--- title: NoGameNoGrowthYGO canonical_url: "https://www.modelscope.cn/studios/kevinfu/NoGameNoGrowthYGO" md_url: "https://www.modelscope.cn/studios/kevinfu/NoGameNoGrowthYGO.md" repository: kevinfu/NoGameNoGrowthYGO chinese_name: "游戏成长(YGO版)+AgentScope挑战赛+SUN" last_updated: 2024-04-10 sdk_type: gradio sdk_version: 4.19.1 downloads: 0 stars: 1 --- # NoGameNoGrowthYGO > NoGameNoGrowthYGO - kevinfu 在 ModelScope 创建的在线 Demo。【介绍】：让完成计划目标、学习、读书、养成良好习惯都像游戏那样，充满乐趣让人欲罢不能。让LLM帮我们制定计划，然后就像游戏那样根据学习、阅读、习惯坚持的实际情况进行奖励，及时奖励。当前版本下，奖励是模拟在游戏王中抽卡，类似集牌游戏，并基于自己生活中赚取的奖励可以组自己的卡组，在YGO游戏王中进行卡牌对战。后续可支持其他开源游戏。【特点】：1.已添加Erniebot支持;2.已添加function… kevinfu/NoGameNoGrowthYGO 是 ModelScope 魔搭社区上的在线可交互 Demo（创空间），基于 gradio 4.19.1 构建，中文名为「游戏成长(YGO版)+AgentScope挑战赛+SUN」。 - **Repository**: kevinfu/NoGameNoGrowthYGO - **SDK**: gradio - **SDK version**: 4.19.1 - **Downloads**: 0 - **Stars**: 1 - **Last updated**: 2024-04-10 Source: https://www.modelscope.cn/studios/kevinfu/NoGameNoGrowthYGO --- English | [**中文**](README_ZH.md) # AgentScope Start building LLM-empowered multi-agent applications in an easier way. [![](https://img.shields.io/badge/cs.MA-2402.14034-B31C1C?logo=arxiv&logoColor=B31C1C)](https://arxiv.org/abs/2402.14034) [![](https://img.shields.io/badge/python-3.9+-blue)](https://pypi.org/project/agentscope/) [![](https://img.shields.io/badge/pypi-v0.0.2-blue?logo=pypi)](https://pypi.org/project/agentscope/) [![](https://img.shields.io/badge/Docs-English%7C%E4%B8%AD%E6%96%87-blue?logo=markdown)](https://modelscope.github.io/agentscope/#welcome-to-agentscope-tutorial-hub) [![](https://img.shields.io/badge/Docs-API_Reference-blue?logo=markdown)](https://modelscope.github.io/agentscope/) [![](https://img.shields.io/badge/ModelScope-Demos-4e29ff.svg?logo=data:image/svg+xml;base64,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)](https://modelscope.cn/studios?name=agentscope&page=1&sort=latest) [![](https://img.shields.io/badge/license-Apache--2.0-black)](./LICENSE) [![](https://img.shields.io/badge/Contribute-Welcome-green)](https://modelscope.github.io/agentscope/tutorial/contribute.html) If you find our work helpful, please kindly cite [our paper](https://arxiv.org/abs/2402.14034). Welcome to join our community on | [Discord](https://discord.gg/eYMpfnkG8h) | DingTalk | WeChat | |----------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------| | | | | ---- ## News - ![new](https://img.alicdn.com/imgextra/i4/O1CN01kUiDtl1HVxN6G56vN_!!6000000000764-2-tps-43-19.png) [2024-03-19] We release **AgentScope** v0.0.2 now! In this new version, AgentScope supports [ollama](https://modelscope.github.io/agentscope/en/tutorial/203-model.html#supported-models)(A local CPU inference engine), [DashScope](https://modelscope.github.io/agentscope/en/tutorial/203-model.html#supported-models) and Google [Gemini](https://modelscope.github.io/agentscope/en/tutorial/203-model.html#supported-models) APIs. - ![new](https://img.alicdn.com/imgextra/i4/O1CN01kUiDtl1HVxN6G56vN_!!6000000000764-2-tps-43-19.png) [2024-03-19] New examples ["Autonomous Conversation with Mentions"](./examples/conversation_with_mentions) and ["Basic Conversation with LangChain library"](./examples/conversation_with_langchain) are available now! - ![new](https://img.alicdn.com/imgextra/i4/O1CN01kUiDtl1HVxN6G56vN_!!6000000000764-2-tps-43-19.png) [2024-03-19] The [Chinese tutorial](https://modelscope.github.io/agentscope/zh_CN/index.html) of AgentScope is online now! - [2024-02-27] We release **AgentScope v0.0.1** now, which is also available in [PyPI](https://pypi.org/project/agentscope/)! - [2024-02-14] We release our paper "AgentScope: A Flexible yet Robust Multi-Agent Platform" in [arXiv](https://arxiv.org/abs/2402.14034) now! --- ## What's AgentScope? AgentScope is an innovative multi-agent platform designed to empower developers to build multi-agent applications with large-scale models. It features three high-level capabilities: - 🤝 **Easy-to-Use**: Designed for developers, with [fruitful components](https://modelscope.github.io/agentscope/en/tutorial/204-service.html#), [comprehensive documentation](https://modelscope.github.io/agentscope/en/index.html), and broad compatibility. - ✅ **High Robustness**: Supporting customized fault-tolerance controls and retry mechanisms to enhance application stability. - 🚀 **Actor-Based Distribution**: Building distributed multi-agent applications in a centralized programming manner for streamlined development. **Supported