初始化

newpython/OUT-v1.py

@@ -0,0 +1,483 @@
+"""
+测试流式接口的完整脚本
+API流程：
+1. 登录获取token
+2. 创建thread（可选，也可以使用无状态接口）
+3. 调用流式接口
+
+两种方式：
+- 方式1: POST /api/runs/stream (无状态，自动创建thread)
+- 方式2: POST /api/threads/{thread_id}/runs/stream (需要先创建thread)
+"""
+import requests
+import json
+import asyncio
+import httpx  # 异步 HTTP 客户端
+# ============ 配置区域 ============
+BASE_URL = "http://127.0.0.1:8001"
+LOGIN_URL = f"{BASE_URL}/api/v1/auth/login/username"
+THREADS_URL = f"{BASE_URL}/api/threads"
+RUNS_STREAM_URL = f"{BASE_URL}/api/runs/stream"  # 无状态流式接口
+
+# ============ 登录获取token ============
+def get_token(username: str) -> str:
+    """登录获取token"""
+    response = requests.post(
+        LOGIN_URL,
+        json={"username": username},
+        headers={"Content-Type": "application/json"}
+    )
+    # print(f"登录状态码: {response.status_code}")
+    data = response.json()
+    # print(f"登录响应: {json.dumps(data, indent=2, ensure_ascii=False)}")
+    
+    # 根据实际返回结构调整
+    token = data.get("token") or data.get("access_token") or data.get("data", {}).get("token")
+    return token
+# ============ 方式2: 先创建thread，再调用流式接口 ============
+def create_thread(token: str) -> str:
+    """创建thread，返回thread_id"""
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {token}"
+    }
+    
+    response = requests.post(
+        THREADS_URL,
+        json={},  # 空body，使用默认值
+        headers=headers
+    )
+    
+    print(f"创建thread状态码: {response.status_code}")
+    data = response.json()
+    print(f"创建thread响应: {json.dumps(data, indent=2, ensure_ascii=False)}")
+    
+    return data.get("thread_id")
+def get_agent_name_description(agent_name="Taiwan-intelligence-gathering-agent"):
+    url = f"{BASE_URL}/api/agents/{agent_name}"
+    token=get_token("ji")
+    headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {token}"
+        }
+    response = requests.get(
+            url,
+            headers=headers,
+        )
+    print(response.json())
+    return response.json()["name"].lower(),"agent_name:"+response.json()["name"].lower()+","+response.json()["description"]
+
+# ============ 创建智能体 ============
+def create_agent(
+    name: str,
+    description: str = "",
+    model: str | None = None,
+    tool_groups: list[str] | None = None,
+    skills: list[str] | None = None,
+    soul: str = "",
+):
+    """
+    创建一个新的智能体
+    
+    Args:
+        name: 智能体名称（必须是 hyphen-case，如 my-agent）
+        description: 智能体描述
+        model: 可选的模型覆盖（如 gpt-4, claude-3-opus 等）
+        tool_groups: 工具组白名单（如 ["web", "file"]）
+        skills: 技能白名单（如 ["skill1", "skill2"]）
+        soul: SOUL.md 内容（智能体人格/身份描述）
+    
+    Returns:
+        创建结果
+    """
+    TOKEN =get_token("ji")
+    url = f"{BASE_URL}/api/agents"
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {TOKEN}"
+    }
+    
+    payload = {
+        "name": name,
+        "description": description,
+        "soul": soul
+    }
+    
+    # 添加可选字段
+    if model is not None:
+        payload["model"] = model
+    if tool_groups is not None:
+        payload["tool_groups"] = tool_groups
+    if skills is not None:
+        payload["skills"] = skills
+    
+    print(f"请求: POST {url}")
+    print(f"请求体: {json.dumps(payload, indent=2, ensure_ascii=False)}")
+    
+    response = requests.post(url, json=payload, headers=headers)
+    
+    print(f"状态码: {response.status_code}")
+    
+    if response.status_code == 201:
+        result = response.json()
+        print(f"✓ 智能体创建成功!")
