add NativeClaudeSession+NativeToolClient, support parallel tool calls, fix tool_calls[-1:], add llm log

agent_loop.py

@@ -17,9 +17,10 @@ class BaseHandler:
     def tool_before_callback(self, tool_name, args, response): pass
     def tool_after_callback(self, tool_name, args, response, ret): pass
     def next_prompt_patcher(self, next_prompt, outcome, turn): return next_prompt
-    def dispatch(self, tool_name, args, response):
+    def dispatch(self, tool_name, args, response, index=0):
         method_name = f"do_{tool_name}"
         if hasattr(self, method_name):
+            args['_index'] = index
             prer = yield from try_call_generator(self.tool_before_callback, tool_name, args, response)
             ret = yield from try_call_generator(getattr(self, method_name), args, response)
             _ = yield from try_call_generator(self.tool_after_callback, tool_name, args, response, ret)
@@ -61,35 +62,34 @@ def agent_runner_loop(client, system_prompt, user_input, handler, tools_schema,
             response = exhaust(response_gen)
             yield response.content
 
-        if not response.tool_calls:
+        if not response.tool_calls: tool_calls = [{'tool_name': 'no_tool', 'args': {}}]
-            tool_name, args = 'no_tool', {}
+        else: tool_calls = [{'tool_name': tc.function.name, 'args': json.loads(tc.function.arguments)}
-        else:
+                          for tc in response.tool_calls]
-            tool_call = response.tool_calls[0] 
+       
-            tool_name = tool_call.function.name
-            args = json.loads(tool_call.function.arguments)
-
-        if tool_name == 'no_tool': pass
-        else: 
-            showarg = get_pretty_json(args)
-            if not verbose and len(showarg) > 200: showarg = showarg[:200] + ' ...'
-            yield f"🛠️ **正在调用工具:** `{tool_name}`  📥**参数:**\n````text\n{showarg}\n````\n" 
-        handler.current_turn = turn + 1
-        gen = handler.dispatch(tool_name, args, response)
-        if verbose:
-            yield '`````\n'
-            outcome = yield from gen
-            yield '`````\n'
-        else: outcome = exhaust(gen)
-
-        if outcome.next_prompt is None: return {'result': 'CURRENT_TASK_DONE', 'data': outcome.data}
-        if outcome.should_exit: return {'result': 'EXITED', 'data': outcome.data}
-        if outcome.next_prompt.startswith('未知工具'): client.last_tools = ''
-
         next_prompt = ""
-        if outcome.data is not None: 
+        for ii, tc in enumerate(tool_calls):
-            datastr = json.dumps(outcome.data, ensure_ascii=False, default=json_default) if type(outcome.data) in [dict, list] else str(outcome.data) 
+            tool_name, args = tc['tool_name'], tc['args']
-            next_prompt += f"<tool_result>\n{datastr}\n</tool_result>\n\n"
+            if tool_name == 'no_tool': pass
-        next_prompt += outcome.next_prompt
+            else: 
-        next_prompt = handler.next_prompt_patcher(next_prompt, outcome, turn+1)
+                showarg = get_pretty_json(args)
+                if not verbose and len(showarg) > 200: showarg = showarg[:200] + ' ...'
+                yield f"🛠️ **正在调用工具:** `{tool_name}`  📥**参数:**\n````text\n{showarg}\n````\n" 
+            handler.current_turn = turn + 1
+            gen = handler.dispatch(tool_name, args, response, index=ii)
+            if verbose:
+                yield '`````\n'
+                outcome = yield from gen
+                yield '`````\n'
+            else: outcome = exhaust(gen)
+
+            if outcome.next_prompt is None: return {'result': 'CURRENT_TASK_DONE', 'data': outcome.data}
+            if outcome.should_exit: return {'result': 'EXITED', 'data': outcome.data}
+            if outcome.next_prompt.startswith('未知工具'): client.last_tools = ''
+
+            if outcome.data is not None: 
+                datastr = json.dumps(outcome.data, ensure_ascii=False, default=json_default) if type(outcome.data) in [dict, list] else str(outcome.data) 
+                next_prompt += f"<tool_result>\n{datastr}\n</tool_result>\n\n"
+            next_prompt += outcome.next_prompt
+        next_prompt = handler.next_prompt_patcher(next_prompt, None, turn+1)
         messages = [{"role": "user", "content": next_prompt}]
     return {'result': 'MAX_TURNS_EXCEEDED'}

