refactor: 优化文件结构

assets/ARCHITECTURE.md

@@ -29,7 +29,11 @@ luxx/
 │   ├── providers.py     # LLM 提供商管理
 │   └── tools.py         # 工具管理
 ├── services/            # 服务层
-│   ├── chat.py         # 聊天服务 (Agentic Loop)
+│   ├── chat.py         # 聊天服务门面
+│   ├── agentic_loop.py # Agentic Loop 执行器
+│   ├── stream_context.py# 流式状态管理
+│   ├── llm_response.py  # LLM 响应解析器
+│   ├── process_result.py# 处理结果
 │   └── llm_client.py   # LLM 客户端
 ├── tools/              # 工具系统
 │   ├── core.py         # 核心类 (ToolRegistry, ToolDefinition, ToolResult)
@@ -308,6 +312,28 @@ ToolExecutor 返回结果
 
 ### 6. 服务层
 
+#### LLMResponseParser (`services/llm_response.py`)
+统一解析器，兼容多种 LLM API 格式：
+- **OpenAI**: `delta.content`, `delta.tool_calls`
+- **DeepSeek**: `delta.content`, `delta.reasoning_content`
+- **Anthropic**: `content_block` 类型事件
+- **MiniMax**: `<|im_start|>thinking...<|im_end|>` 标签
+
+```python
+from luxx.services.llm_response import llm_parser
+
+# 解析 OpenAI 格式
+parsed = llm_parser.parse_openai(delta)
+
+# 解析 Anthropic 格式
+parsed = llm_parser.parse_anthropic(chunk)
+
+# 返回 ParsedDelta
+parsed.thinking    # 思考内容
+parsed.text        # 文本内容
+parsed.tool_calls  # 工具调用
+```
+
 #### ChatService (`services/chat.py`)
 核心聊天服务：
 - Agentic Loop 迭代执行（最多 10 轮）
@@ -315,9 +341,22 @@ ToolExecutor 返回结果
 - 工具调用编排（并行执行）
 - 消息历史管理
 - 自动重试机制
-- 支持 thinking_content 提取
 - Token 用量追踪
 
+#### AgenticLoop (`services/agentic_loop.py`)
+执行 Agentic Loop 的核心循环：
+- 调用 LLM 获取响应
+- 使用 LLMResponseParser 解析响应
+- 管理 thinking/text/tool_call/tool_result 步骤
+- 工具并行执行
+
+#### StreamContext (`services/stream_context.py`)
+流式状态管理：
+- 追踪当前步骤类型和索引
+- 累积 thinking 和 text 内容
+- 管理 tool_calls 列表
+- 生成 SSE 事件
+
 #### LLMClient (`services/llm_client.py`)
 LLM API 客户端：
 - 多提供商：DeepSeek、GLM、OpenAI
@@ -364,23 +403,30 @@ sequenceDiagram
     participant Client
     participant API as POST /messages/stream
     participant CS as ChatService
+    participant AL as AgenticLoop
+    participant Parser as LLMResponseParser
     participant LLM as LLM API
     participant TE as ToolExecutor
     
     Client->>API: POST {content, tools, thinking_enabled}
     API->>CS: stream_response()
+    CS->>AL: execute()
     
     loop MAX_ITERATIONS (10)
-        CS->>LLM: call(messages, tools)
+        AL->>LLM: stream_call(messages, tools)
-        LLM-->>CS: SSE Stream
+        LLM-->>AL: SSE Stream
+        
+        AL->>Parser: parse_chunk()
+        Parser-->>AL: ParsedDelta {thinking, text, tool_calls}
         
         alt tool_calls
-            CS->>TE: process_tool_calls_parallel()
+            AL->>TE: process_tool_calls_parallel()
-            TE-->>CS: tool_results
+            TE-->>AL: tool_results
-            CS->>CS: 追加到 messages
+            AL->>AL: 追加到 messages
         end
     end
     
+    AL->>CS: done event
     CS->>CS: _save_message()
     CS->>API: SSE Stream
     API-->>Client: 流式响应

luxx/services/__init__.py

@@ -1,3 +1,4 @@
 """Services module"""
 from luxx.services.llm_client import LLMClient, llm_client, LLMResponse
 from luxx.services.chat import ChatService, chat_service
+from luxx.services.llm_response import LLMResponseParser, llm_parser, ParsedDelta

