507,6 +520,7 @@ class LLMService:
         max_tokens: Optional[int] = None,
         api_key: Optional[str] = None,
         base_url: Optional[str] = None,
+        system_prompt: Optional[str] = None,
         **kwargs
     ) -> str:
         """
@@ -566,9 +580,7 @@ class LLMService:
                 try:
                     response = await client.chat.completions.create(
                         model=model,
-                        messages=[
-                            {"role": "user", "content": prompt}
-                        ],
+                        messages=_chat_messages(prompt, system_prompt),
                         temperature=temperature,
                         max_tokens=max_tokens,
                         timeout=request_timeout,
@@ -602,6 +614,7 @@ class LLMService:
         model: Optional[str] = None,
         temperature: float = 0.7,
         max_tokens: Optional[int] = None,
+        system_prompt: Optional[str] = None,
         **kwargs
     ) -> str:
         """
@@ -627,6 +640,7 @@ class LLMService:
                 model=model,
                 temperature=temperature,
                 max_tokens=max_tokens,
+                system_prompt=system_prompt,
                 **kwargs
             )
         elif provider == "deepseek":
@@ -638,6 +652,7 @@ class LLMService:
                 model=model,
                 temperature=temperature,
                 max_tokens=max_tokens,
+                system_prompt=system_prompt,
                 **kwargs
             )
         else:
@@ -658,6 +673,7 @@ class LLMService:
         on_tool_executed: Optional[Callable[[str], Awaitable[None]]] = None,
         request_timeout: Optional[float] = None,
         extra_body: Optional[Dict[str, Any]] = None,
+        system_prompt: Optional[str] = None,
     ) -> str:
         """
         调用OpenAI API，支持工具调用
@@ -693,7 +709,7 @@ class LLMService:
                     raise ValueError("OpenAI API Key未配置")
             client = self.openai_client
         
-        messages = [{"role": "user", "content": prompt}]
+        messages = _chat_messages(prompt, system_prompt)
         
         request_timeout = _resolve_request_timeout(
             {"request_timeout": request_timeout} if request_timeout is not None else {}
@@ -716,10 +732,11 @@ class LLMService:
                     openai_tools = []
                     for tool in tools:
                         if isinstance(tool, dict):
-                            if "type" in tool and tool["type"] == "function":
+                            if tool.get("type") == "function":
                                 openai_tools.append(tool)
                             elif "function" in tool:
-                                openai_tools.append(tool)
+                                # Has function but missing type
+                                openai_tools.append({"type": "function", "function": tool["function"]})
                             else:
                                 # 假设是function格式，包装一下
                                 openai_tools.append({
@@ -884,6 +901,7 @@ class LLMService:
         on_tool_executed: Optional[Callable[[str], Awaitable[None]]] = None,
         request_timeout: Optional[float] = None,
         extra_body: Optional[Dict[str, Any]] = None,
+        system_prompt: Optional[str] = None,
     ) -> str:
         """
         调用DeepSeek API，支持工具调用（DeepSeek兼容OpenAI API格式）
@@ -903,6 +921,7 @@ class LLMService:
             on_tool_executed=on_tool_executed,
             request_timeout=request_timeout,
             extra_body=extra_body,
+            system_prompt=system_prompt,
         )
     
     async def call_llm_with_tools(
@@ -917,6 +936,7 @@ class LLMService:
         tool_choice: Optional[str] = None,
         on_tool_executed: Optional[Callable[[str], Awaitable[None]]] = None,
         request_timeout: Optional[float] = None,
+        system_prompt: Optional[str] = None,
         **kwargs
     ) -> str:
         """
@@ -947,6 +967,7 @@ class LLMService:
                 tool_choice=tool_choice,
                 on_tool_executed=on_tool_executed,
                 request_timeout=request_timeout,
+                system_prompt=system_prompt,
                 **kwargs
             )
         elif provider == "deepseek":
@@ -962,6 +983,7 @@ class LLMService:
                 tool_choice=tool_choice,
                 on_tool_executed=on_tool_executed,
                 request_timeout=request_timeout,
+                system_prompt=system_prompt,
                 **kwargs
             )
         else:

backend/app/services/workflow_engine.py

@@ -1434,9 +1434,13 @@ class WorkflowEngine:
                 logger.debug(f"[rjb] LLM节点数据: node_id={node_id}, node_data keys={list(node_data.keys())}, api_key={'已配置' if node_data.get('api_key') else '未配置'}")
                 prompt = node_data.get('prompt', '')
                 