agentmain.py

@@ -5,8 +5,8 @@ if sys.stderr is None: sys.stderr = open(os.devnull, "w")
 elif hasattr(sys.stderr, 'reconfigure'): sys.stderr.reconfigure(errors='replace')
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
 
-from llmcore import SiderLLMSession, LLMSession, ToolClient, ClaudeSession, XaiSession, build_multimodal_content
+from llmcore import SiderLLMSession, LLMSession, ToolClient, ClaudeSession, XaiSession, NativeToolClient, NativeClaudeSession, build_multimodal_content
-from agent_loop import agent_runner_loop, StepOutcome, BaseHandler
+from agent_loop import agent_runner_loop
 from ga import GenericAgentHandler, smart_format, get_global_memory, format_error
 
 script_dir = os.path.dirname(os.path.abspath(__file__))
@@ -44,28 +44,30 @@ class GeneraticAgent:
         for k, cfg in mykeys.items():
             if not any(x in k for x in ['api', 'config', 'cookie']): continue
             try:
-                if 'claude' in k: llm_sessions += [ClaudeSession(cfg=cfg)]
+                if 'native' in k and 'claude' in k: llm_sessions += [NativeToolClient(NativeClaudeSession(cfg=cfg))]
-                if 'oai' in k: llm_sessions += [LLMSession(cfg=cfg)]
+                elif 'claude' in k: llm_sessions += [ToolClient(ClaudeSession(cfg=cfg))]
-                if 'xai' in k: llm_sessions += [XaiSession(cfg=cfg)]
+                elif 'oai' in k: llm_sessions += [ToolClient(LLMSession(cfg=cfg))]
-                if 'sider' in k: llm_sessions += [SiderLLMSession(cfg={'apikey': cfg, 'model': x}) for x in \
+                elif 'xai' in k: llm_sessions += [ToolClient(XaiSession(cfg=cfg))]
+                elif 'sider' in k: llm_sessions += [ToolClient(SiderLLMSession(cfg={'apikey': cfg, 'model': x})) for x in \
                                     ["gemini-3.0-flash", "gpt-5.4"]]
             except: pass
-        if len(llm_sessions) > 0: self.llmclient = ToolClient(llm_sessions, auto_save_tokens=True)
+        self.llmclients = llm_sessions
-        else: self.llmclient = None
         self.lock = threading.Lock()
         self.history = []               
         self.task_queue = queue.Queue() 
         self.is_running, self.stop_sig = False, False
         self.llm_no = 0;  self.inc_out = False
         self.handler = None; self.verbose = True
+        self.llmclient = self.llmclients[self.llm_no]
 
     def next_llm(self, n=-1):
-        self.llm_no = ((self.llm_no + 1) if n < 0 else n) % len(self.llmclient.backends)
+        self.llm_no = ((self.llm_no + 1) if n < 0 else n) % len(self.llmclients)
+        self.llmclient = self.llmclients[self.llm_no]
         self.llmclient.last_tools = ''
-    def list_llms(self): return [(i, f"{type(b).__name__}/{b.default_model}", i == self.llm_no) for i, b in enumerate(self.llmclient.backends)]
+    def list_llms(self): return [(i, f"{type(b.backend).__name__}/{b.backend.default_model}", i == self.llm_no) for i, b in enumerate(self.llmclients)]
     def get_llm_name(self):
-        b = self.llmclient.backends[self.llm_no]
+        b = self.llmclient
-        return f"{type(b).__name__}/{b.default_model}"
+        return f"{type(b.backend).__name__}/{b.backend.default_model}"
 
