agential 0.0.5

A flexible agent library.

Agential

Features

Our primary goal is to provide easy-to-use and clean implementations of popular LLM-based agent methods: an encyclopedia! This library is one of our contributions for our research project empirically surveying and investigating the performance of these methods across a diverse set of reasoning/decision-making tasks. Learn more about this here!

• Easy-to-Use Interface: Provides intuitive and user-friendly functions for rapid prototyping and development.

• Clean Functions: Offers clean and well-structured functions, promoting readability and maintainability of code.

• Modularized Implementations: Includes modularized implementations of popular LLM-based agents and agent-related methods, allowing users to leverage cutting-edge innovations from the literature.

Getting Started

First, install the library with pip:

pip install agential

Next, let's query the ReActAgent!

question = 'Who was once considered the best kick boxer in the world, however he has been involved in a number of controversies relating to his "unsportsmanlike conducts" in the sport and crimes of violence outside of the ring?'

llm = ChatOpenAI(openai_api_key="YOUR_API_KEY")
agent = ReActAgent(llm=llm)
out = agent.generate(question=question)

Project Organization

---

├── agential                       <- Source code for this project.
│   ├── cog   
│   │   ├── agent                  <- Model/agent-related modules.
│   │   │   
│   │   ├── eval                   <- Agent core modules.
│   │   │   
│   │   ├── functional                  
│   │   │
│   │   ├── modules           
│   │   │   ├── memory             <- Memory-related modules.
│   │   │   ├── plan               <- Planning-related modules.
│   │   │   ├── reflect            <- Reflecting-related modules.
│   │   │   └── score              <- Scoring-related modules.
│   │   │
│   │   ├── persona             
│   │   │
│   │   └── prompts             
│   │
│   └── utils                      <- Utility methods.
│       
├── docs                           <- An mkdocs project.
│
├── models                         <- Trained and serialized models, model predictions,
│                                          or model summaries.
│       
├── notebooks                      <- Jupyter notebooks. Naming convention is a number 
│                                    (for ordering), the creator's initials, and a short `-` delimited │ description, e.g. `1.0-jqp-initial-data-exploration`.
│  
│
├── references                     <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports                        <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures                    <- Generated graphics and figures to be used in reporting.
│
└── tests                          <- Tests.

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Contributing

If you want to contribute, please check the contributing.md for guidelines!