luxx/services/agentic_loop.py

@@ -0,0 +1,275 @@
+"""AgenticLoop - Executes the Agentic Loop: LLM + Tools iteration.
+
+The loop:
+1. Call LLM with messages and tools
+2. Check for tool calls in response
+3. Execute tools in parallel
+4. Add results to messages
+5. Repeat (max 10 iterations)
+6. Return final response
+"""
+import json
+import uuid
+import logging
+import traceback
+from typing import List, Dict, Any, AsyncGenerator
+
+from luxx.tools.executor import ToolExecutor
+from luxx.services.llm_client import LLMClient
+from luxx.services.stream_context import StreamContext, _sse_event
+from luxx.services.process_result import ProcessResult
+from luxx.services.llm_response import llm_parser
+
+logger = logging.getLogger(__name__)
+
+# Maximum iterations to prevent infinite loops
+MAX_ITERATIONS = 10
+
+
+def _parse_sse_line(line: str) -> tuple:
+    """Parse SSE line into (event_type, data_str)."""
+    event_type = None
+    data_str = None
+    for part in line.strip().split('\n'):
+        if part.startswith('event: '):
+            event_type = part[7:].strip()
+        elif part.startswith('data: '):
+            data_str = part[6:].strip()
+    return event_type, data_str
+
+
+class AgenticLoop:
+    """Executes the Agentic Loop: LLM + Tools iteration."""
+    
+    def __init__(self, tool_executor: ToolExecutor):
+        self.tool_executor = tool_executor
+    
+    async def execute(
+        self,
+        llm: LLMClient,
+        model: str,
+        messages: List[Dict],
+        tools: list,
+        temperature: float,
+        max_tokens: int,
+        thinking_enabled: bool,
+        context: 'StreamContext',
+        tool_context: dict = None
+    ) -> AsyncGenerator[str, None]:
+        """Execute the agentic loop.
+        
+        Yields SSE events for each step.
+        """
+        total_usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
+        
+        for iteration in range(MAX_ITERATIONS):
+            context.reset()
+            has_error = False
+            
+            # Stream LLM response
+            async for sse_line in llm.stream_call(
+                model=model,
+                messages=messages,
+                tools=tools,
+                temperature=temperature,
+                max_tokens=max_tokens,
+                thinking_enabled=thinking_enabled
+            ):
+                # Process stream line
+                result = self._process_stream_line(sse_line, context, total_usage)
+                
+                # Yield events
+                for event in result.events:
+                    yield event
+                
+                # Check for errors
+                if result.has_error:
+                    has_error = True
+                    break
+            
+            # If error occurred, break the loop
+            if has_error:
+                break
+            
+            # Finalize current step
+            context.finalize_step()
+            
+            # Check for tool calls
+            if context.tool_calls_list:
+                # Execute tools and yield events
+                for event in self._execute_tools(context, messages, tool_context):
+                    yield event
+                continue
+            
+            # No tools - complete
+            for event in self._complete(context, total_usage):
+                yield event
+            return
+        
+        # Max iterations exceeded or error occurred
+        if not has_error:
+            yield _sse_event("error", {"content": "Exceeded maximum tool call iterations"})
+    
+    def _process_stream_line(self, sse_line: str, ctx: 'StreamContext', 
+                             total_usage: dict) -> ProcessResult:
+        """Process single SSE line from LLM, return result with events and flags."""
+        result = ProcessResult()
+        event_type, data_str = _parse_sse_line(sse_line)
+        if not data_str:
+            return result
+        
+        # Handle upstream errors
+        if event_type == 'error':
+            try:
+                error_data = json.loads(data_str)
+                error_content = error_data.get("content", "Unknown error")
+            except json.JSONDecodeError:
+                error_content = data_str
+            result.set_error(error_content)
+            result.add_event(_sse_event("error", {"content": error_content}))
+            return result
+        
+        try:
+            chunk = json.loads(data_str)
+        except json.JSONDecodeError:
+            error_msg = f"Parse error: {data_str[:50]}"
+            result.set_error(error_msg)
+            result.add_event(_sse_event("error", {"content": error_msg}))
+            return result
+        
+        # Extract usage
+        if "usage" in chunk and chunk["usage"]:
+            usage = chunk["usage"]
+            total_usage.update({
+                "prompt_tokens": usage.get("prompt_tokens", 0),
+                "completion_tokens": usage.get("completion_tokens", 0),
+                "total_tokens": usage.get("total_tokens", 0)
+            })
+        
+        # Handle API errors
+        if "error" in chunk:
+            error_msg = chunk["error"].get("message", str(chunk["error"]))
+            result.set_error(error_msg)
+            result.add_event(_sse_event("error", {"content": f"API Error: {error_msg}"}))
+            return result
+        
+        # Get delta
+        choices = chunk.get("choices", [])
+        if not choices:
+            # Non-standard format: check for content directly
+            content = chunk.get("content") or ""
+            if content:
+                # Check for thinking tags in content
+                thinking_part, clean_text = llm_parser._extract_thinking_tags(content)
+                
+                if thinking_part:
+                    ctx.full_thinking = (ctx.full_thinking or "") + thinking_part
+                    if not ctx.current_step_id or ctx.current_step_type != "thinking":
+                        ctx.start_step("thinking")
+                    result.add_event(_sse_event("process_step", {
+                        "step": {"id": ctx.current_step_id, "index": ctx.current_step_idx, "type": "thinking", "content": ctx.full_thinking}
+                    }))
+                    result.set_content()
+                
+                if clean_text:
+                    ctx.full_content = (ctx.full_content or "") + clean_text
+                    if not ctx.current_step_id or ctx.current_step_type != "text":
+                        ctx.start_step("text")
+                    result.add_event(_sse_event("process_step", {
+                        "step": {"id": ctx.current_step_id, "index": ctx.current_step_idx, "type": "text", "content": ctx.full_content}
+                    }))
+                    result.set_content()
+            return result
+        
+        delta = choices[0].get("delta", {})
+        
+        # Parse delta using unified parser
+        parsed = llm_parser.parse_openai(delta)
+        
+        # Process thinking content
+        if parsed.thinking:
+            ctx.full_thinking = parsed.thinking
+            if not ctx.current_step_id or ctx.current_step_type != "thinking":
+                ctx.start_step("thinking")
+            result.add_event(_sse_event("process_step", {
+                "step": {
+                    "id": ctx.current_step_id,
+                    "index": ctx.current_step_idx,
+                    "type": "thinking",
+                    "content": ctx.full_thinking
+                }
+            }))
+            result.set_content()
+        
+        # Process text content
+        if parsed.text:
+            ctx.full_content = parsed.text
+            if not ctx.current_step_id or ctx.current_step_type != "text":
+                ctx.start_step("text")
+            result.add_event(_sse_event("process_step", {
+                "step": {
+                    "id": ctx.current_step_id,
+                    "index": ctx.current_step_idx,
+                    "type": "text",
+                    "content": ctx.full_content
+                }
+            }))
+            result.set_content()
+        
+        # Accumulate tool calls
+        for tc in parsed.tool_calls or delta.get("tool_calls", []):
+            ctx.accumulate_tool_call(tc)
+            result.set_tool_calls()
+        
+        return result
+    
+    def _execute_tools(self, ctx: 'StreamContext', messages: list, 
+                       tool_context: dict = None) -> List[str]:
+        """Execute tools and return list of events."""
+        events = []
+        
+        # Emit tool call steps
+        for event in ctx.emit_tool_calls():
+            events.append(event)
+        
+        # Execute in parallel
+        tool_results = self.tool_executor.process_tool_calls_parallel(
+            ctx.tool_calls_list, tool_context or {}
+        )
+        
+        # Get tool call IDs for result linking
+        tool_ids = [tc.get("id") for tc in ctx.tool_calls_list]
+        tool_step_ids = [
+            s["id"] for s in ctx.all_steps 
+            if s["type"] == "tool_call" and s.get("id_ref") in tool_ids
+        ]
+        
+        # Emit tool result steps
+        for i, (tr, tc) in enumerate(zip(tool_results, ctx.tool_calls_list)):
+            ref_id = tool_step_ids[i] if i < len(tool_step_ids) else f"step-{len(ctx.all_steps) - len(tool_results) + i}"
+            _, event = ctx.emit_tool_result(tr, ref_id)
+            events.append(event)
+        
+        # Prepare for next iteration
+        messages.append({
+            "role": "assistant",
+            "content": ctx.full_content or "",
+            "tool_calls": ctx.tool_calls_list
+        })
+        messages.extend(ctx.all_tool_results[-len(tool_results):])
+        
+        return events
+    
+    def _complete(self, ctx: 'StreamContext', total_usage: dict) -> List[str]:
+        """Complete the loop and return list of events."""
+        token_count = total_usage.get("completion_tokens") or len(ctx.full_content) // 4
+        msg_id = str(uuid.uuid4())
+        logger.info(f"[TOKEN] usage={total_usage}, count={token_count}")
+        
+        ctx.set_completion(msg_id, token_count, total_usage)
+        
+        return [_sse_event("done", {
+            "message_id": msg_id,
+            "token_count": token_count,
+            "usage": total_usage
+        })]

luxx/services/chat.py

@@ -1,34 +1,99 @@
-"""Chat service module"""
+"""Chat service module with Agentic Loop pattern.
-import json
+
-import uuid
+This module provides the core chat service that orchestrates:
-import logging
+- StreamContext: Manages streaming state transitions
-from typing import List, Dict, Any, AsyncGenerator, Optional
+- MessageBuilder: Constructs message lists
+- AgenticLoop: Executes the agentic loop (LLM + tools iteration)
+- ChatService: Core chat service facade
+"""
+import json
+import logging
+import traceback
+import httpx
+from typing import List, Dict, Any, AsyncGenerator
 