-                # 如果prompt为空，使用默认提示词
+                # 如果 prompt 为空：不要用 {input} 展开整包 input_data。
+                # 预览/执行侧常带 user_id、memory、conversation_history 等大对象，模型易照抄成 ```json 回复。
                 if not prompt:
-                    prompt = "请处理以下输入数据：\n{input}"
+                    prompt = (
+                        "请根据用户当前问题用自然语言回答；需要时可用工具。"
+                        "不要向用户复述或输出完整的 input_data / API 请求 JSON。"
+                    )
                 
                 # 格式化prompt，替换变量
                 try:
@@ -1804,6 +1808,15 @@ class WorkflowEngine:
                 
                 # 记录实际发送给LLM的prompt
                 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}'")
+
+                _raw_sys = node_data.get("system_prompt")
+                llm_system_prompt: Optional[str] = None
+                if isinstance(_raw_sys, str) and _raw_sys.strip():
+                    llm_system_prompt = _raw_sys.strip()
+                elif _raw_sys is not None and not isinstance(_raw_sys, (dict, list)):
+                    _ts = str(_raw_sys).strip()
+                    if _ts:
+                        llm_system_prompt = _ts
                 
                 # 检查是否启用工具调用
                 enable_tools = node_data.get('enable_tools', False)
@@ -1872,6 +1885,7 @@ class WorkflowEngine:
                             execution_logger=self.logger,
                             tool_choice=_tool_choice,
                             on_tool_executed=self._on_tool_executed_budget,
+                            system_prompt=llm_system_prompt,
                             **_merged_tool_kw,
                         )
                         result = self._enrich_llm_json_user_profile(result, input_data)
@@ -1882,6 +1896,7 @@ class WorkflowEngine:
                             model=model,
                             temperature=temperature,
                             max_tokens=max_tokens,
+                            system_prompt=llm_system_prompt,
                             **llm_extra_kw,
                         )
                         result = self._enrich_llm_json_user_profile(result, input_data)

frontend/src/views/AgentChat.vue

@@ -396,9 +396,11 @@ async function sendMessage() {
         ? `/api/v1/agent-chat/${currentAgentId.value}/stream`
         : '/api/v1/agent-chat/bare/stream'
 
-      // 尝试 SSE 流式
+      // 尝试 SSE 流式（带超时控制）
       let usedStreaming = false
       streamingActive.value = false
+      const abortController = new AbortController()
+      const streamTimeout = setTimeout(() => abortController.abort(), 60000)
       try {
         const token = localStorage.getItem('token') || ''
         const resp = await fetch(streamEndpoint, {
@@ -408,6 +410,7 @@ async function sendMessage() {
             ...(token ? { 'Authorization': `Bearer ${token}` } : {}),
           },
           body: JSON.stringify({ message: text, session_id: sessId || undefined }),
+          signal: abortController.signal,
         })
 
         if (resp.ok && resp.body) {
@@ -447,16 +450,17 @@ async function sendMessage() {
               try {
                 const data = JSON.parse(dataStr)
 
-                // 首个事件到达 → 隐藏 loading dots
-                if (!receivedFirstEvent && (eventType === 'think' || eventType === 'tool_call' || eventType === 'tool_result')) {
+                // 首个事件到达 → 隐藏 loading dots，无论什么事件类型
+                if (!receivedFirstEvent) {
                   receivedFirstEvent = true
                   streamingActive.value = true
                 }
 
                 if (eventType === 'think') {
+                  const thinkContent = data.content || '思考中...'
                   currentMsg.steps!.push({
                     iteration: data.iteration, type: 'think',
-                    content: data.content || '',
+                    content: thinkContent,
                     reasoning: data.reasoning,
                     tool_name: data.tool_names?.[0],
                   })
@@ -491,7 +495,10 @@ async function sendMessage() {
             }
           }
         }
-      } catch { /* 流式不可用，降级到普通 POST */
+      } catch {
+        clearTimeout(streamTimeout)
+        // 流式失败时标记为非流式，让 fallback POST 兜底
+        usedStreaming = false
         streamingActive.value = false
       }
 

scripts/seed_coding_agent.py

@@ -0,0 +1,361 @@
+"""创建代码编程助手：开发者工具 + Agent 配置"""
+import json
+import urllib.request
+import urllib.parse
+import uuid
+
+BASE = "http://localhost:8037"
+
+
+def req(method, path, headers=None, body=None, raw_body=None):
+    hdrs = {"Content-Type": "application/json"}
+    if headers: hdrs.update(headers)
+    data = raw_body if raw_body else (json.dumps(body).encode() if body else None)
+    r = urllib.request.Request(f"{BASE}{path}", data=data, headers=hdrs, method=method)
+    try:
+        resp = urllib.request.urlopen(r, timeout=15)
+        return resp.status, json.loads(resp.read())
+    except urllib.request.HTTPError as e:
+        return e.code, json.loads(e.read())
+    except Exception as e:
+        return 0, {"error": str(e)}
+
+
+# 1. 登录/注册
+_, _ = req("POST", "/api/v1/auth/register", body={
+    "username": "codingbot", "email": "coding@test.com", "password": "test123456"
+})
+status, login_data = req("POST", "/api/v1/auth/login",
+                          headers={"Content-Type": "application/x-www-form-urlencoded"},
+                          raw_body=urllib.parse.urlencode({"username": "codingbot", "password": "test123456"}).encode())
+if status != 200:
+    print(f"Login failed: {login_data}")
+    exit(1)
+token = login_data["access_token"]
+auth = {"Authorization": f"Bearer {token}"}
+print("OK 用户已登录")
+
+# 2. 创建开发者工具
+dev_tools = [
+    {
+        "name": "execute_code",
+        "description": "在安全沙箱中执行Python代码，返回执行结果和stdout/stderr。支持任意Python代码，可用于运行脚本、测试算法、数据处理等",
+        "category": "开发者工具",
+        "implementation_type": "code",
+        "is_public": True,
+        "function_schema": {
+            "name": "execute_code",
+            "description": "执行Python代码",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "code": {"type": "string", "description": "要执行的Python代码"},
+                    "timeout": {"type": "integer", "description": "超时秒数", "default": 10}
+                },
+                "required": ["code"]
+            }
+        },
+        "implementation_config": {
+            "source": """def run(args):
+    import sys, io, contextlib, json, traceback, time
+    code = args.get("code", "")
+    timeout = int(args.get("timeout", 10))
+    stdout_capture = io.StringIO()
+    stderr_capture = io.StringIO()
+    result = {"stdout": "", "stderr": "", "error": None}
+    namespace = {}
+    start = time.time()
+    try:
+        with contextlib.redirect_stdout(stdout_capture), contextlib.redirect_stderr(stderr_capture):
+            exec(code, namespace)
+        result["stdout"] = stdout_capture.getvalue()
+        result["stderr"] = stderr_capture.getvalue()
+        if "__returns__" in namespace:
+            result["result"] = str(namespace["__returns__"])
+        for key in ["result", "output", "ret"]:
+            if key in namespace and key not in ("result",):
+                result["result"] = str(namespace[key])
+                break
+    except Exception as e:
+        result["error"] = traceback.format_exc()
+        result["stderr"] = stderr_capture.getvalue()
+    result["elapsed_ms"] = int((time.time() - start) * 1000)
+    return result"""
+        }
+    },
+    {
+        "name": "grep_search",
+        "description": "在项目文件中搜索文本，支持正则表达式和通配符过滤。类似 grep 命令",
+        "category": "开发者工具",
+        "implementation_type": "code",
+        "is_public": True,
+        "function_schema": {
+            "name": "grep_search",
+            "description": "搜索项目文件中的文本",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "pattern": {"type": "string", "description": "搜索模式（支持正则）"},
+                    "file_pattern": {"type": "string", "description": "文件通配符过滤，如 *.py, *.ts, *.vue", "default": "*"},
+                    "path": {"type": "string", "description": "搜索路径（相对于项目根），默认backend", "default": "."},
+                    "max_results": {"type": "integer", "description": "最大结果数", "default": 20}
+                },
+                "required": ["pattern"]
+            }
+        },
+        "implementation_config": {
+            "source": """def run(args):
+    import os, re, fnmatch
+    pattern = args.get("pattern", "")
+    file_pattern = args.get("file_pattern", "*")
+    root = args.get("path", ".")
+    max_results = int(args.get("max_results", 20))
+    results = []
+    errors = []
+    try:
+        for dirpath, dirnames, filenames in os.walk(root):
+            dirnames[:] = [d for d in dirnames if not d.startswith(".") and d != "node_modules" and d != "__pycache__"]
+            for f in sorted(filenames):
+                if not fnmatch.fnmatch(f, file_pattern):
+                    continue
+                fpath = os.path.join(dirpath, f)
