Camel多智能体框架初探

技术分享 11个月前 (03-21) 0 999+

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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。

.venvScriptsactivate

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：camelexamplesai_societyrole_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:nn{user_response.msg.content}n"         )         print_text_animated(             Fore.GREEN + "AI Assistant:nn"             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：