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README.md
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README.md
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@ -4,23 +4,9 @@ # MetaGPT: The Multi-Role Meta Programming Framework
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## Objective
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1. Our ultimate goal is to enable GPT to train, fine-tune, and ultimately, utilize itself, aiming to achieve a level of **self-evolution.**
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1. Once GPT can optimize itself, it will have the capacity to continually improve its own performance without the constant need for manual tuning. This kind of self-evolution enables an **autonomous cycle of growth** where the AI can identify areas for its own improvement, make necessary adjustments, and implement those changes to better achieve its objectives. **It could potentially lead to an exponential growth in the system's capabilities.**
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2. Currently, we have managed to enable GPT to work in teams, collaborating to tackle more complex tasks.
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1. For instance, `startup.py` consists of **product manager / architect / project manager / engineer**, it provides the full process of a **software company.**
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2. The team can cooperate and generate **user stories / competetive analysis / requirements / data structures / apis / files etc.**
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### Philosophy
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The core assets of a software company are three: Executable Code, SOP (Standard Operating Procedures), and Team.
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There is a formula:
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```
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Executable Code = SOP(Team)
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```
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We have practiced this process and expressed the SOP in the form of code,
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and the team itself only used large language models.
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1. Currently, we have managed to enable GPT to work in teams, collaborating to tackle more complex tasks.
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1. The team can handle **Boss's one line Requirement** cooperate and generate **user stories / competetive analysis / requirements / data structures / apis / files etc.**
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2. The team consists of **product manager / architect / project manager / engineer**, it provides the full process of a **software company.**
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## Examples (fully generated by GPT-4)
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@ -29,11 +15,11 @@ ## Examples (fully generated by GPT-4)
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1. It requires around **$0.2** (GPT-4 api's costs) to generate one example with analysis and design.
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2. It requires around **$2.0** (GPT-4 api's costs) to generate one example with a full project.
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| | Design an MLOps/LLMOps framework that supports GPT-4 and other LLMs | Design a game like Candy Crush Saga | Design a RecSys like Toutiao | Design a roguelike game like NetHack | Design a search algorithm framework | Design a minimal pomodoro timer |
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|----------------------|---------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------|
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| Competitive Analysis |  |  |  |  |  |  |
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| Data & API Design |  |  |  |  |  |  |
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| Sequence Flow |  |  |  |  |  |  |
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| | Design an MLOps/LLMOps framework that supports GPT-4 and other LLMs | Design a RecSys like Toutiao | Design a search algorithm framework |
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|----------------------|---------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------|
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| Competitive Analysis |  |  |  |
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| Data & API Design |  |  |  |
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| Sequence Flow |  |  |  |
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## Installation
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@ -53,7 +39,7 @@ # Step 3: Clone the repository to your local machine, and install it.
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## Configuration
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- You can configure your `OPENAI_API_KEY` in `config/key.yaml / config/config.yaml / env`
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- Configure your `OPENAI_API_KEY` in `config/key.yaml / config/config.yaml / env`
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- Priority order: `config/key.yaml > config/config.yaml > env`
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```bash
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@ -94,31 +80,7 @@ ### Code walkthrough
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await company.run(n_round=n_round)
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```
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## Tutorial: single role and LLM examples
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### The framework support single role as well, here's a simple sales role use case
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```python
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from metagpt.const import DATA_PATH
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from metagpt.document_store import FaissStore
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from metagpt.roles import Sales
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store = FaissStore(DATA_PATH / 'example.pdf')
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role = Sales(profile='Sales', store=store)
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result = await role.run('Which facial cleanser is good for oily skin?')
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```
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### The framework also provide llm interfaces
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```python
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from metagpt.llm import LLM
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llm = LLM()
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await llm.aask('hello world')
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hello_msg = [{'role': 'user', 'content': 'hello'}]
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await llm.acompletion(hello_msg)
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```
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You can check `examples` for more details on single role (with knowledge base) and LLM only examples.
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## Contact Information
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@ -129,12 +91,6 @@ ## Contact Information
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We aim to respond to all inquiries within 2-3 business days.
