Puppys


NamePuppys JSON
Version 0.0.30 PyPI version JSON
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home_pageNone
SummaryFramework for Developing an Agentic System
upload_time2024-07-15 12:19:58
maintainerNone
docs_urlNone
authorPuppys
requires_python>=3.9
licenseCopyright (c) 2024 Puppy Agent Tech Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
keywords llm agent
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<div align="center">
      <img src="./assets/PuppyAgentLOGOBig.png" alt="Image" height="120">
    <h1 align="center">Puppys</h1>

**Framework for Developing an Agentic System**

**📜 [Document](https://mulberry-magician-e0a.notion.site/Puppys-document-83e2e55cfd27449589a6a721402ff4bc?pvs=4)**
&ensp;&ensp;
**🔌 [Install](https://mulberry-magician-e0a.notion.site/Install-453ccfa356a04eda865c68e489d0e6bf?pvs=4)**
&ensp;&ensp;
**âš½ [QuickStart](https://mulberry-magician-e0a.notion.site/Quick-Start-f4f383324012448180049f78035ccfa2?pvs=74)**

[![Twitter](https://img.shields.io/badge/-PuppyAgent-1DA1F2?style=flat&logo=X&logoColor=ffffff&color=%23000000&)](https://twitter.com/PuppyAgentTech) &ensp;
[![Discord](https://img.shields.io/badge/-PuppyAgent-7289DA.svg?logo=discord&labelColor=%235462eb&logoColor=%23ffffff&color=%235462eb&label=&style=flat)](https://discord.com/channels/1249674961199829053/1249674961644163164)

## 
</div>




<div align="center">


-**Plug and Play**-

*Insert agentic ability into anywhere your existing enterprise code.*

-**Make Agent Robust**-

*Instruct agents via code, leading configurable and robust.*

-**Lite**-

*Less dependency, more scalability.*
</div>

<div align="center">
<img src="./assets/intro_diagram.png" alt="Image" width="800">
</div>



<div align="center">

## Plug-and-Play
</div>

Embed the agent's action into any your existing code, transforming your original code into an agentic system

No DSL. No Workflow. Only Python (We understand that you don't like DSL or Workflow)

<div align="center">

<img src="./assets/plug&play.png" alt="Image" width="800">

</div>




<div align="center">

## Make Agent Robust

</div>

**Autonomous Agents** can do tasks by themselves and work in many situations, but only be able to solve very simple problems.

**RPA** (Robotic Process Automation), can handle complex tasks but isn't very flexible. 

We provide a hybrid solution of Agents and RPA.

<div align="center">
<img src="./assets/AgentRPA.png" alt="Image" width="800">
</div>


<div align="center">

## Code-Driven

</div>

 **An LLM predicts the next token**

 **An agent predicts the next action**. 

We believe that an LLM-based agent needs to predict the next action; in reality, it predicts the **next code**. This is the philosophy of being code-driven.
<div align="center">
<img src="./assets/PuppyVsOthers.png" alt="Image" width="800">
</div>



## Install

```
pip install git+https://github.com/PuppyAgent/Puppys.git
```


## Quick Start & User Case

1. 📢 *Hacker News Reporter*

```
from puppy.pp.mei import Mei

# change the API key to your own
# os.environ["OPENAI_API_KEY"] = ""


def hacker_news_decisiontree(self):

    self.do_check("go to https://news.ycombinator.com/ show the HTML", show_response=True)

    self.do_check("show the top 10 news @llm, and send it to me", show_response=True)

    self.do_check("pick the news that related to Large Language Models, summarize all the news, and send it to me")


hacker_news = Mei(hacker_news_decisiontree)

hacker_news.run()

```

            

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