Rename project: maxclaw → langcode

- Renamed all references from maxclaw to langcode across all files
- Renamed maxclaw.py → langcode.py
- Renamed completion scripts: maxclaw.bash/zsh → langcode.bash/zsh
- Updated Dockerfile, README, all Python files, HTML, JSON, YAML
- New Gitea repository: http://192.168.0.183:8085/truetype/langcode_agent.git

main.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-MaxClaw Agent - A configurable agent harness using DeepAgents
+LangCode Agent - A configurable agent harness using DeepAgents
 Supports multiple LLM providers via LangChain's init_chat_model
 Enhanced with unified provider tools and custom tools
 """
@@ -74,7 +74,7 @@ def load_model_from_config():
 
 def get_custom_tools() -> List[Tool]:
     """
-    Get custom tools for the MaxClaw agent
+    Get custom tools for the LangCode agent
     
     Returns:
         List of custom tools
@@ -114,12 +114,12 @@ def get_custom_tools() -> List[Tool]:
     return custom_tools
 
 
-def create_maxclaw_agent(system_prompt: Optional[str] = None):
+def create_langcode_agent(system_prompt: Optional[str] = None):
     """
     Create and return a DeepAgent instance with configurable model.
     """
     if system_prompt is None:
-        system_prompt = """You are MaxClaw, a helpful AI agent that can plan, use tools, 
+        system_prompt = """You are LangCode, a helpful AI agent that can plan, use tools, 
         manage context, and delegate to subagents. You are designed to be configurable 
         to work with various LLM providers.
         
@@ -371,12 +371,12 @@ def process_command(user_input: str) -> bool:
 
 def main():
     """
-    Main entry point for the MaxClaw agent.
+    Main entry point for the LangCode agent.
     """
-    print("🚀 Initializing MaxClaw Agent...")
+    print("🚀 Initializing LangCode Agent...")
     
     try:
-        agent = create_maxclaw_agent()
+        agent = create_langcode_agent()
         print("✅ Agent initialized successfully!")
         
         # Show available tools
@@ -408,7 +408,7 @@ def main():
                 result = agent.invoke({"messages": [HumanMessage(content=user_input)]})
                 # Extract the AI's response
                 ai_message = result["messages"][-1]
-                print(f"\n🤖 MaxClaw: {ai_message.content}")
+                print(f"\n🤖 LangCode: {ai_message.content}")
             except Exception as e:
                 print(f"\n❌ Error: {str(e)}")