fix: tool call failed problem

agent/protocol/agent_stream.py

@@ -171,54 +171,75 @@ class AgentStreamExecutor:
                 tool_results = []
                 tool_result_blocks = []
 
-                for tool_call in tool_calls:
-                    result = self._execute_tool(tool_call)
-                    tool_results.append(result)
-                    
-                    # Log tool result in compact format
-                    status_emoji = "✅" if result.get("status") == "success" else "❌"
-                    result_data = result.get('result', '')
-                    # Format result string with proper Chinese character support
-                    if isinstance(result_data, (dict, list)):
-                        result_str = json.dumps(result_data, ensure_ascii=False)
-                    else:
-                        result_str = str(result_data)
-                    logger.info(f"  {status_emoji} {tool_call['name']} ({result.get('execution_time', 0):.2f}s): {result_str[:200]}{'...' if len(result_str) > 200 else ''}")
+                try:
+                    for tool_call in tool_calls:
+                        result = self._execute_tool(tool_call)
+                        tool_results.append(result)
+                        
+                        # Log tool result in compact format
+                        status_emoji = "✅" if result.get("status") == "success" else "❌"
+                        result_data = result.get('result', '')
+                        # Format result string with proper Chinese character support
+                        if isinstance(result_data, (dict, list)):
+                            result_str = json.dumps(result_data, ensure_ascii=False)
+                        else:
+                            result_str = str(result_data)
+                        logger.info(f"  {status_emoji} {tool_call['name']} ({result.get('execution_time', 0):.2f}s): {result_str[:200]}{'...' if len(result_str) > 200 else ''}")
 
-                    # Build tool result block (Claude format)
-                    # Format content in a way that's easy for LLM to understand
-                    is_error = result.get("status") == "error"
-                    
-                    if is_error:
-                        # For errors, provide clear error message
-                        result_content = f"Error: {result.get('result', 'Unknown error')}"
-                    elif isinstance(result.get('result'), dict):
-                        # For dict results, use JSON format
-                        result_content = json.dumps(result.get('result'), ensure_ascii=False)
-                    elif isinstance(result.get('result'), str):
-                        # For string results, use directly
-                        result_content = result.get('result')
-                    else:
-                        # Fallback to full JSON
-                        result_content = json.dumps(result, ensure_ascii=False)
-                    
-                    tool_result_block = {
-                        "type": "tool_result",
-                        "tool_use_id": tool_call["id"],
-                        "content": result_content
-                    }
-                    
-                    # Add is_error field for Claude API (helps model understand failures)
-                    if is_error:
-                        tool_result_block["is_error"] = True
-                    
-                    tool_result_blocks.append(tool_result_block)
-
-                # Add tool results to message history as user message (Claude format)
-                self.messages.append({
-                    "role": "user",
-                    "content": tool_result_blocks
-                })
+                        # Build tool result block (Claude format)
+                        # Format content in a way that's easy for LLM to understand
+                        is_error = result.get("status") == "error"
+                        
+                        if is_error:
+                            # For errors, provide clear error message
+                            result_content = f"Error: {result.get('result', 'Unknown error')}"
+                        elif isinstance(result.get('result'), dict):
+                            # For dict results, use JSON format
+                            result_content = json.dumps(result.get('result'), ensure_ascii=False)
+                        elif isinstance(result.get('result'), str):
+                            # For string results, use directly
+                            result_content = result.get('result')
+                        else:
+                            # Fallback to full JSON
+                            result_content = json.dumps(result, ensure_ascii=False)
+                        
+                        tool_result_block = {
+                            "type": "tool_result",
+                            "tool_use_id": tool_call["id"],
+                            "content": result_content
+                        }
+                        
+                        # Add is_error field for Claude API (helps model understand failures)
+                        if is_error:
+                            tool_result_block["is_error"] = True
+                        
+                        tool_result_blocks.append(tool_result_block)
+                
+                finally:
+                    # CRITICAL: Always add tool_result to maintain message history integrity
+                    # Even if tool execution fails, we must add error results to match tool_use
+                    if tool_result_blocks:
+                        # Add tool results to message history as user message (Claude format)
+                        self.messages.append({
+                            "role": "user",
+                            "content": tool_result_blocks
+                        })
+                    elif tool_calls:
+                        # If we have tool_calls but no tool_result_blocks (unexpected error),
+                        # create error results for all tool calls to maintain message integrity
+                        logger.warning("⚠️ Tool execution interrupted, adding error results to maintain message history")
+                        emergency_blocks = []
+                        for tool_call in tool_calls:
+                            emergency_blocks.append({
+                                "type": "tool_result",
+                                "tool_use_id": tool_call["id"],
+                                "content": "Error: Tool execution was interrupted",
+                                "is_error": True
+                            })
+                        self.messages.append({
+                            "role": "user",
+                            "content": emergency_blocks
+                        })
 
