fix: memory and path bug

agent/memory/summarizer.py

@@ -117,7 +117,7 @@ class MemoryFlushManager:
             return user_dir / "MEMORY.md"
         else:
             # Return workspace root MEMORY.md
-            return Path(self.workspace_root) / "MEMORY.md"
+            return Path(self.workspace_dir) / "MEMORY.md"
     
     def create_flush_prompt(self) -> str:
         """
@@ -214,7 +214,7 @@ def create_memory_files_if_needed(workspace_dir: Path, user_id: Optional[str] =
         user_dir.mkdir(parents=True, exist_ok=True)
         main_memory = user_dir / "MEMORY.md"
     else:
-        main_memory = Path(workspace_root) / "MEMORY.md"
+        main_memory = Path(workspace_dir) / "MEMORY.md"
     
     if not main_memory.exists():
         # Create empty file or with minimal structure (no obvious "Memory" header)

agent/prompt/builder.py

@@ -378,7 +378,7 @@ def _build_workspace_section(workspace_dir: str, language: str) -> List[str]:
         "",
         "**首次对话**:",
         "",
-        "如果这是你与用户的首次对话，并且你的人格设定和用户信息还是空白或初始状态：",
+        "如果这是你与用户的首次对话，并你的`SOUL.md`和`USER.md`均是完全空白或初始模板状态的时候才会进行以下流程：",
         "",
         "1. **表达初次启动的感觉** - 像是第一次睁开眼看到世界，带着好奇和期待",
         "2. **简短打招呼后，分点询问三个核心问题**：",
@@ -391,8 +391,7 @@ def _build_workspace_section(workspace_dir: str, language: str) -> List[str]:
         "",
         "**重要**: ",
         "- 在所有对话中，无需提及技术细节（如 SOUL.md、USER.md 等文件名，工具名称，配置等），除非用户明确询问。用自然表达如「我已记住」而非「已更新 SOUL.md」",
-        "- 不要问太多其他信息（职业、时区等可以后续自然了解）",
+        "- 不要问太多其他信息（职业、时区等可以后续自然了解），只要`SOUL.md`和`USER.md`又被填写过真实内容而不是占位则说明已经不是首次对话了，此时不用进行初始流程",
-        "- 保持简洁，避免过度抒情",
         "",
     ]
     

agent/protocol/agent_stream.py

@@ -305,10 +305,20 @@ class AgentStreamExecutor:
             for chunk in stream:
                 # Check for errors
                 if isinstance(chunk, dict) and chunk.get("error"):
-                    error_msg = chunk.get("message", "Unknown error")
+                    # Extract error message from nested structure
+                    error_data = chunk.get("error", {})
+                    if isinstance(error_data, dict):
+                        error_msg = error_data.get("message", chunk.get("message", "Unknown error"))
+                        error_code = error_data.get("code", "")
+                    else:
+                        error_msg = chunk.get("message", str(error_data))
+                        error_code = ""
+                    
                     status_code = chunk.get("status_code", "N/A")
-                    logger.error(f"API Error: {error_msg} (Status: {status_code})")
+                    logger.error(f"API Error: {error_msg} (Status: {status_code}, Code: {error_code})")
                     logger.error(f"Full error chunk: {chunk}")
+                    
+                    # Raise exception with full error message for retry logic
                     raise Exception(f"{error_msg} (Status: {status_code})")
 
                 # Parse chunk
@@ -346,10 +356,11 @@ class AgentStreamExecutor:
 
         except Exception as e:
             error_str = str(e).lower()
-            # Check if error is retryable (timeout, connection, rate limit, etc.)
+            # Check if error is retryable (timeout, connection, rate limit, server busy, etc.)
             is_retryable = any(keyword in error_str for keyword in [
                 'timeout', 'timed out', 'connection', 'network', 
-                'rate limit', 'overloaded', 'unavailable', '429', '500', '502', '503', '504'
+                'rate limit', 'overloaded', 'unavailable', 'busy', 'retry',
+                '429', '500', '502', '503', '504', '512'
             ])
             
             if is_retryable and retry_count < max_retries:

agent/tools/ls/ls.py

@@ -23,7 +23,7 @@ class Ls(BaseTool):
         "properties": {
             "path": {
                 "type": "string",
-                "description": "Directory to list (default: current directory)"
+                "description": "Directory to list. IMPORTANT: Relative paths are based on workspace directory. To access directories outside workspace, use absolute paths starting with ~ or /."
             },
             "limit": {
                 "type": "integer",
@@ -56,7 +56,7 @@ class Ls(BaseTool):
                 return ToolResult.fail(
                     f"Error: Path not found: {path}\n"
                     f"Resolved to: {absolute_path}\n"
-                    f"Hint: If accessing files outside workspace ({self.cwd}), use absolute path like ~/{path} or /full/path/{path}"
+                    f"Hint: Relative paths are based on workspace ({self.cwd}). For files outside workspace, use absolute paths."
                 )
             return ToolResult.fail(f"Error: Path not found: {path}")
         

agent/tools/read/read.py

@@ -22,7 +22,7 @@ class Read(BaseTool):
         "properties": {
             "path": {
                 "type": "string",
-                "description": "Path to the file to read (relative or absolute)"
+                "description": "Path to the file to read. IMPORTANT: Relative paths are based on workspace directory. To access files outside workspace, use absolute paths starting with ~ or /."
             },
             "offset": {
                 "type": "integer",
@@ -68,7 +68,7 @@ class Read(BaseTool):
                 return ToolResult.fail(
                     f"Error: File not found: {path}\n"
                     f"Resolved to: {absolute_path}\n"
-                    f"Hint: If accessing files outside workspace ({self.cwd}), use absolute path like ~/{path}"
+                    f"Hint: Relative paths are based on workspace ({self.cwd}). For files outside workspace, use absolute paths."
                 )
             return ToolResult.fail(f"Error: File not found: {path}")
         