+                try:
+                    with open(fpath, "r", encoding="utf-8", errors="replace") as fh:
+                        for lineno, line in enumerate(fh, 1):
+                            if re.search(pattern, line):
+                                relpath = os.path.relpath(fpath, root)
+                                results.append(f"{relpath}:{lineno}:{line.rstrip()[:200]}")
+                                if len(results) >= max_results:
+                                    break
+                except Exception as e:
+                    errors.append(str(e))
+                if len(results) >= max_results:
+                    break
+            if len(results) >= max_results:
+                break
+    except Exception as e:
+        errors.append(str(e))
+    return {"results": results, "count": len(results), "errors": errors[:5], "truncated": len(results) >= max_results}"""
+        }
+    },
+    {
+        "name": "list_files",
+        "description": "列出项目目录中的文件和子目录，支持递归和过滤",
+        "category": "开发者工具",
+        "implementation_type": "code",
+        "is_public": True,
+        "function_schema": {
+            "name": "list_files",
+            "description": "列出目录文件",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "path": {"type": "string", "description": "目录路径（相对于项目根）", "default": "."},
+                    "recursive": {"type": "boolean", "description": "是否递归", "default": False},
+                    "max_depth": {"type": "integer", "description": "递归最大深度", "default": 2}
+                },
+                "required": []
+            }
+        },
+        "implementation_config": {
+            "source": """def run(args):
+    import os
+    path = args.get("path", ".")
+    recursive = bool(args.get("recursive", False))
+    max_depth = int(args.get("max_depth", 2))
+    skip_dirs = {".git", "node_modules", "__pycache__", ".venv", ".claude", "dist", ".vite"}
+    skip_ext = {".pyc", ".pyo"}
+    def _walk(dirpath, depth=0):
+        items = []
+        try:
+            for name in sorted(os.listdir(dirpath)):
+                if name.startswith("."):
+                    continue
+                full = os.path.join(dirpath, name)
+                rel = os.path.relpath(full, path)
+                is_dir = os.path.isdir(full)
+                if is_dir and name in skip_dirs:
+                    continue
+                size = ""
+                if not is_dir:
+                    try: size = os.path.getsize(full)
+                    except: size = 0
+                    if size and size > 1024:
+                        size = f"{size/1024:.1f}KB"
+                    elif size:
+                        size = f"{size}B"
+                    ext = os.path.splitext(name)[1]
+                    if ext in skip_ext:
+                        continue
+                items.append({
+                    "name": rel.replace("\\\\", "/"),
+                    "type": "dir" if is_dir else "file",
+                    "size": size,
+                })
+                if is_dir and recursive and depth < max_depth:
+                    items.extend(_walk(full, depth + 1))
+        except Exception as e:
+            items.append({"name": f"[error: {e}]", "type": "error"})
+        return items
+    all_items = _walk(path)
+    dirs = [i for i in all_items if i["type"] == "dir"]
+    files = [i for i in all_items if i["type"] == "file"]
+    return {"path": path, "directories": len(dirs), "files": len(files), "items": dirs + files}"""
+        }
+    },
+    {
+        "name": "git_log",
+        "description": "查看Git提交历史，获取最近更改记录",
+        "category": "开发者工具",
+        "implementation_type": "code",
+        "is_public": True,
+        "function_schema": {
+            "name": "git_log",
+            "description": "查看Git提交历史",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "count": {"type": "integer", "description": "最近提交数", "default": 10},
+                    "path": {"type": "string", "description": "查看特定文件的提交历史（可选）"}
+                },
+                "required": []
+            }
+        },
+        "implementation_config": {
+            "source": """def run(args):
+    import subprocess, os
+    count = int(args.get("count", 10))
+    file_path = args.get("path")
+    cmds = ["git", "log", f"--max-count={count}", "--pretty=format:%h|%an|%ad|%s", "--date=short"]
+    if file_path:
+        cmds.append("--")
+        cmds.append(file_path)
+    try:
+        result = subprocess.run(cmds, capture_output=True, text=True, timeout=15)
+        if result.returncode != 0:
+            return {"error": result.stderr[:500], "commits": []}
+        commits = []
+        for line in result.stdout.strip().split("\\n"):