Model Libraries** AgentScope provides a list of `ModelWrapper` to support both local model services and third-party model APIs. | API | Task | Model Wrapper | |------------------------|-----------------|---------------------------------------------------------------------------------------------------------------------------------| | OpenAI API | Chat | [`OpenAIChatWrapper`](https://github.com/modelscope/agentscope/blob/main/src/agentscope/models/openai_model.py) | | | Embedding | [`OpenAIEmbeddingWrapper`](https://github.com/modelscope/agentscope/blob/main/src/agentscope/models/openai_model.py) | | | DALL·E | [`OpenAIDALLEWrapper`](https://github.com/modelscope/agentscope/blob/main/src/agentscope/models/openai_model.py) | | DashScope API | Chat | [`DashScopeChatWrapper`](https://github.com/modelscope/agentscope/blob/main/src/agentscope/models/dashscope_model.py) | | | Image Synthesis | [`DashScopeImageSynthesisWrapper`](https://github.com/modelscope/agentscope/blob/main/src/agentscope/models/dashscope_model.py) | | | Text Embedding | [`DashScopeTextEmbeddingWrapper`](https://github.com/modelscope/agentscope/blob/main/src/agentscope/models/dashscope_model.py) | | Gemini API | Chat | [`GeminiChatWrapper`](https://github.com/modelscope/agentscope/blob/main/src/agentscope/models/gemini_model.py) | | | Embedding | [`GeminiEmbeddingWrapper`](https://github.com/modelscope/agentscope/blob/main/src/agentscope/models/gemini_model.py) | | ollama | Chat | [`OllamaChatWrapper`](https://github.com/modelscope/agentscope/blob/main/src/agentscope/models/ollama_model.py) | | | Embedding | [`OllamaEmbedding`](https://github.com/modelscope/agentscope/blob/main/src/agentscope/models/ollama_model.py) | | | Generation | [`OllamaGenerationWrapper`](https://github.com/modelscope/agentscope/blob/main/src/agentscope/models/ollama_model.py) | | Post Request based API | - | [`PostAPIModelWrapper`](https://github.com/modelscope/agentscope/blob/main/src/agentscope/models/post_model.py) | **Supported Local Model Deployment** AgentScope enables developers to rapidly deploy local model services using the following libraries. - [ollama (CPU inference)](https://github.com/modelscope/agentscope/blob/main/scripts/README.md#ollama) - [Flask + Transformers](https://github.com/modelscope/agentscope/blob/main/scripts/README.md#with-transformers-library) - [Flask + ModelScope](https://github.com/modelscope/agentscope/blob/main/scripts/README.md#with-modelscope-library) - [FastChat](https://github.com/modelscope/agentscope/blob/main/scripts/README.md#fastchat) - [vllm](https://github.com/modelscope/agentscope/blob/main/scripts/README.md#vllm) **Supported Services** - Web Search - Data Query - Retrieval - Code Execution - File Operation - Text Processing **Example Applications** - Conversation - [Basic Conversation](./examples/conversation_basic) - [Autonomous Conversation with Mentions](./examples/conversation_with_mentions) - [Self-Organizing Conversation](./examples/conversation_self_organizing) - [Basic Conversation with LangChain library](./examples/conversation_with_langchain) - Game - [Werewolf](./examples/game_werewolf) - Distribution - [Distributed Conversation](./examples/distribution_conversation) - [Distributed Debate](./examples/distribution_debate) More models, services and examples are coming soon! ## Installation AgentScope requires **Python 3.9** or higher. **_Note: This project is currently in active development, it's recommended to install AgentScope from source._** ### From source - Install AgentScope in editable mode: ```bash # Pull the source code from GitHub git clone https://github.com/modelscope/agentscope.git # Install the package in editable mode cd AgentScope pip install -e . ``` - To build distributed multi-agent applications: ```bash # On windows pip install -e .[distribute] # On mac pip install -e .