+        print(f"响应: {json.dumps(result, indent=2, ensure_ascii=False)}")
+        return result
+    else:
+        print(f"✗ 创建失败: {response.text}")
+        return f"✗ 创建失败: {response.text}"
+
+def create_main_agent(zhihui_name,agent_names):
+    d={}
+    token=get_token("ji")
+    l=[]
+    for i in agent_names:
+        # print(i)
+        name,description=get_agent_name_description(i)
+        thread_id_tmp=create_thread(token)
+        d[name]=thread_id_tmp
+        l.append(description)
+    name="你是总控控制协调智能体,负责协调其他智能体干活"
+    miaoshu=f"""#核心定位：
+    用途：跟用用户的任务目标,调用其他智能体干活,只负责协调汇总,当你觉得已有信息支持回答用户问题时候,停止调度,并输出最后的结果
+    #调度方法：
+    通过使用agent_orchestration技能调用其他智能体干活,使用技能时传入agent_name和需要做的活
+    #可调度智能体：
+    {l}"""
+    thread_id_tmp=create_thread(token)
+    engname=zhihui_name
+    d[engname]=thread_id_tmp
+    tmp_skills=["agent_orchestration"]
+    tmpres=create_agent(
+            name=engname,
+            description=name,
+            model="glm-5",  # 指定模型
+            skills=tmp_skills,  # 关联技能
+            soul=miaoshu)
+    # print("创建智能体响应:",tmpres)
+    if "失败" in tmpres and "exists" not in tmpres:
+        return tmpres
+    print(f"创建协调智能体成功,可以协调列表:{l}")
+    return d
+import re
+def extract_name_task(text="python3 scripts/agent_orchestration.py \"Us-intelligence-gathering-agent\" \"收集和分析美国人民对特朗普的看法，包括支持率和反对率、不同群体的观点差异、主要支持理由和反对理由等\""):
+    try:
+        pattern = r'\.py\s+"([^"]*)"\s+"([^"]*)"'
+        match = re.search(pattern, text)
+        if match:
+            agent_name = match.group(1)
+            task_content = match.group(2)
+        return agent_name,task_content
+    except:
+        return "",""
+def update_agent_message(thread_id,new_content):
+    token=get_token("ji")
+    url = f"{BASE_URL}/api/threads/{thread_id}/state"
+    headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {token}"
+        }
+    tmp = requests.get(
+            url,
+            headers=headers
+        ).json()
+    # print(tmp)
+    if "messages" in tmp["values"]:
+         tmp["values"]["messages"].append({"type": "human", "content": new_content})
+    else:
+         tmp["values"]["messages"]=[]
+         tmp["values"]["messages"].append({"type": "human", "content": new_content})
+    url = f"{BASE_URL}/api/threads/{thread_id}/state"
+    headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {token}"
+        }
+    response = requests.post(
+            url,
+            json=tmp,
+            headers=headers,
+            stream=True
+        )
+    del tmp
+    print(f"线程{thread_id}更新状态码: {response.status_code}")
+def Broadcast_message(message,d_agent_thread_id,pingbi_agent_name):
+    for key in d_agent_thread_id:
+            if key not in pingbi_agent_name:
+                update_agent_message(d_agent_thread_id[key],message)
+                print("广播消息成功:",key,message)
+null=None
+true=True
+false=False
+async def leader_agent(d_agent_thread_id, agent_name, new_message, skill_stop_names):
+    """使用 httpx 异步请求的 leader_agent"""
+    # 使用 to_thread 异步运行同步的 Broadcast_message
+    await asyncio.to_thread(Broadcast_message, "总控智能体收到用户消息:" + new_message, d_agent_thread_id, [agent_name])
+    thread_id = d_agent_thread_id[agent_name]
+    l = []
+    token = get_token("ji")
+    url = f"{BASE_URL}/api/threads/{thread_id}/runs/stream"
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {token}"
+    }
+    payload = {
+        "input": {
+            "messages": [
+                {
+                    "type": "human",
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": new_message
+                        }
+                    ],
+                    "additional_kwargs": {}
+                }
+            ]
+        },
+        "config": {
+            "recursion_limit": 1000
+        },
+        "context": {
+            "agent_name": agent_name,
+            "model_name": "deepseek-chat",
+            "mode": "pro",
+            "reasoning_effort": "medium",
+            "thinking_enabled": true,
+            "is_plan_mode": false,
+            "subagent_enabled": false,
+            "thread_id": thread_id
+        },
+        "stream_mode": [
+            "messages-tuple",
+            "values",
+        ],
+        "stream_subgraphs": true,
+        "stream_resumable": true,
+        "assistant_id": "lead_agent",
+        "on_disconnect": "continue",
+        "excluded_tools": ["web_search"],
+        "skill_stop_names": skill_stop_names,
+    }
+    
+    async with httpx.AsyncClient(timeout=None) as client:
+        async with client.stream("POST", url, json=payload, headers=headers) as response:
+            print(f"状态码: {response.status_code}")
+            if response.status_code != 200:
+                body = await response.aread()
+                raise RuntimeError(
+                    f"upstream {url} returned HTTP {response.status_code}: "
+                    f"{body.decode('utf-8', 'replace')[:1000]}"
+                )
+            async for line in response.aiter_lines():
+                if line:
+                    yield line
+                    if line[:5] == "data:":
+                        print(line)
+                        l.append(line)
+    if len(l) < 2:
+        raise RuntimeError(
+            f"upstream stream produced no usable data lines (got {len(l)} line(s)). "
+            f"Check the server log on {url} for the real failure."
+        )
+    
+    null = None
+    tmpres = eval(l[-2][5:])
+    tmpres = tmpres["messages"][-1]
+    tmp_task = []
+    if tmpres.get("tool_calls", []) == []:
+        yield {"status": []}
+    else:
+        for tool_call in tmpres.get("tool_calls", []):
+            try:
+                agentname, task_text = extract_name_task(str(tool_call["args"]))
+                print("agentname", agentname)
+                print("task_text:", task_text)
+                # 使用 to_thread 异步运行同步的 Broadcast_message
+                await asyncio.to_thread(Broadcast_message, f"总控智能体给{agentname}派活,内容为:" + task_text, d_agent_thread_id, [agent_name, agentname])
+                tmp_task.append([agentname, task_text])
+            except:
+                pass
+        yield {"status": tmp_task}
+
+
+async def special_agent(d_agent_thread_id, agent_name, new_message, skill_stop_names):
+    """使用 httpx 异步请求的 special_agent"""
+    thread_id = d_agent_thread_id[agent_name]
+    l = []
+    token = get_token("ji")
+    url = f"{BASE_URL}/api/threads/{thread_id}/runs/stream"
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {token}"
+    }
+    payload = {
+        "input": {
+            "messages": [
+                {
+                    "type": "human",
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": new_message
+                        }
+                    ],
+                    "additional_kwargs": {}
+                }
+            ]
+        },
+        "config": {
+            "recursion_limit": 1000
+        },
+        "context": {
+            "agent_name": agent_name,
+            "model_name": "deepseek-chat",
+            "mode": "pro",
+            "reasoning_effort": "medium",
+            "thinking_enabled": true,
+            "is_plan_mode": false,
+            "subagent_enabled": false,
+            "thread_id": thread_id
+        },
+        "stream_mode": [
+            "messages-tuple",
+            "values",
+        ],
+        "stream_subgraphs": true,
+        "stream_resumable": true,
+        "assistant_id": "lead_agent",
+        "on_disconnect": "continue",
+        "excluded_tools": ["web_search", "ask_clarification", "present_files", "view_image"],
+        "skill_stop_names": skill_stop_names,
+    }
+    
+    async with httpx.AsyncClient(timeout=None) as client:
+        async with client.stream("POST", url, json=payload, headers=headers) as response:
+            print(f"状态码: {response.status_code}")
+            if response.status_code != 200:
+                body = await response.aread()
+                raise RuntimeError(
+                    f"upstream {url} returned HTTP {response.status_code}: "
+                    f"{body.decode('utf-8', 'replace')[:1000]}"
+                )
+            async for line in response.aiter_lines():
+                if line:
+                    yield line
+                    if line[:5] == "data:":
+                        print(line)
+                        l.append(line)
+    if len(l) < 2:
+        raise RuntimeError(
+            f"upstream stream produced no usable data lines (got {len(l)} line(s)). "
+            f"Check the server log on {url} for the real failure."
+        )
+    
+    null = None
+    tmpres = eval(l[-2][5:])
+    tmpres = tmpres["messages"][-1]
+    # 使用 to_thread 异步运行同步的 Broadcast_message
+    await asyncio.to_thread(Broadcast_message, f"子智能体{agent_name}完成{new_message}工作,交付内容为:" + tmpres["content"], d_agent_thread_id, [agent_name])
+    yield {"status": f"子智能体{agent_name}完成{new_message}工作"}
+
+async def agent_init(agent_names, main_agent_name):
+    """异步的 agent_init，同步函数调用使用 to_thread"""
+    agent_names = [i.lower() for i in agent_names]
+    # 使用 to_thread 异步运行同步的 create_main_agent
+    tmpd = await asyncio.to_thread(create_main_agent, main_agent_name, agent_names)
+    message = """
+你的职责:你只是若干个智能体的其中一员,你们的共同目标是完成一个庞大的任务,总控控制协调智能体会分配特定的活给你,你只需要用你自身的能力来完成这个特定的活
+注意:你只是一个子智能体,你不必把你的分析结果存放到文件里面去,你只关心于完成总控控制协调智能体分配的活即可
+"""
+    # 使用 to_thread 异步运行同步的 Broadcast_message
+    await asyncio.to_thread(Broadcast_message, message, tmpd, [main_agent_name])
+    return main_agent_name, tmpd
+
+
+from fastapi import FastAPI, HTTPException,Body
+from fastapi.responses  import StreamingResponse
+from fastapi.middleware.cors import CORSMiddleware
+import asyncio
+import uvicorn
+
+app = FastAPI()
+ # CORS 配置
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+import time
+@app.post("/generate_mul_agent")
+async def generate_stream(payload: dict = Body(...)):
+    """
+    根据 payload 中的 agent_type 字段判断调用 leader_agent 还是 special_agent
+    
+    payload 结构示例:
+    {
+        "agent_type": "leader",  # 或 "special"
+        "d_agent_thread_id": {...},
+        "agent_name": "xxx",
+        "new_message": "xxx",
+        "skill_stop_names": [...]
