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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 | |
| 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()) |