                 self._emit_event("turn_end", {
                     "turn": turn,
@@ -257,6 +278,9 @@ class AgentStreamExecutor:
         Returns:
             (response_text, tool_calls)
         """
+        # Validate and fix message history first
+        self._validate_and_fix_messages()
+        
         # Trim messages if needed (using agent's context management)
         self._trim_messages()
 
@@ -513,6 +537,27 @@ class AgentStreamExecutor:
             })
             return error_result
 
+    def _validate_and_fix_messages(self):
+        """
+        Validate message history and fix incomplete tool_use/tool_result pairs.
+        Claude API requires each tool_use to have a corresponding tool_result immediately after.
+        """
+        if not self.messages:
+            return
+        
+        # Check last message for incomplete tool_use
+        if len(self.messages) > 0:
+            last_msg = self.messages[-1]
+            if last_msg.get("role") == "assistant":
+                # Check if assistant message has tool_use blocks
+                content = last_msg.get("content", [])
+                if isinstance(content, list):
+                    has_tool_use = any(block.get("type") == "tool_use" for block in content)
+                    if has_tool_use:
+                        # This is incomplete - remove it
+                        logger.warning(f"⚠️ Removing incomplete tool_use message from history")
+                        self.messages.pop()
+
     def _trim_messages(self):
         """
         Trim message history to stay within context limits.

agent/tools/__init__.py

@@ -21,6 +21,7 @@ from agent.tools.memory.memory_get import MemoryGetTool
 
 # Import web tools
 from agent.tools.web_fetch.web_fetch import WebFetch
+from agent.tools.bocha_search.bocha_search import BochaSearch
 
 # Import tools with optional dependencies
 def _import_optional_tools():
@@ -93,6 +94,7 @@ __all__ = [
     'MemorySearchTool',
     'MemoryGetTool',
     'WebFetch',
+    'BochaSearch',
     # Optional tools (may be None if dependencies not available)
     'GoogleSearch',
     'FileSave',

agent/tools/web_fetch/web_fetch.py

@@ -46,7 +46,7 @@ class WebFetch(BaseTool):
     
     def __init__(self, config: dict = None):
         self.config = config or {}
-        self.timeout = self.config.get("timeout", 30)
+        self.timeout = self.config.get("timeout", 20)
         self.max_redirects = self.config.get("max_redirects", 3)
         self.user_agent = self.config.get(
             "user_agent",

bridge/agent_bridge.py

@@ -171,13 +171,15 @@ class AgentLLMModel(LLMModel):
 
 class AgentBridge:
     """
-    Bridge class that integrates single super Agent with COW
+    Bridge class that integrates super Agent with COW
+    Manages multiple agent instances per session for conversation isolation
     """
     
     def __init__(self, bridge: Bridge):
         self.bridge = bridge
+        self.agents = {}  # session_id -> Agent instance mapping
+        self.default_agent = None  # For backward compatibility (no session_id)
         self.agent: Optional[Agent] = None
-    
     def create_agent(self, system_prompt: str, tools: List = None, **kwargs) -> Agent:
         """
         Create the super agent with COW integration
@@ -209,8 +211,8 @@ class AgentBridge:
                 except Exception as e:
                     logger.warning(f"[AgentBridge] Failed to load tool {tool_name}: {e}")
         