+    }
+    """
+    # 从 payload 中提取参数
+    agent_type = payload.get("agent_type", "leader")  # 默认为 leader
+    d_agent_thread_id = payload.get("d_agent_thread_id", {})
+    agent_name = payload.get("agent_name", "")
+    new_message = payload.get("new_message", "")
+    skill_stop_names = payload.get("skill_stop_names", [])
+    
+    # 流式响应生成器
+    async def stream_generator():
+        # 根据 agent_type 选择调用的函数
+        if agent_type == "leader":
+            async_generator = leader_agent(d_agent_thread_id, agent_name, new_message, skill_stop_names)
+        else:  # special
+            async_generator = special_agent(d_agent_thread_id, agent_name, new_message, skill_stop_names)
+        
+        # 异步迭代生成器
+        async for chunk in async_generator:
+            if isinstance(chunk, dict):
+                yield f"data: {json.dumps(chunk, ensure_ascii=False)}\n\n"
+            else:
+                yield f"{chunk}"
+    
+    # 使用 SSE 流式响应
+    return StreamingResponse(
+        stream_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "X-Accel-Buffering": "no",
+            "Connection": "keep-alive",
+        }
+    )
+
+
+@app.post("/agent_init")
+async def init_agents(payload: dict = Body(...)):
+    """
+    初始化智能体接口
+    payload 结构示例:
+    {
+        "agent_names": ["agent1", "agent2"],
+        "main_agent_name": "main-agent"
+    }
+    """
+    agent_names = payload.get("agent_names", [])
+    main_agent_name = payload.get("main_agent_name", "main-agent")
+    
+    # 调用异步的 agent_init
+    name, thread_id_dict = await agent_init(agent_names, main_agent_name)
+    
+    return {
+        "status": "success",
+        "main_agent_name": name,
+        "thread_ids": thread_id_dict
+    }
+if __name__ == "__main__": 
+    uvicorn.run("testapi:app",  host="0.0.0.0", port=8095, workers=1,limit_concurrency=1000)  # 多进程提升并发能力 

newpython/OUT.py

@@ -0,0 +1,483 @@
+"""
+测试流式接口的完整脚本
+API流程：
+1. 登录获取token
+2. 创建thread（可选，也可以使用无状态接口）
+3. 调用流式接口
+
+两种方式：
+- 方式1: POST /api/runs/stream (无状态，自动创建thread)
+- 方式2: POST /api/threads/{thread_id}/runs/stream (需要先创建thread)
+"""
+import requests
+import json
+import asyncio
+import httpx  # 异步 HTTP 客户端
+# ============ 配置区域 ============
+BASE_URL = "http://127.0.0.1:8001"
+LOGIN_URL = f"{BASE_URL}/api/v1/auth/login/username"
+THREADS_URL = f"{BASE_URL}/api/threads"
+RUNS_STREAM_URL = f"{BASE_URL}/api/runs/stream"  # 无状态流式接口
+
+# ============ 登录获取token ============
+def get_token(username: str) -> str:
+    """登录获取token"""
+    response = requests.post(
+        LOGIN_URL,
+        json={"username": username},
+        headers={"Content-Type": "application/json"}
+    )
+    # print(f"登录状态码: {response.status_code}")
+    data = response.json()
+    # print(f"登录响应: {json.dumps(data, indent=2, ensure_ascii=False)}")
+    
+    # 根据实际返回结构调整
+    token = data.get("token") or data.get("access_token") or data.get("data", {}).get("token")
+    return token
+# ============ 方式2: 先创建thread，再调用流式接口 ============
+def create_thread(token: str) -> str:
+    """创建thread，返回thread_id"""
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {token}"
+    }
+    
+    response = requests.post(
+        THREADS_URL,
+        json={},  # 空body，使用默认值
+        headers=headers
+    )
+    
+    print(f"创建thread状态码: {response.status_code}")
+    data = response.json()
+    print(f"创建thread响应: {json.dumps(data, indent=2, ensure_ascii=False)}")
+    
+    return data.get("thread_id")
+def get_agent_name_description(agent_name="Taiwan-intelligence-gathering-agent"):
+    url = f"{BASE_URL}/api/agents/{agent_name}"
+    token=get_token("ji")
+    headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {token}"
+        }
+    response = requests.get(
+            url,
+            headers=headers,
+        )
+    print(response.json())
+    return response.json()["name"].lower(),"agent_name:"+response.json()["name"].lower()+","+response.json()["description"]
+
+# ============ 创建智能体 ============
+def create_agent(
+    name: str,
+    description: str = "",
+    model: str | None = None,
+    tool_groups: list[str] | None = None,
+    skills: list[str] | None = None,
+    soul: str = "",
+):
+    """
+    创建一个新的智能体
+    
+    Args:
+        name: 智能体名称（必须是 hyphen-case，如 my-agent）
+        description: 智能体描述
+        model: 可选的模型覆盖（如 gpt-4, claude-3-opus 等）
+        tool_groups: 工具组白名单（如 ["web", "file"]）
+        skills: 技能白名单（如 ["skill1", "skill2"]）
+        soul: SOUL.md 内容（智能体人格/身份描述）
+    
+    Returns:
+        创建结果
+    """
+    TOKEN =get_token("ji")
+    url = f"{BASE_URL}/api/agents"
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {TOKEN}"
+    }
+    
+    payload = {
+        "name": name,
+        "description": description,
+        "soul": soul
+    }
+    
+    # 添加可选字段
+    if model is not None:
+        payload["model"] = model
+    if tool_groups is not None:
+        payload["tool_groups"] = tool_groups
+    if skills is not None:
+        payload["skills"] = skills
+    
+    print(f"请求: POST {url}")
+    print(f"请求体: {json.dumps(payload, indent=2, ensure_ascii=False)}")
+    
+    response = requests.post(url, json=payload, headers=headers)
+    
+    print(f"状态码: {response.status_code}")
+    
+    if response.status_code == 201:
+        result = response.json()
+        print(f"✓ 智能体创建成功!")
