agent harness initialize repo

agent-with-tools/agent.py

@@ -0,0 +1,91 @@
+import json
+from openai import OpenAI
+from tools import get_tool_registry, get_tool_schemas
+from dotenv import load_dotenv
+
+load_dotenv()
+
+TOOL_REGISTRY = get_tool_registry()
+TOOL_SCHEMAS = get_tool_schemas()
+
+# ---------------------------------------------------------------------------
+# Agent loop
+# ---------------------------------------------------------------------------
+
+
+def get_llm_client():
+    return OpenAI(
+        base_url="http://localhost:11434/v1",
+        api_key=""
+    )
+
+
+def handle_tool_calls(tool_calls, messages):
+    """Execute each tool the LLM requested and append the results to messages."""
+    for tool_call in tool_calls:
+        name = tool_call.function.name
+        args = json.loads(tool_call.function.arguments)
+
+        print(f"  [tool] {name}({args})")
+
+        if name not in TOOL_REGISTRY:
+            result = f"Error: unknown tool '{
+                name}'. Available tools: {list(TOOL_REGISTRY.keys())}"
+        else:
+            result = TOOL_REGISTRY[name](**args)
+
+        print(f"  [tool result] {result[:200]}{
+              '...' if len(result) > 200 else ''}")
+
+        # The LLM needs the result tied back to the specific tool call id
+        messages.append({
+            "role": "tool",
+            "tool_call_id": tool_call.id,
+            "content": result,
+        })
+
+
+def agent_loop(client):
+    messages = [
+        {
+            "role": "system",
+            "content": (
+                "You are a helpful assistant. You have tools to read and write files, "
+                "search the file system, and fetch web pages. Use them to help the user."
+            ),
+        }
+    ]
+
+    while True:
+        user_input = input("You: ")
+        if user_input.lower() == "\\exit":
+            break
+
+        messages.append({"role": "user", "content": user_input})
+
+        # Keep looping until the LLM stops calling tools and gives a final reply
+        while True:
+            response = client.chat.completions.create(
+                model="gemma4",
+                messages=messages,
+                tools=TOOL_SCHEMAS,
+                temperature=0.7,
+            )
+
+            message = response.choices[0].message
+
+            # Always append the assistant turn so the conversation stays intact
+            messages.append(message)
+
+            if message.tool_calls:
+                # The LLM wants to use one or more tools — run them, then loop
+                handle_tool_calls(message.tool_calls, messages)
+            else:
+                # No tool calls: we have the final answer
+                print(f"Assistant: {message.content}")
+                break
+
+
+if __name__ == "__main__":
+    client = get_llm_client()
+    agent_loop(client)