-        # Create the single super agent
-        self.agent = Agent(
+        # Create agent instance
+        agent = Agent(
             system_prompt=system_prompt,
             description=kwargs.get("description", "AI Super Agent"),
             model=model,
@@ -225,21 +227,38 @@ class AgentBridge:
         )
 
         # Log skill loading details
-        if self.agent.skill_manager:
+        if agent.skill_manager:
             logger.info(f"[AgentBridge] SkillManager initialized:")
-            logger.info(f"[AgentBridge]   - Managed dir: {self.agent.skill_manager.managed_skills_dir}")
-            logger.info(f"[AgentBridge]   - Workspace dir: {self.agent.skill_manager.workspace_dir}")
-            logger.info(f"[AgentBridge]   - Total skills: {len(self.agent.skill_manager.skills)}")
-            for skill_name in self.agent.skill_manager.skills.keys():
+            logger.info(f"[AgentBridge]   - Managed dir: {agent.skill_manager.managed_skills_dir}")
+            logger.info(f"[AgentBridge]   - Workspace dir: {agent.skill_manager.workspace_dir}")
+            logger.info(f"[AgentBridge]   - Total skills: {len(agent.skill_manager.skills)}")
+            for skill_name in agent.skill_manager.skills.keys():
                 logger.info(f"[AgentBridge]     * {skill_name}")
 
-        return self.agent
+        return agent
     
-    def get_agent(self) -> Optional[Agent]:
-        """Get the super agent, create if not exists"""
-        if self.agent is None:
-            self._init_default_agent()
-        return self.agent
+    def get_agent(self, session_id: str = None) -> Optional[Agent]:
+        """
+        Get agent instance for the given session
+        
+        Args:
+            session_id: Session identifier (e.g., user_id). If None, returns default agent.
+        
+        Returns:
+            Agent instance for this session
+        """
+        # If no session_id, use default agent (backward compatibility)
+        if session_id is None:
+            if self.default_agent is None:
+                self._init_default_agent()
+            return self.default_agent
+        
+        # Check if agent exists for this session
+        if session_id not in self.agents:
+            logger.info(f"[AgentBridge] Creating new agent for session: {session_id}")
+            self._init_agent_for_session(session_id)
+        
+        return self.agents[session_id]
     