\[distribute\] ``` ### Using pip - Install AgentScope from pip: ```bash pip install agentscope ``` ## Quick Start ### Configuration In AgentScope, the model deployment and invocation are decoupled by `ModelWrapper`. To use these model wrappers, you need to prepare a model config file as follows. ```python model_config = { # The identifies of your config and used model wrapper "config_name": "{your_config_name}", # The name to identify the config "model_type": "{model_type}", # The type to identify the model wrapper # Detailed parameters into initialize the model wrapper # ... } ``` Taking OpenAI Chat API as an example, the model configuration is as follows: ```python openai_model_config = { "config_name": "my_openai_config", # The name to identify the config "model_type": "openai", # The type to identify the model wrapper # Detailed parameters into initialize the model wrapper "model_name": "gpt-4", # The used model in openai API, e.g. gpt-4, gpt-3.5-turbo, etc. "api_key": "xxx", # The API key for OpenAI API. If not set, env # variable OPENAI_API_KEY will be used. "organization": "xxx", # The organization for OpenAI API. If not set, env # variable OPENAI_ORGANIZATION will be used. } ``` More details about how to set up local model services and prepare model configurations is in our [tutorial](https://modelscope.github.io/agentscope/index.html#welcome-to-agentscope-tutorial-hub). ### Create Agents Create built-in user and assistant agents as follows. ```python from agentscope.agents import DialogAgent, UserAgent import agentscope # Load model configs agentscope.init(model_configs="./model_configs.json") # Create a dialog agent and a user agent dialog_agent = DialogAgent(name="assistant", model_config_name="my_openai_config") user_agent = UserAgent() ``` ### Construct Conversation In AgentScope, **message** is the bridge among agents, which is a **dict** that contains two necessary fields `name` and `content` and an optional field `url` to local files (image, video or audio) or website. ```python from agentscope.message import Msg x = Msg(name="Alice", content="Hi!") x = Msg("Bob", "What about this picture I took?", url="/path/to/picture.jpg") ``` Start a conversation between two agents (e.g. dialog_agent and user_agent) with the following code: ```python x = None while True: x = dialog_agent(x) x = user_agent(x) if x.content == "exit": # user input "exit" to exit the conversation_basic break ``` ## Tutorial - [Getting Started](https://modelscope.github.io/agentscope/en/tutorial/quick_start.html) - [About AgentScope](https://modelscope.github.io/agentscope/en/tutorial/101-agentscope.html) - [Installation](https://modelscope.github.io/agentscope/en/tutorial/102-installation.html) - [Quick Start](https://modelscope.github.io/agentscope/en/tutorial/103-example.html) - [Crafting Your First Application](https://modelscope.github.io/agentscope/en/tutorial/104-usecase.html) - [Logging and WebUI](https://modelscope.github.io/agentscope/en/tutorial/105-logging.html#) - [Advanced Exploration](https://modelscope.github.io/agentscope/en/tutorial/advance.html) - [Customize Your Own Agent](https://modelscope.github.io/agentscope/en/tutorial/201-agent.html) - [Pipeline and MsgHub](https://modelscope.github.io/agentscope/en/tutorial/202-pipeline.html) - [Model](https://modelscope.github.io/agentscope/en/tutorial/203-model.html) - [Service](https://modelscope.github.io/agentscope/en/tutorial/204-service.html) - [Memory](https://modelscope.github.io/agentscope/en/tutorial/205-memory.html) - [Prompt Engine](https://modelscope.github.io/agentscope/en/tutorial/206-prompt.html) - [Monitor](https://modelscope.github.io/agentscope/en/tutorial/207-monitor.html) - [Distribution](https://modelscope.github.io/agentscope/en/tutorial/208-distribute.html) - [Get Involved](https://modelscope.github.io/agentscope/en/tutorial/contribute.html) - [Join AgentScope Community](https://modelscope.github.io/agentscope/en/tutorial/301-community.html) - [Contribute to AgentScope](https://modelscope.github.io/agentscope/en/tutorial/302-contribute.html) ## License AgentScope is released under Apache License 2.0. ## Contributing Contributions are always welcomed! We provide a developer version with additional pre-commit hooks to perform checks compared to the official version: ```bash # For windows pip install -e .[dev] # For mac pip install -e .\[dev\] # Install pre-commit hooks pre-commit install ``` Please refer to our [Contribution Guide](https://modelscope.github.io/agentscope/en/tutorial/302-contribute.html) for more details. ## References If you find our work helpful for your research or application, please cite [our paper](https://arxiv.org/abs/2402.14034): ``` @article{agentscope, author = {Dawei Gao and Zitao Li and Weirui Kuang and Xuchen Pan and Daoyuan Chen and Zhijian Ma and Bingchen Qian and Liuyi Yao and Lin Zhu and Chen Cheng and Hongzhu Shi and Yaliang Li and Bolin Ding and Jingren Zhou}, title = {AgentScope: A Flexible yet Robust Multi-Agent Platform}, journal = {CoRR}, volume = {abs/2402.14034}, year = {2024}, } ```