-from luxx.models import Conversation, Message
 from luxx.tools.executor import ToolExecutor
 from luxx.tools.core import registry
 from luxx.services.llm_client import LLMClient
+from luxx.services.stream_context import StreamContext
+from luxx.services.agentic_loop import AgenticLoop
 from luxx.config import config
 
 logger = logging.getLogger(__name__)
-# Maximum iterations to prevent infinite loops
-MAX_ITERATIONS = 10
 
 
-def _sse_event(event: str, data: dict) -> str:
+# ============== MessageBuilder ==============
-    """Format a Server-Sent Event string."""
+
-    return f"event: {event}\ndata: {json.dumps(data, ensure_ascii=False)}\n\n"
+class MessageBuilder:
+    """Builds message lists for LLM requests."""
+    
+    def __init__(self):
+        self.messages = []
+    
+    def add_system(self, content: str) -> 'MessageBuilder':
+        """Add system message."""
+        self.messages.append({"role": "system", "content": content})
+        return self
+    
+    def add_user(self, content: str, attachments: list = None) -> 'MessageBuilder':
+        """Add user message in JSON format."""
+        msg_content = json.dumps({
+            "text": content,
+            "attachments": attachments or []
+        }, ensure_ascii=False)
+        self.messages.append({"role": "user", "content": msg_content})
+        return self
+    
+    def add_assistant(self, content: str, tool_calls: list = None) -> 'MessageBuilder':
+        """Add assistant message."""
+        msg = {"role": "assistant", "content": content}
+        if tool_calls:
+            msg["tool_calls"] = tool_calls
+        self.messages.append(msg)
+        return self
+    
+    def add_tool_result(self, tool_call_id: str, content: str) -> 'MessageBuilder':
+        """Add tool result message."""
+        self.messages.append({
+            "role": "tool",
+            "tool_call_id": tool_call_id,
+            "content": content
+        })
+        return self
+    
+    def build(self) -> List[Dict]:
+        """Build and return message list."""
+        return self.messages.copy()
+    
+    @staticmethod
+    def extract_text(content: str) -> str:
+        """Extract text from message content (supports JSON format)."""
+        if not content:
+            return ""
+        try:
+            parsed = json.loads(content)
+            if isinstance(parsed, dict):
+                return parsed.get("text", content)
+        except (json.JSONDecodeError, TypeError):
+            pass
+        return content
 
 
-def get_llm_client(conversation: Conversation = None):
+# ============== Factory Function ==============
-    """Get LLM client, optionally using conversation's provider. Returns (client, max_tokens)"""
+
+def get_llm_client(conversation=None) -> tuple:
+    """Get LLM client based on conversation provider. Returns (client, max_tokens)"""
+    from luxx.models import LLMProvider
+    from luxx.database import SessionLocal
+    
     max_tokens = None
+    
     if conversation and conversation.provider_id:
-        from luxx.models import LLMProvider
-        from luxx.database import SessionLocal
         db = SessionLocal()
         try:
-            provider = db.query(LLMProvider).filter(LLMProvider.id == conversation.provider_id).first()
+            provider = db.query(LLMProvider).filter(
+                LLMProvider.id == conversation.provider_id
+            ).first()
             if provider:
                 max_tokens = provider.max_tokens
                 client = LLMClient(
@@ -40,184 +105,27 @@ def get_llm_client(conversation: Conversation = None):
         finally:
             db.close()
     
-    # Fallback to global config
+    return LLMClient(), max_tokens
-    client = LLMClient()
-    return client, max_tokens
 
 
-class StreamContext:
+# ============== ChatService ==============
-    """Context for streaming response state management."""
-    
-    def __init__(
-        self,
-        step_index: int = 0,
-        current_step_id: str = None,
-        current_step_idx: int = None,
-        current_stream_type: str = None,
-        full_content: str = "",
-        full_thinking: str = ""
-    ):
-        self.step_index = step_index
-        self.current_step_id = current_step_id
-        self.current_step_idx = current_step_idx
-        self.current_stream_type = current_stream_type
-        self.full_content = full_content
-        self.full_thinking = full_thinking
-        self.all_steps = []
-        self.all_tool_calls = []
-        self.all_tool_results = []
-        self.tool_calls_list = []
-    
-    def reset_iteration(self):
-        """Reset streaming step tracker for new iteration."""
-        self.current_step_id = None
-        self.current_step_idx = None
-        self.current_stream_type = None
-        self.full_content = ""
-        self.full_thinking = ""
-        self.tool_calls_list = []
-    
-    def start_stream_step(self, step_type: str) -> str:
-        """Start a new streaming step. Returns the step_id."""
-        self.current_step_idx = self.step_index
-        self.current_step_id = f"step-{self.step_index}"
-        self.current_stream_type = step_type
-        self.step_index += 1
-        return self.current_step_id
-    
-    def yield_stream_step(self, step_type: str, content: str) -> Dict[str, Any]:
-        """Yield a streaming step event."""
-        return _sse_event("process_step", {
-            "step": {
-                "id": self.current_step_id,
-                "index": self.current_step_idx,
-                "type": step_type,
-                "content": content
-            }
-        })
-    
-    def save_streaming_step(self):
-        """Save the current streaming step to all_steps."""
-        if self.current_step_id is None:
-            return
-        
-        if self.current_stream_type == "thinking":
-            self.all_steps.append({
-                "id": self.current_step_id,
-                "index": self.current_step_idx,
-                "type": "thinking",
-                "content": self.full_thinking
-            })
-        elif self.current_stream_type == "text":
-            self.all_steps.append({
-                "id": self.current_step_id,
-                "index": self.current_step_idx,
-                "type": "text",
-                "content": self.full_content
-            })
-    
-    def handle_thinking_stream(self, delta: Dict) -> Optional[Dict]:
-        """Handle reasoning/thinking delta. Returns yield_obj if step was yielded."""
-        reasoning = delta.get("reasoning_content", "")
-        if not reasoning:
-            return None
-        
-        prev_len = len(self.full_thinking)
-        self.full_thinking += reasoning
-        
-        if prev_len == 0:  # New thinking stream started
-            self.start_stream_step("thinking")
-        
-        return self.yield_stream_step("thinking", self.full_thinking)
-    
-    def handle_text_stream(self, delta: Dict) -> Optional[Dict]:
-        """Handle content delta. Returns yield_obj if step was yielded."""
-        content = delta.get("content", "")
-        if not content:
-            return None
-        
-        prev_len = len(self.full_content)
-        self.full_content += content
-        
-        if prev_len == 0:  # New text stream started
-            self.start_stream_step("text")
-        
-        return self.yield_stream_step("text", self.full_content)
-    
-    def handle_tool_call(self) -> tuple:
-        """Handle tool calls. Returns (tool_call_step_ids, tool_call_steps, yield_objs)."""
-        tool_call_step_ids = []
-        tool_call_steps = []
-        yield_objs = []
-        
-        for tc in self.tool_calls_list:
-            call_step_idx = self.step_index
-            call_step_id = f"step-{self.step_index}"
-            tool_call_step_ids.append(call_step_id)
-            self.step_index += 1
-            
-            call_step = {
-                "id": call_step_id,
-                "index": call_step_idx,
-                "type": "tool_call",
-                "id_ref": tc.get("id", ""),
-                "name": tc["function"]["name"],
-                "arguments": tc["function"]["arguments"]
-            }
-            tool_call_steps.append(call_step)
-            yield_objs.append(_sse_event("process_step", {"step": call_step}))
-        
-        return tool_call_step_ids, tool_call_steps, yield_objs
-    
-    def handle_tool_result(self, tool_result: Dict, tool_call_step_id: str) -> tuple:
-        """Handle single tool result. Returns (result_step, yield_obj)."""
-        result_step_idx = self.step_index
-        result_step_id = f"step-{self.step_index}"
-        self.step_index += 1
-        
-        content = tool_result.get("content", "")
-        success = True
-        try:
-            content_obj = json.loads(content)
-            if isinstance(content_obj, dict):
-                success = content_obj.get("success", True)
-        except:
-            pass
-        
-        result_step = {
-            "id": result_step_id,
-            "index": result_step_idx,
-            "type": "tool_result",
-            "id_ref": tool_call_step_id,
-            "name": tool_result.get("name", ""),
-            "content": content,
-            "success": success
-        }
-        return result_step, _sse_event("process_step", {"step": result_step})
-
 