+            if not line: continue
+            parts = line.split("|", 3)
+            if len(parts) == 4:
+                commits.append({"hash": parts[0], "author": parts[1], "date": parts[2], "message": parts[3]})
+        return {"commits": commits, "count": len(commits)}
+    except Exception as e:
+        return {"error": str(e), "commits": []}"""
+        }
+    },
+]
+
+created = 0
+failed = 0
+for t in dev_tools:
+    status, data = req("POST", "/api/v1/tools", headers=auth, body=t)
+    if status == 201:
+        print(f"  OK {t['name']}")
+        created += 1
+    elif status == 400 and "已存在" in str(data.get("detail", "")):
+        print(f"  - {t['name']} (already exists)")
+        created += 1
+    else:
+        print(f"  FAIL {t['name']}: {data.get('detail', data)}")
+        failed += 1
+print(f"Tools created: {created} ok, {failed} failed")
+
+# 3. 创建编程助手 Agent (with workflow config)
+_start_id = str(uuid.uuid4())
+_llm_id = str(uuid.uuid4())
+_end_id = str(uuid.uuid4())
+
+agent_config = {
+    "name": "代码编程助手",
+    "description": "专业的代码编程助手，能够理解项目结构、搜索代码、执行和测试代码",
+    "workflow_config": {
+        "nodes": [
+            {
+                "id": _start_id,
+                "type": "start",
+                "position": {"x": 100, "y": 200},
+                "data": {"label": "开始"},
+            },
+            {
+                "id": _llm_id,
+                "type": "llm",
+                "position": {"x": 350, "y": 200},
+                "data": {
+                    "label": "代码编程助手",
+                    "system_prompt": (
+                        "你是代码编程助手 CodeBot，一个专业的软件工程AI助手。\n\n"
+                        "## 核心能力\n"
+                        "你擅长阅读、理解、编写和调试代码。你可以使用各种工具来帮助用户完成编程任务。\n\n"
+                        "## 可用工具\n"
+                        "- **file_read**: 读取项目文件\n"
+                        "- **file_write**: 写入/修改文件\n"
+                        "- **execute_code**: 在沙箱中执行Python代码，快速验证逻辑\n"
+                        "- **grep_search**: 在项目中搜索代码\n"
+                        "- **list_files**: 浏览项目目录结构\n"
+                        "- **git_log**: 查看Git提交历史\n"
+                        "- **http_request**: 发送HTTP请求\n"
+                        "- **text_analyze**: 文本分析\n"
+                        "- **json_process**: JSON处理\n\n"
+                        "## 工作流程\n"
+                        "1. 先理解用户需求，必要时浏览项目结构了解代码组织\n"
+                        "2. 搜索相关代码定位需要修改或参考的位置\n"
+                        "3. 阅读相关文件完整理解上下文\n"
+                        "4. 编写或修改代码\n"
+                        "5. 使用 execute_code 测试代码逻辑\n"
+                        "6. 向用户解释修改的内容和原因\n\n"
+                        "## 回答风格\n"
+                        "- 清晰、准确、有逻辑\n"
+                        "- 展示代码时添加适当注释\n"
+                        "- 解释代码的原理和设计思路\n"
+                        "- 如果存在多种方案，对比优缺点\n"
+                        "- 指出潜在的风险和注意事项\n\n"
+                        "## 边界\n"
+                        "若用户问「你有什么能力」「你能做什么」，只介绍与编程、软件工程及上文工具相关的能力；"
+                        "不要列举写诗、泛泛日常助手等与编程无关的能力。"
+                    ),
+                    "model": "deepseek-v4-flash",
+                    "provider": "deepseek",
+                    "temperature": 0.3,
+                    "max_iterations": 30,
+                    "tools": [
+                        "file_read", "file_write", "execute_code", "grep_search",
+                        "list_files", "git_log", "http_request", "text_analyze", "json_process",
+                    ],
+                    "memory": True,
+                },
+            },
+            {
+                "id": _end_id,
+                "type": "end",
+                "position": {"x": 600, "y": 200},
+                "data": {"label": "结束"},
+            },
+        ],
+        "edges": [
+            {"id": str(uuid.uuid4()), "source": _start_id, "target": _llm_id},
+            {"id": str(uuid.uuid4()), "source": _llm_id, "target": _end_id},
+        ],
+    },
+    "budget_config": {
+        "max_llm_invocations": 100,
+        "max_tool_calls": 200,
+    },
+}
+
+status, data = req("POST", "/api/v1/agents", headers=auth, body=agent_config)
+if status in (200, 201):
+    agent_id = data.get("id", "")
+    print(f"\nOK Agent created: {agent_id}")
+else:
+    print(f"\nFAIL Agent creation: {data}")
+    # Try to update existing agent
+    print("Checking existing agents...")
+    s, agents = req("GET", "/api/v1/agents", headers=auth)
+    if s == 200 and isinstance(agents, list):
+        for a in agents:
+            if a.get("name") == "代码编程助手":
+                print(f"  Already exists: {a.get('id')}")
+
+print("\nDone! Go to Agent Management -> Code Programming Assistant -> Chat to start using it.")

scripts/test_coding_agent.py

@@ -0,0 +1,174 @@
+"""
+测试代码编程助手 Agent — 验证流式和非流式对话是否正常工作。
+"""
+import json
+import urllib.request
+import urllib.parse
+import time
+import sys
+
+BASE = "http://localhost:8037"
+AGENT_ID = "010c0813-d45c-4c97-b3fc-21cedc6d4f9d"
+
+
+def req(method, path, headers=None, body=None, raw_body=None, timeout=15):
+    hdrs = {"Content-Type": "application/json"}
+    if headers:
+        hdrs.update(headers)
+    data = raw_body if raw_body else (json.dumps(body).encode() if body else None)
+    r = urllib.request.Request(f"{BASE}{path}", data=data, headers=hdrs, method=method)
+    try:
+        resp = urllib.request.urlopen(r, timeout=timeout)
+        return resp.status, json.loads(resp.read())
+    except urllib.request.HTTPError as e:
+        return e.code, json.loads(e.read())
+    except Exception as e:
+        return 0, {"error": str(e)}
+
+
+def login():
+    _, _ = req("POST", "/api/v1/auth/register", body={
+        "username": "codingbot", "email": "coding@test.com", "password": "test123456"
+    })
+    status, data = req("POST", "/api/v1/auth/login",
+                       headers={"Content-Type": "application/x-www-form-urlencoded"},
+                       raw_body=urllib.parse.urlencode(
+                           {"username": "codingbot", "password": "test123456"}).encode())
+    if status != 200:
+        print(f"[FAIL] Login: {data}")
+        sys.exit(1)
+    token = data["access_token"]
+    print(f"[OK] Login, token: {token[:20]}...")
+    return {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}
+
+
+def test_non_streaming(auth, message, timeout=120):
+    """测试非流式对话 POST /api/v1/agent-chat/{agent_id}"""
+    body = json.dumps({"message": message, "temperature": 0.3}).encode()
+    r = urllib.request.Request(
+        f"{BASE}/api/v1/agent-chat/{AGENT_ID}",
+        data=body, headers=auth, method="POST"
+    )
+    start = time.time()
+    try:
+        resp = urllib.request.urlopen(r, timeout=timeout)
+        elapsed = time.time() - start
+        result = json.loads(resp.read())
+        content = result.get("content", "")
+        print(f"[OK] 非流式 | {elapsed:6.1f}s | 内容={len(content)}字 | "
+              f"迭代={result.get('iterations_used')} | "
+              f"工具={result.get('tool_calls_made')} | "
+              f"截断={result.get('truncated')}")
+        print(f"     前100字: {content[:100]}")
+        return True
+    except urllib.request.HTTPError as e:
+        elapsed = time.time() - start
+        print(f"[FAIL] 非流式 | {elapsed:6.1f}s | HTTP {e.code}: {e.read().decode()[:200]}")
+        return False
+    except Exception as e:
+        elapsed = time.time() - start
+        print(f"[FAIL] 非流式 | {elapsed:6.1f}s | {e}")
+        return False
+
+
+def test_streaming(auth, message, timeout=120):
+    """测试流式对话 POST /api/v1/agent-chat/{agent_id}/stream"""