     def abort(self):
         print('Abort current task...')
@@ -95,7 +97,6 @@ class GeneraticAgent:
                 handler.working['passed_sessions'] = ps = self.handler.working.get('passed_sessions', 0) + 1
                 if ps > 0: handler.working['key_info'] += f'\n[SYSTEM] 此为 {ps} 个对话前设置的key_info，若已在新任务，先更新或清除工作记忆。\n'
             self.handler = handler
-            self.llmclient.backend = self.llmclient.backends[self.llm_no]
             user_input = raw_query
             if source == 'feishu' and len(self.history) > 1:   # 如果有历史记录且来自飞书，注入到首轮 user_input 中（支持/restore恢复上下文）
                 user_input = handler._get_anchor_prompt() + f"\n\n### 用户当前消息\n{raw_query}"

assets/tools_schema.json

@@ -1,7 +1,7 @@
 [
   {"type": "function", "function": {
     "name": "code_run",
-    "description": "代码执行器。优先使用python，仅在必要系统操作时使用 powershell。注意：执行的代码必须放在在回复正文中，以 ```python 或 ```powershell 代码块的形式。严禁在代码中硬编码大量数据，如有需要应通过文件读取。",
+    "description": "代码执行器。优先使用python，仅在必要系统操作时使用 powershell。注意：不能同时调用多个，执行的代码放在回复正文中，以 ```python 或 ```powershell 代码块的形式。严禁在代码中硬编码大量数据，如有需要应通过文件读取。",
     "parameters": {"type": "object", "properties": {
       "type": {"type": "string", "enum": ["python", "powershell"], "description": "执行环境类型，默认为 python。", "default": "python"},
       "timeout": {"type": "integer", "description": "执行超时时间（秒），默认 60。", "default": 60},

ga.py

@@ -256,6 +256,7 @@ class GenericAgentHandler(BaseHandler):
         return os.path.abspath(os.path.join(self.cwd, path))
     
     def tool_after_callback(self, tool_name, args, response, ret):
+        if args.get('_index', 0) > 0: return 
         rsumm = re.search(r"<summary>(.*?)</summary>", response.content, re.DOTALL)
         if rsumm: summary = rsumm.group(1).strip()[:200]
         else:
@@ -268,6 +269,7 @@ class GenericAgentHandler(BaseHandler):
     def do_code_run(self, args, response):
         '''执行代码片段，有长度限制，不允许代码中放大量数据，如有需要应当通过文件读取进行。
         '''
+        if args.get('_index', 0) > 0: return StepOutcome("[BLANK]", next_prompt="no multi code_run in one round!") 
         code_type = args.get("type", "python")
         # 从 response.content 中提取代码块, 匹配 ```python ... ``` 或 ```powershell ... ```
         pattern = rf"```{code_type}\n(.*?)\n```"
@@ -502,4 +504,4 @@ def get_global_memory():
         prompt += structure + '\n../memory/global_mem_insight.txt:\n'
         prompt += insight + "\n"
     except FileNotFoundError: pass
-    return prompt
+    return prompt

llmcore.py

@@ -397,25 +397,127 @@ class XaiSession:
             yield f"[XaiError] {e}"
     def reset(self): self._last_response_id = None
 