     def _init_default_agent(self):
         """Initialize default super agent with new prompt system"""
@@ -307,6 +326,12 @@ class AgentBridge:
                         tool.cwd = file_config.get("cwd", tool.cwd if hasattr(tool, 'cwd') else None)
                         if 'memory_manager' in file_config:
                             tool.memory_manager = file_config['memory_manager']
+                    # Apply API key for bocha_search tool
+                    elif tool_name == 'bocha_search':
+                        bocha_api_key = conf().get("bocha_api_key", "")
+                        if bocha_api_key:
+                            tool.config = {"bocha_api_key": bocha_api_key}
+                            tool.api_key = bocha_api_key
                     tools.append(tool)
                     logger.debug(f"[AgentBridge] Loaded tool: {tool_name}")
             except Exception as e:
@@ -370,6 +395,127 @@ class AgentBridge:
         if memory_manager:
             agent.memory_manager = memory_manager
             logger.info(f"[AgentBridge] Memory manager attached to agent")
+        
+        # Store as default agent
+        self.default_agent = agent
+    
+    def _init_agent_for_session(self, session_id: str):
+        """
+        Initialize agent for a specific session
+        Reuses the same configuration as default agent
+        """
+        from config import conf
+        import os
+
+        # Get workspace from config
+        workspace_root = os.path.expanduser(conf().get("agent_workspace", "~/cow"))
+
+        # Initialize workspace
+        from agent.prompt import ensure_workspace, load_context_files, PromptBuilder
+
+        workspace_files = ensure_workspace(workspace_root, create_templates=True)
+
+        # Setup memory system
+        memory_manager = None
+        memory_tools = []
+
+        try:
+            from agent.memory import MemoryManager, MemoryConfig
+            from agent.tools import MemorySearchTool, MemoryGetTool
+
+            memory_config = MemoryConfig(
+                workspace_root=workspace_root,
+                embedding_provider="local",
+                embedding_model="all-MiniLM-L6-v2"
+            )
+            
+            memory_manager = MemoryManager(memory_config)
+            memory_tools = [
+                MemorySearchTool(memory_manager),
+                MemoryGetTool(memory_manager)
+            ]
+            
+        except Exception as e:
+            logger.debug(f"[AgentBridge] Memory system not available for session {session_id}: {e}")
+
+        # Load tools
+        from agent.tools import ToolManager
+        tool_manager = ToolManager()
+        tool_manager.load_tools()
+
+        tools = []
+        file_config = {
+            "cwd": workspace_root,
+            "memory_manager": memory_manager
+        } if memory_manager else {"cwd": workspace_root}
+
+        for tool_name in tool_manager.tool_classes.keys():
+            try:
+                tool = tool_manager.create_tool(tool_name)
+                if tool:
+                    if tool_name in ['read', 'write', 'edit', 'bash', 'grep', 'find', 'ls']:
+                        tool.config = file_config
+                        tool.cwd = file_config.get("cwd", tool.cwd if hasattr(tool, 'cwd') else None)
+                        if 'memory_manager' in file_config:
+                            tool.memory_manager = file_config['memory_manager']
+                    elif tool_name == 'bocha_search':
+                        bocha_api_key = conf().get("bocha_api_key", "")
+                        if bocha_api_key:
+                            tool.config = {"bocha_api_key": bocha_api_key}
+                            tool.api_key = bocha_api_key
+                    tools.append(tool)
+            except Exception as e:
+                logger.warning(f"[AgentBridge] Failed to load tool {tool_name} for session {session_id}: {e}")
+
+        if memory_tools:
+            tools.extend(memory_tools)
+
+        # Load context files
+        context_files = load_context_files(workspace_root)
+
+        # Check if this is the first conversation
+        from agent.prompt.workspace import is_first_conversation, mark_conversation_started
+        is_first = is_first_conversation(workspace_root)
+        
+        # Build system prompt
+        prompt_builder = PromptBuilder(
+            workspace_dir=workspace_root,
+            language="zh"
+        )
+
+        runtime_info = {
+            "model": conf().get("model", "unknown"),
+            "workspace": workspace_root,
+            "channel": conf().get("channel_type", "unknown")
+        }
+
+        system_prompt = prompt_builder.build(
+            tools=tools,
+            context_files=context_files,
+            memory_manager=memory_manager,
+            runtime_info=runtime_info,
+            is_first_conversation=is_first
+        )
+        
+        if is_first:
+            mark_conversation_started(workspace_root)
+
+        # Create agent for this session
+        agent = self.create_agent(
+            system_prompt=system_prompt,
+            tools=tools,
+            max_steps=50,
+            output_mode="logger",
+            workspace_dir=workspace_root,
+            enable_skills=True
+        )
+
+        if memory_manager:
+            agent.memory_manager = memory_manager
+        
+        # Store agent for this session
+        self.agents[session_id] = agent
+        logger.info(f"[AgentBridge] Agent created for session: {session_id}")
     
     def agent_reply(self, query: str, context: Context = None, 
                    on_event=None, clear_history: bool = False) -> Reply:
@@ -378,7 +524,7 @@ class AgentBridge:
         
         Args:
             query: User query
-            context: COW context (optional)
+            context: COW context (optional, contains session_id for user isolation)
             on_event: Event callback (optional)
             clear_history: Whether to clear conversation history
             
@@ -386,8 +532,13 @@ class AgentBridge:
             Reply object
         """
         try:
-            # Get agent (will auto-initialize if needed)
-            agent = self.get_agent()
+            # Extract session_id from context for user isolation
+            session_id = None
+            if context:
+                session_id = context.kwargs.get("session_id") or context.get("session_id")
+            
+            # Get agent for this session (will auto-initialize if needed)
+            agent = self.get_agent(session_id=session_id)
             if not agent:
                 return Reply(ReplyType.ERROR, "Failed to initialize super agent")
             