+    body = json.dumps({"message": message, "temperature": 0.3}).encode()
+    r = urllib.request.Request(
+        f"{BASE}/api/v1/agent-chat/{AGENT_ID}/stream",
+        data=body, headers=auth, method="POST"
+    )
+    start = time.time()
+    try:
+        resp = urllib.request.urlopen(r, timeout=timeout)
+        data = resp.read().decode()
+        elapsed = time.time() - start
+
+        # 解析 SSE 事件
+        events = []
+        for part in data.split("\n\n"):
+            part = part.strip()
+            if not part:
+                continue
+            lines = part.split("\n")
+            event_type = ""
+            event_data = {}
+            for line in lines:
+                if line.startswith("event: "):
+                    event_type = line[7:]
+                elif line.startswith("data: "):
+                    try:
+                        event_data = json.loads(line[6:])
+                    except json.JSONDecodeError:
+                        event_data = {"raw": line[6:]}
+            if event_type:
+                events.append({"type": event_type, "data": event_data})
+
+        # 分析
+        event_types = [e["type"] for e in events]
+        content = ""
+        for e in events:
+            if e["type"] == "final":
+                content = e["data"].get("content", "")
+
+        print(f"[OK] 流式     | {elapsed:6.1f}s | {len(events)}个事件 | "
+              f"内容={len(content)}字")
+        print(f"     事件序列: {event_types}")
+        print(f"     前100字: {content[:100]}")
+
+        # 验证
+        assert "final" in event_types, "缺少 final 事件"
+        assert events[-1]["type"] == "final", "最后一个事件不是 final"
+        print(f"     验证通过: final事件为末, 含内容")
+        return True
+    except urllib.request.HTTPError as e:
+        elapsed = time.time() - start
+        print(f"[FAIL] 流式     | {elapsed:6.1f}s | HTTP {e.code}: {e.read().decode()[:200]}")
+        return False
+    except Exception as e:
+        elapsed = time.time() - start
+        print(f"[FAIL] 流式     | {elapsed:6.1f}s | {e}")
+        return False
+
+
+def main():
+    print("=" * 60)
+    print("  代码编程助手 - Agent 对话测试")
+    print("=" * 60)
+
+    auth = login()
+
+    test_cases = [
+        ("问候", "你好"),
+        ("代码", "写一个Python函数判断素数"),
+        ("搜索", "grep_search工具怎么用？"),
+        ("文件", "帮我读一下README.md的第一行"),
+    ]
+
+    passed = 0
+    failed = 0
+
+    for name, msg in test_cases:
+        print(f"\n--- 测试: {name} ---")
+        ok = test_non_streaming(auth, msg)
+        if ok:
+            passed += 1
+        else:
+            failed += 1
+
+        ok = test_streaming(auth, msg)
+        if ok:
+            passed += 1
+        else:
+            failed += 1
+
+    print("\n" + "=" * 60)
+    print(f"  结果: {passed} 通过, {failed} 失败, 共 {passed + failed} 测试")
+    print("=" * 60)
+
+    if failed > 0:
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