+
+
+
+class NativeClaudeSession:
+    def __init__(self, cfg):
+        self.api_key = cfg['apikey']; self.api_base = cfg['apibase'].rstrip('/')
+        self.default_model = cfg.get('model', 'claude-opus')
+        self.context_win = cfg.get('context_win', 24000)
+        self.history = []  
+        self.system = None
+        self.lock = threading.Lock()
+    def set_system(self, system_text): self.system = system_text
+
+    def raw_ask(self, messages, tools=None, system=None, model=None, temperature=0.5, max_tokens=6144):
+        """底层API调用。yields text chunks，generator return = list[content_block]"""
+        model = model or self.default_model
+        headers = {"x-api-key": self.api_key, "Content-Type": "application/json", "anthropic-version": "2023-06-01"}
+        payload = {"model": model, "messages": messages, "temperature": temperature, "max_tokens": max_tokens, "stream": True}
+        if tools: payload["tools"] = tools
+        if system: payload["system"] = system
+        try:
+            resp = requests.post(auto_make_url(self.api_base, "messages"), headers=headers, json=payload, stream=True, timeout=120)
+            if resp.status_code != 200:
+                error_msg = f"Error: HTTP {resp.status_code} {resp.text[:500]}"
+                yield error_msg
+                return [{"type": "text", "text": error_msg}]
+        except Exception as e:
+            error_msg = f"Error: {e}"
+            yield error_msg
+            return [{"type": "text", "text": error_msg}]
+
+        content_blocks = []; current_block = None; tool_json_buf = ""
+        for line in resp.iter_lines():
+            if not line: continue
+            line = line.decode('utf-8') if isinstance(line, bytes) else line
+            data_str = line[6:]
+            if data_str.strip() == "[DONE]": break
+            try: evt = json.loads(data_str)
+            except: continue
+            evt_type = evt.get("type", "")
+            if evt_type == "content_block_start":
+                block = evt.get("content_block", {})
+                if block.get("type") == "text": current_block = {"type": "text", "text": ""}
+                elif block.get("type") == "tool_use":
+                    current_block = {"type": "tool_use", "id": block.get("id", ""), "name": block.get("name", ""), "input": {}}
+                    tool_json_buf = ""
+            elif evt_type == "content_block_delta":
+                delta = evt.get("delta", {})
+                if delta.get("type") == "text_delta":
+                    text = delta.get("text", "")
+                    if current_block: current_block["text"] += text
+                    yield text
+                elif delta.get("type") == "input_json_delta": tool_json_buf += delta.get("partial_json", "")
+            elif evt_type == "content_block_stop":
+                if current_block:
+                    if current_block["type"] == "tool_use":
+                        try: current_block["input"] = json.loads(tool_json_buf) if tool_json_buf else {}
+                        except: current_block["input"] = {"_raw": tool_json_buf}
+                    content_blocks.append(current_block)
+                    current_block = None
+        return content_blocks
+
+    def ask(self, msg, tools=None, model=None):
+        """增量ask。msg: str|list[content_block]|dict。yields text chunks, return MockResponse"""
+        if isinstance(msg, str): msg = {"role": "user", "content": msg}
+        elif isinstance(msg, list): msg = {"role": "user", "content": msg}  
+        with self.lock:
+            self.history.append(msg)
+            while len(self.history) > 2:
+                cost = sum(len(json.dumps(m, ensure_ascii=False)) for m in self.history) + len(self.system or '')
+                if cost <= self.context_win * 4: break
+                self.history.pop(0); self.history.pop(0)  # 砍一对
+            messages = list(self.history)
+
+        content_blocks = None
+        gen = self.raw_ask(messages, tools, self.system, model)
+        try:
+            while True: yield next(gen)
+        except StopIteration as e: content_blocks = e.value or []
+        if content_blocks and not (len(content_blocks) == 1 and content_blocks[0].get("text", "").startswith("Error:")):
+            self.history.append({"role": "assistant", "content": content_blocks})
+        thinking = ''
+        text_parts = [b["text"] for b in content_blocks if b.get("type") == "text"]
+        content = "\n".join(text_parts).strip()
+        tool_calls = []
+        for b in content_blocks:
+            if b.get("type") == "tool_use":
+                tool_calls.append(MockToolCall(b["name"], b.get("input", {}), id=b.get("id", "")))
+        return MockResponse(thinking, content, tool_calls, str(content_blocks))
+
+def openai_tools_to_claude(tools):
+    """[{type:'function', function:{name,description,parameters}}] → [{name,description,input_schema}]. 幂等"""
+    result = []
+    for t in tools:
+        if 'input_schema' in t: result.append(t); continue  # 已是claude格式
+        fn = t.get('function', t)
+        result.append({
+            'name': fn['name'], 'description': fn.get('description', ''),
+            'input_schema': fn.get('parameters', {'type': 'object', 'properties': {}})
+        })
+    return result
+
+
 class MockFunction:
     def __init__(self, name, arguments): self.name, self.arguments = name, arguments  
          
