Update example.py

examples/agents-sdk-python/example.py

@@ -1,6 +1,6 @@
 import asyncio
-from pathlib import Path
 import shutil
+from pathlib import Path
 
 from openai import AsyncOpenAI
 from agents import (
@@ -21,19 +21,29 @@ async def prompt_user(question: str) -> str:
     return await loop.run_in_executor(None, input, question)
 
 
+@function_tool
+async def get_weather(location: str) -> str:
+    """Dummy weather tool"""
+    return f"The weather in {location} is sunny."
+
+
 async def main():
-    # Set up OpenAI client for local server (e.g., Ollama)
+    # Check if npx is installed
+    if not shutil.which("npx"):
+        raise RuntimeError(
+            "❌ 'npx' is not installed. Please install it with `npm install -g npx`."
+        )
+
+    # OpenAI client (e.g., for Ollama or local LLM)
     openai_client = AsyncOpenAI(
         api_key="local",
         base_url="http://localhost:11434/v1",
     )
 
-    # Get current working directory
+    # Set up ModelContextProtocol (MCP) server using npx
     samples_dir = str(Path.cwd())
-
-    # Create MCP server for filesystem operations
     mcp_server = MCPServerStdio(
-        name="Filesystem MCP Server, via npx",
+        name="Filesystem MCP Server (via npx)",
         params={
             "command": "npx",
             "args": [
@@ -52,51 +62,38 @@ async def main():
     set_default_openai_client(openai_client)
     set_default_openai_api("chat_completions")
 
-    # Define weather tool
-    @function_tool
-    async def get_weather(location: str) -> str:
-        return f"The weather in {location} is sunny."
-
-    # Create agent
+    # Create the agent
     agent = Agent(
         name="My Agent",
         instructions="You are a helpful assistant.",
         tools=[get_weather],
-        model="gpt-oss:20b-test",
+        model="gpt-oss:20b-test",  # Ensure this model is available in your Ollama instance
         mcp_servers=[mcp_server],
     )
 
     # Get user input
     user_input = await prompt_user("> ")
 
-    # Run agent with streaming
+    # Run agent with streamed output
     result = Runner.run_streamed(agent, user_input)
 
-    # Process streaming results
+    # Stream processing
     async for event in result.stream_events():
         if event.type == "raw_response_event":
             continue
         elif event.type == "agent_updated_stream_event":
-            print(f"Agent updated: {event.new_agent.name}")
+            print(f"[Agent updated]: {event.new_agent.name}")
         elif event.type == "run_item_stream_event":
-            if event.item.type == "tool_call_item":
+            item = event.item
+            if item.type == "tool_call_item":
                 print("-- Tool was called")
-            elif event.item.type == "tool_call_output_item":
-                print(f"-- Tool output: {event.item.output}")
-            elif event.item.type == "message_output_item":
-                print(
-                    f"-- Message output:\n {ItemHelpers.text_message_output(event.item)}"
-                )
-            else:
-                pass
+            elif item.type == "tool_call_output_item":
+                print(f"-- Tool output: {item.output}")
+            elif item.type == "message_output_item":
+                print(f"-- Message output:\n{ItemHelpers.text_message_output(item)}")
 
-    print("=== Run complete ===")
+    print("✅ Run complete.")
 
 
 if __name__ == "__main__":
-
-    if not shutil.which("npx"):
-        raise RuntimeError(
-            "npx is not installed. Please install it with `npm install -g npx`."
-        )
     asyncio.run(main())