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## Demo
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| blackjack | adventure-game | 2048 | pomodoro-timer |
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|-----------|-----------|-----------|-----------|
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|  |  |  |  |
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https://github.com/geekan/MetaGPT/assets/2707039/5e8c1062-8c35-440f-bb20-2b0320f8d27d
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56
README_CN.md
56
README_CN.md
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@ -4,21 +4,9 @@ # MetaGPT：多角色元编程框架
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## 目标
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1. 我们的最终目标是让 GPT 能够训练、微调，并最终利用自身，以实现**自我进化**
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1. 一旦 GPT 能够优化自身，它将有能力持续改进自己的性能，而无需经常手动调整。这种自我进化使得 AI 能够识别自身改进的领域，进行必要的调整，并实施那些改变以更好地达到其目标。**这可能导致系统能力的指数级增长**
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2. 目前，我们已经使 GPT 能够以团队的形式工作，协作处理更复杂的任务
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1. 例如，`startup.py` 包括**产品经理 / 架构师 / 项目经理 / 工程师**，它提供了一个**软件公司**的全过程
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2. 该团队可以合作并生成**用户故事 / 竞品分析 / 需求 / 数据结构 / APIs / 文件等**
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### 哲学
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软件公司核心资产有三：可运行的代码，SOP，团队。有公式：
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```
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可运行的代码 = SOP(团队)
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```
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我们践行了这个过程，并且将SOP以代码形式表达了出来，而团队本身仅使用了大模型
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1. 目前，我们已经使 GPT 能够以团队的形式工作，协作处理更复杂的任务
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1. 该团队可以消化**一句话的老板需求**合作并生成**用户故事 / 竞品分析 / 需求 / 数据结构 / APIs / 文件等**
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2. 该团队包括**产品经理 / 架构师 / 项目经理 / 工程师**，它提供了一个**软件公司**的全过程
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## 示例（均由 GPT-4 生成）
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@ -27,11 +15,11 @@ ## 示例（均由 GPT-4 生成）
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1. 生成一个带有分析和设计的示例大约需要**$0.2** (GPT-4 api 的费用)
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2. 生成一个完整项目的示例大约需要**$2.0** (GPT-4 api 的费用)
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| | 设计一个支持 GPT-4 和其他 LLMs 的 MLOps/LLMOps 框架 | 设计一个像 Candy Crush Saga 的游戏 | 设计一个像今日头条的 RecSys | 设计一个像 NetHack 的 roguelike 游戏 | 设计一个搜索算法框架 | 设计一个简约的番茄钟计时器 |
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|-------------|-------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------|
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| 竞品分析 |  |  |  |  |  |  |
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| 数据 & API 设计 |  |  |  |  |  |  |
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| 序列流程图 |  |  |  |  |  |  |
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| | 设计一个支持 GPT-4 和其他 LLMs 的 MLOps/LLMOps 框架 | 设计一个像今日头条的 RecSys | 设计一个搜索算法框架 |
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|-------------|-------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|
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| 竞品分析 |  |  |  |
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| 数据 & API 设计 |  |  |  |
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| 序列流程图 |  |  |  |
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## 安装
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@ -50,7 +38,7 @@ # 第 3 步：克隆仓库到您的本地机器，并进行安装。
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## 配置
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- 您可以在 `config/key.yaml / config/config.yaml / env` 中配置您的 `OPENAI_API_KEY`
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- 在 `config/key.yaml / config/config.yaml / env` 中配置您的 `OPENAI_API_KEY`
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- 优先级顺序：`config/key.yaml > config/config.yaml > env`
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```bash
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@ -93,31 +81,7 @@ ### 代码实现
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await company.run(n_round=n_round)
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```
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## 示例：单角色能力与底层LLM调用
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### 框架同样支持单角色能力，以下是一个销售角色（完整示例见examples）
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```python
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from metagpt.const import DATA_PATH
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from metagpt.document_store import FaissStore
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from metagpt.roles import Sales
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store = FaissStore(DATA_PATH / 'example.pdf')
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role = Sales(profile='Sales', store=store)
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result = await role.run('Which facial cleanser is good for oily skin?')
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```
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### 框架也支持LLM的直接接口
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```python
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from metagpt.llm import LLM
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llm = LLM()
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await llm.aask('hello world')
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hello_msg = [{'role': 'user', 'content': 'hello'}]
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await llm.acompletion(hello_msg)
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```
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你可以查看`examples`，其中有单角色（带知识库）的使用例子与仅LLM的使用例子。
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## 联系信息
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