 class ChatService:
-    """Chat service with tool support"""
+    """Core chat service with Agentic Loop support."""
     
     def __init__(self):
         self.tool_executor = ToolExecutor()
+        self.agentic_loop = AgenticLoop(self.tool_executor)
     
-    def build_messages(
+    def build_messages(self, conversation, include_system: bool = True) -> List[Dict]:
-        self,
+        """Build message list from conversation history."""
-        conversation: Conversation,
-        include_system: bool = True
-    ) -> List[Dict[str, str]]:
-        """Build message list"""
         from luxx.database import SessionLocal
         from luxx.models import Message
         
         messages = []
         
         if include_system and conversation.system_prompt:
-            messages.append({
+            messages.append({"role": "system", "content": conversation.system_prompt})
-                "role": "system",
-                "content": conversation.system_prompt
-            })
         
         db = SessionLocal()
         try:
@@ -226,28 +134,23 @@ class ChatService:
             ).order_by(Message.created_at).all()
             
             for msg in db_messages:
-                # Parse JSON content if possible
+                content = MessageBuilder.extract_text(msg.content)
-                try:
+                messages.append({"role": msg.role, "content": content})
-                    content_obj = json.loads(msg.content) if msg.content else {}
-                    if isinstance(content_obj, dict):
-                        content = content_obj.get("text", msg.content)
-                    else:
-                        content = msg.content
-                except (json.JSONDecodeError, TypeError):
-                    content = msg.content
-                
-                messages.append({
-                    "role": msg.role,
-                    "content": content
-                })
         finally:
             db.close()
         
         return messages
     
+    def _get_tools(self, enabled_tools: list) -> list:
+        """Filter tools based on enabled_tools list."""
+        if not enabled_tools:
+            return []
+        return [t for t in registry.list_all() 
+                if t.get("function", {}).get("name") in enabled_tools]
+    
     async def stream_response(
         self,
-        conversation: Conversation,
+        conversation,
         user_message: str,
         thinking_enabled: bool = False,
         enabled_tools: list = None,
@@ -255,256 +158,118 @@ class ChatService:
         username: str = None,
         workspace: str = None,
         user_permission_level: int = 1
-    ) -> AsyncGenerator[Dict[str, str], None]:
+    ) -> AsyncGenerator[str, None]:
-        """
+        """Streaming response with Agentic Loop."""
-        Streaming response generator
-        
-        Yields raw SSE event strings for direct forwarding.
-        """
         try:
+            # Build initial messages
             messages = self.build_messages(conversation)
-            
             messages.append({
                 "role": "user",
                 "content": json.dumps({"text": user_message, "attachments": []})
             })
             
-            # Get tools based on enabled_tools filter
+            # Get tools and LLM client
-            if enabled_tools:
+            tools = self._get_tools(enabled_tools)
-                tools = [t for t in registry.list_all() if t.get("function", {}).get("name") in enabled_tools]
-            else:
-                tools = []
-            
             llm, provider_max_tokens = get_llm_client(conversation)
             model = conversation.model or llm.default_model or "gpt-4"
-            # 直接使用 provider 的 max_tokens
+            max_tokens = provider_max_tokens or 8192
-            max_tokens = provider_max_tokens
             
-            # Token usage tracking
+            # Tool execution context
-            total_usage = {
+            tool_context = {
-                "prompt_tokens": 0,
+                "workspace": workspace,
-                "completion_tokens": 0,
+                "user_id": user_id,
-                "total_tokens": 0
+                "username": username,
+                "user_permission_level": user_permission_level
             }
-            actual_token_count = 0
             
-            # Streaming context for state management
+            # Stream context
             ctx = StreamContext()
             
-            for iteration in range(MAX_ITERATIONS):
+            # Execute agentic loop
-                # Reset streaming context for this iteration
+            async for event in self.agentic_loop.execute(
-                ctx.reset_iteration()
+                llm=llm,
-                
+                model=model,
-                async for sse_line in llm.stream_call(
+                messages=messages,
-                    model=model,
+                tools=tools,
-                    messages=messages,
+                temperature=conversation.temperature,
-                    tools=tools,
+                max_tokens=max_tokens,
-                    temperature=conversation.temperature,
+                thinking_enabled=thinking_enabled or conversation.thinking_enabled,
-                    max_tokens=max_tokens or 8192,
+                context=ctx,
-                    thinking_enabled=thinking_enabled or conversation.thinking_enabled
+                tool_context=tool_context
-                ):
+            ):
-                    # Parse SSE line
+                yield event
-                    # Format: "event: xxx\ndata: {...}\n\n"
-                    event_type = None
-                    data_str = None
-                    
-                    for line in sse_line.strip().split('\n'):
-                        if line.startswith('event: '):
-                            event_type = line[7:].strip()
-                        elif line.startswith('data: '):
-                            data_str = line[6:].strip()
-                    
-                    if data_str is None:
-                        continue
-                    
-                    # Handle error events from LLM
-                    if event_type == 'error':
-                        try:
-                            error_data = json.loads(data_str)
-                            yield _sse_event("error", {"content": error_data.get("content", "Unknown error")})
-                        except json.JSONDecodeError:
-                            yield _sse_event("error", {"content": data_str})
-                        return
-                    
-                    # Parse the data
-                    try:
-                        chunk = json.loads(data_str)
-                    except json.JSONDecodeError:
-                        yield _sse_event("error", {"content": f"Failed to parse response: {data_str}"})
-                        return
-                    
-                    # 提取 API 返回的 usage 信息
-                    if "usage" in chunk:
-                        usage = chunk["usage"]
-                        total_usage["prompt_tokens"] = usage.get("prompt_tokens", 0)
-                        total_usage["completion_tokens"] = usage.get("completion_tokens", 0)
-                        total_usage["total_tokens"] = usage.get("total_tokens", 0)
-                    
-                    # Check for error in response
-                    if "error" in chunk:
-                        error_msg = chunk["error"].get("message", str(chunk["error"]))
-                        yield _sse_event("error", {"content": f"API Error: {error_msg}"})
-                        return
-                    
-                    # Get delta
-                    choices = chunk.get("choices", [])
-                    if not choices:
-                        # Check if there's any content in the response (for non-standard LLM responses)
-                        if chunk.get("content") or chunk.get("message"):
-                            content = chunk.get("content") or chunk.get("message", {}).get("content", "")
-                            if content:
-                                prev_len = len(ctx.full_content)
-                                ctx.full_content += content
-                                if prev_len == 0:  # New text stream started
-                                    ctx.start_stream_step("text")
-                                yield _sse_event("process_step", {
-                                    "step": {
-                                        "id": ctx.current_step_id if prev_len == 0 else f"step-{ctx.step_index - 1}",
-                                        "index": ctx.current_step_idx if prev_len == 0 else ctx.step_index - 1,
-                                        "type": "text",
-                                        "content": ctx.full_content
-                                    }
-                                })
-                        continue
-                    
-                    delta = choices[0].get("delta", {})
-                    
-                    # Handle reasoning (thinking)
-                    yield_obj = ctx.handle_thinking_stream(delta)
-                    if yield_obj:
-                        yield yield_obj
-                    
-                    # Handle content
-                    yield_obj = ctx.handle_text_stream(delta)
-                    if yield_obj:
-                        yield yield_obj
-                    
-                    # Accumulate tool calls
-                    tool_calls_delta = delta.get("tool_calls", [])
-                    for tc in tool_calls_delta:
-                        idx = tc.get("index", 0)
-                        if idx >= len(ctx.tool_calls_list):
-                            ctx.tool_calls_list.append({
-                                "id": tc.get("id", ""),
-                                "type": "function",
-                                "function": {"name": "", "arguments": ""}
-                            })
-                        func = tc.get("function", {})
-                        if func.get("name"):
-                            ctx.tool_calls_list[idx]["function"]["name"] += func["name"]
-                        if func.get("arguments"):
-                            ctx.tool_calls_list[idx]["function"]["arguments"] += func["arguments"]
-                
-                # Save streaming step (thinking or text)
-                ctx.save_streaming_step()
-                
-                # Handle tool calls
-                if ctx.tool_calls_list:
-                    ctx.all_tool_calls.extend(ctx.tool_calls_list)
-                    
-                    # Handle tool_call steps
-                    tool_call_step_ids, tool_call_steps, yield_objs = ctx.handle_tool_call()
-                    ctx.all_steps.extend(tool_call_steps)
-                    for yield_obj in yield_objs:
-                        yield yield_obj
-                    
-                    # Execute tools
-                    tool_context = {
-                        "workspace": workspace,
-                        "user_id": user_id,
-                        "username": username,
-                        "user_permission_level": user_permission_level
-                    }
-                    tool_results = self.tool_executor.process_tool_calls_parallel(
-                        ctx.tool_calls_list, tool_context
-                    )
-                    
-                    # Handle tool_result steps
-                    for i, tr in enumerate(tool_results):
-                        tool_call_step_id = tool_call_step_ids[i] if i < len(tool_call_step_ids) else f"step-{i}"
-                        result_step, yield_obj = ctx.handle_tool_result(tr, tool_call_step_id)
-                        ctx.all_steps.append(result_step)
-                        yield yield_obj
-                        
-                        ctx.all_tool_results.append({
-                            "role": "tool",
-                            "tool_call_id": tr.get("tool_call_id", ""),
-                            "content": tr.get("content", "")
-                        })
-                    
-                    # Add assistant message with tool calls for next iteration
-                    messages.append({
-                        "role": "assistant",
-                        "content": ctx.full_content or "",
-                        "tool_calls": ctx.tool_calls_list
-                    })
-                    messages.extend(ctx.all_tool_results[-len(tool_results):])
-                    ctx.all_tool_results = []
-                    continue
-                
-                # No tool calls - final iteration, save message
-                msg_id = str(uuid.uuid4())
-                
-                # 使用 API 返回的真实 completion_tokens，如果 API 没返回则降级使用估算值
-                actual_token_count = total_usage.get("completion_tokens", 0) or len(ctx.full_content) // 4
-                logger.info(f"[TOKEN] total_usage: {total_usage}, actual_token_count: {actual_token_count}")
             
+            # Save message after successful completion (only if we have content)
+            if ctx._last_message_id and (ctx.full_content or ctx.all_tool_calls):
                 self._save_message(
                     conversation.id,
-                    msg_id,
+                    ctx._last_message_id,
                     ctx.full_content,
                     ctx.all_tool_calls,
                     ctx.all_tool_results,
                     ctx.all_steps,
-                    actual_token_count,
+                    ctx._last_token_count,
-                    total_usage
+                    ctx._last_usage
                 )
             
-                yield _sse_event("done", {
-                    "message_id": msg_id,
-                    "token_count": actual_token_count,
-                    "usage": total_usage
-                })
-                return
-            
-            # Max iterations exceeded - save message before error
-            if ctx.full_content or ctx.all_tool_calls:
-                msg_id = str(uuid.uuid4())
-                self._save_message(
-                    conversation.id,
-                    msg_id,
-                    ctx.full_content,
-                    ctx.all_tool_calls,
-                    ctx.all_tool_results,
-                    ctx.all_steps,
-                    actual_token_count,
-                    total_usage
-                )
-            yield _sse_event("error", {"content": "Exceeded maximum tool call iterations"})
-            
         except Exception as e:
-            logger.error(f"Stream error: {e}")
+            logger.error(f"Stream error: {e}\n{traceback.format_exc()}")
             yield _sse_event("error", {"content": str(e)})
     
