Camel多智能体框架初探

Camel介绍

CAMEL 是一个开源社区，致力于探索代理的扩展规律。我们相信，在大规模研究这些代理可以提供对其行为、能力和潜在风险的宝贵见解。为了促进该领域的研究，我们实现了并支持各种类型的代理、任务、提示、模型和模拟环境。

CAMEL ：找到智能体的扩展规律。第一个也是最好的多智能体框架。

CAMEL 框架设计原则

可演化性

该框架通过生成数据并与环境交互，使多智能体系统能够持续进化。这种进化可以由可验证奖励驱动的强化学习或监督学习驱动。

规模性

该框架旨在支持百万级代理的系统，确保在大规模情况下实现高效的协调、通信和资源管理。

有状态性

代理保持状态记忆，使它们能够进行多步与环境的交互，并高效地应对复杂的任务。

代码即提示

每一行代码和注释都作为代理的提示。代码应编写得清晰易读，确保人类和代理都能有效解读。

GitHub地址：https://github.com/camel-ai/camel。

Camel初探

我使用从源代码中使用 uv 这种方式进行安装。

git clone https://github.com/camel-ai/camel.git

cd camel

如果没安装uv需要安装。

pip install uv

创建一个虚拟环境。

uv venv .venv --python=3.10

激活虚拟环境。

.venv\Scripts\activate

安装CAMEL及其依赖。

uv pip install -e ".[all, dev, docs]"

开发者可以安装pre-commit hooks 与 mypy。

uv pip install pre-commit mypy

pre-commit install

现在先随便跑个例子看看。

我想要使用硅基流动的模型，就可以在.env文件中这样写：

Silicon_Model_ID="Qwen/Qwen2.5-72B-Instruct"
SiliconCloud_API_KEY="你的api_key"
SiliconCloud_Base_URL="https://api.siliconflow.cn/v1"

我跑的例子是这个：camel\examples\ai_society\role_playing_multi_lingual.py

将代码修改为如下的形式即可：

# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
from colorama import Fore

from camel.societies import RolePlaying
from camel.utils import print_text_animated


def main(model=None) -> None:
    task_prompt = "Develop a trading bot for the stock market"
    role_play_session = RolePlaying(
        assistant_role_name="Python Programmer",
        assistant_agent_kwargs=dict(model=model),
        user_role_name="Stock Trader",
        user_agent_kwargs=dict(model=model),
        task_prompt=task_prompt,
        with_task_specify=True,
        task_specify_agent_kwargs=dict(model=model),
        output_language="Chinese",  # Arabic, French, Spanish, ...
    )

    print(
        Fore.GREEN
        + f"AI Assistant sys message:\n{role_play_session.assistant_sys_msg}\n"
    )
    print(
        Fore.BLUE + f"AI User sys message:\n{role_play_session.user_sys_msg}\n"
    )

    print(Fore.YELLOW + f"Original task prompt:\n{task_prompt}\n")
    print(
        Fore.CYAN
        + "Specified task prompt:"
        + f"\n{role_play_session.specified_task_prompt}\n"
    )
    print(Fore.RED + f"Final task prompt:\n{role_play_session.task_prompt}\n")

    chat_turn_limit, n = 50, 0
    input_msg = role_play_session.init_chat()
    while n < chat_turn_limit:
        n += 1
        assistant_response, user_response = role_play_session.step(input_msg)

        if assistant_response.terminated:
            print(
                Fore.GREEN
                + (
                    "AI Assistant terminated. Reason: "
                    f"{assistant_response.info['termination_reasons']}."
                )
            )
            break
        if user_response.terminated:
            print(
                Fore.GREEN
                + (
                    "AI User terminated. "
                    f"Reason: {user_response.info['termination_reasons']}."
                )
            )
            break

        print_text_animated(
            Fore.BLUE + f"AI User:\n\n{user_response.msg.content}\n"
        )
        print_text_animated(
            Fore.GREEN + "AI Assistant:\n\n"
            f"{assistant_response.msg.content}\n"
        )

        if "CAMEL_TASK_DONE" in user_response.msg.content:
            break

        input_msg = assistant_response.msg


if __name__ == "__main__":
    from camel.models import ModelFactory
    from camel.types import ModelPlatformType, ModelType
    import pathlib
    import os
    from dotenv import load_dotenv

    base_dir = pathlib.Path(__file__).parent.parent.parent
    env_path = base_dir / ".env"
    load_dotenv(dotenv_path=str(env_path))

    modeltype = os.getenv("Silicon_Model_ID")
    api_key = os.getenv("SiliconCloud_API_KEY")
    base_url = os.getenv("SiliconCloud_Base_URL")
    siliconcloud_model = ModelFactory.create(
        model_platform=ModelPlatformType.OPENAI_COMPATIBLE_MODEL,
                model_type=modeltype,
                api_key=api_key,
                url=base_url,
                model_config_dict={"temperature": 0.4, "max_tokens": 4096},
    )
    main(siliconcloud_model)

运行效果：

算是把环境搭建好了。

现在就可以开始学习Camel这个多智能体框架了。