@@ -402,4 +553,21 @@ class AgentBridge:
             
         except Exception as e:
             logger.error(f"Agent reply error: {e}")
-            return Reply(ReplyType.ERROR, f"Agent error: {str(e)}")
+            return Reply(ReplyType.ERROR, f"Agent error: {str(e)}")
+    
+    def clear_session(self, session_id: str):
+        """
+        Clear a specific session's agent and conversation history
+        
+        Args:
+            session_id: Session identifier to clear
+        """
+        if session_id in self.agents:
+            logger.info(f"[AgentBridge] Clearing session: {session_id}")
+            del self.agents[session_id]
+    
+    def clear_all_sessions(self):
+        """Clear all agent sessions"""
+        logger.info(f"[AgentBridge] Clearing all sessions ({len(self.agents)} total)")
+        self.agents.clear()
+        self.default_agent = None

channel/web/web_channel.py

@@ -49,8 +49,6 @@ class WebChannel(ChatChannel):
         self.msg_id_counter = 0  # 添加消息ID计数器
         self.session_queues = {}  # 存储session_id到队列的映射
         self.request_to_session = {}  # 存储request_id到session_id的映射
-        # web channel无需前缀
-        conf()["single_chat_prefix"] = [""]
 
 
     def _generate_msg_id(self):
@@ -122,18 +120,30 @@ class WebChannel(ChatChannel):
             if session_id not in self.session_queues:
                 self.session_queues[session_id] = Queue()
             
+            # Web channel 不需要前缀，确保消息能通过前缀检查
+            trigger_prefixs = conf().get("single_chat_prefix", [""])
+            if check_prefix(prompt, trigger_prefixs) is None:
+                # 如果没有匹配到前缀，给消息加上第一个前缀
+                if trigger_prefixs:
+                    prompt = trigger_prefixs[0] + prompt
+                    logger.debug(f"[WebChannel] Added prefix to message: {prompt}")
+            
             # 创建消息对象
             msg = WebMessage(self._generate_msg_id(), prompt)
             msg.from_user_id = session_id  # 使用会话ID作为用户ID
             
-            # 创建上下文
-            context = self._compose_context(ContextType.TEXT, prompt, msg=msg)
+            # 创建上下文，明确指定 isgroup=False
+            context = self._compose_context(ContextType.TEXT, prompt, msg=msg, isgroup=False)
+            
+            # 检查 context 是否为 None（可能被插件过滤等）
+            if context is None:
+                logger.warning(f"[WebChannel] Context is None for session {session_id}, message may be filtered")
+                return json.dumps({"status": "error", "message": "Message was filtered"})
 
-            # 添加必要的字段
+            # 覆盖必要的字段（_compose_context 会设置默认值，但我们需要使用实际的 session_id）
             context["session_id"] = session_id
+            context["receiver"] = session_id
             context["request_id"] = request_id
-            context["isgroup"] = False  # 添加 isgroup 字段
-            context["receiver"] = session_id  # 添加 receiver 字段
             
             # 异步处理消息 - 只传递上下文
             threading.Thread(target=self.produce, args=(context,)).start()

config.py

@@ -185,7 +185,8 @@ available_setting = {
     "Minimax_base_url": "",
     "web_port": 9899,
     "agent": False,  # 是否开启Agent模式
-    "agent_workspace": "~/cow"  # agent工作空间路径，用于存储skills、memory等
+    "agent_workspace": "~/cow",  # agent工作空间路径，用于存储skills、memory等
+    "bocha_api_key": ""
 }
 
 

skills/skill-creator/SKILL.md

@@ -68,10 +68,11 @@ skill-name/
 - Must test scripts before including
 
 **references/** - When to include:
-- Documentation for agent to reference
-- Database schemas, API docs, domain knowledge
+- **ONLY** when documentation is too large for SKILL.md (>500 lines)
+- Database schemas, complex API specs that agent needs to reference
 - Agent reads these files into context as needed
-- For large files (>10k words), include grep patterns in SKILL.md
+- **NOT for**: API reference docs, usage examples, tutorials (put in SKILL.md instead)
+- **Rule of thumb**: If it fits in SKILL.md, don't create a separate reference file
 
