开源智能体框架 OWL（未完成）

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安装配置

以 windows11 为例。

安装 OWL 框架包

shell

git clone https://github.com/camel-ai/owl.git
cd owl
conda create -n owl_env python=3.12 -y
conda activate owl_env
pip install -r requirements.txt

一键安装包下载地址：owl.rar

（可选）安装 Playwright，用来控制浏览器行为。首先安装 nodeJS，之后安装 Playwright

shell

# 全局安装 playwright
npm install -g playwright
# 下载浏览器（如 Chromium、Firefox 和 WebKit）才能运行（自动下载所需的浏览器并安装到默认路径 C:\Users\Administrator\AppData\Local\ms-playwright）
npx playwright install

修改 run_ollama.py（如果需要运行本地模型）

shell

from dotenv import load_dotenv
from camel.models import ModelFactory
from camel.toolkits import (
    CodeExecutionToolkit,
    ExcelToolkit,
    ImageAnalysisToolkit,
    SearchToolkit,
    WebToolkit,
    FileWriteToolkit,
)
from camel.types import ModelPlatformType

from utils import OwlRolePlaying, run_society

from camel.logger import set_log_level

set_log_level(level="DEBUG")

load_dotenv()


def construct_society(question: str) -> OwlRolePlaying:
    r"""Construct a society of agents based on the given question.

    Args:
        question (str): The task or question to be addressed by the society.

    Returns:
        OwlRolePlaying: A configured society of agents ready to address the question.
    """

    # Create models for different components
    models = {
        "user": ModelFactory.create(
            model_platform=ModelPlatformType.OLLAMA,
            model_type="qwq", # todo 修改为自己的模型
            url="http://localhost:11434/v1",
            model_config_dict={"temperature": 0.8, "max_tokens": 1000000},
        ),
        "assistant": ModelFactory.create(
            model_platform=ModelPlatformType.OLLAMA,
            model_type="qwq", # todo 修改为自己的模型
            url="http://localhost:11434/v1",
            model_config_dict={"temperature": 0.2, "max_tokens": 1000000},
        ),
        "web": ModelFactory.create(
            model_platform=ModelPlatformType.OLLAMA,
            model_type="qwq", # todo 修改为自己的模型
            url="http://localhost:11434/v1",
            model_config_dict={"temperature": 0.4, "max_tokens": 1000000},
        ),
        "planning": ModelFactory.create(
            model_platform=ModelPlatformType.OLLAMA,
            model_type="qwq", # todo 修改为自己的模型
            url="http://localhost:11434/v1",
            model_config_dict={"temperature": 0.4, "max_tokens": 1000000},
        ),
        "image": ModelFactory.create(
            model_platform=ModelPlatformType.OLLAMA,
            model_type="qwq", # todo 修改为自己的模型
            url="http://localhost:11434/v1",
            model_config_dict={"temperature": 0.4, "max_tokens": 1000000},
        ),
    }

    # Configure toolkits
    tools = [
        *WebToolkit(
            headless=False,  # Set to True for headless mode (e.g., on remote servers)
            web_agent_model=models["web"],
            planning_agent_model=models["planning"],
        ).get_tools(),
        *CodeExecutionToolkit(sandbox="subprocess", verbose=True).get_tools(),
        *ImageAnalysisToolkit(model=models["image"]).get_tools(),
        SearchToolkit().search_duckduckgo,
        # SearchToolkit().search_google,  # Comment this out if you don't have google search
        SearchToolkit().search_wiki,
        *ExcelToolkit().get_tools(),
        *FileWriteToolkit(output_dir="./").get_tools(),
    ]

    # Configure agent roles and parameters
    user_agent_kwargs = {"model": models["user"]}
    assistant_agent_kwargs = {"model": models["assistant"], "tools": tools}

    # Configure task parameters
    task_kwargs = {
        "task_prompt": question,
        "with_task_specify": False,
    }

    # Create and return the society
    society = OwlRolePlaying(
        **task_kwargs,
        user_role_name="user",
        user_agent_kwargs=user_agent_kwargs,
        assistant_role_name="assistant",
        assistant_agent_kwargs=assistant_agent_kwargs,
    )

    return society


def main():
    r"""Main function to run the OWL system with an example question."""
    # Example research question todo 修改为自己的提示语
    question = "Navigate to Amazon.com and identify one product that is attractive to coders. Please provide me with the product name and price. No need to verify your answer."

    # Construct and run the society
    society = construct_society(question)
    answer, chat_history, token_count = run_society(society)

    # Output the result
    print(f"\033[94mAnswer: {answer}\033[0m")


if __name__ == "__main__":
    main()

开始使用

shell

python run_ollama.py

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开源智能体框架 OWL（未完成） ​

安装配置 ​

开始使用 ​

开源智能体框架 OWL（未完成）

安装配置

开始使用