-    def _save_message(
+    async def non_stream_response(
         self,
-        conversation_id: str,
+        conversation,
-        msg_id: str,
+        user_message: str,
-        full_content: str,
+        tools_enabled: bool = True,
-        all_tool_calls: list,
+        thinking_enabled: bool = False
-        all_tool_results: list,
+    ) -> Dict[str, Any]:
-        all_steps: list,
+        """Non-streaming response for simple requests."""
-        token_count: int = 0,
+        try:
-        usage: dict = None
+            messages = self.build_messages(conversation)
-    ):
+            messages.append({
-        """Save the assistant message to database."""
+                "role": "user",
+                "content": json.dumps({"text": user_message, "attachments": []})
+            })
+            
+            tools = [] if not tools_enabled else None
+            llm, max_tokens = get_llm_client(conversation)
+            model = conversation.model or llm.default_model or "gpt-4"
+            
+            response = await llm.sync_call(
+                model=model,
+                messages=messages,
+                tools=tools,
+                temperature=conversation.temperature,
+                max_tokens=max_tokens or 8192,
+                thinking_enabled=thinking_enabled or conversation.thinking_enabled
+            )
+            
+            return {
+                "success": True,
+                "content": response.content,
+                "tool_calls": response.tool_calls,
+                "usage": response.usage
+            }
+            
+        except httpx.HTTPStatusError as e:
+            error_msg = f"HTTP {e.response.status_code}: {e.response.text[:200] if e.response else 'No response body'}"
+            logger.error(f"Non-stream HTTP error: {error_msg}")
+            return {"success": False, "error": error_msg}
+        except httpx.TimeoutException as e:
+            logger.error(f"Non-stream timeout: {e}")
+            return {"success": False, "error": "Request timeout"}
+        except Exception as e:
+            logger.error(f"Non-stream error: {type(e).__name__}: {e}\n{traceback.format_exc()}")
+            return {"success": False, "error": f"{type(e).__name__}: {str(e)}"}
+    
+    def _save_message(self, conversation_id: str, msg_id: str, full_content: str,
+                       all_tool_calls: list, all_tool_results: list, all_steps: list,
+                       token_count: int = 0, usage: dict = None):
+        """Save assistant message to database."""
         from luxx.database import SessionLocal
         from luxx.models import Message
         
-        content_json = {
+        content_json = {"text": full_content, "steps": all_steps}
-            "text": full_content,
-            "steps": all_steps
-        }
         if all_tool_calls:
             content_json["tool_calls"] = all_tool_calls
         
@@ -520,12 +285,18 @@ class ChatService:
             )
             db.add(msg)
             db.commit()
-        except Exception as e:
+        except Exception:
             db.rollback()
             raise
         finally:
             db.close()
 
 
-# Global chat service
+def _sse_event(event: str, data: dict) -> str:
+    """Format a Server-Sent Event string."""
+    return f"event: {event}\ndata: {json.dumps(data, ensure_ascii=False)}\n\n"
+
+
+# ============== Global Singleton ==============
+
 chat_service = ChatService()

luxx/services/llm_client.py

@@ -2,6 +2,7 @@
 import json
 import httpx
 import logging
+import traceback
 from typing import Dict, Any, Optional, List, AsyncGenerator
 
 from luxx.config import config
@@ -172,7 +173,7 @@ class LLMClient:
             logger.error(f"HTTP error: {status_code}")
             yield f"event: error\ndata: {json.dumps({'content': f'HTTP {status_code}: Request failed'})}\n\n"
         except Exception as e:
-            logger.error(f"Exception: {type(e).__name__}: {str(e)}")
+            logger.error(f"Exception: {type(e).__name__}: {str(e)}\n{traceback.format_exc()}")
             yield f"event: error\ndata: {json.dumps({'content': str(e)})}\n\n"
 
 

