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# coding: utf-8
# Copyright (c) 2025 inclusionAI.
import asyncio
import json
import logging
import os
from dotenv import load_dotenv
from aworld.agents.llm_agent import Agent
from aworld.config.conf import AgentConfig, TaskConfig
from aworld.core.task import Task
from aworld.runner import Runners
from aworld.runners.callback.decorator import reg_callback
from aworld.tools.mcp_tool import async_mcp_tool
from examples.sandbox.inner_tools import aworldsearch_function_tools
@reg_callback("print_content")
def simple_callback(content):
"""Simple callback function, prints content and returns it
Args:
content: Content to print
Returns:
The input content
"""
print(f"callback content: {content}")
return content
async def run():
load_dotenv()
llm_provider = os.getenv("LLM_PROVIDER_WEATHER", "openai")
llm_model_name = os.getenv("LLM_MODEL_NAME_WEATHER")
llm_api_key = os.getenv("LLM_API_KEY_WEATHER")
llm_base_url = os.getenv("LLM_BASE_URL_WEATHER")
llm_temperature = os.getenv("LLM_TEMPERATURE_WEATHER", 0.0)
agent_config = AgentConfig(
llm_provider=llm_provider,
llm_model_name=llm_model_name,
llm_api_key=llm_api_key,
llm_base_url=llm_base_url,
llm_temperature=llm_temperature,
)
#mcp_servers = ["filewrite_server", "fileread_server"]
#mcp_servers = ["amap-amap-sse","filewrite_server", "fileread_server"]
#mcp_servers = ["file_server"]
#mcp_servers = ["amap-amap-sse"]
mcp_servers = ["aworldsearch_server"]
#mcp_servers = ["gen_video_server"]
# mcp_servers = ["picsearch_server"]
#mcp_servers = ["gen_audio_server"]
#mcp_servers = ["playwright"]
#mcp_servers = ["tavily-mcp"]
path_cwd = os.path.dirname(os.path.abspath(__file__))
mcp_path = os.path.join(path_cwd, "mcp.json")
with open(mcp_path, "r") as f:
mcp_config = json.load(f)
print("-------------------mcp_config--------------",mcp_config)
#sand_box = Sandbox(mcp_servers=mcp_servers,mcp_config=mcp_config)
# You can specify sandbox
#sand_box = Sandbox(mcp_servers=mcp_servers, mcp_config=mcp_config,env_type=SandboxEnvType.K8S)
#sand_box = Sandbox(mcp_servers=mcp_servers, mcp_config=mcp_config,env_type=SandboxEnvType.SUPERCOMPUTER)
search_sys_prompt = "You are a versatile assistant"
search = Agent(
conf=agent_config,
name="search_agent",
system_prompt=search_sys_prompt,
mcp_config=mcp_config,
mcp_servers=mcp_servers,
#sandbox=sand_box,
)
# Run agent
# Runners.sync_run(input="Use tavily-mcp to check what tourist attractions are in Hangzhou", agent=search)
task = Task(
# input="Use tavily-mcp to check what tourist attractions are in Hangzhou",
# input="Use the file_server tool to analyze this audio link: https://amap-aibox-data.oss-cn-zhangjiakou.aliyuncs.com/.mp3",
# input="Use the amap-amap-sse tool to find hotels within one kilometer of West Lake in Hangzhou",
input="Use the aworldsearch_server tool to search for the origin of the Dragon Boat Festival",
# input="Use the picsearch_server tool to search for Captain America",
# input="Make sure to use the human_confirm tool to let the user confirm this message: 'Do you want to make a payment to this customer'",
# input="Use the gen_audio_server tool to convert this sentence to audio: 'Nice to meet you'",
#input="Use the gen_video_server tool to generate a video of this description: 'A cat walking alone on a snowy day'",
#input="How's the weather in New York, Shanghai, and Beijing right now? These are three cities, I hope the large model returns three tools when it identifies tool calls",
# input="First call the filewrite_server tool, then call the fileread_server tool",
# input="Use the playwright tool, with Google browser, search for the latest news about the Trump administration on www.baidu.com",
# input="Use tavily-mcp",
agent=search,
conf=TaskConfig(),
event_driven=True
)
#result = Runners.sync_run_task(task)
#result = Runners.sync_run_task(task)
#result = await Runners.streamed_run_task(task)
# result = await Runners.run_task(task)
# print(
# "----------------------------------------------------------------------------------------------"
# )
# print(result)
# async for chunk in Runners.streamed_run_task(task).stream_events():
# print(chunk, end="", flush=True)
async for output in Runners.streamed_run_task(task).stream_events():
print(f"Agent Ouput: {output}")
if __name__ == "__main__":
asyncio.run(run()) |