 class MockToolCall:
-    def __init__(self, name, args):
+    def __init__(self, name, args, id=''):
         arg_str = json.dumps(args, ensure_ascii=False) if isinstance(args, dict) else args
-        self.function = MockFunction(name, arg_str)
+        self.function = MockFunction(name, arg_str); self.id = id
 
 class MockResponse:
-    def __init__(self, thinking, content, tool_calls, raw):
+    def __init__(self, thinking, content, tool_calls, raw, stop_reason='end_turn'):
-        self.thinking = thinking        # 存放 <thinking> 内部的思维过程
+        self.thinking = thinking; self.content = content          
-        self.content = content          # 存放去除标签后的纯文本回复
+        self.tool_calls = tool_calls; self.raw = raw
-        self.tool_calls = tool_calls    # 存放 MockToolCall 列表 或 None
+        self.stop_reason = 'tool_use' if tool_calls else stop_reason
-        self.raw = raw
     def __repr__(self):    
         return f"<MockResponse thinking={bool(self.thinking)}, content='{self.content}', tools={bool(self.tool_calls)}>"
 
 class ToolClient:
-    def __init__(self, backends, auto_save_tokens=False):
+    def __init__(self, backends, auto_save_tokens=True):
         if isinstance(backends, list): self.backends = backends
         else: self.backends = [backends]
         self.backend = self.backends[0]
@@ -424,8 +526,6 @@ class ToolClient:
         self.total_cd_tokens = 0
 
     def chat(self, messages, tools=None):
-        script_dir = os.path.dirname(os.path.abspath(__file__))
-        log_path = os.path.join(script_dir, f'./temp/model_responses_{os.getpid()}.txt')
         if self._should_use_structured_messages(messages):
             backend_messages = self._build_backend_messages(messages, tools)
             print("Structured prompt length:", sum(self._estimate_content_len(m.get("content")) for m in backend_messages), 'chars')
@@ -436,8 +536,7 @@ class ToolClient:
             print("Full prompt length:", len(full_prompt), 'chars')
             prompt_log = full_prompt
             gen = self.backend.ask(full_prompt, stream=True)
-        with open(log_path, 'a', encoding='utf-8', errors="replace") as f:
+        _write_llm_log('Prompt', prompt_log)
-            f.write(f"=== Prompt === {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n{prompt_log}\n")
         raw_text = ''; summarytag = '[NextWillSummary]'
         for chunk in gen:
             raw_text += chunk
@@ -445,8 +544,7 @@ class ToolClient:
         print('Complete response received.')
         if raw_text.endswith(summarytag):
             self.last_tools = ''; raw_text = raw_text[:-len(summarytag)]
-        with open(log_path, 'a', encoding='utf-8', errors="replace") as f:
+        _write_llm_log('Response', raw_text)
-            f.write(f"=== Response === {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n{raw_text}\n\n")
         return self._parse_mixed_response(raw_text)
 
     def _should_use_structured_messages(self, messages):
@@ -474,7 +572,7 @@ class ToolClient:
 请按照以下步骤思考并行动，标签之间需要回车换行：
 1. **思考**: 在 `<thinking>` 标签中先进行思考，分析现状和策略。
 2. **总结**: 在 `<summary>` 中输出*极为简短*的高度概括的单行（<30字）物理快照，包括上次工具调用结果产生的新信息+本次工具调用意图。此内容将进入长期工作记忆，记录关键信息，严禁输出无实际信息增量的描述。
-3. **行动**: 如需调用工具，请在回复正文之后输出一个 **<tool_use>块**，然后结束，我会稍后给你返回<tool_result>块。
+3. **行动**: 如需调用工具，请在回复正文之后输出一个（或多个）**<tool_use>块**，然后结束，我会稍后给你返回<tool_result>块。
    格式: ```<tool_use>\n{{"name": "工具名", "arguments": {{参数}}}}\n</tool_use>\n```
 
 ### 可用工具库（已挂载，持续有效）
@@ -589,7 +687,13 @@ class ToolClient:
                 print(e['err'])
                 if 'bad_json' in e: tool_calls.append(MockToolCall('bad_json', {'msg': e['bad_json']}))
         content = remaining_text.strip()
-        return MockResponse(thinking, content, tool_calls[-1:], text)
+        return MockResponse(thinking, content, tool_calls, text)
+
+def _write_llm_log(label, content):
+    log_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), f'temp/model_responses_{os.getpid()}.txt')
+    ts = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    with open(log_path, 'a', encoding='utf-8', errors='replace') as f:
+        f.write(f"=== {label} === {ts}\n{content}\n\n")
 
 def tryparse(json_str):
     try: return json.loads(json_str)
@@ -602,30 +706,43 @@ def tryparse(json_str):
     if '}' in json_str: json_str = json_str[:json_str.rfind('}') + 1]
     return json.loads(json_str)
 
-if __name__ == "__main__":
-    sider_cookie = mykeys.get("sider_cookie")
-    oai_configs = {
-        k: v for k, v in mykeys.items() if k.startswith("oai_config") and v
-    }
-    google_api_key = mykeys.get("google_api_key")
-    cfg = oai_configs.get("oai_config")
-    llmclient = ToolClient(LLMSession(cfg))
-    def get_final(gen):
-        try:
-            while True: print('mid:', next(gen))
-        except StopIteration as e:
-            return e.value
-        
-    response = get_final(llmclient.chat(
-        messages=[{"role": "user", "content": "我的IP是多少"}], 
-        tools=[{"name": "get_ip", "parameters": {}}]
-    ))
-    print(f"思考: {response.thinking}") 
-    if response.tool_calls:
-        cmd = response.tool_calls[0]
-        print(f"调用: {cmd.function.name} 参数: {cmd.function.arguments}")
 
-    response = get_final(llmclient.chat(
+class NativeToolClient:
-        messages=[{"role": "user", "content": "<tool_result>10.176.45.12</tool_result>"}] 
+    THINKING_PROMPT = """
-    ))
+### 行动规范（持续有效）
-    print(response.content)
+每次回复请遵循：
+1. 在 <thinking> 标签中先分析现状和策略
+2. 在 <summary> 中输出极简单行（<30字）物理快照：上次结果新信息+本次意图。此内容进入长期工作记忆。
+3. 如需调用工具，直接使用工具调用能力，然后结束回复。
+""".strip()
+    def __init__(self, backend):
+        self.backend = backend
+        self.backend.system = self.THINKING_PROMPT
+        self.tools = {}
+    def set_system(self, extra_system):
+        combined = f"{extra_system}\n\n{self.THINKING_PROMPT}" if extra_system else self.THINKING_PROMPT
+        self.backend.system = combined
+    def chat(self, messages, tools=None):
+        if tools: self.tools = openai_tools_to_claude(tools) if isinstance(self.backend, NativeClaudeSession) else tools
+        combined_content = []; resp = None
+        for msg in messages:
+            c = msg.get('content', '')
+            if isinstance(c, str): combined_content.append({"type": "text", "text": c})
+            elif isinstance(c, list) or isinstance(c, dict): combined_content.extend(c)
+        merged = {"role": "user", "content": combined_content}
+        _write_llm_log('Prompt', json.dumps(merged, ensure_ascii=False, indent=2))
+        gen = self.backend.ask(merged, self.tools); 
+        try:
+            while True: 
+                chunk = next(gen); yield chunk
+        except StopIteration as e: resp = e.value
+        print('Complete response received.')
+        if resp:
+            _write_llm_log('Response', resp.raw)
+            text = resp.content
+            think_match = re.search(r'<thinking>(.*?)</thinking>', text, re.DOTALL)
+            if think_match:
+                resp.thinking = think_match.group(1).strip()
+                text = re.sub(r'<thinking>.*?</thinking>', '', text, flags=re.DOTALL)
+            resp.content = text.strip()
+        return resp