 **assets/** - When to include:
 - Files used in output (not loaded to context)
@@ -82,11 +83,15 @@ skill-name/
 
 ### What NOT to Include
 
-Do NOT create auxiliary documentation:
-- README.md
-- INSTALLATION_GUIDE.md
-- CHANGELOG.md
-- Other non-essential files
+Do NOT create auxiliary documentation files:
+- README.md - Instructions belong in SKILL.md
+- INSTALLATION_GUIDE.md - Setup info belongs in SKILL.md
+- CHANGELOG.md - Not needed for local skills
+- API_REFERENCE.md - Put API docs directly in SKILL.md
+- USAGE_EXAMPLES.md - Put examples directly in SKILL.md
+- Any other documentation files - Everything goes in SKILL.md unless it's too large
+
+**Critical Rule**: Only create files that the agent will actually execute (scripts) or that are too large for SKILL.md (references). Documentation, examples, and guides ALL belong in SKILL.md.
 
 ## Skill Creation Process
 
@@ -133,22 +138,31 @@ To turn concrete examples into an effective skill, analyze each example by:
 1. Considering how to execute on the example from scratch
 2. Identifying what scripts, references, and assets would be helpful when executing these workflows repeatedly
 
+**Planning Checklist**:
+- ✅ **Always needed**: SKILL.md with clear description and usage instructions
+- ✅ **scripts/**: Only if code needs to be executed (not just shown as examples)
+- ❌ **references/**: Rarely needed - only if documentation is >500 lines and can't fit in SKILL.md
+- ✅ **assets/**: Only if files are used in output (templates, boilerplate, etc.)
+
 Example: When building a `pdf-editor` skill to handle queries like "Help me rotate this PDF," the analysis shows:
 
 1. Rotating a PDF requires re-writing the same code each time
 2. A `scripts/rotate_pdf.py` script would be helpful to store in the skill
+3. ❌ Don't create `references/api-docs.md` - put API info in SKILL.md instead
 
 Example: When designing a `frontend-webapp-builder` skill for queries like "Build me a todo app" or "Build me a dashboard to track my steps," the analysis shows:
 
 1. Writing a frontend webapp requires the same boilerplate HTML/React each time
 2. An `assets/hello-world/` template containing the boilerplate HTML/React project files would be helpful to store in the skill
+3. ❌ Don't create `references/usage-examples.md` - put examples in SKILL.md instead
 
 Example: When building a `big-query` skill to handle queries like "How many users have logged in today?" the analysis shows:
 
 1. Querying BigQuery requires re-discovering the table schemas and relationships each time
-2. A `references/schema.md` file documenting the table schemas would be helpful to store in the skill
+2. A `references/schema.md` file documenting the table schemas would be helpful to store in the skill (ONLY because schemas are very large)
+3. ❌ Don't create separate `references/query-examples.md` - put examples in SKILL.md instead
 
-To establish the skill's contents, analyze each concrete example to create a list of the reusable resources to include: scripts, references, and assets.
+To establish the skill's contents, analyze each concrete example to create a list of the reusable resources to include: scripts, references, and assets. **Default to putting everything in SKILL.md unless there's a compelling reason to separate it.**
 
 ### Step 3: Initialize the Skill
 
@@ -200,6 +214,12 @@ These files contain established best practices for effective skill design.
 
 To begin implementation, start with the reusable resources identified above: `scripts/`, `references/`, and `assets/` files. Note that this step may require user input. For example, when implementing a `brand-guidelines` skill, the user may need to provide brand assets or templates to store in `assets/`, or documentation to store in `references/`.
 
+**Important Guidelines**:
+- **scripts/**: Only create scripts that will be executed. Test all scripts before including.
+- **references/**: ONLY create if documentation is too large for SKILL.md (>500 lines). Most skills don't need this.
+- **assets/**: Only include files used in output (templates, icons, etc.)
+- **Default approach**: Put everything in SKILL.md unless there's a specific reason not to.
+
 Added scripts must be tested by actually running them to ensure there are no bugs and that the output matches what is expected. If there are many similar scripts, only a representative sample needs to be tested to ensure confidence that they all work while balancing time to completion.
 
 If you used `--examples`, delete any placeholder files that are not needed for the skill. Only create resource directories that are actually required.