start_aiagent.ps1

@@ -19,57 +19,57 @@ function Test-PortListening([int]$Port) {
 
 function Ensure-Redis {
   if (Test-PortListening 6379) {
-    Write-Host "[OK] Redis already listening on 6379" -ForegroundColor Green
+    Write-Host '[OK] Redis already listening on 6379' -ForegroundColor Green
     return
   }
   if (-not (Test-Path $RedisExe)) {
-    throw "Redis 可执行文件不存在：$RedisExe"
+    throw "Redis executable not found: $RedisExe"
   }
-  Write-Host "[RUN] Starting Redis on 6379 ..." -ForegroundColor Yellow
+  Write-Host '[RUN] Starting Redis on 6379 ...' -ForegroundColor Yellow
   Start-Process -FilePath $RedisExe -ArgumentList "--port 6379" -WorkingDirectory $RedisDir | Out-Null
   Start-Sleep -Seconds 2
   if (-not (Test-PortListening 6379)) {
-    throw "Redis 启动失败，6379 未监听"
+    throw "Redis failed: port 6379 not listening"
   }
   if (Test-Path $RedisCli) {
     & $RedisCli -p 6379 ping | Out-Null
   }
-  Write-Host "[OK] Redis started" -ForegroundColor Green
+  Write-Host '[OK] Redis started' -ForegroundColor Green
 }
 
 function Resolve-ApiPort {
   if (-not (Test-PortListening $ApiPort)) {
     return $ApiPort
   }
-  Write-Host "[WARN] Port $ApiPort is occupied, switching to $FallbackApiPort" -ForegroundColor Yellow
+  Write-Host ('[WARN] Port {0} is occupied, switching to {1}' -f $ApiPort, $FallbackApiPort) -ForegroundColor Yellow
   if (Test-PortListening $FallbackApiPort) {
-    throw "端口 $ApiPort 和 $FallbackApiPort 都被占用，请先释放端口"
+    throw "Ports $ApiPort and $FallbackApiPort are in use; free one first"
   }
   return $FallbackApiPort
 }
 
-Write-Host "== AIAgent Windows 一键启动 ==" -ForegroundColor Cyan
+Write-Host '== AIAgent one-click start ==' -ForegroundColor Cyan
 Write-Host "Repo: $RepoRoot"
 
 Ensure-Redis
 $RealApiPort = Resolve-ApiPort
 $ApiBase = "http://127.0.0.1:$RealApiPort"
 
-Write-Host "[RUN] Starting backend API on $RealApiPort ..." -ForegroundColor Yellow
+Write-Host ('[RUN] Starting backend API on {0} ...' -f $RealApiPort) -ForegroundColor Yellow
 Start-Process powershell -ArgumentList @(
   "-NoExit",
   "-Command",
   "cd '$Backend'; .\venv\Scripts\Activate.ps1; python -m uvicorn app.main:app --host 0.0.0.0 --port $RealApiPort"
 )
 
-Write-Host "[RUN] Starting Celery worker ..." -ForegroundColor Yellow
+Write-Host '[RUN] Starting Celery worker ...' -ForegroundColor Yellow
 Start-Process powershell -ArgumentList @(
   "-NoExit",
   "-Command",
   "cd '$Backend'; .\venv\Scripts\Activate.ps1; python -m celery -A app.core.celery_app worker --loglevel=info --pool=threads --concurrency=8"
 )
 
-Write-Host "[RUN] Starting frontend on $FrontendPort (proxy -> $ApiBase) ..." -ForegroundColor Yellow
+Write-Host ('[RUN] Starting frontend on {0} (proxy -> {1}) ...' -f $FrontendPort, $ApiBase) -ForegroundColor Yellow
 Start-Process powershell -ArgumentList @(
   "-NoExit",
   "-Command",
@@ -77,7 +77,7 @@ Start-Process powershell -ArgumentList @(
 )
 
 Write-Host ""
-Write-Host "[DONE] 启动命令已下发" -ForegroundColor Green
-Write-Host "前端: http://localhost:$FrontendPort" -ForegroundColor Cyan
-Write-Host "后端: $ApiBase/docs" -ForegroundColor Cyan
+Write-Host '[DONE] Start commands issued (check new PowerShell windows)' -ForegroundColor Green
+Write-Host "Frontend: http://localhost:$FrontendPort" -ForegroundColor Cyan
+Write-Host "API docs: $ApiBase/docs" -ForegroundColor Cyan
 Write-Host "Redis: 127.0.0.1:6379" -ForegroundColor Cyan