luxx/services/llm_response.py

@@ -0,0 +1,309 @@
+"""LLM Response Parser - Unified parser for multiple LLM API formats.
+
+Supported Providers:
+- OpenAI: delta.content, delta.tool_calls
+- DeepSeek: delta.content, delta.reasoning_content, delta.tool_calls
+- Anthropic: content_block with thinking/text types
+- MiniMax: <|im_start|>thinking...<|im_end|> tags in content
+
+Data Flow:
+```
+LLM API Response (SSE)
+    │
+    ▼
+LLMResponseParser.parse_chunk()
+    │
+    ├──► ParsedDelta { thinking, text, tool_calls }
+    │
+    ▼
+AgenticLoop._process_stream_line()
+    │
+    ▼
+SSE Events (process_step)
+    │
+    ├──► type: "thinking"
+    ├──► type: "text"  
+    └──► type: "tool_call"
+```
+
+API Response Formats:
+
+1. OpenAI Standard (DeepSeek, OpenAI):
+```json
+{
+  "choices": [{
+    "delta": {
+      "content": "Hello",
+      "reasoning_content": "Let me think...",
+      "tool_calls": [{"id": "call_1", "function": {...}}]
+    }
+  }]
+}
+```
+
+2. Anthropic Streaming:
+```json
+{"type": "content_block_start", "content_block": {"type": "thinking", "thinking": "..."}}
+{"type": "content_block_delta", "delta": {"type": "thinking_delta", "thinking": "..."}}
+{"type": "content_block_delta", "delta": {"type": "text_delta", "text": "..."}}
+{"type": "content_block_stop"}
+```
+
+3. MiniMax (with thinking tags in content):
+```json
+{
+  "choices": [{
+    "delta": {
+      "content": "<|im_start|>thinking分析中...<|im_end|>回复内容"
+    }
+  }]
+}
+```
+
+4. Standard thinking tags:
+```json
+{
+  "choices": [{
+    "delta": {
+      "content": "<think>思考内容</think>回复内容"
+    }
+  }]
+}
+```
+"""
+from typing import Dict, Any, Optional, List
+from dataclasses import dataclass
+
+
+@dataclass
+class ParsedDelta:
+    """Parsed response delta from LLM.
+    
+    Attributes:
+        thinking: Thinking/reasoning content
+        text: Regular text content
+        tool_calls: Tool call requests
+        is_complete: Whether this delta completes a content block
+    """
+    thinking: str = ""
+    text: str = ""
+    tool_calls: List[Dict] = None
+    is_complete: bool = False
+    
+    def __post_init__(self):
+        if self.tool_calls is None:
+            self.tool_calls = []
+
+
+class LLMResponseParser:
+    """Unified parser for LLM API response formats.
+    
+    Usage:
+        from luxx.services.llm_response import llm_parser
+        
+        # Parse OpenAI format
+        delta = {"content": "Hello", "reasoning_content": "Thinking..."}
+        parsed = llm_parser.parse_openai(delta)
+        
+        # Parse Anthropic format
+        chunk = {"type": "content_block_delta", "delta": {"type": "thinking_delta", "thinking": "..."}}
+        parsed = llm_parser.parse_anthropic(chunk)
+        
+        # Auto-detect format
+        parsed = llm_parser.parse_chunk(chunk, provider="anthropic")
+    """
+    
+    # Content block types
+    BLOCK_THINKING = "thinking"
+    BLOCK_TEXT = "text"
+    BLOCK_TOOL_USE = "tool_use"
+    BLOCK_TOOL_RESULT = "tool_result"
+    
+    def __init__(self):
+        self._buffer = ""
+        self._thinking_buffer = ""
+        self._text_buffer = ""
+    
+    def reset(self):
+        """Reset parser state for new message."""
+        self._buffer = ""
+        self._thinking_buffer = ""
+        self._text_buffer = ""
+    
+    def parse_openai(self, delta: Dict) -> ParsedDelta:
+        """Parse OpenAI format delta.
+        
+        Handles:
+        - OpenAI: delta.content, delta.tool_calls
+        - DeepSeek: delta.content, delta.reasoning_content, delta.tool_calls
+        - MiniMax: <|im_start|>thinking...<|im_end|> in content
+        - Standard: <think>...</think> in content
+        
+        Args:
+            delta: Delta object from LLM API response
+            
+        Returns:
+            ParsedDelta with extracted thinking, text, and tool_calls
+        """
+        result = ParsedDelta()
+        
+        # Get thinking content (DeepSeek uses reasoning_content)
+        thinking = delta.get("reasoning_content") or delta.get("reasoning") or ""
+        if thinking:
+            self._thinking_buffer += thinking
+            result.thinking = self._thinking_buffer
+        
+        # Get text content
+        text = delta.get("content") or ""
+        if text:
+            # Check for embedded thinking tags (MiniMax format)
+            thinking_part, clean_text = self._extract_thinking_tags(text)
+            if thinking_part:
+                self._thinking_buffer += thinking_part
+                result.thinking = self._thinking_buffer
+            if clean_text:
+                self._text_buffer += clean_text
+                result.text = self._text_buffer
+        elif thinking_part := delta.get("thinking"):
+            # Some providers use "thinking" field directly
+            self._thinking_buffer += thinking_part
+            result.thinking = self._thinking_buffer
+        
+        # Tool calls
+        result.tool_calls = delta.get("tool_calls") or []
+        
+        return result
+    
+    def parse_anthropic(self, chunk: Dict) -> ParsedDelta:
+        """Parse Anthropic streaming format.
+        
+        Anthropic uses a different event structure:
+        - content_block_start: Begin a content block
+        - content_block_delta: Incremental content
+        - content_block_stop: End of content blocks
+        
+        Content block types:
+        - thinking: Model reasoning
+        - text: Regular text
+        - tool_use: Tool invocation
+        - tool_result: Tool output
+        
+        Args:
+            chunk: Anthropic SSE event chunk
+            
+        Returns:
+            ParsedDelta with extracted content
+        """
+        result = ParsedDelta()
+        chunk_type = chunk.get("type", "")
+        
+        if chunk_type == "content_block_start":
+            block = chunk.get("content_block", {})
+            if block.get("type") == self.BLOCK_THINKING:
+                thinking = block.get("thinking", "")
+                if thinking:
+                    self._thinking_buffer = thinking
+                    result.thinking = self._thinking_buffer
+        
+        elif chunk_type == "content_block_delta":
+            delta = chunk.get("delta", {})
+            delta_type = delta.get("type", "")
+            
+            if delta_type == "thinking_delta":
+                thinking = delta.get("thinking", "")
+                self._thinking_buffer += thinking
+                result.thinking = self._thinking_buffer
+            
+            elif delta_type == "text_delta":
+                text = delta.get("text", "")
+                self._text_buffer += text
+                result.text = self._text_buffer
+            
+            elif delta_type == "partial_json":
+                # Partial JSON for tool calls
+                pass
+        
+        elif chunk_type == "content_block_stop":
+            result.is_complete = True
+        
+        return result
+    
+    def parse_chunk(self, chunk: Dict, provider: str = "openai") -> ParsedDelta:
+        """Parse chunk based on provider.
+        
+        Args:
+            chunk: Response chunk from LLM
+            provider: Provider name ("openai", "anthropic", "deepseek", "minimax")
+            
+        Returns:
+            ParsedDelta with extracted content
+        """
+        if provider == "anthropic":
+            return self.parse_anthropic(chunk)
+        
+        # Default to OpenAI format
+        return self.parse_openai(chunk)
+    
+    def _extract_thinking_tags(self, content: str) -> tuple:
+        """Extract thinking content from tags.
+        
+        Handles multiple tag formats:
+        - MiniMax: <|im_start|>thinking...<|im_end|>
+        - Standard: <think>...</think>
+        
+        Args:
+            content: Raw content string from LLM
+            
+        Returns:
+            Tuple of (thinking_content, clean_text)
+        """
+        thinking_parts = []
+        clean_parts = []
+        i = 0
+        
+        while i < len(content):
+            remaining = content[i:].lower()
+            
+            # Check for MiniMax format
+            if remaining.startswith("<|im_start|>thinking"):
+                end_tag = "<|im_end|>"
+                start = i + 21  # len("<|im_start|>thinking")
+                end = content.find(end_tag, start)
+                if end != -1:
+                    thinking_parts.append(content[start:end])
+                    i = end + len(end_tag)
+                    continue
+            
+            # Check for standard format
+            if remaining.startswith("<think>"):
+                end_tag = "</think>"
+                start = i + 7  # len("<think>")
+                end = content.find(end_tag, start)
+                if end != -1:
+                    thinking_parts.append(content[start:end])
+                    i = end + len(end_tag)
+                    continue
+            
+            # Regular character
+            clean_parts.append(content[i])
+            i += 1
+        
+        return "".join(thinking_parts), "".join(clean_parts)
+    
+    def has_thinking_tags(self, content: str) -> bool:
+        """Check if content contains thinking tags.
+        
+        Args:
+            content: Raw content string
+            
+        Returns:
+            True if content contains thinking tags
+        """
+        if not content:
+            return False
+        lower = content.lower()
+        return "<|im_start|>thinking" in lower or "<think>" in lower
+
+
+# Global parser instance
+llm_parser = LLMResponseParser()

luxx/services/process_result.py

@@ -0,0 +1,37 @@
+"""ProcessResult - Result of processing an SSE line."""
+
+
+class ProcessResult:
+    """Result of processing an SSE line.
+    
+    Attributes:
+        events: List of SSE event strings to yield
+        has_error: Whether an error occurred
+        error_content: Error message if any
+        has_content: Whether content was received
+        has_tool_calls: Whether tool calls were received
+    """
+    
+    def __init__(self):
+        self.events: list = []
+        self.has_error: bool = False
+        self.error_content: str = ""
+        self.has_content: bool = False
+        self.has_tool_calls: bool = False
+    
+    def add_event(self, event: str):
+        """Add an event to the result."""
+        self.events.append(event)
+    
+    def set_error(self, content: str):
+        """Set error state."""
+        self.has_error = True
+        self.error_content = content
+    
+    def set_content(self):
+        """Mark that content was received."""
+        self.has_content = True
+    
+    def set_tool_calls(self):
+        """Mark that tool calls were received."""
+        self.has_tool_calls = True

luxx/services/stream_context.py

@@ -0,0 +1,185 @@
+"""StreamContext - Manages streaming state transitions during LLM response.
+
+Tracks steps in order:
+- thinking: Model reasoning content
+- text: Model response text
+- tool_call: Tool invocation request
+- tool_result: Tool execution result
+
+Each step has unique id and index for frontend rendering.
+"""
+import json
+from typing import List, Dict, Optional
+
+
+def _sse_event(event: str, data: dict) -> str:
+    """Format a Server-Sent Event string."""
+    return f"event: {event}\ndata: {json.dumps(data, ensure_ascii=False)}\n\n"
+
+
+class StreamContext:
+    """Manages streaming state transitions during LLM response."""
+    
+    def __init__(self):
+        self.step_index = 0
+        self.current_step_id = None
+        self.current_step_idx = None
+        self.current_step_type = None
+        self.full_content = ""
+        self.full_thinking = ""
+        self.all_steps = []
+        self.all_tool_calls = []
+        self.all_tool_results = []
+        self.tool_calls_list = []
+        self._last_message_id = None
+        self._last_token_count = 0
+        self._last_usage = None
+    
+    def reset(self):
+        """Reset state for new iteration."""
+        self.current_step_id = None
+        self.current_step_idx = None
+        self.current_step_type = None
+        self.full_content = ""
+        self.full_thinking = ""
+        self.tool_calls_list = []
+    
+    def start_step(self, step_type: str) -> str:
+        """Start a new step with unique ID."""
+        self.current_step_idx = self.step_index
+        self.current_step_id = f"step-{self.step_index}"
+        self.current_step_type = step_type
+        self.step_index += 1
+        return self.current_step_id
+    
+    def finalize_step(self):
+        """Save current step to all_steps."""
+        if self.current_step_id is None:
+            return
+        
+        content = self.full_content if self.current_step_type == "text" else self.full_thinking
+        self.all_steps.append({
+            "id": self.current_step_id,
+            "index": self.current_step_idx,
+            "type": self.current_step_type,
+            "content": content
+        })
+    
+    def handle_thinking(self, delta: Dict) -> Optional[str]:
+        """Handle reasoning delta from LLM."""
+        reasoning = delta.get("reasoning_content", "")
+        if not reasoning:
+            return None
+        
+        if not self.full_thinking:
+            self.start_step("thinking")
+        
+        self.full_thinking += reasoning
+        return _sse_event("process_step", {
+            "step": {
+                "id": self.current_step_id,
+                "index": self.current_step_idx,
+                "type": "thinking",
+                "content": self.full_thinking
+            }
+        })
+    
+    def handle_text(self, delta: Dict) -> Optional[str]:
+        """Handle content delta from LLM."""
+        content = delta.get("content", "")
+        if not content:
+            return None
+        
+        if not self.full_content:
+            self.start_step("text")
+        
+        self.full_content += content
+        return _sse_event("process_step", {
+            "step": {
+                "id": self.current_step_id,
+                "index": self.current_step_idx,
+                "type": "text",
+                "content": self.full_content
+            }
+        })
+    
+    def accumulate_tool_call(self, tc_delta: Dict):
+        """Accumulate tool call delta."""
+        idx = tc_delta.get("index", 0)
+        if idx >= len(self.tool_calls_list):
+            self.tool_calls_list.append({
+                "id": tc_delta.get("id", ""),
+                "type": "function",
+                "function": {"name": "", "arguments": ""}
+            })
+        
+        func = tc_delta.get("function", {})
+        if func.get("name"):
+            self.tool_calls_list[idx]["function"]["name"] += func["name"]
+        if func.get("arguments"):
+            self.tool_calls_list[idx]["function"]["arguments"] += func["arguments"]
+    
+    def emit_tool_calls(self) -> List[str]:
+        """Emit tool call steps, return SSE events."""
+        events = []
+        for tc in self.tool_calls_list:
+            step_id = f"step-{self.step_index}"
+            self.step_index += 1
+            
+            step = {
+                "id": step_id,
+                "index": self.step_index - 1,
+                "type": "tool_call",
+                "id_ref": tc.get("id", ""),
+                "name": tc["function"]["name"],
+                "arguments": tc["function"]["arguments"]
+            }
+            self.all_steps.append(step)
+            self.all_tool_calls.append(tc)
+            events.append(_sse_event("process_step", {"step": step}))
+        
+        return events
+    
+    def emit_tool_result(self, result: Dict, ref_step_id: str) -> tuple:
+        """Emit tool result step, return (step, event)."""
+        step_id = f"step-{self.step_index}"
+        self.step_index += 1
+        
+        content = result.get("content", "")
+        success = True
+        try:
+            parsed = json.loads(content)
+            if isinstance(parsed, dict):
+                success = parsed.get("success", True)
+        except (json.JSONDecodeError, TypeError):
+            pass
+        
+        step = {
+            "id": step_id,
+            "index": self.step_index - 1,
+            "type": "tool_result",
+            "id_ref": ref_step_id,
+            "name": result.get("name", ""),
+            "content": content,
+            "success": success
+        }
+        self.all_steps.append(step)
+        self.all_tool_results.append({
+            "role": "tool",
+            "tool_call_id": result.get("tool_call_id", ""),
+            "content": content
+        })
+        
+        return step, _sse_event("process_step", {"step": step})
+    
+    def set_completion(self, msg_id: str, token_count: int, usage: dict):
+        """Set completion info for saving."""
+        self._last_message_id = msg_id
+        self._last_token_count = token_count
+        self._last_usage = usage
+    
+    def reset_completion(self):
+        """Reset completion info."""
+        self._last_message_id = None
+        self._last_token_count = 0
+        self._last_usage = None