bot/gemini/google_gemini_bot.py

@@ -245,8 +245,7 @@ class GoogleGeminiBot(Bot):
             gen_config = {}
             if kwargs.get("temperature") is not None:
                 gen_config["temperature"] = kwargs["temperature"]
-            if kwargs.get("max_tokens"):
+
-                gen_config["maxOutputTokens"] = kwargs["max_tokens"]
             if gen_config:
                 payload["generationConfig"] = gen_config
             

bot/linkai/link_ai_bot.py

@@ -4,6 +4,7 @@
 import re
 import time
 import requests
+import json
 import config
 from bot.bot import Bot
 from bot.openai_compatible_bot import OpenAICompatibleBot
@@ -463,7 +464,7 @@ class LinkAISessionManager(SessionManager):
             session.add_query(query)
         session.add_reply(reply)
         try:
-            max_tokens = conf().get("conversation_max_tokens", 2500)
+            max_tokens = conf().get("conversation_max_tokens", 8000)
             tokens_cnt = session.discard_exceeding(max_tokens, total_tokens)
             logger.debug(f"[LinkAI] chat history, before tokens={total_tokens}, now tokens={tokens_cnt}")
         except Exception as e:
@@ -504,6 +505,31 @@ def _linkai_call_with_tools(self, messages, tools=None, stream=False, **kwargs):
         Formatted response in OpenAI format or generator for streaming
     """
     try:
+        # Debug logging
+        logger.info(f"[LinkAI] ⭐ LinkAI call_with_tools method called")
+        logger.info(f"[LinkAI] messages count (before conversion): {len(messages) if messages else 0}")
+        
+        # Convert messages from Claude format to OpenAI format
+        # This is important because Agent uses Claude format internally
+        messages = self._convert_messages_to_openai_format(messages)
+        logger.info(f"[LinkAI] messages count (after conversion): {len(messages) if messages else 0}")
+        
+        # Convert tools from Claude format to OpenAI format
+        if tools:
+            tools = self._convert_tools_to_openai_format(tools)
+        
+        # Handle system prompt (OpenAI uses system message, Claude uses separate parameter)
+        system_prompt = kwargs.get('system')
+        if system_prompt:
+            # Add system message at the beginning if not already present
+            if not messages or messages[0].get('role') != 'system':
+                messages = [{"role": "system", "content": system_prompt}] + messages
+            else:
+                # Replace existing system message
+                messages[0] = {"role": "system", "content": system_prompt}
+        
+        logger.info(f"[LinkAI] Final messages count: {len(messages)}, tools count: {len(tools) if tools else 0}, stream: {stream}")
+        
         # Build request parameters (LinkAI uses OpenAI-compatible format)
         body = {
             "messages": messages,
@@ -515,16 +541,6 @@ def _linkai_call_with_tools(self, messages, tools=None, stream=False, **kwargs):
             "stream": stream
         }
 
-        # Add max_tokens if specified
-        if kwargs.get("max_tokens"):
-            body["max_tokens"] = kwargs["max_tokens"]
-        
-        # Add app_code if provided
-        app_code = kwargs.get("app_code", conf().get("linkai_app_code"))
-        if app_code:
-            body["app_code"] = app_code
-        
-        # Add tools if provided (OpenAI-compatible format)
         if tools:
             body["tools"] = tools
             body["tool_choice"] = kwargs.get("tool_choice", "auto")

bridge/agent_bridge.py

@@ -66,14 +66,20 @@ class AgentLLMModel(LLMModel):
         self.bridge = bridge
         self.bot_type = bot_type
         self._bot = None
+        self._use_linkai = conf().get("use_linkai", False) and conf().get("linkai_api_key")
     
     @property
     def bot(self):
         """Lazy load the bot and enhance it with tool calling if needed"""
         if self._bot is None:
-            self._bot = self.bridge.get_bot(self.bot_type)
+            # If use_linkai is enabled, use LinkAI bot directly
-            # Automatically add tool calling support if not present
+            if self._use_linkai:
-            self._bot = add_openai_compatible_support(self._bot)
+                logger.info("[AgentBridge] Using LinkAI bot for agent")
+                self._bot = self.bridge.find_chat_bot(const.LINKAI)
+            else:
+                self._bot = self.bridge.get_bot(self.bot_type)
+                # Automatically add tool calling support if not present
+                self._bot = add_openai_compatible_support(self._bot)
         return self._bot
 
     def call(self, request: LLMRequest):
@@ -88,11 +94,18 @@ class AgentLLMModel(LLMModel):
                 kwargs = {
                     'messages': request.messages,
                     'tools': getattr(request, 'tools', None),
-                    'stream': False
+                    'stream': False,
+                    'model': self.model  # Pass model parameter
                 }
                 # Only pass max_tokens if it's explicitly set
                 if request.max_tokens is not None:
                     kwargs['max_tokens'] = request.max_tokens
+                
+                # Extract system prompt if present
+                system_prompt = getattr(request, 'system', None)
+                if system_prompt:
+                    kwargs['system'] = system_prompt
+                
                 response = self.bot.call_with_tools(**kwargs)
                 return self._format_response(response)
             else:
@@ -122,7 +135,8 @@ class AgentLLMModel(LLMModel):
                     'messages': request.messages,
                     'tools': getattr(request, 'tools', None),
                     'stream': True,
-                    'max_tokens': max_tokens
+                    'max_tokens': max_tokens,
+                    'model': self.model  # Pass model parameter
                 }
                 
                 # Add system prompt if present