+        print(f"响应: {json.dumps(result, indent=2, ensure_ascii=False)}")
+        return result
+    else:
+        print(f"✗ 创建失败: {response.text}")
+        return f"✗ 创建失败: {response.text}"
+
+def create_main_agent(zhihui_name,agent_names):
+    d={}
+    token=get_token("ji")
+    l=[]
+    for i in agent_names:
+        # print(i)
+        name,description=get_agent_name_description(i)
+        thread_id_tmp=create_thread(token)
+        d[name]=thread_id_tmp
+        l.append(description)
+    name="你是总控控制协调智能体,负责协调其他智能体干活"
+    miaoshu=f"""#核心定位：
+    用途：跟用用户的任务目标,调用其他智能体干活,只负责协调汇总,当你觉得已有信息支持回答用户问题时候,停止调度,并输出最后的结果
+    #调度方法：
+    通过使用agent_orchestration技能调用其他智能体干活,使用技能时传入agent_name和需要做的活
+    #可调度智能体：
+    {l}"""
+    thread_id_tmp=create_thread(token)
+    engname=zhihui_name
+    d[engname]=thread_id_tmp
+    tmp_skills=["agent_orchestration"]
+    tmpres=create_agent(
+            name=engname,
+            description=name,
+            model="glm-5",  # 指定模型
+            skills=tmp_skills,  # 关联技能
+            soul=miaoshu)
+    # print("创建智能体响应:",tmpres)
+    if "失败" in tmpres and "exists" not in tmpres:
+        return tmpres
+    print(f"创建协调智能体成功,可以协调列表:{l}")
+    return d
+import re
+def extract_name_task(text="python3 scripts/agent_orchestration.py \"Us-intelligence-gathering-agent\" \"收集和分析美国人民对特朗普的看法，包括支持率和反对率、不同群体的观点差异、主要支持理由和反对理由等\""):
+    try:
+        pattern = r'\.py\s+"([^"]*)"\s+"([^"]*)"'
+        match = re.search(pattern, text)
+        if match:
+            agent_name = match.group(1)
+            task_content = match.group(2)
+        return agent_name,task_content
+    except:
+        return "",""
+def update_agent_message(thread_id,new_content):
+    token=get_token("ji")
+    url = f"{BASE_URL}/api/threads/{thread_id}/state"
+    headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {token}"
+        }
+    tmp = requests.get(
+            url,
+            headers=headers
+        ).json()
+    # print(tmp)
+    if "messages" in tmp["values"]:
+         tmp["values"]["messages"].append({"type": "human", "content": new_content})
+    else:
+         tmp["values"]["messages"]=[]
+         tmp["values"]["messages"].append({"type": "human", "content": new_content})
+    url = f"{BASE_URL}/api/threads/{thread_id}/state"
+    headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {token}"
+        }
+    response = requests.post(
+            url,
+            json=tmp,
+            headers=headers,
+            stream=True
+        )
+    del tmp
+    print(f"线程{thread_id}更新状态码: {response.status_code}")
+def Broadcast_message(message,d_agent_thread_id,pingbi_agent_name):
+    for key in d_agent_thread_id:
+            if key not in pingbi_agent_name:
+                update_agent_message(d_agent_thread_id[key],message)
+                print("广播消息成功:",key,message)
+null=None
+true=True
+false=False
+async def leader_agent(d_agent_thread_id, agent_name, new_message, skill_stop_names):
+    """使用 httpx 异步请求的 leader_agent"""
+    # 使用 to_thread 异步运行同步的 Broadcast_message
+    await asyncio.to_thread(Broadcast_message, "总控智能体收到用户消息:" + new_message, d_agent_thread_id, [agent_name])
+    thread_id = d_agent_thread_id[agent_name]
+    l = []
+    token = get_token("ji")
+    url = f"{BASE_URL}/api/threads/{thread_id}/runs/stream"
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {token}"
+    }
+    payload = {
+        "input": {
+            "messages": [
+                {
+                    "type": "human",
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": new_message
+                        }
+                    ],
+                    "additional_kwargs": {}
+                }
+            ]
+        },
+        "config": {
+            "recursion_limit": 1000
+        },
+        "context": {
+            "agent_name": agent_name,
+            "model_name": "deepseek-chat",
+            "mode": "pro",
+            "reasoning_effort": "medium",
+            "thinking_enabled": true,
+            "is_plan_mode": false,
+            "subagent_enabled": false,
+            "thread_id": thread_id
+        },
+        "stream_mode": [
+            "messages-tuple",
+            "values",
+        ],
+        "stream_subgraphs": true,
+        "stream_resumable": true,
