diff --git a/experiment/llm_test.py b/experiment/llm_test.py
index 4e37711..90115b0 100644
--- a/experiment/llm_test.py
+++ b/experiment/llm_test.py
@@ -2,16 +2,14 @@ import os
 import uuid
 from dotenv import load_dotenv
 from ra_aid.agent_utils import run_agent_with_retry
-from typing import Dict, Any, Generator, List, Optional
-from langchain_core.messages import AIMessage, HumanMessage
 from langchain_openai import ChatOpenAI
 from langchain_core.tools import tool
 from ra_aid.tools.list_directory import list_directory_tree
 from ra_aid.tool_configs import get_read_only_tools
-import inspect
 from rich.panel import Panel
 from rich.markdown import Markdown
 from rich.console import Console
+from ra_aid.agents.ciayn_agent import CiaynAgent
 
 console = Console()
 
@@ -33,121 +31,6 @@ def output_message(message: str, prompt_user_input: bool = False) -> str:
         return user_input
     return ""
 
-class CiaynAgent:
-    def get_function_info(self, func):
-        """
-        Returns a well-formatted string containing the function signature and docstring,
-        designed to be easily readable by both humans and LLMs.
-        """
-        signature = inspect.signature(func)
-        docstring = inspect.getdoc(func)
-        if docstring is None:
-            docstring = "No docstring provided"
-        full_signature = f"{func.__name__}{signature}"
-        info = f"""{full_signature}
-\"\"\"
-{docstring}
-\"\"\"  """
-        return info
-
-    def __init__(self, model, tools: list):
-        """Initialize the agent with a model and list of tools."""
-        self.model = model
-        self.tools = tools
-        self.available_functions = []
-        for t in tools:
-            self.available_functions.append(self.get_function_info(t.func))
-
-    def _build_prompt(self, last_result: Optional[str] = None) -> str:
-        """Build the prompt for the agent including available tools and context."""
-        base_prompt = ""
-        if last_result is not None:
-            base_prompt += f"\n<last result>{last_result}</last result>"
-            
-        base_prompt += f"""
-<available functions>
-{"\n\n".join(self.available_functions)}
-</available functions>
-
-<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.
-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.
-</agent instructions>
-
-<example response>
-check_weather("London")
-</example response>
-    
-<example response>
-output_message(\"\"\"How can I help you today?\"\"\", True)
-</example response>
-
-Output **ONLY THE CODE** and **NO MARKDOWN BACKTICKS**"""
-        return base_prompt
-
-    def _execute_tool(self, code: str) -> str:
-        """Execute a tool call and return its result."""
-        globals_dict = {
-            tool.func.__name__: tool.func
-            for tool in self.tools
-        }
-        
-        try:
-            result = eval(code.strip(), globals_dict)
-            return result
-        except Exception as e:
-            error_msg = f"Error executing code: {str(e)}"
-            console.print(f"[red]Error:[/red] {error_msg}")
-            return error_msg
-
-
-    def _create_agent_chunk(self, content: str) -> Dict[str, Any]:
-        """Create an agent chunk in the format expected by print_agent_output."""
-        return {
-            "agent": {
-                "messages": [AIMessage(content=content)]
-            }
-        }
-
-    def _create_error_chunk(self, content: str) -> Dict[str, Any]:
-        """Create an error chunk in the format expected by print_agent_output."""
-        return {
-            "tools": {
-                "messages": [{"status": "error", "content": content}]
-            }
-        }
-
-    def stream(self, messages_dict: Dict[str, List[Any]], config: Dict[str, Any] = None) -> Generator[Dict[str, Any], None, None]:
-        """Stream agent responses in a format compatible with print_agent_output."""
-        initial_messages = messages_dict.get("messages", [])
-        chat_history = []
-        last_result = None
-        first_iteration = True
-        
-        while True:
-            base_prompt = self._build_prompt(None if first_iteration else last_result)
-            chat_history.append(HumanMessage(content=base_prompt))
-            
-            try:
-                full_history = initial_messages + chat_history
-                response = self.model.invoke(full_history)
-                
-                last_result = self._execute_tool(response.content)
-                chat_history.append(response)
-                first_iteration = False
-                yield {}
-                
-            except Exception as e:
-                error_msg = f"Error: {str(e)}"
-                yield self._create_error_chunk(error_msg)
-                break
-
 if __name__ == "__main__":
     # Initialize the chat model
     chat = ChatOpenAI(
diff --git a/ra_aid/agents/ciayn_agent.py b/ra_aid/agents/ciayn_agent.py
index 8f21f35..cb9f06f 100644
--- a/ra_aid/agents/ciayn_agent.py
+++ b/ra_aid/agents/ciayn_agent.py
@@ -20,7 +20,7 @@ class CiaynAgent:
 \"\"\""""
         return info
 
-    def __init__(self, model, tools: list, max_history_messages: int = 50, max_tokens: Optional[int] = None):
+    def __init__(self, model, tools: list, max_history_messages: int = 50, max_tokens: Optional[int] = 100000):
         """Initialize the agent with a model and list of tools.
         
         Args: