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.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 sophos.png filter=lfs diff=lfs merge=lfs -text
+Qwen3-4B-Instruct-2507-Q3_K_S.gguf filter=lfs diff=lfs merge=lfs -text

Qwen3-4B-Instruct-2507-Q3_K_S.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0ce20058cc0ed6b6c9213bb383589327e458c12ffce0842fc96867042d669c75
+size 1886997600

README.md

@@ -1,14 +1,10 @@
----
-license: mit
-language:
-- en
----
 <img src="sophos.png" alt="Sophos Logo" width="500"/>
 
 # Sophos 
+
 ## -- Wisdom --
 ### Artificial General Intelligence Agent Framework
 
-Sophos is a near-AGI level AI Agent framework designed for tool usage and autonomous task completion. It uses `pollinations` as a default language model, but can be configured to use any model by changing the `sophos_models.py` file.
+Sophos is a near-AGI level Agent designed for tool usage and autonomous task completion. It uses `qwen-3` as a default language model, but can be configured to use any model by changing the `sophos_models.py` file.
 
-Sophos intelligence is limited only by the language model it uses. It is designed to be a flexible and powerful AI agent framework that can be used for a wide variety of tasks.
+Sophos intelligence is limited only by the language model it uses. It is designed to be a flexible and powerful AI agent that can be used for a wide variety of tasks.

main.py

@@ -9,9 +9,9 @@ def favourite_color() -> str:
 agent = Agent(
     name="Sophos Agent",
     instructions="You are an AI Agent.",
-    model="Pollinations",
+    model="Qwen3-4B-Instruct-2507-Q3_K_S.gguf",
     tools=[get_weather, get_time, favourite_color],
 )
 
-response = agent.run("What time is it? Can you tell me my favourite color too? tell me who you are")
+response = agent.run("What is my favorite color? and what is the weather in New York?")
 print(response)

sophos.py

@@ -1,4 +1,4 @@
-from sophos_models import ask
+from sophos_models import Sophos
 from sophos_tools import *
 
 class Agent:
@@ -7,6 +7,7 @@ class Agent:
         self.instructions = instructions
         self.model = model
         self.tools = {tool.__name__: tool for tool in tools if hasattr(tool, "_is_tool")}
+        self.sophos = Sophos(model_path=model)
 
     def process_tool_calls(self, response_content: str):
         """Process tool calls from response content and return results"""
@@ -69,11 +70,13 @@ CRITICAL:
 ```tool
 <tool_name>(<arg>)
 ```
+REMEMBER ALL TOOLS MUST BE INSIDE THE SAME BLOCK
 
-If you need live information, you MUST use the tool before responding.
-Continue calling tools as needed until you have all the information required.
-When you don't need any more tools, provide your final response without tool calls.
-ALL Tools must be in the same tool block.
+IMPORTANT WORKFLOW:
+1. If you need live information, use the appropriate tool(s) first
+2. Once you have the information you need, provide your final response WITHOUT any tool calls
+3. Do NOT call the same tool repeatedly unless the user asks for updated information
+4. After calling a tool once and getting results, provide your answer to the user
 
 user prompt: {prompt}
 """
@@ -86,12 +89,13 @@ user prompt: {prompt}
 
         max_iterations = 99
         iteration = 0
+        last_tool_calls = []
         
         while iteration < max_iterations:
             iteration += 1
             print(f"\n--- Iteration {iteration} ---")
 
-            response = ask(str(messages))
+            response = self.sophos.ask(str(messages))
 
             current_response = response 
             print(f"AI Response: {current_response}")
@@ -103,8 +107,18 @@ user prompt: {prompt}
                 return current_response
         
             all_results = "\n".join(tool_results)
-            messages.append({"role": "assistant", "content": current_response})
-            messages.append({"role": "user", "content": f"Tool results:\n{all_results}\n\nContinue with your task. Use more tools if needed, or provide your final response."})
+            
+            # Check if we're repeating the same tool calls
+            current_tool_calls = [result.split(":")[0] for result in tool_results]
+            if current_tool_calls == last_tool_calls and iteration > 1:
+                print("⚠️ Detected repeated tool calls - forcing final response")
+                messages.append({"role": "assistant", "content": current_response})
+                messages.append({"role": "user", "content": f"Tool results:\n{all_results}\n\nYou have already called these tools. Now provide your final answer to the user without calling any more tools."})
+            else:
+                messages.append({"role": "assistant", "content": current_response})
+                messages.append({"role": "user", "content": f"Tool results:\n{all_results}\n\nNow provide your final response to the user. Do NOT call any more tools unless absolutely necessary."})
+            
+            last_tool_calls = current_tool_calls
         
         print(f"⚠️ Reached maximum iterations ({max_iterations})")
         return current_response

sophos_models.py

@@ -1,22 +1,26 @@
-import requests
-import urllib.parse
+from llama_cpp import Llama
 
-def ask_pollinations(prompt: str) -> str:
-    escaped = urllib.parse.quote(prompt, safe='')
-    url = f"https://text.pollinations.ai/{escaped}"
-    try:
-        resp = requests.get(url)
-        resp.raise_for_status()
-        return resp.text
-    except requests.HTTPError as e:
-        return f"HTTP error: {e} (status {resp.status_code})"
-    except Exception as e:
-        return f"Request failed: {e}"
+class Sophos:
+    def __init__(self, model_path="Qwen3-4B-Instruct-2507-Q3_K_S.gguf"):
+        self.model_path = model_path
+        self.model = Llama(model_path=model_path, n_ctx=2048, n_gpu_layers=35)
 
-def ask(prompt: str) -> str:
-    return ask_pollinations(prompt)
+    def ask(self, prompt):
+        """Ask Sophos a question and return the answer."""
+        messages = [
+            {"role": "user", "content": prompt}
+        ]
+
+        output = self.model.create_chat_completion(messages, max_tokens=1050, temperature=0.7)
+        return output["choices"][0]["message"]["content"]
 
 if __name__ == "__main__":
-    prompt = "Hello, how are you?"
-    response = ask(prompt)
-    print(response)
+    sophos = Sophos()
+    
+    while True:
+        prompt = input("\nEnter your prompt: ")
+        if prompt.lower() in ['exit', 'quit', 'q']:
+            break
+        
+        response = sophos.ask(prompt)
+        print(response)