+        "assistant_id": "lead_agent",
+        "on_disconnect": "continue",
+        "excluded_tools": ["web_search"],
+        "skill_stop_names": skill_stop_names,
+    }
+    
+    async with httpx.AsyncClient(timeout=None) as client:
+        async with client.stream("POST", url, json=payload, headers=headers) as response:
+            print(f"状态码: {response.status_code}")
+            if response.status_code != 200:
+                body = await response.aread()
+                raise RuntimeError(
+                    f"upstream {url} returned HTTP {response.status_code}: "
+                    f"{body.decode('utf-8', 'replace')[:1000]}"
+                )
+            async for line in response.aiter_lines():
+                if line:
+                    yield line
+                    if line[:5] == "data:":
+                        print(line)
+                        l.append(line)
+    if len(l) < 2:
+        raise RuntimeError(
+            f"upstream stream produced no usable data lines (got {len(l)} line(s)). "
+            f"Check the server log on {url} for the real failure."
+        )
+    
+    null = None
+    tmpres = eval(l[-2][5:])
+    tmpres = tmpres["messages"][-1]
+    tmp_task = []
+    if tmpres.get("tool_calls", []) == []:
+        yield {"status": []}
+    else:
+        for tool_call in tmpres.get("tool_calls", []):
+            try:
+                agentname, task_text = extract_name_task(str(tool_call["args"]))
+                print("agentname", agentname)
+                print("task_text:", task_text)
+                # 使用 to_thread 异步运行同步的 Broadcast_message
+                await asyncio.to_thread(Broadcast_message, f"总控智能体给{agentname}派活,内容为:" + task_text, d_agent_thread_id, [agent_name, agentname])
+                tmp_task.append([agentname, task_text])
+            except:
+                pass
+        yield {"status": tmp_task}
+
+
+async def special_agent(d_agent_thread_id, agent_name, new_message, skill_stop_names):
+    """使用 httpx 异步请求的 special_agent"""
+    thread_id = d_agent_thread_id[agent_name]
+    l = []
+    token = get_token("ji")
+    url = f"{BASE_URL}/api/threads/{thread_id}/runs/stream"
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {token}"
+    }
+    payload = {
+        "input": {
+            "messages": [
+                {
+                    "type": "human",
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": new_message
+                        }
+                    ],
+                    "additional_kwargs": {}
+                }
+            ]
+        },
+        "config": {
+            "recursion_limit": 1000
+        },
+        "context": {
+            "agent_name": agent_name,
+            "model_name": "deepseek-chat",
+            "mode": "pro",
+            "reasoning_effort": "medium",
+            "thinking_enabled": true,
+            "is_plan_mode": false,
+            "subagent_enabled": false,
+            "thread_id": thread_id
+        },
+        "stream_mode": [
+            "messages-tuple",
+            "values",
+        ],
+        "stream_subgraphs": true,
+        "stream_resumable": true,
+        "assistant_id": "lead_agent",
+        "on_disconnect": "continue",
+        "excluded_tools": ["web_search", "ask_clarification", "present_files", "view_image"],
+        "skill_stop_names": skill_stop_names,
+    }
+    
+    async with httpx.AsyncClient(timeout=None) as client:
+        async with client.stream("POST", url, json=payload, headers=headers) as response:
+            print(f"状态码: {response.status_code}")
+            if response.status_code != 200:
+                body = await response.aread()
+                raise RuntimeError(
+                    f"upstream {url} returned HTTP {response.status_code}: "
+                    f"{body.decode('utf-8', 'replace')[:1000]}"
+                )
+            async for line in response.aiter_lines():
+                if line:
+                    yield line
+                    if line[:5] == "data:":
+                        print(line)
+                        l.append(line)
+    if len(l) < 2:
+        raise RuntimeError(
+            f"upstream stream produced no usable data lines (got {len(l)} line(s)). "
+            f"Check the server log on {url} for the real failure."
+        )
+    
+    null = None
+    tmpres = eval(l[-2][5:])
+    tmpres = tmpres["messages"][-1]
+    # 使用 to_thread 异步运行同步的 Broadcast_message
+    await asyncio.to_thread(Broadcast_message, f"子智能体{agent_name}完成{new_message}工作,交付内容为:" + tmpres["content"], d_agent_thread_id, [agent_name])
+    yield {"status": f"子智能体{agent_name}完成{new_message}工作"}
+
+async def agent_init(agent_names, main_agent_name):
+    """异步的 agent_init，同步函数调用使用 to_thread"""
+    agent_names = [i.lower() for i in agent_names]
+    # 使用 to_thread 异步运行同步的 create_main_agent
+    tmpd = await asyncio.to_thread(create_main_agent, main_agent_name, agent_names)
+    message = """
+你的职责:你只是若干个智能体的其中一员,你们的共同目标是完成一个庞大的任务,总控控制协调智能体会分配特定的活给你,你只需要用你自身的能力来完成这个特定的活
+注意:你只是一个子智能体,你不必把你的分析结果存放到文件里面去,你只关心于完成总控控制协调智能体分配的活即可
+"""
+    # 使用 to_thread 异步运行同步的 Broadcast_message
+    await asyncio.to_thread(Broadcast_message, message, tmpd, [main_agent_name])
+    return main_agent_name, tmpd
+
+
+from fastapi import FastAPI, HTTPException,Body
+from fastapi.responses  import StreamingResponse
+from fastapi.middleware.cors import CORSMiddleware
+import asyncio
+import uvicorn
+
+app = FastAPI()
+ # CORS 配置
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+import time
+@app.post("/generate_mul_agent")
+async def generate_stream(payload: dict = Body(...)):
+    """
+    根据 payload 中的 agent_type 字段判断调用 leader_agent 还是 special_agent
+    
+    payload 结构示例:
+    {
+        "agent_type": "leader",  # 或 "special"
+        "d_agent_thread_id": {...},
+        "agent_name": "xxx",
+        "new_message": "xxx",
+        "skill_stop_names": [...]
+    }
+    """
+    # 从 payload 中提取参数
+    agent_type = payload.get("agent_type", "leader")  # 默认为 leader
+    d_agent_thread_id = payload.get("d_agent_thread_id", {})
+    agent_name = payload.get("agent_name", "")
+    new_message = payload.get("new_message", "")
+    skill_stop_names = payload.get("skill_stop_names", [])
+    
+    # 流式响应生成器
+    async def stream_generator():
+        # 根据 agent_type 选择调用的函数
+        if agent_type == "leader":
+            async_generator = leader_agent(d_agent_thread_id, agent_name, new_message, skill_stop_names)
+        else:  # special
+            async_generator = special_agent(d_agent_thread_id, agent_name, new_message, skill_stop_names)
+        
+        # 异步迭代生成器
+        async for chunk in async_generator:
+            if isinstance(chunk, dict):
+                yield f"data: {json.dumps(chunk, ensure_ascii=False)}\n\n"
+            else:
+                yield f"{chunk}"
+    
+    # 使用 SSE 流式响应
+    return StreamingResponse(
+        stream_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "X-Accel-Buffering": "no",
+            "Connection": "keep-alive",
+        }
+    )
+
+
+@app.post("/agent_init")
+async def init_agents(payload: dict = Body(...)):
+    """
+    初始化智能体接口
+    payload 结构示例:
+    {
+        "agent_names": ["agent1", "agent2"],
+        "main_agent_name": "main-agent"
+    }
+    """
+    agent_names = payload.get("agent_names", [])
+    main_agent_name = payload.get("main_agent_name", "main-agent")
+    
+    # 调用异步的 agent_init
+    name, thread_id_dict = await agent_init(agent_names, main_agent_name)
+    
+    return {
+        "status": "success",
+        "main_agent_name": name,
+        "thread_ids": thread_id_dict
+    }
+if __name__ == "__main__": 
+    uvicorn.run("testapi:app",  host="0.0.0.0", port=8095, workers=1,limit_concurrency=1000)  # 多进程提升并发能力 

newpython/demo_call.py

@@ -0,0 +1,132 @@
+"""
+OUT.py 的调用链 demo —— 跑通 agent_init → leader 派活 → 各 special agent 干活 这一整套流程。
+
+前置条件:
+  1. DeerFlow 后端在 http://127.0.0.1:8001 在跑
+  2. OUT.py 已经 `python -m uvicorn OUT:app --host 0.0.0.0 --port 8095` 起好
+  3. 下面 SUB_AGENT_NAMES 里的 agent 已经在 DeerFlow 里创建好(/api/agents 能查到)
+
+直接 `python demo_call.py` 即可。
+"""
+import asyncio
+import json
+
+import httpx
+
+# ============ 改这里 ============
+OUT_BASE = "http://127.0.0.1:8095"
+
+# 已经在 DeerFlow 里建好的子 agent 名字 (会被 OUT.py 强制 lowercase)
+SUB_AGENT_NAMES = [
+    "us-intelligence-gathering-agent",
+    "taiwan-intelligence-gathering-agent",
+]
+
+# 总控 agent 名字 —— OUT.py 会现场创建它,所以填一个新名字即可 (hyphen-case)
+MAIN_AGENT_NAME = "demo-coordinator"
+
+# 用户提问 —— 总控会基于此派活
+USER_QUESTION = "对比中美两国 2024 年在 AI 监管政策上的差异"
+
+# leader 阶段命中即停的 skill 名字单 (OUT.py 用 agent_orchestration 作为派活信号)
+SKILL_STOP_NAMES = ["agent_orchestration"]
+# ================================
+
+
+async def call_agent_init() -> tuple[str, dict[str, str]]:
+    """步骤 1: 初始化 —— 拿回 thread_ids 字典 {agent_name → thread_id}."""
+    print(">>> [1/3] POST /agent_init")
+    async with httpx.AsyncClient(timeout=300.0) as client:
+        r = await client.post(
+            f"{OUT_BASE}/agent_init",
+            json={
+                "agent_names": SUB_AGENT_NAMES,
+                "main_agent_name": MAIN_AGENT_NAME,
+            },
+        )
+        r.raise_for_status()
+        body = r.json()
+    main_name = body["main_agent_name"]
+    thread_ids = body["thread_ids"]
+    print(f"    main_agent_name = {main_name}")
+    print(f"    thread_ids = {json.dumps(thread_ids, ensure_ascii=False, indent=2)}")
+    return main_name, thread_ids
+
+
+async def stream_generate(payload: dict) -> object | None:
+    """通用 SSE 消费:边收边打印,最后返回 OUT.py 自己 yield 的 {'status': ...} 里的 status 字段."""
+    final_status = None
+    async with httpx.AsyncClient(timeout=None) as client:
+        async with client.stream("POST", f"{OUT_BASE}/generate_mul_agent", json=payload) as resp:
+            print(f"    HTTP {resp.status_code}")
+            if resp.status_code != 200:
+                err = await resp.aread()
+                raise RuntimeError(f"OUT.py /generate_mul_agent failed: {err.decode('utf-8', 'replace')[:500]}")
+
+            async for line in resp.aiter_lines():
+                if not line:
+                    continue
+                # OUT.py 把 DeerFlow 的原始 SSE 流直接转发,自己最后会再 yield 一行
+                # `data: {"status": ...}\n\n`。两边都以 `data:` 开头,我们都尝试 JSON parse
+                # 一下,能解析成 {"status": ...} 的就是 OUT.py 的终态。
+                if line.startswith("data:"):
+                    body_text = line[5:].strip()
+                    try:
+                        obj = json.loads(body_text)
+                    except json.JSONDecodeError:
+                        continue
+                    if isinstance(obj, dict) and "status" in obj:
+                        final_status = obj["status"]
+                # 想看上游 DeerFlow 的完整流,把下面打开即可
+                # else:
+                #     print("   raw:", line[:200])
+    return final_status
+
+
+async def call_leader(main_agent_name: str, thread_ids: dict[str, str]) -> list[list[str]]:
+    """步骤 2: leader 阶段 —— 总控读完用户问题,产出派活清单."""
+    print(f"\n>>> [2/3] POST /generate_mul_agent (leader, agent={main_agent_name})")
+    status = await stream_generate(
+        {
+            "agent_type": "leader",
+            "d_agent_thread_id": thread_ids,
+            "agent_name": main_agent_name,
+            "new_message": USER_QUESTION,
+            "skill_stop_names": SKILL_STOP_NAMES,
+        }
+    )
+    dispatch = status or []
+    print(f"    派活清单 = {json.dumps(dispatch, ensure_ascii=False, indent=2)}")
+    return dispatch
+
+
+async def call_specials(thread_ids: dict[str, str], dispatch: list[list[str]]) -> None:
+    """步骤 3: 把派活清单里每一项交给对应子 agent 跑."""
+    if not dispatch:
+        print("\n>>> [3/3] 总控没派出任何任务,直接结束。")
+        return
+    for idx, item in enumerate(dispatch, 1):
+        agent_name, task_text = item[0], item[1]
+        print(f"\n>>> [3/3] ({idx}/{len(dispatch)}) POST /generate_mul_agent (special, agent={agent_name})")
+        print(f"    task = {task_text[:120]}{'...' if len(task_text) > 120 else ''}")
+        result = await stream_generate(
+            {
+                "agent_type": "special",
+                "d_agent_thread_id": thread_ids,
+                "agent_name": agent_name,
+                "new_message": task_text,
+                "skill_stop_names": [],
+            }
+        )
+        print(f"    完成: {result}")
+
+
+async def main() -> None:
+    main_agent_name, thread_ids = await call_agent_init()
+    dispatch = await call_leader(main_agent_name, thread_ids)
+    await call_specials(thread_ids, dispatch)
+    print("\n✅ demo 跑完")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())