Use create_react_agent for claude.

ra_aid/__main__.py

@@ -191,13 +191,6 @@ def main():
             # Get initial request from user
             initial_request = ask_human.invoke({"question": "What would you like help with?"})
 
-            # Create chat agent with appropriate tools
-            chat_agent = create_agent(
-                model,
-                get_chat_tools(expert_enabled=expert_enabled, web_research_enabled=web_research_enabled),
-                checkpointer=MemorySaver()
-            )
-
             # Run chat agent with CHAT_PROMPT
             config = {
                 "configurable": {"thread_id": uuid.uuid4()},
@@ -216,6 +209,13 @@ def main():
             _global_memory['config']['expert_provider'] = args.expert_provider
             _global_memory['config']['expert_model'] = args.expert_model
 
+            # Create chat agent with appropriate tools
+            chat_agent = create_agent(
+                model,
+                get_chat_tools(expert_enabled=expert_enabled, web_research_enabled=web_research_enabled),
+                checkpointer=MemorySaver()
+            )
+
             # Run chat agent and exit
             run_agent_with_retry(chat_agent, CHAT_PROMPT.format(
                     initial_request=initial_request,

ra_aid/agent_utils.py

@@ -92,20 +92,16 @@ def create_agent(
         The created agent instance
     """
     try:
-        # Extract model info from module path
-        module_path = model.__class__.__module__.split('.')
-        if len(module_path) > 1:
-            provider = module_path[1] # e.g. anthropic from langchain_anthropic
-        else:
-            provider = None
-            
         # Get model name if available
-        model_name = getattr(model, 'model_name', '').lower()
+        provider = _global_memory.get('config', {}).get('provider')
+        model_name = _global_memory.get('config', {}).get('model')
         
         # Use REACT agent for Anthropic Claude models, otherwise use CIAYN
         if provider == 'anthropic' and 'claude' in model_name:
+            logger.debug("Using create_react_agent to instantiate agent.")
             return create_react_agent(model, tools, checkpointer=checkpointer)
         else:
+            logger.debug("Using CiaynAgent agent instance.")
             return CiaynAgent(model, tools)
             
     except Exception as e:

ra_aid/agents/ciayn_agent.py

@@ -82,10 +82,9 @@ class CiaynAgent:
 
 <agent instructions>
 You are a ReAct agent. You run in a loop and use ONE of the available functions per iteration.
-If the current query does not require a function call, just use output_message to say what you would normally say.
 The result of that function call will be given to you in the next message.
 Call one function at a time. Function arguments can be complex objects, long strings, etc. if needed.
-The user cannot see the results of function calls, so you have to explicitly call output_message if you want them to see something.
+The user cannot see the results of function calls, so you have to explicitly use a tool like ask_human if you want them to see something.
 You must always respond with a single line of python that calls one of the available tools.
 Use as many steps as you need to in order to fully complete the task.
 Start by asking the user what they want.

ra_aid/prompts.py

@@ -623,17 +623,19 @@ Remember:
     - Do communicate results/responses from tools that you call as it pertains to the users request.
     - If the user interrupts/cancels an operation, you may want to ask why.
     - If the user gives you key facts, record them using emit_key_facts.
+    - Typically, you will already be in the directory of a new or existing project.
+      - If the user implies that a project exists, assume it does and make the tool calls as such.
 
 You have often been criticized for:
     - You sometimes call request_research_and_implementation which makes the full implementation successfully, but act like it has only been planned and still needs to be implemented.
     - Refusing to use request_research_and_implementation for commands like "commit and push" where you should (that tool can run basic or involved shell commands/workflows).
     - Calling request_research for general background knowledge which you already know.
-    - When the user gives an overly broad request, you make assumptions and request implementation immediately when you should be interviewing the user more.
     - Assuming the user is always right. Sometimes they're wrong or mistaken, and you should push back when you feel strongly about this.
     - Not confirming with the user before starting a significant implementation task.
     - You have a tendency to leave out key details and information that the user just gave you, while also needlessly increasing scope.
       - Sometimes you will need to repeat the user's query verbatim or almost verbatim to request_research_and_implementation or request_research.
     - Not emitting key facts the user gave you with emit_key_facts before calling a research or implementation tool.
+    - Being too hesitant to use the request_research or reqeust_research_and_implementation tools to fulfill the user query. These are your bread and butter.
 
 <initial request>
 {initial_request}